Leslie Jacquemin
email: leslie.jacquemin@ensiacet.fr

Pierre-Yves Pontalier

Caroline Sablayrolles

Life cycle assessment (LCA) applied to the process industry: a review

Keywords: Life Cycle Assessment, Process Systems Engineering, Environmental Design and Optimization, Eco-Friendly processes, Industrial Ecology

Purpose: Life cycle assessment (LCA) methodology is a well-established analytical method to quantify environmental impacts, which has been mainly applied to products. However, recent literature would suggest that it has also the potential as an analysis and design tool for processes, and stresses that one of the biggest challenges of this decade in the field of process systems engineering (PSE) is the development of tools for environmental considerations.

Method: This article attempts to give an overview of the integration of LCA methodology in the context of industrial ecology (IE), and focuses on the use of this methodology for environmental considerations concerning process design and optimization.

Results:

The review identifies that LCA is often used as a multi-objective optimization of processes: practitioners use LCA to obtain the inventory and inject the results into the optimization model. It also shows that most of the LCA studies undertaken on process analysis consider the unit processes as black boxes and build the inventory analysis on fixed operating conditions.

Conclusions:

The article highlights the interest to better assimilate PSE tools with LCA methodology, in order to produce a more detailed analysis. This will allow optimizing the influence of process operating conditions on environmental impacts and including detailed environmental results into process industry.

Introduction

Since the beginning of the eighteenth century, the industrial society has grown up and spurred an increase in production of goods and services. Because the availability of raw materials is not unending and the ecosystem is not able to absorb infinite quantities of pollutants, environmental damages have risen. This has stimulated the birth of environmental policy [START_REF] Eliceche | Environmental life cycle impact as a tool for process optimisation of a utility plant[END_REF], and thus the development of environmental assessment methodologies in order to lower the environmental footprints of product manufacturing [START_REF] Jolliet | Life Cycle Assessment: understand and perform an Eco-balance[END_REF][START_REF] Telenko | A compilation of design for environment principles and guidelines[END_REF]. This awareness of environmental concerns has led the manufacturing industry to become proactive in the design of new products, improve those which already exist and develop cleaner manufacturing processes [START_REF] Harold | Process engineering in the evolving chemical industry[END_REF]. Alongside this phenomenon, the concepts of "industrial ecology" (IE) and "design for environment" (DfE) have appeared. IE is defined as a "systems-based view of how, where and why environmental improvements can be made to develop a sustainable industry", which means "meeting the needs of current generations, without sacrificing the needs of the futures ones" [START_REF] Anastas | Life cycle assessment and green chemistry: the yin and yang of industrial ecology[END_REF]. [START_REF] Seager | A uniform definition and quantitative basis for industrial ecology[END_REF] defined IE as "a field of study concerned with the inter relationships of human industrial systems and their environments". The notion of DfE is the "field of product design methodology that includes tools, methods and principles to help designers reduce environmental impacts". Both are general concepts where environmental tools are developed, especially life cycle assessment (LCA) which is considered as a well-established analytical method to quantify environmental impacts of a product, a service or a production process.

During the early years of LCA, the methodology was mostly applied to products but recent literature suggests that it also has the potential as an analysis and design tool for processes [START_REF] Burgess | Application of life cycle assessment to chemical processes[END_REF][START_REF] Gillani | Review of Life Cycle Assessment in Agro-Chemical Processes[END_REF].

Simultaneously, in the field of process systems engineering (PSE), which deals with the design, operation, control and optimization of processes thanks to systematic computer-based methods, literature reveals the need to include environmental considerations in order to develop a more sustainable industry, and stresses the opportunity for adapting LCA methodology to PSE tools [START_REF] Allen | Green engineering: Environmentally conscious design of chemical processes and products[END_REF][START_REF] Grossmann | Challenges in the new millennium: Product discovery and design, enterprise and supply chain optimization, global life cycle assessment[END_REF]Grossmann and Westerberg 2000;[START_REF] Harold | Process engineering in the evolving chemical industry[END_REF]. This article will attempt to give an overview of the state of LCA methodology in the context of IE, and more precisely, to focus on LCA used as a methodology for environmental considerations affecting process design and optimization. The review will begin with a general description of the DfE principle and the opportunities regarding the integration of sustainability principles into PSE. Then the focus will shift to the interest of LCA, especially in the process industry.

Lastly, the opportunities to integrate PSE tools with LCA methodology are highlighted. Actually this will allow producing more detailed analysis on the influence of process operating conditions on environmental impacts. The systematic integration of PSE tools into LCA for the environmental evaluation of industrial processes implies the need to adapt both LCA and PSE tools but will bring more comprehensive results.

Industrial Ecology and Process Systems Engineering

Design for environment: history and principles

During the second part of the twentieth century, the industrial sector became aware of the negative impacts generated by human activities. This induced reactivity and the development of new behaviours in order to avoid environmental damage. The first industries to come under scrutiny were the chemical processes and heavy industry sector, however this has tended to evolve to cover other sectors and different sizes of industry. The response of industry to mounting environmental pressure was progressive (Figure 1): it began with a "reactive period" (1970s) and crossed a "compliant period" (1970s-80s) before reaching a "proactive period" (1990s) with a real industrial response to environmental issues [START_REF] Young | Manufacturing and the environment[END_REF]. The most recent period is an "integrated and progressive period". Actually, a framework has been found thanks to the rapid evolution of DfE concept and its standardization into the ISO 14062 EMS (ISO 2003), which allows integrating environmental preoccupations from the early stages of the conception to the industrial production process. Moreover, the more and more widespread use of strong concepts ("industrial ecology", "eco-efficient manufacturing"), the development of some tools like green chemistry principles [START_REF] Anastas | Green Chemistry: Theory and Practice[END_REF] and the generalization of LCA methodology show the progressive behaviour and the ambition of the process industry to improve their environmental footprint.

Figure 1. Industrial response to environmental issues (inspired from Young et al. 1997)

DfE is a preventive approach, which involves the incorporation of environmental considerations into the design and optimization of products, processes and management systems at the early stages of conception, in order to minimize environmental impacts [START_REF] Sroufe | The new product design process and design for environment -"Crossing the chasm[END_REF]) and avoid having subsequent reduction measures [START_REF] Gasafi | Using Life-Cycle Assessment in Process Design[END_REF]. For example, in the chemical industry, DfE is apparent via development of "green" chemical routes, process intensification and process redesign [START_REF] Bakshi | The quest for sustainability: Challenges for process systems engineering[END_REF]. According to [START_REF] Ernzer | An international study on utilization of design for environment methods (DfE) -a pre-study[END_REF], most results end up in scientific publications rather than being transferred into practice, which implies that the number of design methods and tools used in the industry is relatively small compared to the number of existing ones. However, with the development of the EMS ISO 14062 standard (ISO 2003), which gives the general environmental integration principles and defines the effects on the environment to be considered during design and development stages, DfE principles are increasingly structured and have begun to be widely applied in various sectors.

It is difficult to draw up an exhaustive list of DfE tools. Moreover, it is a pity to restrict the analysis to the design of processes, because many other tools are conceived in order to improve these processes even when they are already developed, which is why in the following part, a description of the main environmental assessment techniques will be made, first on DfE, and then widened to assessment techniques in order to give a broader description and situate the application of LCA within it.

Environmental assessment techniques

Various assessment techniques can be applied, during the conceptual and embodiment design phases preceding LCA, when lack of time and detailed information prohibit a full LCA or simply when, for many reasons, it is just more suitable.

One of the first examples quoted in the literature is the elaboration of principles and guidelines to guide designers [START_REF] Ernzer | An international study on utilization of design for environment methods (DfE) -a pre-study[END_REF]. For example the twelve principles of Green Chemistry, were created in order to design chemical processes and products that reduce or eliminate the use and generation of hazardous substances. And [START_REF] Anastas | Life cycle assessment and green chemistry: the yin and yang of industrial ecology[END_REF] established a list of principles to prevent pollution during the life cycle step of chemical products or processes.

However, these kinds of DfE principles have been developed by different designers in a large variety of industries [START_REF] Hauschild | Design for Environment --Do We Get the Focus Right[END_REF], and the guidelines scattered throughout the literature are often focused on individual life cycle stages (design for recycling, design for energy efficiency…). [START_REF] Telenko | A compilation of design for environment principles and guidelines[END_REF] gives an overview of the different DfE principles, guidelines and checklists thus available. The aim was to synthesize them into comprehensive categories and hierarchical levels by developing an original methodology, and this resulted in the birth of 6 principles and 67 guidelines. However, if these guidelines often improved products, sometimes they were not well adapted to the context. For example, longevity of a product often means lower environmental impact, but if the product consumes large amounts of material or energy during its use, a short product life may be preferable. [START_REF] Hauschild | Design for Environment --Do We Get the Focus Right[END_REF] argued that an intuitive approach to DfE can fail to optimize overall environmental performance, which could be avoided by adopting a systematic, analytical approach and building a hierarchy of importance, which may explain why, in DfE methodologies, those which are more systematic and detailed are the most frequently adopted.

One of the most widespread methods is multi-objective optimization that consists of simultaneously optimizing two or more conflicting objectives [START_REF] Alexander | Process synthesis and optimisation tools for environmental design: methodology and structure[END_REF]Baratto et al. 2005;[START_REF] Dietz | Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations[END_REF][START_REF] Hermann | Assessing environmental performance by combining life cycle assessment, multi-criteria analysis and environmental performance indicators[END_REF]). The initial idea was that it was impossible to satisfy simultaneously economic, social and environmental objectives, but possible to define a tradeoff between these objectives, thanks to a multi-objective optimization [START_REF] Alexander | Process synthesis and optimisation tools for environmental design: methodology and structure[END_REF].

