Fabio Zottele

Etienne Delay

How much can a virtual territory help us to understand the resilience of the heroic viticulture? A case study in Trentino (IT)

Keywords: Viticulture, agent based modeling, landscape dynamic

In Trentino (IT) two types of vineyard landscape coexist in dynamic equilibrium: the valley is mechanized and plots have a great extent, on the steep slopes a terraced viticulture developes with small size plots and little mechanization is possible. In this work we assume that the emergence of two different contexts -steep slopes and the valleys -result from the complexity of the processes that underlie the development of a territory, and the different factors in play should be studied in their intrinsic diversity because none of them should be considered as the main cause of the emergence of the landscape structure. With some simple (but not simplistic) assumptions on the landscape structure the authors developed a parsimonious agentbased model for the simulation of these dynamics. The results obtained were compared with the historical evolution of the landscape documented in historical archives and with the current state of the territory. The goal is to determine the plausibility of such a simplified model that can describe the evolution of the landscape to the current configurations. We will show, finally, the representations of the model and the dynamics of two viticultural landscapes of the Trentino region (IT).

Introduction

The traditional vine growing terraced landscapes retain a strong local identity: for example the Douro Valley (PT), Lavaux (CH) and the Cinque Terre (IT) are so worldwide known that some of them were enrolled in the UNESCO World Heritage List. But these are only few famous examples among less known landscapes pulverized across the entire world: for the prohibitive working conditions are often recognized as heroic viticulture and CERVIM (the Center for Research, Environmental Sustainability and Advancement of Mountain Viticulture) has compiled a list of these areas in Europe. Some years ago the authors developed an automatic methodology to identify the terraces of the vineyards in Val di Cembra and Banyuls sur Mer to be applied to the description of the heterogeneity of the different traditional terraced landscapes (Delay and Zottele 2012a) knowing that the terraces are the symbol used to create a strong link between the wine and the consumer [START_REF] Delay | Cartographie web : comment construire le lien entre territoire et consommateur ?[END_REF]. The strong identity of these two viticultures do not protect the vine growers form the dynamics of global market that dictates lower prices for the same quality: the higher costs of managements of the vineyards on the steep slopes than the mechanizable ones on the bottom of the valleys push the mountain wines out of the market. However, in a warmer climate, the terraced vineyards with their excursion of altitudes are becoming interesting for the wine producers because the grapes can reach those quality standards that are more and more difficult to attain at lower altitudes [START_REF] Caffarra | Projecting the impacts of climate change on the phenology of grapevine in a mountain area[END_REF]. At the time of this writing, some wine producers pay more for white grapes cultivated at higher elevation than 400 m, paying even more when a low environmental impact protocol is set in production. In the "Valsugana Trentina" region (Trentino, IT) we have examples of the two co-existent and adjacent agricultural landscapes: a flat, bottom-of-the-valley mechanized orchards and a traditional terraced viticulture that climbs the slopes made up of small plots that have successfully resisted the abandonment and reforestation. With this work we want to show a model to investigate the emergence of this dynamic equilibrium as the interaction of different factors and to exploit the attitude of the vine growers to maintain the production in an area that would appear at first sight disadvantaged (Figure 1). The model has been published by [START_REF] Zottele | È possibile descrivere la resistenza dei paesaggi vitati di montagna utilizzando un territorio virtuale? Il caso trentino[END_REF]

Results and Discussion

Some spatial pattern emerged conditioned to some ranges of values set in the behavior space. One of the most notable result is linked to R p : the resilience of the traditional terraced landscape, in terms of active vine growers is enhanced by the possibility for them to access distant plots (Figure 3). Then, the ratio between the mechanization benefits and the climatic benefits must be less than 0.3 (Figure 4): with this value both viticulture (on the bottom of the valley and on the steep slope) coexist and expand. Even with high values of F u (expected utility) the traditional viticulture survives but with few active growers that owns plots both on the bottom of the valley and on the slopes. For a deeper analysis of the results see [START_REF] Zottele | È possibile descrivere la resistenza dei paesaggi vitati di montagna utilizzando un territorio virtuale? Il caso trentino[END_REF]. The importance of Rp shows that a healthy traditional viticulture could survive if it is ensured farmers' mobility. A well maintained system of roads should be fundamental for the landscape resilience. In a similar way, the benefit of mechanization depress the viticulture of the mountain: a good policy of remuneration for the vine growers that works on the steep slope is also fundamental. In the work of [START_REF] Zottele | È possibile descrivere la resistenza dei paesaggi vitati di montagna utilizzando un territorio virtuale? Il caso trentino[END_REF] is discussed how a superficial consideration of the importance of the ratio between mechanization and climatic benefit could lead to a possible destruction and trivialization of the traditional landscape.

Conclusion

The authors hope to improve this parsimonious and simple (but not simplistic) model introducing a refined parametrization of the benefits used to define the utility function. This implies a even more multidisciplinary approach to the ABM

Figure 1 .

 1 Figure 1. The evolution of the traditional viticultural landscape in Valsugana (Trentino, ITALY). Left: in the 50s all the slopes are cultivated (Marchesoni, 2010, Sartori's archive). Right: 2013: the upper part of the hill and its terraces are completely abandoned, while the actual plots maintain the traditional landscape matrix.

Figure 3 .

 3 Figure 3. Land use (%) evolution for different ratio between the mechanization and climatic benefit. In green the land use for the bottom of the valley, in red for the steep-slope traditional landscape. The solid lines shows the median values, the colored area shows the 25th and 75th percentile respectively. The dashed lines show the minima and the maxima respectively.

Figure 2 .

 2 Figure 2.Two initialization of the model. The big circle are the position of the vine growers, at each vine grower 25 plots are assigned. The black cells are the bottom of the valley, the elevation is represented in shades of gray

Materials and methods

We will describe shortly the developed model of landscape dynamic describing emerging pattern based on the choice of the vine growers to maintain or abandon their activities depending to the benefits of each owned plots. In the bottom of the valley, the management costs are lower (mechanization benefit) than on the steep slopes where the production can benefit higher remunerations (climatic benefit). A synthetic landscape has been used: the computation matrix is 200 2 cells, a quarter of the landscape is flat, the other cells have a slope of 45° simulating the steep-slope viticulture (Figure 2). The mutual interaction between the landscape and the vine growers has been codified with an Agent Based model (ABM) that mimics a complex system of human behaviors and spatial patterns [START_REF] Batty | A Generic Framework for Computational Spatial Modelling[END_REF]. To describe the system we use a variable of state that computes the expected benefit of the vine grower to continue to operate on each plot. The reason to continue to operate, to expand the activity or to abandon is described with a utility function that changes in space and time and that is proportional to the benefits (number and position of the plots).

(Equation 1) T c represents the mean costs for all the plot (of the i-th vine grower that could own fields on the slopes and in the valley), T g is the mean profit and Tr is the climatic benefit. We used the work of [START_REF] Lang | Costi e ricavi della fruttiviticoltura altoatesina. Centro di consulenza per la fruttiviticoltura dell[END_REF] for the different management costs in the bottom of the valley and on the slopes. The climatic benefit has been modeled to have a maximum benefits at higher altitudes and no benefit for the plots at the bottom of the valley. For a complete description of the individual terms of (Eq. 1) see [START_REF] Zottele | È possibile descrivere la resistenza dei paesaggi vitati di montagna utilizzando un territorio virtuale? Il caso trentino[END_REF]. The α and β parameters measure the mechanization benefit and the climatic benefit respectively. We then introduce a utility threshold F u, so that, when the value of (Eq. 1) is lower than the threshold the vine grower has less benefits than charges and will abandon one of the plots (the one minimum value of climatic benefit ) .Otherwise, the vine grower expand occupying one the neighboring cells (at a distance < R p , the radius of interest) of his plots and choosing the one with the maximum climatic benefit. In our synthetic experiment we place 100 vine growers randomly on the matrix each with 25 random plots (Fig 2 .). The authors ran 23100 simulations to explore the spatial pattern emerging by all the combination (behavior space) -with different discretization steps, replicated 10 times -of the parameters α, R p , F u .


Marc Allan Redecker

Klaus-Dieter Thoben

An Approach for Energy Saving in the Compound Feed Production

Keywords: raw material, uncertainty, agricultural commodities, expert system, manufacturing processes, quality

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The dynamics of price developments in recent years for raw materials and energy will tend to persist. The global competition for resources and the legally mandated emission limits placed will determine the conditions of stronger companies. The competitiveness of an increasing productivity can be achieved only if the available resources such as energy, materials and personnel will be handled efficiently. The resulting gaps have to be closed by increasing efficiency [START_REF] Neugebauer | Energieeffizienz in der Produktion -Untersuchung zum Handlungs-und Forschungsbedarf[END_REF].

Effectiveness and efficiency are to be distinguished from each other. The effectiveness generally referred to the Ratio of the achieved to a defined target. The efficiency in turn describes the performance and economic efficiency. Efficiency is thus understood as the ratio between a defined input and a fixed size of an output. Energy efficiency means reducing energy consumption in a system to providing a service [START_REF] Pehnt | Energieeffizienz: Ein Lehr-und Handbuch[END_REF]. The European Union defines energy efficiency as the ratio of the return of performance, services, goods or energy to use of energy [START_REF]Parlaments und des Rates vom 5[END_REF].