Environmental risk assessment (ERA) is another interesting design tool for the improvement of existing processes. The general principle consists of estimating and evaluating risk to the environment caused by a particular activity or exposure [START_REF] Burgess | Application of life cycle assessment to chemical processes[END_REF], and then developing risk management in order to reduce the risks of harmful effects to man and/or the environment [START_REF] Olsen | Life cycle impact assessment and risk assessment of chemicals -a methodological comparison[END_REF].

Cost-benefit analysis is a totally different approach relative to the "environmental economics" field, consisting of evaluating project quality by estimating its "real economic value". This means taking into account the economic value of any loss or gain of environmental quality in the costs and benefits evaluation of a project. Thus, the total value of a project is obtained by summing all market and environmental costs and benefits [START_REF] Pearce | Cost-Benefit Analysis and the Environment: Recent Developments[END_REF].

Environmental impact assessment (EIA) aims to predict and evaluate the environmental consequences of human activities, before they begin (Morgan 1998). This technique considers both environmental and socio-economic issues relative to a proposed project, usually by using checklists of potential environmental impacts, in order to provide qualitative and quantitative information, which then permits minimization of environmental impact and identification of benefits [START_REF] Burgess | Application of life cycle assessment to chemical processes[END_REF]. However, because this method focuses on a specific project (site specific, activity specific, and time specific), it is often more a legal procedure than a detailed environmental assessment tool [START_REF] Jolliet | Life Cycle Assessment: understand and perform an Eco-balance[END_REF].

At least, life cycle assessment, is the most well-known and powerful tool within DfE which will be described later.

However, it appears that LCA is more reliable when coupled with other environmental approaches. In 2001, Olsen et al. produced a comparative study of LCA and ERA applied to chemicals that described the two methodologies and identified harmonies, discrepancies and relations between them. In the context of chemicals, the authors highlight differences between ERA, as an "absolute tool" able to predict the occurrence of adverse effects, and LCA, as a "comparative tool" used for environmental improvement of products. They also concluded that because they fulfil different purposes, both are necessary and cannot substitute for each other; they are complementary. [START_REF] Hermann | Assessing environmental performance by combining life cycle assessment, multi-criteria analysis and environmental performance indicators[END_REF] described an environmental assessment combining LCA, multicriteria analysis, and environmental performance indicators. The authors developed a new tool to perform an overall environmental assessment, involving solely the strengths of the three methods, releasing the user from their weaknesses: COMPLIMENT (COMbining environmental Performance indicators, LIfe cycle approach and Multi-criteria to assess the overall ENvironmental impacT). As well as applying this methodology to the specific case of eucalyptus pulp production in Thailand, the article gives an overview of studies that have combined several assessment tools. Recently, the coupling of exergy and environmental analysis in order to determine the environmental efficiency of the biological energy conversion process revealed the dependence between the thermodynamic parameters of the process, the operating conditions used and its environmental impacts [START_REF] Buchgeister | Exergoenvironmental Analysis -A New Approach to Support the Design for Environment of Chemical Processes?[END_REF].

Towards sustainable PSE?

Process systems engineering is a relatively young field of chemical engineering (about 35 years old), focusing on the design, operation, control and optimization of processes via the systematic aid of computer-based methods. This field "develops methods and tools that allow industry to meet its needs by tying science to engineering" [START_REF] Grossmann | Challenges in the new millennium: Product discovery and design, enterprise and supply chain optimization, global life cycle assessment[END_REF], and encompasses a vast range of industries, such as petrochemical, mineral processing, advanced material, food, pharmaceutical and biotechnological. The significant accomplishments and the future challenges for PSE are summed up in Table 1 [START_REF] Bakshi | The quest for sustainability: Challenges for process systems engineering[END_REF][START_REF] Grossmann | Challenges in the new millennium: Product discovery and design, enterprise and supply chain optimization, global life cycle assessment[END_REF]Grossmann and Westerberg 2000). As well as these accomplishments and challenges, PSE has played an important role over the last decade by developing many useful concepts, tools and techniques for improving the viability of chemical processes, making them more and more industrially feasible [START_REF] Grossmann | Challenges in the new millennium: Product discovery and design, enterprise and supply chain optimization, global life cycle assessment[END_REF]), e.g. the use of statistical signal processing techniques in process operation, or the optimization and use of artificial intelligence methods in process design. In 2000, Grossman and Westerberg broadened the definition of PSE to "the improvement of decision-making processes for the creation and operation of the chemical supply chain. It deals with the discovery, design, manufacture and distribution of chemical products in the context of many conflicting goals". This broadening to encompass the whole chemical supply chain (from the molecular to the company level) gradually led to the integration of safety and environmental factors as well as economics.

Consequently, the emergence of environmental considerations and sustainability as a new industrial challenge give to PSE the opportunity to play an important role, by modifying the design and operation of existing processes, and then developing new products and technologies that are designed according to environmental considerations [START_REF] Bakshi | The quest for sustainability: Challenges for process systems engineering[END_REF]. In the same vein, Grossman et al. (2004) argued that environmental protection will become an important challenge for the process industry, which must be urgently and effectively addressed, because it has a profound effect on the long term viability and acceptance of the chemical industry. The author stressed that a stronger interaction between product and process design as part of LCA could be an interesting improvement. A full LCA would include a "cradle-to-grave" approach by considering each step of the life cycle: design/development of the product, raw material acquisition, manufacturing, distribution use/maintenance/re-use and end-of-life activities. The methodology is usually described under four different steps:

• Goal and scope definition: This step consists of drawing the studied system boundaries to ensure that no relevant part is omitted.

• Inventory analysis: Often based on a mass and energy balance, this step compiles and quantifies inputs (raw materials and energy) and outputs (wastes and others emissions) relative to the system throughout its life cycle. A review and comparison of life cycle inventory (LCI) methodologies was given by [START_REF] Suh | Methods for Life Cycle Inventory of a product[END_REF], which identified six different methods and three hybrid approaches.

• Impact assessment: This step consists of aggregating and identifying the environmental burdens quantified in the inventory analysis, into environmental impact categories [START_REF] Azapagic | The application of life cycle assessment to process optimisation[END_REF] • Interpretation: This last part allows conclusions to be drawn concerning environmental damages generated by the system, using results provided by the impact assessment step.

LCA methodology and limitations have been widely described and improved over the last three decades, and are covered in many articles [START_REF] Ayres | Life cycle analysis: A critique[END_REF][START_REF] Guinée | Life Cycle Assessment: Past, Present, and Future[END_REF][START_REF] Thorn | Life-cycle assessment as a sustainability management tool: Strengths, weaknesses, and other considerations[END_REF]). [START_REF] Rebitzer | Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications[END_REF] provided a welldetailed two-part methodology review, covering the framework, goal and scope definition, inventory analysis and application in the first part and current impact assessment practice in the second [START_REF] Pennington | Life cycle assessment Part 2: Current impact assessment practice[END_REF][START_REF] Rebitzer | Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications[END_REF]). Recently, Finnveden et al. (2009) published a review dealing with recent developments in LCA methodology.

This article focused on areas with significant methodological development such as definition of attributional and consequential analysis, system boundaries and the improvement of allocation rules, the development of new inventory databases, current developments in LCIA and lastly improvements made regarding consideration of uncertainties.

Concerning consequential LCA, which represents the convergence between LCA and economic modelling methods, research and applications are in their infancy although a very detailed review has been made by [START_REF] Earles | Consequential life cycle assessment: a review[END_REF],

where the authors have covered the historical development of this particular methodology, plus previous literature on the topic, bringing an interesting perspective to this new methodological approach.

Historical review of LCA methodology

Azapagic (1999) and [START_REF] Burgess | Application of life cycle assessment to chemical processes[END_REF] provided a brief history of the methodology from its original form ('net energy analysis studies' 1970), to its slow evolution (the consideration of waste and emissions), and then the creation in 1993 of a general method for conducting effective LCA studies by the Society for Environmental Toxicology and Chemistry (SETAC 1993). They also described and discussed aspects of the ISO standard (ISO 1997) and stressed the specificity of including a sensitive analysis for this latter. [START_REF] Young | Manufacturing and the environment[END_REF] proposed a chronological study of the industrial response to environmental preoccupations, and more recently, [START_REF] Guinée | Life Cycle Assessment: Past, Present, and Future[END_REF] provided an article dealing with a detailed history of LCA methodology and its probable evolution in the years to come, totally in line with the previous work. This article describes how LCA, which was basically a tool for evaluating environmental impact, was integrated and promoted by governments all around the world as the core element of their environmental policy. The authors present an original point of view and divide the last four decades into three main categories:

• 1970-1990 is named 'the decade of conception', because widely diverging approaches, terminologies and results were developed during this period. This can be explained by a clear lack of international communication concerning the methodology employed. The evolution between 1970 and 1990 as a reactive period moving to a compliant period explains the discrepancy in methodology by the fact that it takes time to become aware of environmental aspects [START_REF] Young | Manufacturing and the environment[END_REF]).