The significant energy cost increases in recent years have been creating high economic incentives for rational energy budgets in the compound feed industry [START_REF] Stelling | Notwendige Schritte zur Implementierung eines Energiemanagementsystems in Mischfutterwerken[END_REF]. In Germany the annual production output of the compound feed production exceeding approximately by 22,500,000 tons in 319 factories [START_REF] Kunkel | Struktur der Mischfutterhersteller in Deutschland -Wirtschaftsjahr 2009/10[END_REF]. The total yearly electricity consumption of this branch of industry lies at 1.65 billion kWh per annum [START_REF] Heidenreich | Gestaltung der Zukunft durch Schüren von Ängsten?[END_REF]. Our case study is the production of compound feed in a commercial feed processing plant in Northern Germany. The energy consumption accounts up to 4-6 GWh per annum by an annual production output up to 240,000 tons of feed. The biggest part represents the pig feed with 80 %, followed by chicken feed with 15 % and 5 % bovine animal feed. For various costumers the plant is producing 200 different feed recipes with over 60 different ingredients. All these recipes are produced batchwise on one single plant.

Characteristics in the compound feed production

The industrial feed compounding is focused on the refining of natural resources like grains. Feed recipes are consisted of up to 40 different natural resources, for example wheat, barley, corn, soy, molasses, limestone and micro components like vitamins and enzymes. The composition of feed recipes depends on the animal species as well as the period of growth and is currently adjusted for example by availability and market price of raw materials.

In the compound feed production the demands of customer are at the forefront and the lead-time of the order of feed are only few hours. In our case study the production plant has to produce 200 different composites in short-term.

The compound feed production process could be divided into four process steps. At first the feed processing starts with the incoming of different natural resources like wheat, barley, rye and so on which are delivered by trucks, discharged into collecting vessels and short-term stored in silo compartments.

On the respective feed recipes the components could mixed automatically. All recipes are registered in a control system. If the process is starting, the refinement begins with the transportation of the raw materials to the following process step to weigh and to mix the natural resources. The next step includes the comminution of the grainy raw materials by the use of hammer mills or roller mills. Every time it is necessary to aspirate the dust, which comes out by hurrying the materials. After the milling process the mealy material gets an admixture of molasses, linoleic acid and other ingredients. To produce the final product the raw materials have to be pressed to pellets. Thus the mealy material with the admixture has to be transported to the pelletizing process [START_REF] Kirchner | Manufacturing with minimal energy consumptiona product perspective[END_REF].

The pelletizing process comprised the pressure grouting of the mealy material. Before the mealy material could be pressed the floury texture will be added with steam and grease in the conditioner. The next step includes the compactor process. This step manufactured the pellets with high quality performance and physical impacts against pressure, abrasion and critical strength. The quality of a pellet must have an uniform length, hardness and has to be resistant against all strains. Pelleting in combination with short-term conditioning is most common as hydrothermal treatment in feed mills. After the pelletizing process the material has to be cooled and as a last process step transported in the corresponding silo compartments.

Analysis of high energy consumption

Among the most important forms of energy in a compound feed production process are the thermal energy, mechanical energy and electrical energy. Figure 1 shows the percentage of the consumption of electricity of the compound feed production in the case study.

Fig. 1. Consumption of electricity in percentage of compound feed production in the case study

The pelletizing process provides the highest energy consumption in front of the milling process. These two processes have a total electricity consumption by more than 60 %. Figure 2 gives an overview of the process steps in the feed industry as well as an allocation of the current energy consumption of the individual process steps. It is necessary to have a closer look at the process step pelletizing and milling in order to ensure a more efficient production.

Fig. 2. Process chain of compound feed production

To actuate a hammer mill more energy-efficient it must be known the texture about the raw materials. In connection with the conclusion of milling it concerns the elastic force and the strength of the mill material. By increasing influence on the elastic force and the strength, the product moisture and the energy consumption are rising. Therefor it is important that the output data are known for the humidity and the bulk weight.

A pelletizing process should have a high throughput rate and low energy consumption. The quality and the efficiency of the compactor process will be influenced by the physical quality of the pellets and the specific energy demand (kW/t). These requirements are difficult to reach. The manufacturing of hard pressed pellets demanded high pressure and therefore high energy consumption as soft pressed pellets. Depending on the composition of the raw materials the energy consumption fluctuates between 10 up to 25 kWh/t by pressing pellets based on the ratio of moisture and quality [START_REF] Kersten | Mischfutterherstellung. Agrimedia[END_REF].

Essential for the energy consumption and the product quality is in addition to time, the temperature and the moisture. The temperature is determined by adding steam. The temperature rising by 10 degrees by increasing the raw material from 0.6 to 0.7 of the moisture. On account of this it has to be detected the optimum between the quantity of steam and product quality to reduce the energy consumption.

For example industrial products (like automobile, aircraft, etc.) can be constructed by reducing the weight without loosing quality properties. But in the food and feed processing industry reducing weight means that we put the animals on diet. The research focus therefore must be placed on the investigation of energy efficiency of the production process without changing the end-product quality.

Uncertainties in the compound feed production

The uncertainties in the compound feed production can be divided into two parts, on the one hand in properties that can be influenced and on the other hand in properties that cannot be influenced. Figure 3 shows graphically the general connection between the product quality and the uncertainty of the process steps compacting (pressing), comminution (milling) and adding steam (conditioning). The bars in the chart show that fluctuations of the product properties are available. Mainly for exam-ple the water content of raw materials for the compound feed production can changed by a controlled steam entry.

Fig. 3. Uncertainties of product properties

For example different natural resources will trigger different grinding characteristics and will reveal different particle size distributions.

Efficient control of the compound feed production process

In the case study, the product quality will be considered only at the end of the process chain. To save the energy it is important to analyse the different ingredients in different process steps, which does not exist in the current compound feed production. For example, information is needed about the particle sizes during milling process. The same applies to the moisture content of raw materials before the pelletizing process is starting.

One possibility is to detect the uncertainties during the production process with measurement instruments. For Example the analysis of the grain sizes during the production process could be done with computerized particle analyser (CPA) which could be integrated in the milling process step to analyse the grain size at the beginning of the manufacturing process. They, in turn, support information about how the parameters must be set in the milling process in order to the optimal size of the raw material before passing into the pressing process and thus provide information on how the machine settings must be made so that energy efficiency works. CPA-Measuring instruments used to perform particle size and particle shape analysis of dry and nonagglomerated particles in ranges from 0.010 mm to 400. The systems can also be used as particle counters. CPA measuring instruments are used for digital image processing for analyzing the particles. A CPA measuring device is connected after a comminution unit to monitor the particle size distribution of the comminuted material. In strong or less comminution the measuring device can guide them on the desired particle size distribution. However, this requires a good calibration of the instrument and to be determined at the beginning experience on the process parameters. Furthermore a pre-sieve should be installed to separate fines before grinding in order to unload the milling process and reduce its energy consumption.

The diode array base spectrometer (NIR) is used for the ingredients description and causes the optimized control of the conditioning and compacting process steps. The sample material is irradiated with light in the range of the near-infrared wavelength range. The reflected light is read out by a diode array spectrometer. The reflections vary depending on which of the parameters of concentration in the raw materials and feed mixtures and provide information on the material composition of sample materials. With this device single samples can be measured. In addition to product moisture is usually the protein, fat and crude fiber and starch content can be determined. In addition to product moisture content of protein, fat and fiber content of the starch content and can be determined.

Fig. 4. Process control in real time

Figure 4 shows a possible process control in real time to get high quality products with low energy consumption. The system receives information during the production process about the grain size and moisture content and is directly put in a position to influence the machine parameters.

Conclusion

The integration of these instruments within the process can provide information on the uncertainties and allow a process control. The development of the control system of the production process is performed according to uncertainty management. Uncertainties are present at any time in the process and has therefore taken into account during the decision making process. The uncertainties are formed into fuzzy rules and are implemented as a new software tool that supports the process control. It will be possible to simulate the production process with varying parameters to evaluate the result of the simulation.

Acknowledgement -The authors thanks the German Ministry of Economy and Technology and the Project Management Jülich for funding the project in the compound feed production.


Manuel Rippel
email: mrippel@ethz.ch

Olga Willner

Johannes Plehn

Paul Schönsleben

Bridging the Gap between Energy Management Systems and Machine Tools -Embedded Energy Efficiency in Production Planning and Control

Keywords: PPC, Process Chain, Energy Efficiency, Energy profile

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Motivation and purpose

Scarcity of resources and rising energy prices caused by an increasing primary energy demand started to change society and business values. Today, companies have to tackle three essential challenges related to global environmental issues: (1) legislation and governmental regulations, (2) consumer and public awareness, (3) resource and energy prices and CO 2 -costs [START_REF] Bunse | Integrating energy efficiency performance in production management -gap analysis between industrial needs and scientific literature[END_REF]. These challenges lead to an increasing importance of resource efficiency and in particular energy efficiency in European manufacturing industry. Manufacturing is directly connected to natural resources, since this sector is responsible for about 33% of the primary energy use and for 38% of the CO 2 emissions [START_REF]Worldwide Trends in Energy Use and Efficiency, Key Insights from IEA Indicator Analysis[END_REF].

The efficient use of energy in manufacturing is seen as an effective approach to decrease the energy needs and the associated CO 2 emissions [START_REF] Bunse | Integrating energy efficiency performance in production management -gap analysis between industrial needs and scientific literature[END_REF]. Moreover, it is, and will be even more in future, a central competitive factor, since resource efficiency is gaining importance next to economic objectives. The challenge for manufacturing companies is to tackle the assumed dichotomy between competitive and environmental friendly operations.