• 1990-2000 is, for the authors, the 'decade of standardization'. The period was one where the drawbacks in the methodology were identified and a more common theoretical framework developed. The 1990s was a proactive period during which the SETAC coordination and the ISO standardization converged on the different framework developed. However, the ISO never provided a detailed standardization because "there is no single method for concluding LCA".

LCA application fields

At the end of the twentieth century, the adoption by industry of the LCA approach was recognized as relatively slow, but the methodology was progressively gaining acceptance. Some sectors such as plastics, detergents, personal care products and automobiles were identified as pioneers investing in LCA. They were closely followed by agriculture, mining and oil and gas extraction, the construction/building material sector, manufacturing industries and retailing, and more recently by infrastructure industries (electricity, gas and water supply, transport, storage and communication). This methodology was also considered as one of the best tools for developing integrated and efficient environmental policies [START_REF] Berkhout | The adoption of life-cycle approaches by industry: patterns and impacts[END_REF]. There are many areas in which LCA can be applied: in the macro-scale analyses sector as well as in microscale areas, in the public sector as well as in individual organizations, in ecodesign and in product engineering... Currently, in the industrial sector, the approach is largely applied to biofuels (Lim and [START_REF] Ortiz | Sustainability in the construction industry: A review of recent developments based on LCA[END_REF][START_REF] Pehnt | Life cycle assessment of carbon dioxide capture and storage from lignite power plants[END_REF]. LCA could also be used in EMS, as a tool for identifying the significant environmental aspects of products and services in an organization engaged in the ISO 14001 standardization process [START_REF] Lewandowska | Environmental life cycle assessment as a tool for identification and assessment of environmental aspects in environmental management systems (EMS) part 1: methodology[END_REF].

LCA can be used with several aims, at different stages of a product life cycle [START_REF] Keoleian | The application of life cycle assessment to design[END_REF]. The methodology was traditionally used to understand three types of problem: assessment of single products to learn about their environmental impacts, comparison of process routes in the production of substitutable products or processes, and comparison of alternative ways for delivering a given function [START_REF] Berkhout | The adoption of life-cycle approaches by industry: patterns and impacts[END_REF]. More recently, it was similarly argued that LCA is mainly used to compare different products, processes and activities delivering similar functions, but this methodology can also be used as a standalone tool to identify hotspots in a life cycle [START_REF] Gasafi | Using Life-Cycle Assessment in Process Design[END_REF].

A new approach for LCA is to focus on the product conception in order to build eco-friendly processes. This approach can lead to detailed analyses of processes and to the development of process specific LCA methodology, confirming that the indisputable opportunity for LCA is in the field of PSE, already discussed in the first part of this review. As Grossman et al. ( 2004) argued, "global LCA is a major research challenge in the PSE area over the next decade".

LCA and process design: state of the art and challenges

The two previous sections of this paper have argued that DfE contains efficient tools, which have become more and more useful in our industrial and environmentally open-minded society. Moreover, the interest of LCA has been proved: a powerful tool, gaining in complexity and in maturity and increasingly accepted as a valuable methodology for a large field of applications. However, it is clear from the literature that the applications of LCA to industrial process analysis are not widespread, but becoming more and more important through the building of new integrated methodology. The following part deals especially with this aspect.

Interest of an application in process design and optimization

Most of the initial life cycle studies published compared product alternatives, and it was rare to find studies dealing with process design in the early stages of the methodology [START_REF] Burgess | Application of life cycle assessment to chemical processes[END_REF]. A detailed treatment of the application of LCA to process selection, design and optimization was published [START_REF] Azapagic | Life cycle assessment and its application to process selection, design and optimisation[END_REF], and since this early review, methodological aspects of LCA have been improved and the methodology is more and more accepted within the scientific community. The starting point for the use of LCA for sustainable development has been the design of "environmentally friendly" products, and this approach was progressively extended to the process industry [START_REF] Young | Manufacturing and the environment[END_REF]. Basically, the methodology was mainly applied to products, by developing the "from cradle-to-grave" approach to the life cycle (as described in the previous section), targeting just the product because the process, from this point of view, is considered as a part of the product life cycle (manufacturing the product). However, today, its application to process assessment is increasing. Little by little, works are appearing in the literature that develop another perception of life cycle and process. In fact, the process could also be seen with its own life cycle: design of the process (planning, design, R&D), installation, use of the process (manufacture of the product), disassembly of the process and remediation of the used lands [START_REF] Allen | Green engineering: Environmentally conscious design of chemical processes and products[END_REF]. Figure 2 illustrates the different LCA approaches that could be adopted and the main alternative uses of LCA to products, and LCA to processes. LCA applied to process can also adopt a "cradle-to-gate" approach, which means that the study stops at the gate of the factory: the manufacturing product end of life of is not considered. For example, Serres et al. ( 2011) presented a detailed study on a direct additive laser manufacturing process, which allows the direct manufacturing of small parts with complex shapes, giving equivalent properties as conventional machining or casting techniques. They built their study on the fabrication of a selected titanium pieces, and this allows comparing this new process with a more classical one (machining), showing the environmental benefice of the laser manufacturing process. Moreover, another approach has been developed, which considers the process from "gate-to-gate", meaning that the system boundaries of the LCA end at the manufacture gate and do not consider the whole life cycle. This approach is rarely used but finds an application in chemical engineering process design, when factual or literature information is unavailable for a study [START_REF] Jiménez-Gonzalez | Methodology for developing gate-to-gate Life cycle inventory information[END_REF]. For example, [START_REF] Portha | Life Cycle Assessment Applied to Naphtha Catalytic Reforming[END_REF] studied a naphtha catalytic reforming process, by considering only the heat production and distribution and the tree main steps of the process (reaction, separation and catalyst regeneration).

The gate-to-gate approach, combined with process simulation, helped the authors to study the influence of temperature on environmental impacts.

Thus, regarding the process industry, it was suggested that LCA could be used in various contexts as for example "the use at the research and development phase of a process, in guiding process evolution; in process design for comparison and selection of options; in business planning for identifying weak links in a processing chain or in comparing processes with those of business competitors" [START_REF] Burgess | Application of life cycle assessment to chemical processes[END_REF]. Thus specialists' recent interest in the application of LCA to processes would seem obvious, and actually the technique could represent an efficient tool for the design and improvement of processes, by taking into account classical criteria like yield and cost concerns, and incorporating LCAderived environmental considerations.

LCA applied to processes: state-of-the-art and future perspectives

Pre 2000 studies

One of the first works dealing with LCA applied to process application is attributed to [START_REF] Furuholt | Life cycle assessment of gasoline and diesel[END_REF], comparing the production and use of different petrochemical products. The originality of this study is that instead of considering the refinery step as an unknown and nondetailed process (as a black box, with just input and output known), the author divided it into several subunits and tried to quantify the energy demand and emission of pollutants relative to these subunits. At the same time, [START_REF] Stefanis | Minimizing the environmental impact of process Plants: A process systems methodology[END_REF] were working on the minimization of environmental impacts of vinyl chloride monomers from ethylene production process, and were considering the opportunity of applying LCA as a tool for process optimization. In 1996, [START_REF] Kniel | Life cycle assessment applied to process design: Environmental and economic analysis and optimization of a nitric acid plant[END_REF] linked LCA to an economic analysis, in order to achieve a multiobjective analysis for the optimization of a nitric acid plant, and this study is one of the first where the aim of using LCA as a tool for process design and optimization is clearly displayed. The paper concluded that it was possible to outline a best solution thanks to this technique and stressed the multiple ramifications and improvements possible via the methodology. The authors asserted that LCA is one of the best methodologies able to link unit processes, environmental impact and economic aspects of processes. Using the same multi-objective approach, [START_REF] Mann | Economic and life-cycle assessment of an integrated biomass gasification combined cycle system[END_REF] combined LCA and economic studies on a biomass gasification energy production process.

One of the most relevant authors on the "LCA and process application" topic was obviously A. Azapagic. She wrote a very detailed review on the application of LCA in process selection, design and optimization, as a tool for identifying clean technologies, and published several works on LCA and multi-objective optimization of processes [START_REF] Azapagic | Life cycle assessment and its application to process selection, design and optimisation[END_REF][START_REF] Azapagic | The application of life cycle assessment to process optimisation[END_REF].

LCA and processes: the current state-of-the-art

Since 2000, the field of multi-objective optimization has been largely completed (Table 2). [START_REF] Alexander | Process synthesis and optimisation tools for environmental design: methodology and structure[END_REF] developed an environmental economic multi-objective optimization on a nitric acid plant study, where they used LCA to obtain the environmental impact information, which was then used to define environmental objectives to introduce into the optimization algorithm. More recently, LCA was used by [START_REF] Dietz | Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations[END_REF] to obtain a pollution index, which was then included as input environmental data for solving a cost/environment multi-objective system. Similarly, environmental life cycle impacts have been used as a tool for process optimization in a utility plant by [START_REF] Eliceche | Environmental life cycle impact as a tool for process optimisation of a utility plant[END_REF].

However, the incorporation of LCA in multi-objective optimization was not the only use to be under the scrutiny of process design and analysis practitioners. For example, some works in the literature use LCA combined with other tools.

In the field of supercritical water gasification, [START_REF] Gasafi | Using Life-Cycle Assessment in Process Design[END_REF] presented one application of LCA in an early phase of process design. They adopted an original approach that consisted of coupling LCA with a hierarchical approach to quantify environmental impact throughout the process chain, and identify the environmental damage "hotspots" which were then focused on for optimization of environmental performance. Recently, Da Silva et al. (2009) worked on an integrated methodology to analyse a generic production process, considering both environmental impacts and related costs. They applied the methodology to an example of incinerator production and combined different existing methodologies like LCA, activity-based costing, environmental management accounting, economic model for control and evaluation of environmental impacts and risk matrix. [START_REF] Hermann | Assessing environmental performance by combining life cycle assessment, multi-criteria analysis and environmental performance indicators[END_REF], quoted earlier for their work, applied the COMPLIMENT tool to the case study of eucalyptus pulp production using soda treatment and chlorine bleaching processes. The interesting point of this work was that it was run at two different levels: large system boundaries (cradleto-grave approach: from the eucalyptus plantation to the finished product) and at a process level (gate-to-gate approach: considering all the processes connected to the soda pulping production of eucalyptus, and also the on-site processes (waste treatment, chemical recovery, etc.)).

Another common utilization of LCA on processes that stood out in this advancement review was that of comparing different scenarios (existing or under design). [START_REF] Brentner | Combinatorial Life Cycle Assessment to Inform Process Design of Industrial Production of Algal Biodiesel[END_REF] presented an LCA that compared various methods for a sustainable, full-scale production of algae biodiesel. The innovation is inherent to the fact that a number of technology options were considered for each process stage, and different technology combinations were assessed to identify the most preferable process. The authors also aimed to identify design parameters that collectively indicated the most potentially sustainable system. Still in the field of biodiesel, Kenthorai Raman et al. ( 2011) developed a cradle-togate approach to analyse three different catalytic processes. Concerning gas treatment, [START_REF] Benko | Comparison of flue gas desulphurization processes based on life cycle assessment[END_REF] where it was possible to intervene to develop the most technologically advanced solution. Other fields were also investigated with such approach for the comparison of municipal solid waste incineration [START_REF] Chevalier | Environmental assessment of flue gas cleaning processes of municipal solid waste incinerators by means of the life cycle assessment approach[END_REF] for hydrogen fuel production [START_REF] Koroneos | Life cycle assessment of hydrogen fuel production processes[END_REF]) and for metal production processes [START_REF] Norgate | Assessing the environmental impact of metal production processes[END_REF]).

Lastly, another interesting point that could be treated by LCA is the selection of operating conditions for a unit process.

Such studies are quite rare, but some exist in the field of a microfiltration process [START_REF] Tangsubkul | Environmental life cycle assessment of the microfiltration process[END_REF], in the Naphtha catalytic reforming process [START_REF] Portha | Life Cycle Assessment Applied to Naphtha Catalytic Reforming[END_REF], in the biomass-derived resin production process [START_REF] Kikuchi | Integration of CAPE and LCA Tools in Environmentally-Conscious Process Design: A Case Study on Biomass-Derived Resin[END_REF]) and in the assessment of an energy conversion system [START_REF] Gerber | Systematic integration of LCA in process systems design: Application to combined fuel and electricity production from lignocellulosic biomass[END_REF].

PSE tools/LCA methodology coupling: future perspectives?

The previous section has given an overview of the different studies made over the last two decades, concerning LCA applied to process design and optimization. It allows us to conclude that there are three ways of applying LCA to the process issue:

• multi-objective optimization where LCA is used for inventory data and the result of the assessment is injected into the optimization model [START_REF] Dietz | Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations[END_REF][START_REF] Eliceche | Environmental life cycle impact as a tool for process optimisation of a utility plant[END_REF][START_REF] Gerber | Systematic integration of LCA in process systems design: Application to combined fuel and electricity production from lignocellulosic biomass[END_REF]);

• coupling LCA with other assessment tools to complete the studies and improve the limitations of the LCA and • analyzing environmental impact of processes by using the LCA methodology alone, in order to compare different scenarios, or for identifying the hotspots.

Nevertheless this latter option often sees processes as black boxes and constructs LCIs using the literature or industrial data at fixed operating conditions, without taking into account operating parameter variations [START_REF] Benko | Comparison of flue gas desulphurization processes based on life cycle assessment[END_REF][START_REF] Brentner | Combinatorial Life Cycle Assessment to Inform Process Design of Industrial Production of Algal Biodiesel[END_REF][START_REF] Gasafi | Using Life-Cycle Assessment in Process Design[END_REF][START_REF] Kenthorai Raman | Life cycle assessment of biodiesel production using alkali, soluble and immobilized enzyme catalyst processes[END_REF][START_REF] Koroneos | Life cycle assessment of hydrogen fuel production processes[END_REF][START_REF] Norgate | Assessing the environmental impact of metal production processes[END_REF][START_REF] Scipioni | LCA to choose among alternative design solutions: The case study of a new Italian incineration line[END_REF]). This approach is of interest when the aim is to assess the process via an overall approach or to compare different processes in their global nature, but it is limiting when dealing with analysing each process unit as a complex system, and determining what are the best operating conditions. However, in the last few years, some authors have become aware of this problem and point out the opportunity to incorporate LCA into the PSE approach for process design and analysis [START_REF] Alexander | Process synthesis and optimisation tools for environmental design: methodology and structure[END_REF]. At the same time, they pointed out that because of the difficulty of translating process information into environmental objectives, incorporating environmental sensitivity into the PSE approach was unsatisfactory. They proposed a multi-objective optimization in the PSE approach and used LCA linked with process simulation tools (Hysys) to identify the environmental objectives: an illustration of the advantage of injecting PSE results into LCA. Some years later, [START_REF] Chevalier | Environmental assessment of flue gas cleaning processes of municipal solid waste incinerators by means of the life cycle assessment approach[END_REF] demonstrated how to develop collaboration between the LCA approach and chemical engineering, in order to make process inventory data more accurate and test other process configurations, thus improving knowledge of unit processes.

In their very detailed work on microfiltration process assessment, [START_REF] Tangsubkul | Environmental life cycle assessment of the microfiltration process[END_REF] have in turn demonstrated how to determine optimal operating conditions for a membrane unit process, from an environmental perspective. They did not use any modelling software for the process simulation and the study was quite laborious and obviously could not be applied as a generalized application in the process industry, but they have shown the interest of such an approach.

The integration of operating conditions was sometimes achieved in part by using mass transfer models and by introducing modelling tools upstream of the LCA (Baratto and Diwekar 2005). And very recently in the oil and gas industry field, [START_REF] Portha | Life Cycle Assessment Applied to Naphtha Catalytic Reforming[END_REF] applied LCA to the naphtha catalytic reforming process. Process simulation tools were used with LCA in order to study the influence of operating parameters on environmental impacts, by performing a comparative study on two processes, studying the influence of furnace inlet temperature and the influence of feed on this impact. Very recently, estimating missing data using process simulation was done in a case study dealing with the design of a process for the production of biomass-derived polypropylene [START_REF] Kikuchi | Integration of CAPE and LCA Tools in Environmentally-Conscious Process Design: A Case Study on Biomass-Derived Resin[END_REF]. The authors presented a framework integrating computer-aided process engineering and LCA. In the field of energy production from lignocellulosic biomass, a flowsheeting model, providing material and energy flows and equipment sizes, was exploited to calculate the LCI of emissions and extraction flows associated with the process equipment and its operation [START_REF] Gerber | Systematic integration of LCA in process systems design: Application to combined fuel and electricity production from lignocellulosic biomass[END_REF]). The aim is then to propose a systematic approach for integrating LCA in process systems design using multiobjective optimization, which allows the simultaneous consideration of the influence of the process design and its integration, on the thermodynamic, economic and environmental life cycle performance in the early stages of the conceptual process synthesis.

These latter studies and their recentness testify to the fact that recognition of the operating parameters injected into LCA applied to processes is very important, and thus imply that the coupling between LCA and PSE, illustrated in Figure 3, is a future challenge for LCA when applied to the process industry.

Figure 3. Illustration of PSE tool integration into LCA methodology for process Conclusion

Over the three last decades, LCA has been identified as one of the most interesting tools for environmental assessment.

Its current wide use denotes that since its first application, the methodology appears to have evolved from a very specific tool for product assessment to a far ranging one, with an application to products, services, EMS, environmental policies, processes, as a standalone tool or combined with other environmental assessment tools. At the same time, the interest in the tools developed for the design of new processes and the improvement of older ones (PSE tools) has risen significantly.

This literature review has highlighted the fact that the use of LCA on processes has taken time to develop; but in the last few years, this field of application has been much under the spotlight and so today, studies on LCA applied to process analysis are readily available. In addition, LCA is often used to obtain input data for multi-objective optimization of processes. However, the coupling between LCA and PSE tools must be improved, notably to produce more detailed analysis on the influence of process operating conditions on environmental impacts. The systematic integration of PSE tools into the elaboration of environmental assessment of processes will bring scientific legitimacy to environmental evaluation by LCA.

Figure 2 .