The significance of this field can be illustrated by looking at the German industry, which accounts for around 46% of the country's overall energy consumption [START_REF] König | Energieeffiziente Produktherstellung[END_REF]. Experts estimate that 10-15% of this amount is consumed by machine tools for machining operations [START_REF] König | Energieeffiziente Produktherstellung[END_REF]. Since 99% of the environmental impacts of machine tools are due to the consumption of electrical energy, the energy consumption is solely considered in this paper [START_REF]CECIMO: Concept Description for CECIMO's Self-Regulatory Initiative (SRI) for the Sector Specific Implementation of the Directive[END_REF].

The Smart Manufacturing Consultation Group of the European Commission for energy efficiency declares that "it is necessary to develop tools and relevant algorithms which will be able to take energy efficiency into account as a significant parameter of the process, and to calculate best energy performance results along with other desired process results" [START_REF]European Commission: ICT and Energy Efficiency in the Case for Manufacturing. Recommendations of the Consultation Group[END_REF]. Hence, the objective is to identify and analyze the potential of production planning and control (PPC) systems, in order to increase the energy efficiency in manufacturing processes.

Methodological approach

This paper is mainly based on qualitative research. A literature analysis is conducted with a focus on energy efficiency in the manufacturing industry both from a managerial and a technical background. A "Top-down/Bottom-up" process is used as a hierarchical analysis, in order to assure a structured approach. As depicted in Figure 1, this paper conceptually and methodologically systematizes existing concepts from Energy Management Systems (EMS) as top-down perspective and from process chain level respectively machine tools as bottom-up perspective. The process for achieving energy efficiency in production systems is categorized in four steps: data collection, data visualization, evaluation and improvement measures. The key elements for embedding energy efficiency in a PPC-logic are reviewed and allocated to four overlaying levels of analysis: data, model, system and logic.

Findings

This section presents the findings by identifying essential elements for increasing energy efficiency in production systems as a combination of superior management perspective and the technical entities within the process chain. Manufacturing companies underestimate their potential to save energy and reduce related costs. For example, studies have shown saving potential of 25-30% in production [START_REF] Neugebauer | Abschlussbericht -Untersuchung zur Energieeffizienz in der Produktion[END_REF]. Due to this promising impact, the focus of the bottom-up perspective is mainly on ongoing research to improve the energy efficiency of machine tools as operating elements of process chains. Most machine tool suppliers and end users lack knowledge about time-dependent energy demand during operation [START_REF] Brecher | Ressourceneffizienzbewertung einer Werkzeugmaschine zur Steigerung ihrer Wirtschaftlichkeit[END_REF]. Significant improvement potential is expected by enhancing transparency of energy consumption of machine tools and its components [START_REF] Neugebauer | Abschlussbericht -Untersuchung zur Energieeffizienz in der Produktion[END_REF]. The measurement of machine tools with regard to energy used and material consumed is seen as the first step in order to understand, characterize and improve environmental impact and energy consumption of manufacturing processes [3] [8].

Energy demand of machine tools is primarily considered as an estimated value under either nominal or extreme conditions [START_REF] Li | An Investigation into Fixed Energy Consumption of Machine Tools[END_REF]. However, energy consumption is distinctly dynamic due to the temporal power demand throughout a machining process. It significantly depends on the operating states, like e.g. turn-off, start-up, warm-up, stand-by, processing or stopping [START_REF] Brecher | Ressourceneffizienz von Werkzeugmaschinen im Fokus der Forschung[END_REF] [10]. Furthermore, power demand while processing depends on several further factors, e.g. process design, selected machine tool, machine dynamics and process parameters [START_REF] Li | An Investigation into Fixed Energy Consumption of Machine Tools[END_REF] [START_REF] Brecher | Ressourceneffizienzbewertung einer Werkzeugmaschine zur Steigerung ihrer Wirtschaftlichkeit[END_REF]. Taking into account these characteristics, it is not sufficient to consider energy consumption on average, but to provide time-dependent energy profiles for further evaluation. It is detected that the energy demand of machine tools consists of the fixed power as base load, which sets the process-time depending share of required energy for the machining of the work piece, and the variable power, which depends on the process [START_REF] Brecher | Ressourceneffizienz von Werkzeugmaschinen im Fokus der Forschung[END_REF].

The metering and monitoring of energy consumption of single machinery is time consuming and expensive. Furthermore, it is not feasible to measure energy demands considering all potential influencing factors among all machines in a factory [START_REF] Li | An Investigation into Fixed Energy Consumption of Machine Tools[END_REF]. Therefore, it is intended to define standards for the measurement considering operating state, consumer and exemplary processes and to formalize the results in appropriate models supporting the case-specific quantification [START_REF] Brecher | Ressourceneffizienz von Werkzeugmaschinen im Fokus der Forschung[END_REF]. Thereby, it is at least possible to rate and evaluate the scale of the consumption ex-ante or ex-post. Upcoming new sensor technologies within Information & Communication Technologies (ICT) enable to perform in-process measurement, which will provide more accurate information about temporal energy demands [START_REF] Karnouskos | Towards the energy efficient future Factory[END_REF]. Thereby, the energy-oriented evaluation can be conducted continuously based on real-time data and decision makers can react instantaneously by taking dynamic measures.

Furthermore, ongoing research activities are analyzing when and under which conditions components can be deactivated or at least reduced in non-productive situations. However, the optimization is limited to single machines. There is no technological approach pursued in machine tool industry to aim at a dynamic energetic optimum for the whole process chain.

Top-Down Perspective: Energy Management Systems

Up to now, solely one third of all companies have a systematic evaluation and optimizing approach for their energy consumption [START_REF] Neugebauer | Abschlussbericht -Untersuchung zur Energieeffizienz in der Produktion[END_REF]. Mostly, they just have an overall recording of data referring to their energy consumption, whereby they are not aware of their specific energy use and loss within production [START_REF] Neugebauer | Abschlussbericht -Untersuchung zur Energieeffizienz in der Produktion[END_REF]. Several tools and methods are presently developed in this research area.

Monitoring and simulation are seen as essential elements of energy management [START_REF] Bunse | Integrating energy efficiency performance in production management -gap analysis between industrial needs and scientific literature[END_REF]. Key Performance Indicators (KPIs) are developed to evaluate if changes in the process for increasing energy efficiency have a verifiable benefit [START_REF] Bunse | Evaluating Energy Efficiency Improvements in Manufacturing Processes In[END_REF]. Their monitoring supports assessment of optimization potentials and visualization of benefits from improvement measures. There exist various structural and explanatory indicators, since the variety and complexity of industrial processes set different requirements [START_REF] Bunse | Integrating energy efficiency performance in production management -gap analysis between industrial needs and scientific literature[END_REF].

The evaluation of energy costs, environmental impacts and technical performance are of major importance in order to achieve a holistic image of the factory [START_REF] Herrmann | Energy oriented simulation of manufacturing systems -Concept and application[END_REF]. Different analytical techniques can be consulted for evaluating energy efficiency improvements in terms of economic impact, e.g. net present value method (NPV), the method of annualized value, the method of internal rate of return or payback period. However, it is difficult to quantify potential benefits in monetary terms for an economic evaluation [START_REF] Bunse | Evaluating Energy Efficiency Improvements in Manufacturing Processes In[END_REF].

Simulation can support to analyze the dynamic energy consumption behavior, operational data and interdependencies of machine tools and other technical equipment involved. It can be used to provide an overview of the system's performance on a realistic base. Simulation tools enable decision making to assess the expected effects of measures for improving the environmental performance both on process chain or machine level [START_REF] Vijayaraghavan | Automated energy monitoring of machine tools[END_REF] [START_REF] Hermann | A Holistic Framework for Increasing Energy and Resource Efficiency in Manufacturing[END_REF].

However, a comprehensive portfolio of measures is still missing. The range of energy efficiency measures is limited to static approaches such as replacement of components, acquisition of new machinery or installation of fixed equipment. Dynamic approaches for reacting on short-term changes of the system's energy state are missing or are not fully developed.

Synthetic link: Embedded energy efficiency in PPC

The above mentioned data and concepts from each perspective have to be utilized and adjusted with regard to a multi-object approach. Bunse et al. proposed to extend the scope of MES, ERP and simulation software to optimize processes throughout the whole production in terms of energy efficiency [START_REF] Bunse | Integrating energy efficiency performance in production management -gap analysis between industrial needs and scientific literature[END_REF]. In the following, an integral PPCconcept is outlined, since PPC can exploit existing configuration flexibility within economic optimization of production systems for embedding also energy efficiency as optimization criteria. PPC will not solely be used as information source for the economic values for monitoring of KPIs anymore. Therefore, the defined and monitored KPIs in EMS will be split into operational variables. Prior PPC algorithms solely focused on direct economic and technical aspects. They have to be extended for environmental aspects. By doing so, both energy-related costs and environmental footprint of the products can be enhanced. The extended PPC-logic provides the organizational conditions for exploiting several characteristics and technological developments of machine tools for increasing energy efficiency as follows:

First, the above mentioned characteristic of the energy profiles of time-dependent energy consumption consisting of fixed and variable power can be exploited. It is possible to reduce energy consumption per part by enhancing the productivity of the machine tool respectively the capacity utilization [START_REF] Herrmann | Energy Labels for Production Machines -An Approach to Facilitate Energy Efficiency in Production Systems[END_REF]. The resulting shorter production time enables savings of fixed power of the machine's base load. This saving is higher than the additional consumption within the process-dependent variable power [START_REF] Brecher | Ressourceneffizienz im Werkzeugmaschinenbau[END_REF]. PPC-algorithms have to consider within machine scheduling that the material removal rate (MRR) can be maximized in order to reduce the impact of fixed power share. Furthermore, the machine tool consumes a fixed amount of energy during non-value adding tasks (e.g. setting-up, handling) [START_REF] Zein | Energy Efficiency Measures for the Design and Operation of Machine Tools: An Axiomatic Approach[END_REF]. Those tasks have to be avoided as far as possible or the time required needs to be reduced by energy-oriented process and production planning, since the energy is wasted without adding value on the product.

Second, stand-by strategies for machine tools should be integrated in production scheduling in order to reduce energy consumption within the remaining nonproductive times [START_REF] Brecher | Ressourceneffizienzbewertung einer Werkzeugmaschine zur Steigerung ihrer Wirtschaftlichkeit[END_REF]. For example, to switch off a machine tool if stand-by time is long enough [START_REF] Herrmann | Energy Labels for Production Machines -An Approach to Facilitate Energy Efficiency in Production Systems[END_REF]. PPC should use existing flexibility in machine schedule to stop, postpone or bring forward different process steps for implementing such stand-by strategies of machine tools. The Consultation Group of the European Commission estimates the saving potential of the optimization of waiting/start-up times at 10-25% [START_REF]European Commission: ICT and Energy Efficiency in the Case for Manufacturing. Recommendations of the Consultation Group[END_REF]. However, the task of an appropriate PPC-algorithm is to evaluate if shut-down and a start-up of a machine tool and its components is reasonable from the perspective of the whole production system and underlying process chain, since several important aspects have to be considered for the implementation of those strategies. For example, specific time and energy demand (e.g. maximum instantaneous peak power requirement, total energy consumption for start-up) is required both for start-up and poweroff stage [START_REF] Li | An Investigation into Fixed Energy Consumption of Machine Tools[END_REF]. Therefore, non-productive times have to be distributed appropriately in order to realize stand-by concepts [START_REF] Brecher | Ressourceneffizienz von Werkzeugmaschinen im Fokus der Forschung[END_REF]. Furthermore, intervals need to be long enough, e.g. in order to ensure thermal stability of machine tools by sufficient lead times [START_REF] Brecher | Ressourceneffizienz von Werkzeugmaschinen im Fokus der Forschung[END_REF]. PPC has to determine the holistic optimum: On the one hand it has to examine total energetic benefit including multiple machines, which might be affected by a redistribution of orders. On the other the PPC has to assess further organizational consequences, e.g. additional changeover time, operational readiness of the machine (since the gained energy saving should not sacrifice the process stability) product quality or machine tool availability [START_REF] Li | An Investigation into Fixed Energy Consumption of Machine Tools[END_REF] [START_REF] Brecher | Ressourceneffizienz von Werkzeugmaschinen im Fokus der Forschung[END_REF].

Third, Brecher et al. indicate that current control systems of machine tools should be extended, e.g. by systems for forecasting load detecting and visualization [START_REF] Brecher | Ressourceneffizienz von Werkzeugmaschinen im Fokus der Forschung[END_REF]. Based on information derived from the above mentioned measurement of energy consumption on component level, abilities of machine's path planning simulations can be enhanced, since the cumulative energy consumption can be calculated by the iterative summation of all individual cuts [START_REF] Brecher | Ressourceneffizienzbewertung einer Werkzeugmaschine zur Steigerung ihrer Wirtschaftlichkeit[END_REF]. Those results should be linked with PPC so that the technical potential of machine tools can be used by providing them conditions for operating in their optimal range of efficiency.

To conclude, to equip PPC with advanced algorithms is a measure for achieving energy efficiency performance on a high scale. Benefits will be as follows: PPC works as leverage for EMS by putting findings from monitoring and simulation tasks into action. It also is in an enabler to use effectively the potential of energy optimized machine tools. PPC is a cost-effective instrument for using the given configuration of machine tool infrastructure in regard to energy efficiency as best as possible. Besides, it is a tool to detect the energy efficiency boundaries and to determine promising improvement potential at shop-floor level. Finally, it takes over the dynamic optimization in continuation of static improvement measures like investments in single components of machine tool or the new acquisition of whole machine tools with less energy consumption.

Limitations and practical implications

The elaborated concept is restricted to an outline for embedding energy efficiency in the planning and control logic of a conventional PPC. The conducted analysis focuses on creating the conceptual basis as well as the influencing factors and interconnected elements for further research. The conceptual integration of environmental-oriented optimization targets in existing PPC-algorithms will be conducted in a next step. The principles will be transferred to other processes than machining, e.g. milling and turning. The final concept has to be tested and validated within a manufacturing company. Due to the high significance of the energy consumption of machine tools, energy consumption is solely considered in this concept [START_REF]European Commission: ICT and Energy Efficiency in the Case for Manufacturing. Recommendations of the Consultation Group[END_REF]. After validating energy-oriented PPC-logic variables should be supplemented by the consideration of other environmental factors, e.g. compressed air, coolant and lubricants. The impact of the embedded energy efficiency PPC consists in achieving energy efficiency in interconnected processes and in particular beyond individual entities. This will result in a decrease of the environmental impact of the production site, saving of resources and reduction of costs: Total energy consumption will be reduced by exploiting load characteristics of machine tools as useful potential and embed them in the scheduling algorithms. Besides, production schedule can be aligned to advantageous time periods, e.g. when renewable energy is available or day-time dependent prices for electricity are low [START_REF] Weinert | Methodology for planning and operating energyefficient production systems[END_REF]. Furthermore, PPC can distribute machining tasks so that undesirable, high impact marginal energy peak loads can be avoided, which reduces energy costs and the environmental footprint of a production site [START_REF]European Commission: ICT and Energy Efficiency in the Case for Manufacturing. Recommendations of the Consultation Group[END_REF]. A prospective, schematic application example for a scheduling approach considering peak power demands is given in Figure 2.

Furthermore, the adapted PPC will improve Life-Cycle-Assessments (LCA) by supporting the calculation and evaluation of the employed resources on individual product and process level. The PPC will deliver detailed information to enhance transparency and validity of product-specific environmental footprints.

Conclusion

Existing conceptual elements from a top-down and bottom-up perspective are analyzed and systemized in regards to their contribution to embed energy efficiency in PPC. The integration of energy efficiency criteria in PPC will enable to achieve an economic and environmental performance optimum while fundamental technical and organizational requirements are assured at once. As automated, integrated solution it provides the ability of responsiveness of EMS by taking appropriate situationdepending, dynamic improvement measures. Hence, the value proposition of embedded energy efficiency in a PPC is the ability to link managerial targets with technical entities on the shop-floor by balancing and combining organizational prerequisites with technical capabilities.

Fig. 1 .

 1 Fig. 1. Top-down and bottom-up analysis process

Fig. 2 .

 2 Fig. 2. Schematic example for scheduling considering peak power demands

Embedded energy efficiency in PPC Current


Connor Upton
email: connor.upton@intel.com

Fergus Quilligan
email: fergus.quilligan@icmr.ie

Carlos García-Santiago

Asier González-González

Asier González- González

Energy Efficient Production Planning

Keywords: visualisation, optimisation, energy efficiency, manufacturing

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Energy efficient manufacturing is a key research challenge for both industry and academia. Systemic energy waste is closely tied to strategic production decisions and therefore poses a complex operations-research problem. An example of this involves switching idle machine into a low-power mode. While this strategy is an effective way to save energy, it is not a straightforward task in many industrial environments. Energy savings are often subservient to production targets and decisions about changing machine states involve weighing up a complex set of goals and constraints. These include hard metrics such as production capacity, predicted inventory and product priorities as well as soft constraints such as technician skill level, engineering requests and machine recovery risks. Operations managers currently apply human expertise to cope with this complexity. In high product mix factories this problem can become very challenging and even before energy-saving is considered. Optimisation algorithms can be applied to reduce the problem space associated with this decision and to highlight energy saving opportunities; however an algorithmic approach is challenged by soft constraints and unpredictable changes in goals. In addition operations manag-ers tend to be wary of decision support tools due to their perceived brittleness and lack of transparency [START_REF] Carlsson | Introduction: DSS: Directions for the next decade[END_REF].

A potential solution to this is to treat the human operators and automated systems not as autonomous agents but as team members in a joint-cognitive system [START_REF] Hollnagel | Joint cognitive systems: Foundations of cognitive systems engineering[END_REF]. Joint cognitive systems, where responsibility for control is shared between human and machine 'intelligence', are becoming increasingly important in all modern workplaces. System observability plays a critical role in the success of joint cognitive control as it ensures that human and automated agents can co-ordinate their actions and collaborate effectively [START_REF] Christoffersen | How to make automated systems team players[END_REF]. This paper describes the design of a Decision Support System (DSS) using "grey box" optimisation and an interactive visualisation to ensure observability. This approach aims to expose aspects of an optimisation engine to increase flexibility in terms of goal and constraint settings and to communicate outputs in a manner that are easily interpretable to the end user. In this manner end user trust and the overall effectiveness of the system will be improved.