 2 Figure 2. Illustration of different life cycle approaches (inspired from Allen and Shonnard 2001; Chevalier et al. 2003)

  proposed a comparison of flue gas desulphurization processes based on a classical LCA, and[START_REF] Scipioni | LCA to choose among alternative design solutions: The case study of a new Italian incineration line[END_REF] developed a study, interesting in that it concerned an incineration plant under design, and analysed different scenarios in order to choose from several design solutions. The authors then outlined the opportunities for detecting 'priority' points(hotspots)

Table 1 . Accomplishments and future challenges in PSE

 1 (inspired from Grossmann and Westerberg 2000)

	SIGNIFICANT ACCOMPLISHMENTS IN PSE IN THE PAST THREE DECADES	FUTURES CHALLENGES FOR PSE
	Process Design		Process and Product Design
	Synthesis of energy recovery networks, distillation systems, reactor	Design of new molecules
	networks		Develop predictive capabilities for properties of compounds
	Hierarchical decomposition flowsheets	Process intensification
	Superstructure optimization		Design of sustainable and environmentally benign processes
	Design multiproduct batch plants
	Process Control	Statistical Process Control	Process Control
	Model predictive control	Process Monitoring	Tight integration between design and control
	Controllability measures	Thermodynamics-based control	Integrate discrete events and safety functions in process control
	Robust control		Improvement of sensors
	Non linear control
	Process Operation		R&D and Process Operations
	Scheduling of process networks		Expansion of process operations: upstream to R&D and
	Multiperiod planning and optimization	downstream to logistics and product distribution
	Data reconciliation		Process verification and synthesis of operation procedures
	Real time optimization		Large scale continuous processes and small scale batch processes
	Flexibility measures Fault diagnosis		Modeling More flexible modeling environments
			Automating problem formulation through higher level physical
			descriptions
	Supporting tools		Integration
	Sequential modular simulation		Multiscale modeling
	Equation based process simulation		Life cycle modeling
	AI/Expert systems Large scale Non linear programming Optimization of differential algebraic equations Mixed-integer nonlinear programming Global optimization	Supporting methods and tools Large scale differential-algebraic methods for simulating systems on multiple scales Methods for simulating and optimizing under uncertainty Advanced optimization tools
			Improvement of tools for conceptual design
			Development of information modeling tools
	2

. Life Cycle assessment 2.1. Methodology for LCA

	Life cycle assessment is a methodological framework for quantifying and analysing environmental impacts attributable to
	the life cycle of products, services and, more rarely, processes. Nowadays, this is a well-integrated tool in environmental
	management (Azapagic and Clift 1999), normalized by the ISO 14040-14044 (ISO 2006) environmental management
	system (EMS).

  such as climate change, stratospheric ozone depletion, tropospheric ozone creation (smog), eutrophication, acidification, toxicological stress on human health and ecosystems, resource depletion, water use, land use, noise and others. Moving from inventory to impact assessment is one of the most difficult steps of LCA, largely discussed in the literature and implying many inconsistencies between LCA practitioners. Even if[START_REF] Owens | Life-Cycle Assessment: Constraints on Moving from Inventory to Impact Assessment[END_REF] had already observed this before, it is still one of the main limits voiced concerning LCA methodology, and is why different methodologies have been developed for life cycle impact assessment (LCIA) over the last decade: EDIP97, Eco-indicator 99, CML 2001[START_REF] Dreyer | Comparison of Three Different LCIA Methods: EDIP97, CML2001 and Eco-indicator 99[END_REF], IMPACT 2002+(Jolliet et al. 2003), etc.

  • The period2000[START_REF] Buchgeister | Exergoenvironmental Analysis -A New Approach to Support the Design for Environment of Chemical Processes?[END_REF] , according to Guinnée et al. (2011) ) is the 'decade of elaboration'. LCA was becoming a generalized tool for environmental assessment, but new divergences in methodologies appeared. Because the ISO standardization was very wide and did not aim to develop the methodology in detail, many studies have been

performed within the spirit of LCA methodology, but with differences regarding methodological approach. To deal with this problem, a new LCA textbook was published during this decade (European Commission 2010;

[START_REF] Guinée | Handbook on life cycle assessment -operational guide to the ISO standards[END_REF][START_REF] Jolliet | Life Cycle Assessment: understand and perform an Eco-balance[END_REF]

, and an effort was made to harmonize and update LCI data via the development of the Ecoinvent database, which made available more than 2,500 product and service LCIs

[START_REF] Frischknecht | The ecoinvent Database: Overview and Methodological Framework (7 pp)[END_REF]

. Very recently, the UNEP/SETAC provide a guide which aims to give good practices for improving generation, compilation and accessibility of LCA data, and develop the interlinkages between worldwide databases (UNEP and SETAC 2011).

Acknowledgments

The financial support allocated to this project by the French National Research Agency (ANR) is gratefully acknowledged.


Schetrite Sylvie
email: sylvie.schetrite@ensiacet.fr

Frances Christine

Albasi Claire

Alliet Marion

Caractérisation du fonctionnement du pilote : Références Perspectives

Imposer des contraintes de cisaillement contrôlées, par exemple en réacteur couette • Mettre en relation sucres protéines et paramètres morphologiques pour diverses contraintes de cisaillement et des boues d'origine diverses. • Déterminer un paramètre morphologiques ou un couplage de paramètre morphologiques indicateur de la capacité colmatante des boues. • Un outil à développer!

ANALYSE MORPHOLOGIQUE DES BOUES BIOLOGIQUES d'EPURATION

Descriptifs les plus pertinents et propriétés physico-chimiques des boues d'épuration

•Circularité

  La forme des flocs de boues activées varie en fonction des paramètres de fonctionnement comme l'âge des boues , la charge ou l'aération.. • Pour les Bioréacteurs à membrane, les forces de cisaillement induites par les grosses bulles de décolmatage des filtres viennent apporter également leur contribution.. • L'appréhension des boues par leur morphologie est un travail à caractère fondamental, qui a pour objectif ici de relier des critères morphologiques à un pouvoir colmatant des boues, habituellement appréhendé par des indicateurs globaux comme les concentration en EPS ou en DCO du surnageant, et la dérive de pression transmembranaire. 25 (m³/h d'air par m² de membrane) SADp moyen : 41.67 m3/h air par m3/h de perméat filtré Seuil de coupure des filtres: 80 nm Charge du pilote principal Cm = 0.057 g DCO/g MES*JOUR ; Cv = 0.57gDCO/L*jour Concentration des boues : 10 g/L en MES; volume du réacteur : 15L Boues prélevées dans un pilote de trainement d'eau par bioréacteur à membranes immergées Balayage par microscope automatisée d'un échantillon de boues dispersée dans une cellule en voie liquide Séparation des particules par seuillage en niveau de gris Traitement de l'image de chacune des particules isolée : détermination de ses caractères morphologiques Grande quantité de particules traitées (50 000) Examen des corrélations possibles avec les paramètres de suivi du pilote : analyses et enregistrements Méthodologie Paramètres morphologiques moyens de la distribution volumique de taille Image de gauche : flocs produits dans un Réacteur à membrane immergée -écoulement frontal, cisaillement induit essentiellement par des bulles d'air Image de droite : flocs produits dans Réacteur à membrane externe -écoulement tangentiel, cisaillement induit par la circulation des boues à grande vitesse (même échelle) Résultats moyens J. 1. Liao B.Q., Droppo I.G., Leppard G.G., Liss S.N., 2006. Wat. Res. 40,2583-2591 2-Da Motta M., Pons M.N, Roche N., Amaral L., Ferreira E., Mota M., 1999., International Research Conference Water Reuse, 3, Toulouse, 3 Wilèn B-M., Balmer P., 1999. Wat.Res. 33,391-400 4. Frances C., Le Bolay N., Belaroui K., Pons M.N., 2001. Int. J. Miner. Process 61, 41-56 5 Devloo M.O., Drakides C., 1983.Powder Technology 35, 139-142. Corrélations observées • taille moyenne/ dérive de pression • taille moyenne/ DCO surnageant •Taille moyenne/pH (à confirmer) •Aucune corrélation avec les EPS Périmètre du cercle de même aire divisé par le périmètre vrai Convexité Surface réelle divisé par la surface convexe englobante = rugosité Elongation 1 -largeur / longueur Solidité A/(A+B)


Anh Toan

Bui Long
email: builonga@ensma.fr

Yassine Ouhammou
email: yassine.ouhammou@ensma.fr

Emmanuel Grolleau
email: grolleau@ensma.fr

Loïc Fejoz
email: loic.fejoz@realtimeatwork.com

Laurent Rioux
email: laurent.rioux@thalesgroup.com

Anh-Toan Bui

Bridging the gap between practical cases and temporal performance analysis: a models repository-based approach

Keywords: Computer systems organization → Real-time system specification, Real-time system architecture, • Software and its engineering → Scheduling, Process validation, Real-Time, Embedded System, critical embedded system, model, meta-model, modeling, model transformation

come   L'archive ouverte pluridisciplinaire

INTRODUCTION

Critical real-time systems need to be analyzed to check their temporal performance. That is, tasks, messages and end-to-end flows have to meet the requirements in terms of scheduling, deadlines, delays, processors utilization, etc.

Schedulability tests are among techniques that can be used to conclude about the temporal performance of a given system. They are basically based on algebraic methods and are known by their scalability referring to the number of tasks and messages of systems especially industrial ones. Since the 1970s, the real-time community has continuously proposed a plethora of schedulability tests [START_REF] Sha | Real time scheduling theory: A historical perspective[END_REF] while each test is dedicated to a specific analysis model (a.k.a. task model). This latter relies on a set of assumptions describing the architecture, the tasks structure and the system worst-case behavior. In other words, the result of an analysis test can never be reliable unless the analyzed system fits accurately with the analysis model of the applied test.

Context and problem statement

The design of real-time systems becomes more and more complex since -in addition to the functional requirements -it spreads across various domains (e.g., time, energy, safety, security, etc.). The design process may then spread over several years and is essentially composed of two main phases: modeling and analysis.

The modeling phase leads to get a representation combining many artifacts originating from different domains while the analysis phase is separated into different domain-specific analyses. That is, the temporal validation is among those analyses that are applied on the qualitative and quantitative information derived and/or extracted from the modeling. In fact, the transition from the modeling phase to the analysis phase faces two main inter-dependant problems. The first problem is the difficulty of choosing the appropriate schedulability test according to the input model. The second one is that despite the existence of numerous tests related to different architectures, tests do not cover all possible situations of real-time systems. Hence, the modeling has to be transformed to conform to an existing test. Moreover, this transformation must be conservative (see Section 2.1), i.e., the worst case behavior of the target model should never be optimistic compared to the source model.

Currently, the transition process form the modeling to the analysis is still driven by designers experiences which makes the transition endeavour tedious, time-consuming and error-prone due to the complexity and precision required to manipulate real-time theory.

Work positioning

While our recent works [START_REF] Ouhammou | Model-based Framework for Using Advanced Scheduling Theory in Real-Time Systems Design[END_REF][START_REF] Yassine | Reducing the gap between Design and Scheduling[END_REF][START_REF] Yassine | Towards a model-based approach guiding the scheduling analysis of real-time systems design[END_REF] proposed to cope with the first problem by helping designers to choose the suitable analysis tests, the second problem still lacks a solution that can help designers to choose which conservative adaptations their models have to undergo in order to generate models compliant with the source ones in terms of temporal behaviour and to take benefits from analysis tests.

In this paper, we propose an approach to conceptualize the required knowledge and to capitalize efforts to ease the transformation and the adaptation of practical cases to match suitable analysis models. Our proposition is based on a an extensible models repository, which allows experts to share their knowledge and that can be used by non-experts as a decision support. Our proposition is based on model-driven engineering settings to make it utilizable with design languages.

Paper outline

The rest of the paper is organized as follows. Section 2 the state of the art and related works. Section 3 is dedicated to present the contribution. The implementation and tooling are presented Section 4. Section 5 presents a case study stressing the paper contribution. Finally, Section 6 summarizes and concludes this paper.

STATE OF THE ART

In this section, we present the theoretical and technological background of this work. We introduce some motivational examples and we discuss related work.

Background

Software engineers and architects design their real-time systems by using modeling languages [START_REF] Bernardeschi | Modeling and generation of secure component communications in AUTOSAR[END_REF][START_REF] Mraidha | Optimum: A MARTE-based Methodology for Schedulability Analysis at Early Design Stages[END_REF]. As such, their designs are described referring to functional and non-functional specifications to be easily understood. The description done by the design engineer do often not correspond to "conventionnal" analyses.

In the following, we present some of the cases that we considered as motivational examples for this work.

Example 1: alternative task triggering.

A task can be activated by several ways: through a predetermined arrival pattern, or by other tasks. Figure 1 presents a case of OR precedence relationship. We consider three communicating tasks. Tasks τ 1 and τ 2 are sporadic tasks. Task τ 3 is activated by either task τ 1 , or τ 2 , through a message to τ 3 . Therefore, τ 3 is preceded by τ 1 , or τ 2 . To the best of our knowledge, there is no off-the-shelf tool developed especially for this kind of architecture. That is, to analyze the system of Figure 1, one has to transform the architecture to be adapted to another analyzable structure. Moreover, the temporal behavior of the transformation result model has to dominate the origin model.

Since it is difficult to determine finely the execution of task τ 3 , this latter can be represented as two tasks, τ 3A and τ 3B , as it is 1 shown in Figure 2 . This leads in a task activated by τ 1 and the other one is activated by τ 2 . Indeed, τ 3 is a task with two intertwinned sporadic activations. Since in the original model, τ 3 is usually considered as non-reentrant (i.e. FIFO activations), τ 3A and τ 3B should not be able to preempt each other in the second model.

Finally, note that this transformation is valid in a lot of contexts (uniprocessor, global multiprocessor, paritioned multiprocessor) as long as we can enforce that the two subtasks may never overlap, or are executed on the same processor if we have a partitioned scheduler. The priority should also remain consistant with the source model, for example, if the scheduler based on fixed-task priority, the two subtasks shall have the same priority to reflect reality.

2.1.2 Example 2: probabilistic task execution. Probabilistic schedulability analysis [START_REF] Santinelli | A Probabilistic Calculus for Probabilistic Real-Time Systems[END_REF] allows to get an approached estimation of the system. Nevertheless, very few tools handle probabilistic worstcase execution time as input, but an upper bound on this wcet. Then, in case of probabilistic distributions of execution times (which for instance can be calculated via Weibull law), the designers need to consider the worst-case execution time to obtain a valid response time value. Consequently, a model transformation should provide the designers with an automatic mean to transform probabilistic tasks parameters into worst-case ones.

Example 3: sliding window arrival pattern.

A sliding window is an arrival pattern type of task activation. A task τ i activated in a sliding window is such that for any window time of size w i , there are at most n i activations of τ i .

We came across this model in an industrial case study, and, there again, no available tool considers this pattern as a possible activation pattern.

We consider a task-set constituted by n tasks τ i,i=1...n with worst-case execution time C i and a sliding window defined by n i and w i . We cannot determine the exact position of the activations to determine the evolution of the system and analyze the system's schedulability.

Nevertheless, if we are in a context where the request bound function (RBF) [START_REF] Baruah | Preemptively scheduling hardreal-time sporadic tasks on one processor[END_REF] (resp. the demand bound function [START_REF] Baruah | Preemptively scheduling hardreal-time sporadic tasks on one processor[END_REF] (DBF)) can be used to represent the worst-case activation pattern of tasks (resp. the worst-case cumulative processor demand to be satisfied), we can observe that it is possible to envelop every possible set of releases (resp. every possible set of deadlines) in a sliding window by a single sporadic release of n i jobs with a period w i . It shows that, in a context where the RBF or the DBF is used for analysis, a sliding window activation pattern can be equivalently represented by sporadic activations. • the blue curve represents the request bound function for a sporadic task τ i with WCET n i C i and period equal w i , • the black (or dashed) curve represents different possible activation patterns for τ i activated with a sliding window pattern.

This transformation from sliding window to sporadic preserves the interference as well as the worst-case response time for a sliding window task in the case of fixed-task priorities on a single processor. Feasibility is can also be studied using the RBF of a sporadic task rather than the sliding window task.

Example 4: watchdog tasks.

The Watchdog is also a particular type of task activation. A task activated by a watchdog of timeout w is such that, if the watchdog is not reset during w, the task is activated. This kind of tasks usually plays the role of an alert to perform some action (e.g., switch to degraded mode, resolve errors, rebooting the system, sound an alarm, etc.). As watchdogs should not be triggered under normal operations, it is uncertain of when they can occur. For a safe and accurate temporal behavior analysis, the watchdog task can be assumed to be triggered every w time units at most, and, therefore, be modeled as a sporadic task with period (minimum inter-arrival time) equals to the timeout of the watchdog.

Several other examples could be found to highlight the gap between practical use cases and analysis models.

Model-driven Engineering for modeling and transformation

The model-driven engineering (MDE) [START_REF] Douglas | Model-driven engineering[END_REF] is a generative process enabling to get, treat and produce information from structured data entities called models. MDE settings are mainly based on modeling, meta-modeling and transformation aspects. Another important MDE aspect is the conformity relationship. A model conforms to the meta-model as a program conforms to the programming language's grammar in which it is written. This means that a metamodel describes the different types of model elements and how they are arranged. Thanks to the transformations techniques the transition of data from models to other models is possible. There are two kinds of model-to-model transformations, the exogenous and the endogenous transformation. While the first kind consists of having meta-models of the source models different to meta-models of the target models, the second kind means that source and target models are both conform to the same meta-model. The need of MDE is increasingly growing to ease development and reduce its costs by following correct-by-construction approaches. Real-time systems actors have taken benefits form the MDE paradigm by proposing and using modeling languages such as the standards UML-MARTE (Modeling and Analysis of Real-Time Embedded systems) [START_REF] Omg | UML profile for MARTE: modeling and analysis of real-time embedded systems[END_REF] and AADL (Architecture Analysis and Design Language) [START_REF] Peter | Model-Based Engineering with AADL: An Introduction to the SAE Architecture Analysis & Design Language[END_REF]. Several temporal performance analysis tools have also been proposed based on MDE technologies and have been integrated in the design process tool-chain (e.g., MAST [START_REF] Harbour | MAST: Modeling and Analysis Suite for Real Time Applications[END_REF], Cheddar [START_REF] Singhoff | Cheddar: A Flexible Real Time Scheduling Framework[END_REF], etc.).

Related work and discussion

Transformations are nowadays lead by either the analyst expertise or automatically by frameworks but opaque, non-extensible and without any information given to users.

There exist only few tools that explicit the transition from design models to analysis models: Tempo and MoSaRT propose such mechanism. Tempo [START_REF] Henia | TEMPO: Performance Viewpoint for Component-based Design of Real-time Systems[END_REF] is a MARTE-compliant tool based on a subset of scheduling analysis concepts. Its transformations are driven by concepts semantics and are expressed by annotations. That is, the knowledge of the target schedulability test should be known by the user beforehand. Hence, the non-expert user cannot be totally autonomous. Moreover, the transformation is more guided by the initial input models of the analysis tools supported by Tempo. In other words, the goal of the transformation is to adapt models to analysis tools and not to adapt practical cases.