Applied Use Case

An individual operation within a manufacturing production process was selected for this research. This operation supports multiple products and involves a large fleet of parallel machines. Each machine requires a manual product configuration (a setup) before processing can occur. This means that the production capacity for each product can be changed in response to demand. As well as set-ups and processing, machines may be in idle, maintenance, engineering, down or powersave states. The optimisation problem investigated here involves allocating machines to states over time under multiple constraints. Some constraints are hard e.g. meeting production targets by specified date, while others are soft, e.g. technician skill level. An adaptation of the Cognitive Work Analysis framework [START_REF] Vicente | Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-Based Work[END_REF] was used as a requirements engineering technique to understand how operations managers currently access information, prioritise goals and communicate decisions. Initially the supervisors answered questionnaires, followed by interviews and observations. Additional questionnaires were provided to further analyse the work flow and to complete task analyses. Supervisors decision making strategies were analysed using the think-aloud protocol [START_REF] Lewis | Using the 'thinking-aloud' method in cognitive interface design[END_REF] and the supervisors actions and thoughts were saved for extended analysis using audio and screen capture software. During interviews and observations three critical requirements of the solution were identified.

Trust

A key challenge with any automated support system is that the final responsibility lies with the human agent. As a result an end user may not respond to a suggestion that they do not fully understand if the possible consequences are severe. Trust in automated systems is a well-known challenge in DSS and observability of system constraints is of key importance to increase user's confidence in the system. The representation of optimisation outputs in a relevant, interpretable format will be critical. In addition, to overcome the perceived brittleness of optimisation engines it is important that the end user can view, assess and edit goals, constraints and rules that act as inputs to the system.

Speed

Responsiveness is another key factor [6]. If a user is to interact and modifying goals and constraints it is important that they can get feedback on the impact of these changes without a prolonged waiting period. Furthermore, as this tool aims to optimise a current schedule it is critical that responses are provided before the current state changes.

Accuracy

The nature of this dynamic scheduling problem favours a satisficing [START_REF] Simon | Rational choice and the structure of the environment[END_REF] approach over maximal optimisation. On top of the speed requirement, it is important to realise that goals can change, different scheduling strategies can be used to arrive at the same outputs and some constraints may not be available to the system and will require adjustments by the end user.

At a high level the main objectives are to optimise energy efficiency by maximising the time that machines can remain in power-save state, without compromising production goals. The impact of the system will be to optimise for these key performance indicators resulting in maximised output, minimised cost and increased machine utilisation.

3

Optimisation Approach

Optimisation Engine Design and User Interaction

The optimisation engine has been designed to allow for a close integration with the final user. The visualisation and user interaction requirements have heavily affected the design approach taken for the optimisation engine.

One of these main requirements is increasing the trust from the user in the results obtained from the optimisation engine. This has been addressed in two different ways, affecting several basic components of the optimiser. First, the user is able to influence the solution selection by inputting his preferences, because depending on the dynamic situation at the plant the user will prefer different alternatives. To provide these options, the optimisation engine follows 2 different algorithms. In the first one the user provides his preferences "a priori", before the optimisation starts. This input will then be used by the algorithms as parameters that will guide the search for the optimum solution that best uses it.

The second approach does not need this prior info. In this case the optimisation algorithm will search for different solutions, together forming what is known as a "Pareto front" (the set of all solutions that are nondominated with respect to each other [START_REF] Martínez-Iranzo | Applied Pareto multi-objective optimization by stochastic solvers[END_REF]). In this set of solutions, no one can be selected as better than another until the user selects one of them, based on his current preferences. This selection involves a certain amount of decrease in optimality in one objective to achieve a gain in another. The maximum number of solutions presented should be limited so as not to overload the supervisor and allow him to concentrate on a few possibilities. The user interview has shown that a maximum of 3 options is needed. The optimisation engine uses this requirement to select 3 solutions among the whole Pareto front that are different enough to provide a significant diversity of options to the user. The algorithm being currently under testing is the NSGA-II [START_REF] Deb | A fast and elitist multiobjective genetic algorithm: NSGA-II[END_REF] which uses an elitist, non-domination approach, together with a crowded-comparison operator. There plans to test other algorithms in the future, like for example Harmony Search.

The other design decision to increase trust has been using a simulation-based optimisation so the user gets detailed info on all the consequences/implications of the proposed solution. For example, machine utilisation, energy savings per machine, % of committed production in time, utilisation of tools in machines, etc. A discrete-event simulation is used, which provides a descriptive environment for such highcomplexity systems as the one under study [START_REF] Can | A comparative study of genetic algorithm components in simulation-based optimisation[END_REF]. In this way the system observability and transparency is greatly increased. Figure 1 shows a high-level abstraction scheme of the DSS.

Fig. 1. Optimisation Engine -Visualisation Relation

This simulation design approach also opens the possibility to use this run-plan as a base for the introduction of new user-defined constraints directly over it. The user can introduce particular changes by using drag and drop or he can introduce them as general rules. For example, the user may want to select any machine to do some engineering work, but does not care which one. Then the optimisation engine will decide which machine to select.

The main features of the proposed optimisation engine are as follows:

 Multi-Objective. In a realistic manufacturing environment the final user has to balance different, often conflicting goals. This issue is explained below in more detail.  Use of meta-heuristic algorithms e.g. simulated annealing and genetic algorithms.

These types of techniques are approximated, meaning that they are not able to always produce the optimal solution, but have the potential to produce good solutions in a short time.  Simulation-based: the optimisation algorithm relies on a simulation of the process to provide the fitness values for each solution. An advantage of this approach is that it makes possible to provide the final user with increased environment descriptiveness, increasing the trust.  Integration of PPIs (Production Performance Indicators) that act as goals and evaluation criteria for both the optimisation engine and the end user. PPIs are contextual information that provides common ground for human and automated agents.  The user-defined constraints pose a problem of dynamic rescheduling, with the added difficulty that the time to get a solution adapted to the new scenario is limited.

To provide a solution to the new problem, the initial solution used for the rescheduling problem is obtained from the last solution generated for the old problem. This new starting solution must be first updated to the new problem, by deleting the operations already started. Starting with an already "good" solution, together with the reduction in the search space, will decrease the runtime needed to provide the new solution [START_REF] Bierwirth | Production scheduling and rescheduling with genetic algorithms[END_REF]. It is also necessary to adjust different algorithm parameters, like for example crossover operator and mutation rate in the case of genetic algorithms, to adapt to the new situation.

Multi-Objective Optimisation

This problem is defined as a multi-objective optimisation problem (MOO). There is no single global optimum solution in MOO problems. Instead of a single solution, there are a set of optimal valid solutions with different objectives magnitudes (fitness values) [START_REF] Marler | Survey of multi-objective optimization methods for engineering[END_REF].

A general MOO problem can be defined as:

Minimise: ( ) [ ( ) ( ) ( )]
Subject to: ( ) where ( ) is the set of n objective functions, x is the vector of decision variables and are the m independent constraints. In this use case the two following objectives are considered:  Maximise duration in power saving state.  Minimise the number of machine setups.

Multiple constraints are present in the model. Among the most important we can list the following:  Meet production target. This is the hardest constraint in the model, involving a given number of different product types at a predefined time.  Inventory arrival time.  Machine setups require product-specific collateral equipment, of which there is a limited amount.  User-Defined constraints: the user is able to change constraints online through the visualisation, such as fixing the state of a machine in a specified time.

The constraints impose limits in what is called the "feasible space", or the set of valid solutions that meet all constraints. The optimisation algorithms then try to find the Pareto front that optimises all objectives subject to those constraints.

In addition, the optimisation engine should be able to suggest maintenance timing within a defined window.

Visualisation approach

The output of the optimisation algorithm is a schedule of machines transitioning between states across a production period. This schedule is presented to the user in the form of a run-plan (outline shown in Figure 1). Different machine are listed top to bottom on the left of the display. Machine states are shown over time from left to right and are colour coded so that idle states are highly salient and demand attention. The user can zoom into different time periods to assess production schedules across a shift, a day or an entire week. The current time is clearly marked and past events are shaded out to indicate that they are non-editable. On loading the screen, the user is presented with the current schedule but this may be manually edited or re-optimised by clicking on the appropriate function buttons located above and below the runplan. User defined rules may be generated based on an expert users knowledge. For example a user may wish to spread out machine setups due to an unexpected resource constraint. This is achieved by simply dragging the setup event forward or backward in time. Similar actions can also be carried out to input or modify maintenance or power saving states. The panels on the right of the run-plan communicate important production performance indicators. These are updated as the user modifies the run plan in order to allow them to assess the impact of their actions. Following updates the end user can commit the run plan or may choose to re-optimise with their newly defined constraints. The tabbed menu at the top of the screen allows the user to try out multiple runplans to assess which strategy best suits their needs.

Conclusions

This paper describes a decision support system (DSS) for improving the energy efficiency of production plans. The research aims to improve trust in DSS, and in turn its overall effectiveness, by combining an optimisation engine with a dynamic visualisation that:

 Communicates the rules and results of an optimisation engine in an intuitive manner  Allows end-users to generate dynamic constraints using simple drag and drop actions  Supports experimentation using what-if scenario planning  Generates a production plan incorporating energy saving opportunities This concept applies principles from joint cognitive system research to support complex decision making through the combination of human flexibility with computational power. A functional prototype has been developed and future work will focus of improving the performance of the algorithms, integration with a live production system and evaluation with end users.

Fig. 2 .