MoSaRT analysis repository [START_REF] Ouhammou | Model-based Framework for Using Advanced Scheduling Theory in Real-Time Systems Design[END_REF][START_REF] Yassine | Towards a model-based approach guiding the scheduling analysis of real-time systems design[END_REF] proposes to capitalize the transformation from design language to analysis tools. It can be enriched by real-time researchers and practitioners by adding their assumptions that input analysis modelsmust meet and adding transformations towards associated analysis tools. However, the repository proposed by MoSaRT supports only conventional analyzable models and helps users to find the appropriate scheduling tests that match their models.

Since we focus on critical real-time systems, we realize that current transformations from the design models to the adapted design models still requires expertise from a scheduling analysis expert. The very important point is to verify that the output model (adapted model) is either equivalent to, or a worst-case of, the original model.

In this paper, we take benefits from MoSaRT analysis repository principles and we propose an extensible repository of endogenous model transformations called CONSERT (CONServative Endogenous Repository-based Transformations). It is in charge of finding and applying the necessary transformations to models based on experience of expert designers and analysts. CONSERT repository enables to store assumptions of models that need to be adapted and the appropriate transformations that should be run to obtain analyzable models that will be fed to schedulability analysis tools. This repository will also help designers to have a traceability of the changes applied to their original input model before analysis. Even if CONSERT does not depend on a particular design language and it can be compliant with several languages, in this paper we show the usefulness of our contribution by integrating it within a development framework called Time4Sys (which is a new evolution of MARTE). It stems from the Waruna project 1 where we are involved with other academic and practitioners partners.

CONSERT FUNDAMENTALS

The underlying idea behind proposing such repository is to store the knowledge of design/analyst experts and to share it with other collaborators and users. Hence, the repository can play the role of a decision support and make users autonomous during the analysis phase.

Transformation contexts

The context represents the required assumptions for a model to be considered as input model for the transformation. For example, the transformation from sliding window to sporadic can only be applied if all the jobs are to be executed on the same processor. We define a context C as a set of assumptions {a 1 , a 2 , .., a n } such as "the hardware architecture is uniprocessor", or "tasks priorities are fixed". Each assumption a i is then a set of propositions, whose values are expressed as constraints on the meta-model, that an input model satisfies, or not. For example, characteristics of contexts tackled in Section 2.1 can be described by a set of assumptions as follows:

• Excerpt of alternative task activation context (C A ):

-"The hardware architecture is uniprocessor" -"There exists a least one task preceded by two or more tasks and the precedence kind is an OR relationship" -"Every precedence constraint is either one-to-one or OR" -etc.

• Excerpt of sliding window context (C B ):

-"The hardware architecture is uniprocessor" -"There is at least one task activated with a sliding window" -"There is no self-suspension" -etc.

• Excerpt of tasks with probabilistic parameters context (C C ):

-"There is at least a probabilistic parameter" -"Task(s) concerned by a probabilistic value: * "is independent" * "is not self-suspended" -etc.

• Excerpt of watchdog task activation context (C D ):

-"There is at least a watchdog activation" 1 www.waruna-projet.fr -etc.

CONSERT can also be enriched with transformations requiring analysis, as long as the output model of the analysis can be expressed in the same meta-model as the source meta-model. For example, if tasks are periodic, and offset free [START_REF] Goossens | Scheduling of offset free systems[END_REF], a third-party analysis tool could be used to assign a value to these offset such that the system is schedulable by a giving scheduling algorithm. A chain of subtasks with a final deadline could also be modified by a third-party tool proposing intermediate (tasks) deadlines. We could also see response time computation tools as third-party tool used for endogenous transformation, as long as the response time can be expressed in the meta-model. It is not yet implemented in our framework, but is a serious reflection for future work, in order to achieve an holistic analysis per part on an input system.

Detection rules

Each design which might be transformed/adapted would conform to a context stored in the repository. For this, we have to define detection rules to spot the assumptions in the design to apply the correct transformations.

Let D R = d 1 , d 2 , .., d n be a set of detection rules. Since it is common to find several contexts characterized by the same subset of assumptions, each rule d i may or not verify an assumption a i and an assumption belongs to several contexts C j . Indeed, each rule d i is an interrogation checking the system design and leads to an assumption characterizing one or several contexts. For example, d p is "number of processor equals to 1", thus, if d p is true, it verifies assumption a 0 "the hardware architecture is uniprocessor", and if d p is false, it verifies the assumption "the hardware architecture is not uniprocessor". Therefore, for factoring the number of assumptions and to guarantee the scalability and the incremental enrichment of CONSERT repositories, let S f be a function for specifying contexts and defined as S f : C × D R → Boolean. For instance, S f (C A , d p ) = True. [START_REF] Steinberg | EMF: Eclipse Modeling Framework[END_REF] which is a graphical meta-modeling language with concepts quite similar to UML class diagram [START_REF] Omg | Unified Modeling Language (UML) version 2[END_REF]. Indeed, the root element is the Repository class which each of their instances is composed of a set of detection rules (i.e. instances of DetectionRule class) and a set of contexts (i.e., instances of ContextModel class). Each detection rule is characterized by an id, a description written in native language and a formal expression. This latter relies on the modeling language expressing the design which requires transformations. For example, let Sys = {τ 1 , τ 2 , .., τ n } be a system composed of a set of tasks, where every task τ i is characterized by a period T i and a relative deadline D i , then τ i :=<T i , D i >. Therefore, the formal expression attribute of a detection rule aiming to check if the deadlines of all tasks are constrained will be expressed as follows: ∀ τ i ∈ Sys, T i ≤ D i . In our case the formal expression will be written in OCL (Object Constraint Language [START_REF] Omg | Object Constraint Language, OMG Available Specification[END_REF]) since we would like to make our contribution compliant with Time4Sys, see Section 4).

CONSERT metamodel

Figure 4 also shows the interpretation of S f function to two relations (TrueRules and FalseRules) between ContextModel and DetectionRule classes. Besides, every instance of ContextModel is characterized by an adaption (instance of ModelAdaptation) which consists of describing the necessary transformations and their implementations as a transformation program. The fileLocation defines the location where it exists the program to run to get transformed designs. Thanks to the trackModel operation, every execution of the transformation program on a design model generates a model that stores the transformations and then helps designers to follow the traceability and the evolution of initial models. The generated model conforms to the traceability meta-model that we present hereafter.

Traceability -Transformation tracking

The transformations conducted on a given model impact the contained information. As such, after a transformation, it is impossible to go back to the previous model unless we keep all the models in memory, i.e. giving a high amount of memory to unused models. We propose then to track the modifications made on the system model, by user or by the framework. Figure 5 shows the meta-model to which every generated traceability instance will be conform. The cornerstone of the traceability meta-model is the Mapping class that represents the link between source and target elements (both of them are instances of MappingArtefact class). Source and target elements can be distinguished thanks to the instances of the ResourceArtefact class since they are containers of these elements. In addition, every ResourceArtefact instance is characterized by a URI (Uniform Resource Identifier) and a resource (i.e., the root element of the model that contains the resource artefact). The mapping is characterized by a set of transformation rules expressed by a transformation language (like ATL [START_REF] Jouault | ATL: A model transformation tool[END_REF] or QVT [START_REF]Query/view/transformation QVT[END_REF]). Moreover, the mapping can be a result of merging many source elements to get one target element or one source element leading to a bifurcation to many target elements.

TOOLING AND IMPLEMENTATION

This section presents the implementation of CONSERT and the software tools and the support of development used for our proposal.

Waruna Project

Waruna Project is a collaborative project reuniting industrial and academic partners from the field of real-time systems. It comes from the need of industrials to analyze real-time systems in their functional and non-functional requirements by using MDE paradigm. It would reduce the time of testing and reduce the cost of real-time related errors.

Integration in Time4Sys

4.2.1 Time4Sys in a nutshell. Time4sys is a framework developed as a Polarsys plugin2 and available on GitHub 3 . The framework reuse the philosophy from MoSaRT and Tempo frameworks presented in Section 2.3. We present main functionalities.

Design. Time4Sys makes available a concrete syntax to model Real-Time Embedded Systems on a large panoply of classes extracted from UML -MARTE profile.

Traceability. There is a gap between design and analysis. Analysis models and design models are expressed on the same metamodel. Because there are some differences between the analysis and design model, there is a need of a tracking of modifications. Time4Sys proposes a traceability meta-model made with QVT [START_REF]Query/view/transformation QVT[END_REF] and ATL.

Analysis. Time4Sys provides connections with analysis tools such as MAST [START_REF] Harbour | MAST: Modeling and Analysis Suite for Real Time Applications[END_REF] or RTaW-Pegase [START_REF]RTaW-Pegase[END_REF]. Exogenous transformation from the design model to the analysis input is provided in the framework.

Result feedback. The plug-in intends to provide tools from modeling to analysis feedback (including model to model transformation to the analysis tool). To this extent, Time4Sys is capable to handle traces to track modifications made on the model.

The Time4Sys framework proposes an approach of Real-Time systems from the design to the analysis and the redesign if necessary. Figure 6 concrete syntax embedded in the framework. Then, the model is compared in the transformation context repository to check for any available transformation to simplify the analysis. After the endogenous transformation, creating a model also conforms to the Time4Sys design language profile. The model is then compared to the analysis repository to verify for analysis that suits the model context or at least that approaches the model context.