 2 Fig. 2. Run plan Visualisation

Acknowledgements:

The presented work has been funded by the European Union FP7 "Factories of the Future" research project "KAP: Knowledge, Awareness and Prediction" (Grant agreement no. 260111, http://www.kap-project.eu). The authors would like to acknowledge the support and contributions of KAP industrial and academic partners in the development of this work. We also extend our thanks to the Irish Centre for Manufacturing Research (www.icmr.ie) which is supported by Enterprise Ireland and the Industrial Development Authority of Ireland.


Paolo Calefati
email: paolo.calefati@primapower.com

John Pandremenos

Apostolos Fysikopoulos

George Chryssolouris

Energy Efficient Process Planning System -The ENEPLAN Project

Keywords: Energy efficiency, process planning, Meta-CAM tool, process optimization

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Today's manufacturing plants provide a number of different processing possibilities for the manufacturing of a specific product. Each one of these processing possibilities poses different advantages and limitations that are functions of both the geometry and the lot size of the part to be manufactured. However, one of the main driving forces in today's production, as shown below, is the environmental friendliness and the energy efficiency of the production itself. Additionally, the manufacturing processes are required for quickly enabling the shifting between diverse manufac-turing operations with short transfer, program and set-up times without compromises to quality, reliability and life-cycle costs.

The machine tools sector is one of the most relevant in Europe in terms of GDP (180 billion € of new orders before the crisis, 74 billion €/y in Q2 2010) [START_REF]CECIMO Statistical Toolbox[END_REF], the sector mainly comprises SMEs (among machine users: 99.7% by number, 78.2% by GDP, 82.8 by persons employed among machine manufacturers: 98.7% by number, 49.7 by GDP, 56.5 by persons employed) [START_REF]EUROSTAT 2010[END_REF]. In the same way the relevance of the sector in terms of energy consumption is high (>10,000 PJ/y) [START_REF]Draft Working Plan of the Ecodesign Directive[END_REF] and consequently, the financial pressure is high due to the unbalanced demand/offer of energy, which results in increasing the energy price (as in [2007][2008]. Additionally, the recent developments in manufacturing production technologies brought about machines and processes of higher performance and higher energy utilization, in order for the productivity and reliability of the process to be increased. Besides, the growing environmental awareness of people worldwide has led to legislative pressure (EuP Directive) [4] and it is highly probable that tool machines will be subjected to energy efficiency regulations and classification in the years to come [START_REF] Dietmair | Energy consumption forecasting and optimization for tool machines[END_REF]. The potential of energy savings in the machine tool sector is high (e.g. low power factor of 0.7-0.8), as the potential efficiency increases by exploiting the Design for Environment strategies (e.g. reduction of mass, materials used, extension of tool lifetime, optimised PWB, CPU, monitor) [START_REF]Draft Working Plan of the Ecodesign Directive[END_REF]. Recent studies have shown that there is heavy unnecessary energy use in the industrial sector; typical figures for machining equipment are in the range of 20%-50%.

Today's European market has to deal, more than ever before, with highly demanding customers. In order for these advanced needs to be addressed, a large number of alternative products are being produced from the same production line within the industry. Additionally, the products are characterized by a short lifecycle since they are quickly depreciated. Thus, flexible manufacturing processes should enable the immediate shifting between diverse manufacturing operations with short transfer, program and set-up times as required, without compromises to quality, reliability and life-cycle costs.

Today's process planning [START_REF] Papakostas | On the configuration and planning of dynamic manufacturing networks[END_REF] approach is based on expert systems that are able to propose alternative process plans for the manufacturing of a specific product. Additionally, there are tools that can simulate the operation of each machine station both from technological (process parameters, energy efficiency, etc) and economical point of view. All of the systems available though, can cover only portion of the production, requiring the exchange of data between different systems and thus, making the overall optimization of the plant operation a hard task.

ENEPLAN Project [START_REF]ENEPLAN site[END_REF], as an NMP (Nanotechnology and nanosciences, knowledge-based multifunctional materials and new production processes and devices), is the Third Thematic Priority of the Focusing and Integrating research part of the Sixth Framework Programme aiming at Industrial research -which remains a key objective of FP6 in an evolutionary world. ENEPLAN is co-ordinated by the PRIMA INDUSTRIE Group and is initiated and managed by the Laboratory for Manufacturing Systems and Automation of the University of Patras. This project aims at delivering a manufacturing planning decision support tool for the optimization of the plant operation that will be able to be used from the conceptual phase of the product up to its final dispatch to the customer. The ENEPLAN consortium consists of 17 partners:

• two top-level OEMs from two different industrial sectors (CRF and GORENJE) • one aeronautics sector supplier (NEW) • four metal forming machine builders (PRIMA, FINNPOWER, GIZELIS and GIGANT) • three users of those machines and suppliers of metal formed components to the above mentioned OEMS (TEKS, IAM and EXALCO) • four high-ranked European university Labs (LMS, VTT, ITIA-CNR and AMRC) • three "knowledge intensive" companies as RTD providers (CADCAMATION, CASP and IDEKO).

ENEPLAN's View Beyond the State of the Art

ENEPLAN aims at minimizing the inactivity time and waste streams, parameters that cover more than 30% percent of the energy used by machine tools. By using LCA methods and taking into consideration the consumption of natural resources, the waste streams will be accurately estimated and their environmental impact is to become clear. Furthermore, reliable and flexible process monitoring and controlling systems, for both conventional and non-conventional production technologies, will be developed. Finally, the introduction of novel simulation systems will result in reducing the required pre-production setup experimentation effort and subsequently increase the energy efficiency of the processes and the relevant machine tools.

The problem of energy management is solved by Load Management Systems; when a peak occurs, usually the system reactively reduces the power, instead of suggesting a better production planning [START_REF] Jebaraj | A review of energy models[END_REF][START_REF] Bhatt | Energy audit case studies I -steam systems[END_REF][START_REF] Trygg | Swedish Industrial and Energy Supply Measures in a European System Perspective[END_REF]. Within ENEPLAN, a set of developments such as the identification of product's unique signature (CAD model, engineering requirements etc.), the monitoring systems and the multi-objective simulation techniques, will be all embedded into a Meta-CAM tool in order to be utilized for the optimization of the production planning, in terms of energy efficiency and other defined aspects. The existing systems are not capable of addressing the simultaneous consideration of many parameters, during optimization.

Since simulation engineering has since long been believed to be an enabling technology for life-cycle decision support [START_REF]Blue Ribbon Panel: Simulation Based Engineering Science[END_REF][START_REF] Fu | Integrating Optimisation and Simulation: Research and Practice[END_REF][START_REF] Tempelmeier | Practical considerations in the optimization of flow production systems[END_REF], ENEPLAN will introduce a breakthrough to the current problem of optimization and decision making with simulation. Novel multi-objective simulation techniques will be introduced, with integrated techno-economical parameters and the assessment of different automation options will be applied to the evaluation of the process plans and the optimization of the production planning. In order for the process plan to be accurately evaluated, special attention will be paid towards the precise estimation of demand and production requirements throughout the product's life cycle. By utilizing analytical and numerical models on macro and micro/nano-scales, new reliable, production oriented process models, based on process parameters and particular process conditions, will be developed. Furthermore, models, either analytical and computational, or empirical, will also be developed for the analytical description of conventional and non-conventional processes.

Taking into consideration the state of the art [START_REF] Chryssolouris | Decision Making on the Factory Floor: An Integrated Approach to Process Planning and Scheduling[END_REF][START_REF] Chryssolouris | Manufacturing Systems: Theory and Practice[END_REF][START_REF] Chryssolouris | Digital manufacturing: history, perspectives, and outlook[END_REF][START_REF] Chryssolouris | A perspective on manufacturing strategy: Produce more with less[END_REF], the proposed research work will aim at the development of a simple, nonetheless, an effective method that will guide the process engineer in constructing generic process plans adaptable to all possible different scenarios that market needs. Therefore, shortcomings might be revealed in the design of the product and alternative approaches will be assessed for the production of the product (such as use of alternative equipment, resources, or even subcontracting).

During the development of the meta-CAM tool modules, various optimization algorithms and methodologies will be evaluated and compared by their applicability and efficiency. In particular, heuristic methods, specific for energy efficiency optimization, will be used, in conjunction with Pareto-optimal based multi-objective optimization in order for the optimization efficiency to be enhanced. The meta-CAM tool, integrating the output of ENEPLAN RTD efforts, will offer multi-disciplinary and multi-level simulation, optimization and information modeling components. In addition, in order for the integration of them to be supported, a common/open source integration architecture will be used offering extensive compatibility with various production related software.

3

The ENEPLAN Project

The current industrial status indicates that the key factor to success, in a competitive energy consumption reduction, is the effective involvement of SMEs both in a more efficient use of more efficient machines and in the Design for Environment of new products. In the ENEPLAN project, the main objective is the development of manufacturing systems that will be highly flexible, and at the same time, are closely adapted to the single product. These manufacturing systems need an engineering tool that can cover the entire plant operation, from the overall planning of the plant operation (such as the routes that the product follow within the plant and the scheduling of the production) down to the individual process programming. Today's process planning approach is based on expert systems capable of proposing alternative process plans for the manufacturing of a specific product. Additionally, there are tools able to simulate the operation of each machine station techno-economically. All of the systems available though, can cover only part of the production, requiring the exchange of data between different systems and thus, rendering the overall optimization of the plant operation a hard task. The ENEPLAN project will deliver a manufacturing planning decision support tool for the optimization of the plant operation to be used from the conceptual phase of the product up to its final dispatch to the customer.

The Concept

A specific product, based on the cost optimization rather than on production flexibility and energy efficiency, can be manufactured in different ways and by different suppliers. ENEPLAN aims at the development of a digital and real, energy-efficient, multi-process, networked, manufacturing system adapted to the functional specifications of metal formed or machined parts for automotive, aeronautic and domestic appliances: • digital and real: a new CAPP-CAM digital platform will be developed with a DSS that would allow choosing among alternative processing cycles, based on the best compromise between energy and cost efficiency. • energy-efficient: a new optimization strategy will be implemented on the digital platform to minimize lifecycle energy consumption of the selected process. In this optimization materials, process technologies and machine cycles will be taken into account. Furthermore, all the end-users involved will evaluate the substitution of the existing machines with more efficient ones of the next generation.

• multi-process: on the digital platform, alternative processes will be considered for the same product emphasizing a breakthrough potential for energy reduction through the introduction of new technologies in the process (e.g. cutting and forming as alternative to machining). • networked: optimization focus will be given to the manufacturing processes involving both multiple steps and companies along the supply chain, as well as SMEs. The involvement of SMEs is one of the pillars of ENEPLAN and, as a matter of fact, the SMEs partners in ENEPLAN account for more than 40% in number and 25% in budget.

The ENEPLAN's Meta CAM tool functions will perform the following:

• Process CAD data inserted either from a mathematical model or from shape acquisition of physical models through scanning • Accept engineering requirements and material from a human operator • On-line (or short-time) configuration of the manufacturing system • Simulation of an overall manufacturing system, optimization and tuning for top production efficiency and minimum environmental impact • Determine material selection, work sequences, cycles and process routes

The Main ENEPLAN Objectives and Targets

The main objective of ENEPLAN is the development of the Meta-CAM tool for optimum process planning from a given set of production requirements.

ENEPLAN Business Cases

Besides these technical breakthroughs, additional "non-technical" ones will be triggered that will enable a vertical integration in the value chain and a horizontal integration by the ability to transfer the knowledge to other industrial sectors. ENEPLAN will promote breakthroughs on scientific and technical excellence, moving three at least industrial sectors (automotive, aeronautics and domestic appliances) and their involved partners in the vertical value chain, towards a multi-objective optimized production, by combining excellent research capacities of EU OEMs, SMEs, academic partners and technology providers.

The automotive business case will investigate a seat frame. The current automotive seat frame is manufactured by shaping different steel metal sheets or tubes. The Meta-CAM tool will facilitate the evaluation of different technological scenarios and materials, most suitable for a targeted volume production by taking into account a weight and energy manufacturing process reduction. The results will provide an optimization of the final production cost and energy and raw material consumption within the target of a specific production rate at the same original product mechanical performance. Alternatives, in terms of forming technologies and the selection of different raw mate-rials, will be evaluated. Optimization of the process by the elimination of steps, due to a reduction in the parts, may be integrated.

Fig. 3. Application of ENEPLAN's approach to the automotive pilot case

The Aeronautics Business case will use as pilot case the Lading Ramp Hinge of a military aircraft. The target with the use of the ENEPLAN's Meta-CAM tool is the energy consumption reduction and the factory's carbon footprint improvement. Moreover, establishing whether other processes are preferable to milling (main processes currently used), for the machining of the produced part and propose alternate process routes.

The household business will use as a pilot case six versions of the front panels of refrigerator doors. These parts contain high quality demands (Visible surfaces without aesthetics faults and all dimensions in tolerances). Currently, the existing process plan requires a lot of transportation between the different stations that increase the energy consumption. The LCA algorithms that will be included in the Meta-CAM tool will aid in finding alternatives and reducing the environmental footprint in the current factory.

Conclusions

The ENEPLAN project having as its primary objective to help the industry save energy, time and become more flexible, has come up with the development of the ENEPLAN Meta-CAM tool that has already set the specifications of the problem, selected and defined as well as the pilot cases that the Meta-CAM tool will run. The project now is in the phase of modeling and monitoring the business cases processes in order to provide input for the development of the tool. The finalization and validation of the tool is estimated to become true after the April of 2013.

Fig. 1 .

 1 Fig. 1. ENEPLAN Concept

Fig. 2 .

 2 Fig. 2. The Meta-CAM tool This system and its related technologies will enable the: • Environmental footprint reduction for a metal formed component by selecting a more energy efficient combination of processes among those available in the already existing supply chain ─ Target: -40% of the lifecycle energy consumption (from cradle to grave), estimated 8 MJ per component, 14.4 GJ per production life. • Energy efficiency improvement in working conditions ─ Target: estimated energy consumption -30%, 2 MJ per component, 3.6 GJ per production life. • Multi-process, multi-company distributed control ─ Target: use of the same control along the supply chain, possibility to use the system that would adapt the work sequences, the process routes and machines' behavior to the most efficient working conditions.

Acknowledgements

The work reported in this paper was supported by the collaborative program entitled "Energy Efficient Process planning System -ENEPLAN" which in under the seventh framework program -FoF.NMP.2011-1: "The Eco-Factory: cleaner and more resource-efficient production in manufacturing Program".


Iñigo Mendikoa
email: inigo.mendikoa@tecnalia.com

Mikel Sorli
email: mikel.sorli@tecnalia.com

Alberto Armijo
email: alberto.armijo@tecnalia.com

Laura Garcia
email: laura.garciazambrano@tecnalia.com

Luis Erausquin
email: lerausquin@estanda.com

Mario Insunza

Jon Bilbao
email: jbilbao@sisteplant.com

Hakan Friden
email: hakan.friden@ivl.se

Anders Björk
email: anders.bjork@ivl.se

Linus Bergfors
email: linus.bergfors@ivl.se

Romualdas Skema
email: skema@mail.lei.lt

Robertas Alzbutas
email: robertas@mail.lei.lt

Tomas Iesmantas
email: iesmantas@mail.lei.lt

Energy efficiency optimisation in heat treatment process design

Keywords: Energy efficiency, Process design optimisation, Heat treatment

Information and Communication Technology (ICT) tools to support design in terms of energy efficiency optimisation are beginning to come into the market, in particular for energy savings and management in buildings, however little has yet been developed for manufacturing. The work here described is related to the enhancement of existing manufacturing process design methodology and tools with a set of ICT components developed allowing process knowledge based design, simulation and optimisation in terms of energy efficiency. In particular the work described is focused on the heat treatment process of steel casting parts. This process design traditionally consists of a suitable predefined temperature-time curve selection based on personal experience and given customer requirements (material and mechanical properties), yet this curve can actually be optimised in terms of energy consumption and maintenance cost, while keeping required mechanical properties.

Introduction

There are several ICT tools to support process design, addressing especially installation customization to the users' needs. Many of them include simulation of processes, which might be used to implicitly analyse energy use within the design processes.

Research and technology development attempts to address sustainability issues in process design have been made and several tools are available on the market intending to support users in consideration of sustainability problems. However, a systemat-2 ic analysis of energy use within the designed processes and functionality to explicitly support the designer in making decisions relevant for energy efficiency are not provided in most of the currently available design and simulation tools. The current design tools do not explicitly support decisions regarding selection of methods, equipment and materials to use for meeting specifications and for energy efficiency over the whole process life cycle, especially in flexible manufacturing systems asking often for reconfiguration of installations. ICT tools to support design for energy efficiency optimisation are beginning to come into the market, in particular for energy savings and management in buildings, however little has yet been developed for manufacturing.

The work here described is related to the enhancement of existing manufacturing process design methodology and tools with a set of ICT components developed in the framework of DEMI project (FP7-ICT-2009-247831). In particular the work here described is focused on the heat treatment process of steel casting parts and is related to the company Fundiciones del Estanda in Spain, allowing this company to address energy efficiency in this process design.

Manufacturing Process and Energy Consumption Characteristics

Manufacturing process at this company is related to steel casting parts including subprocesses like melting, machining, grinding, cutting and cleaning and heat treatment. This heat treatment process is powered by gas energy, and considering that over 33% of total gas at the company is consumed here, it is reasonable to focus the energy efficient design on this specific sub-process. The main goal is therefore to reduce the energy consumption on the heat treatment process while at least keeping the productivity and resulting mechanical requirements in the steel parts, as well as maintenance costs acceptable. The specific thermal process design for each piece begins with the selection of the suitable temperature-time curve for the part to be treated, which is defined based on personal experience of experts in charge of process design. The "process design" basically consists of the definition of the Temperature-Time curve for the heat treatment for each type of part, given customer requirements (material and mechanical properties). Predefined Temperature-time curves are normally used, yet these curves can actually be optimised in terms of energy consumption, however this optimisation is currently not done at the company. The DEMI tool being developed is intended to provide the company with the capability of designing optimal heat treatment processes.

Heat treatment process, described by the T-t curve, usually consists of some few steps, which can be simplified as an initial heating, temperature holding time t, and a final cooling, but it might be more complex. The considered case study will focus on a particular steel part: brake discs for the high-speed train treated in the Guinea 4 batch furnace. DEMI system will propose the optimal T-t curve, consuming minimum gas providing the mechanical requirements, while maintenance costs must remain acceptable so that the new process is profitable. The software can also optimise the process design in terms of minimal maintenance cost, or even use a multicriteria optimisation strategy considering both weighted energy and maintenance cost.

3

Process optimisation with DEMI tools DEMI ICT tool consists of several ICT components, three of which are involved in the work being implemented at the manufacturing company, namely, 'Energy Dependency Selector' (EDS) supporting a knowledge based preliminary process design, 'Energy Analyser' (EA) for process optimisation and the supporting 'Energy Simulator' (ES) for energy consumption estimation in specific process configuration. Fig. 1 shows the generic process design procedure based on DEMI tools (in grey). All DEMI components communicate each other via web services and sharing information in a Knowledge Repository (KR). In earlier paper the connection between KR-EA-ES for optimisation of configuration of compressed air systems has been described [START_REF] Friden | Energy and LCC Optimised Design of Compressed Air Systems: A Mixed Integer Optimisation Approach with General Applicability[END_REF]. Case Based Reasoning. This functionality provides historic process designs 'similar' to the target process requirements given by the designer. In this case the requirements are those from the customer, basically material and mechanical properties required. This functionality requires historic cases database defined and populated including parameters describing the requirements, in addition to those describing the process design. The 'similarity' of historic processes with respect to required parameters values provided by the user is estimated according to the relative weights given for each parameter. The tool finally provides a list of historic processes sorted by similarity. This functionality implementation is based on the JColibri tool [START_REF] Díaz-Agudo | JColibri Tutorial[END_REF][START_REF] Díaz-Agudo | Building CBR systems with Jcolibri[END_REF] and the process shown in Fig. 2 (left).

Rule Based Reasoning. This functionality provides preliminary process design based on experts' experience implemented in terms of 'rules'. The design sequence implemented with the rule based reasoning approach reproduces the way a process designer follows when designing a process: starting from process requirements the tool provides rule based feedback about the basic process design characteristics. This functionality is based on the tool KnowWE [START_REF] Baumeister | KnowWE -Community based Knowledge Capture with Knowledge Wikis[END_REF][START_REF] Baumeister | Web-Based Knowledge Engineering with KnowledgeWikis[END_REF], for which several plug-ins have been developed in order to connect it to KR. A basic implementation is shown in Fig. 2 (right), where the user introduces Material and Constraints (mechanical properties required) and the tool provides process parameters intervals based on implemented rules, in this way defining a preliminary process design, matching user requirements, for optimisation.

Process optimisation

Once a preliminary process design is done with EDS component, Energy Analyser (EA) component provides process optimisation in terms of energy efficiency and/or maintenance cost, keeping required mechanical properties as constraints. EA provides optimal process design, using in turn Energy Simulator (ES) for energy consumption estimation of each potentially optimal design configuration considered.

Process optimisation with EA. DEMI component EA provides the energy optimised process design from the preliminary design provided by EDS, finding minimum energy given ranges on process variables and constraints on requirements variables. Process variables have to be within their given boundaries and constrained by requirements within constraints given by the user. So EA requires information related to the preliminary process definition (provided by EDS) and boundary limits for all the relevant parameters values.

The procedure is basically as follows: EA Client calls EA with the process configuration ID; EA reads configuration ID with info about the nodes from the Knowledge Repository (KR), as well as initial data, boundaries for process variables, and constraints for quality variables from KR; EA starts Matlab, as a server, for process optimisation; For energy process optimisation, EA uses function fmincon to find minimum energy given ranges on process variables and constraints on quality variables; EA uses ES as a service to evaluate energy for each iterated process during optimisation, gets energy consumption estimations from ES and is able to predict quality variables (mechanical properties) for each configuration; EA finally sends to KR the process parameters values describing the optimal process design.

Process energy consumption simulation. Energy Simulator (ES) estimates the energy consumption of each possible specific process configuration provided by EA component, so that the minimum energy consumption process configuration can be finally selected.

In general, ES is a tool intended for simulation of network system or process, when energy consumption is of interest. ES is constructed as a simulation service able to simulate new/innovative network or system designs and estimate energy use for them. The analytical modelling and simulation is based on "block-based modelling" concept rather than on purely "process-based modelling". It means that whole physical process is decomposed into sub-processes represented by independent blocks (one block for one sub-process). Sub-process is process variable (output of block) related to physical behaviour of other variables (inputs of block) with their weighting coefficients (block parameters). Sub-process may be expressed as a process variable or function dependent on other process variables [START_REF] Iešmantas | Designing of energy efficient system considering power consumption and reliability[END_REF].

ES requires a theoretical model for energy use estimation provided a process configuration. In the case of heat treatment process at Fundiciones del Estanda a basic thermodynamic model has been considered. This model expresses fuel (gas) consumption and energy balance (see an example in Fig. 3) of the heat treatment process, taking into consideration the following energy terms: Energy provided by the gas, Energy lost by fumes, Heat stored by parts after treatment, Heat stored by furnace refractory, Heat losses through the furnace walls. 6

Fig. 3. Example of fuel consumption and energy balance of the heat treatment process

This theoretical model is the basis of ES component, also running in Matlab, which receives a given process design from EA. The data needed for this energy consumption estimation are the following, given by the process parameters values describing the process: Material type (specific heat) of the treated parts, Parts load (mass) to be treated, Difference between parts final and initial temperature (T fin -T in ), Total process time (t fin -t in )=t i , Configuration of heat treatment process.

Energy balance equation of such a process can be simplified as follows:

      in fin W W in ref fin ref refr refr in fin st st F F CI gas t t A P T T c M T T c M h M P Q         
These terms, considered along the whole process time, have the following meaning:

 Energy provided by the gas, depending on gas consumption (time dependent) and gas calorific power. Total gas consumption is the parameter to be estimated.  Energy lost by fumes, depending on fumes mass and enthalpy.  Heat stored by parts after treatment, depending on the parts load, steel specific heat and temperature net increment of the parts.  Heat stored by furnace refractory, depending on the furnace refractory mass, its specific heat and temperature net increment.  Heat losses through the furnace walls, depending on energy loss per unit time (time dependent), furnace walls area and process duration.

In order to structure the workflow of power consumption estimation, ES employs four main functionalities: Get configuration (after unique ID from EA is received, ES will connect via web services to the KR and retrieve information about the process configuration), Add blocks (identifies different phases of heat treatment process), Specify connections (creates the heating model from specific phases identified in the previous functionality), Calculate energy use of process configuration (performs estimation of used gas volume together with consumed power).

3.3

Mathematical modeling approach for process optimisation

As described above, in order to optimise the heat treatment process design keeping the resulting steel parts' mechanical properties, some mathematical models have to be ready relating the different sets of parameters involved. If an analytical model considering the physics involved is not achievable due to the problem complexity, an empirical model approach should be followed, which would require a huge historic database allowing correlations among parameters be found.

The different sets of parameters considered are the following: In order to optimise the process design these sets of parameters have to be linked through mathematical models and expressed as objective function. It is important to remark that these models are intended to allow a comparison between different process designs performance, rather than accurate predictions on energy use, maintenance cost or mechanical properties, so that alternative process designs proposed by EA can be simulated by ES, compared by EA and finally an improved process design provided as result part of the optimisation procedure. The relations between sets of parameters and mathematical modelling approach considered are the following:


 Relationship between process design parameters and mechanical properties required. This model is based on experts' considerations about the influence of process parameters over the resulting mechanical properties. In this way a process optimisation can be performed while keeping constraints on mechanical properties. For example, optimal process might have a lower heating speed or maximum temperature, in this way decreasing the energy consumption, while resulting mechanical properties are still kept within specifications.  Relationship between process design parameters and maintenance cost. According to experts' criteria, the impact of process parameters modifications in maintenance cost can be estimated, and optimisation in terms of maintenance cost performed.  Relationship between process design parameters and energy consumption. A simplified analytical model has been created, as described in section 3.2, allowing an estimation of energy consumption given a process configuration.

Conclusions

The set of tools developed and here described allows a manufacturing company dealing with heat treatment to optimise the process in terms of energy efficiency and maintenance costs while keeping the mechanical properties of the treated parts requested by the customer. Tools supporting knowledge based decision making as those described in Energy Dependency Selector component allow the company to keep and reuse the experts' knowledge and experience for a quick preliminary process design, while Energy Analyser and Energy Simulator components support the process design optimisation. All these components can work in a distributed way with a common Knowledge Repository, based on web services. On the other hand, this set of tools can be adapted to other manufacturing processes where correlations between process parameters and resulting quality parameters can be established, as well as a model for energy consumption estimation, so the approach and tools here presented are portable to other manufacturing processes and therefore to other manufacturing companies.

Fig. 1 .

 1 Fig. 1. Manufacturing process optimal design with DEMI components

Fig. 2 .

 2 Fig. 2. CBR (left) and RBR (right) implementation for preliminary process design

  Heat treatment process parameters, including: Date/time, Material Load, Type of material and Heat treatment Temperature-time curve, for a simplified process defined by initial temperature, maximum temperature, time at maximum temperature and heating/cooling speed.  Measured resulting mechanical properties of parts after heat treatment processes, currently stored in quality tests documentation at the company. Mechanical properties considered are the following: Yield and Ultimate strength, Strain, Resilience and Hardness.  Maintenance cost parameters of historic processes, currently stored at Fundiciones del Estanda by PRISMA software database, provided by the company Sisteplant [7]. The main maintenance cost parameters considered are: Mean Time Between Failures (MTBF), Number of breakdowns (per year), Cost of breakdown (average working hours, material, non productive time).

Acknowledgements. The work here described is being developed in the context of project DEMI "Product and Process Design for AmI Supported Energy Efficient Manufacturing Installations" (2010-2013), funded by the European Commission under the FP7 (ICT-2009-247831) [8].