4.2.2

Positioning of CONSERT inside Time4Sys. Figure 7 shows the detailed process of analysis in Time4Sys, integrating our workflow. Our repository intends to be the first step after modeling in Time4Sys framework (marked as (1) on the figure). From instances of rules provided by CONSERT, the framework is able to compare (marked (2) on the figure) with the model and determine the transformation(s) to retrieve the adapted model. The transformations (marked (3) on the figure) are conducted by the framework. Afterwards, the model is compared with the analysis repository to determine the analysis tool (marked (4) and (5) on the figure). A transformation from the adapted model to the analysis tool input leads the model to its analysis (marked (6) on the figure). The output is retrieved to enrich the model for eventual further analyses.

General structure and scenario usage

The meta-model is established on EMF (Eclipse Modeling Framework [START_REF] Steinberg | EMF: Eclipse Modeling Framework[END_REF]). To obtain the context and compare to the repository, OCL (Object Constraint Language [START_REF] Omg | Object Constraint Language, OMG Available Specification[END_REF]) is used to implement transformation rules between models. The endogenous transformation is done through the ATL (Atlas Transformation Language [START_REF] Jouault | ATL: A model transformation tool[END_REF]) If Time4Sys is used as support of our proposal, Figures 8 and9 presents examples of instances of CONSERT and trace repositories 4.3.1 Pessimism on transformations. To transform real-time systems, we need to be conservative. In software certification, this means the transformation cannot make the new model more optimistic, scheduling-wise, than the model considered in the previous step.

4.3.2

Classes transformation with Time4Sys. The transformations impact few classes on the meta-model. It is possible then to verify the model obtained at each step. Table 1 summarizes all transformations applied on Time4Sys classes for the cases we presented at Section 2.1.

In the case of precedence constraints, a step can be preceded by one or more step. It can be triggered by any step. All properties of the triggered step, such as priority, blocking time, are duplicated into a new task. The duplicated steps and tasks have the same properties, except these have to access a mutual exclusion resource to avoid these tasks preempting each other.

For probabilistic values, we convert the class associated to the probabilistic value to a simple duration and take the minimum or the maximum depending on the probabilistic parameter it was affected to.

For Sliding windows activation, classes are transformed into a burst pattern retrieving the parameters contained in the previous class.

A watchdog is currently designed as an Alarm with a reset service avoiding the execution of the alarm. The service is converted into an independent sporadic task.

CASE STUDY -APPLICATION ON A MINEPUMP

We present a case study of a simple example. First, we present the initial design of the case study expressed in Time4Sys. Then, we emphasize the practical case that should be adapted. We show how the usage of a CONSERT repository helps to perform automatically

A sample practical use case

We consider a deep well filled with water inspired from [START_REF] Brendan | A case-study in timed refinement: A mine pump[END_REF]. It is required to keep the water between Low and High levels. A methane sensor is located in the well, to shut down the pump when the air is explosive (in case of a sparkle), but also to evacuate the employees when the level is dangerous for a human being. If the methane level is over L1, an alarm should be triggered. If the methane level is over L2, the pump shall be deactivated. At Low level, the pump should be off as it cannot pump any more water. All software components are executed on a uniprocessor controller. Figure 10 presents a schematic of the system.

Initial modeling

The software architecture is composed of three tasks. Task 1 is periodic, and acquires the methane level every 50ms, task 2 retrieves periodically information from Low level and High level sensors, and task 3 has to compute whether or not the pump should work and check if alarm should be triggered or stopped.

Task 1 presents three steps corresponding to functions of the task defined at functional modeling stage: acquire signal, compare Task 1 has a periodicity of 50ms and Task 2 a periodicity of 500ms.

There is a watchdog in the system to be reset every 50ms. The watchdog is reset at each successful frame read. A trigger of the watchdog implies a late arrival of a frame or a bad transmission of the message.

We first try to analyze this system. For this kind of system, MAST software is proposed (version 1.5.1.0). This implies a single transaction for the system in MAST.

Task T i C i Priority T 1 50ms 10ms 15 → Step1.1 - 2ms - → Step1.2 - 3ms - → Step1.3 - 5ms - T 2 500ms 10ms 13 → SingleStep 500ms 10ms - T 3 - 10ms 5 → Step3.1 - 10ms - → Step3.2 - 5ms - Watchdog - 5ms 1

First step

In this modeling, there is an "OR" precedence: task 3 preceded by either Task 1 or 2. Figure 11 presents the Time4Sys model of the case study. In Time4Sys, a watchdog cannot be represented at this moment in the graphical syntax. The steps are given periods and computation times C i as follows in Table 2.

Second step of the transformation

The transformation conduct to the creation of two transactions, instead of one transaction as previously, beginning with the activation of tasks 1 and 2. The tasks must be in mutual exclusion, to avoid two jobs of task 3 preempting each other during the process.

Third Step

The transformation creates a watchdog task as a sporadic task of minimum interarrival time equals to 50ms. Figure 13 presents the model transformed after transformation.

As the watchdog activates a service on task 3, alerting it of a frame failure, the transformation implies the creation of a third instance of Task 3 in mutual exclusion with the other instances of "task 3" As it is a safety task on system, it is affected the highest priority on the processor. Table 3 presents the results on the system with a watchdog.

Analysis

We conduct the analysis on MAST. Table 2 summarizes the results obtained through the analysis software. Column Case 1 designates worst case response time excluding the watchdog in the computation, and column Case 1 including the watchdog as a sporadic task.

Task

CONCLUSIONS

This papers tackled the difficulty faced by designers to analyze some practical use cases. The designs of these use cases require to be adapted to become analyzable (in a temporal performance point of view) hence fit a model analysis to conclude about their schedulability.

Basically, the required adaptation and transformation are guided by analysts expertise since it is important that the resulting design should never be optimistic compared to the source one. To capitalize this expertise and make designers autonomous during the analysis phase, we proposed CONSERT (CONServative Endogenous Repository-based Transformations). Thanks to CONSERT, any expert can share his/her knowledge with other collaborators by creating a CONSERT repository where it is possible to store the necessary transformation for each kind of design. Consequently, the transformation can be performed automatically by connecting the repository to a modeling language. Moreover, the utilization of CONSERT repositories enables users to get, in addition to transformed models, the evolution traces (also as model) of their initial model that underwent the transformations.

CONSERT has been integrated within Time4Sys framework and supports OCL to enable users express detection rules. The transformation and the traceability is managed thanks to ATL. A sample use case has been treated in this paper to show how such work can be needful in terms of safety and time-shortening since the content of the repository is supposed to be done by an expert and the implicit and manual transformations become explicit and automatic.

Figure 1 :Figure 2 :

 12 Figure 1: OR precedence relationship between 3 tasks

Figure 3 :

 3 Figure 3: RBF of a sliding window

Figure 3

 3 Figure 3 present two cases considering, for task τ i , n i = 5:• the blue curve represents the request bound function for a sporadic task τ i with WCET n i C i and period equal w i , • the black (or dashed) curve represents different possible activation patterns for τ i activated with a sliding window pattern.

Figure 4 :

 4 Figure 4: Excerpt of CONSERT metamodel

Figure 4

 4 Figure4shows the metamodel that embodied our proposition and which any CONSERT repository has to be conform to. is expressed in Ecore language[START_REF] Steinberg | EMF: Eclipse Modeling Framework[END_REF] which is a graphical meta-modeling language with concepts quite similar to UML class diagram[START_REF] Omg | Unified Modeling Language (UML) version 2[END_REF]. Indeed, the root element is the Repository class which each of their instances is composed of a set of detection rules (i.e. instances of DetectionRule class) and a set of contexts (i.e., instances of ContextModel class). Each detection rule is characterized by an id, a description written in native language and a formal expression. This latter relies on the modeling language expressing the design which requires transformations. For example, let Sys = {τ 1 , τ 2 , .., τ n } be a system composed of a set of tasks, where every task τ i is characterized by a period T i and a relative deadline D i , then τ i :=<T i , D i >. Therefore, the formal expression attribute of a detection rule aiming to check if the deadlines of all tasks are constrained will be expressed as follows: ∀ τ i ∈ Sys, T i ≤ D i . In our case the formal expression will be written in OCL (Object Constraint Language[START_REF] Omg | Object Constraint Language, OMG Available Specification[END_REF]) since we would like to make our contribution compliant with Time4Sys, see Section 4).Figure4also shows the interpretation of S f function to two relations (TrueRules and FalseRules) between ContextModel and DetectionRule classes. Besides, every instance of ContextModel is characterized by an adaption (instance of ModelAdaptation) which consists of describing the necessary transformations and their implementations as a transformation program. The fileLocation defines the location where it exists the program to run to get transformed designs. Thanks to the trackModel operation, every execution of the transformation program on a design model generates a model that stores the transformations and then helps designers to follow the traceability and the evolution of initial models. The generated model conforms to the traceability meta-model that we present hereafter.

Figure 5 :

 5 Figure 5: Traceability meta-model

Figure 6 :

 6 Figure 6: Abstract of the process of Time4Sys

Figure 7 :

 7 Figure 7: Workflow of Time4Sys and integration of CONSERT

Figure 8 :

 8 Figure 8: Instance of the transformation repository

Figure 9 :Figure 10 :

 910 Figure 9: Example of an trace instance

Figure 11 :

 11 Figure 11: Transformation separating the transactions

Figure 12 :

 12 Figure 12: Transformed model separating the transactions

Figure 13 :

 13 Figure 13: Transformation adding a watchdog task

Table 1 :

 1 Table of transformations a series of transformation in order to get an analyzable design while remaining the pessimist temporal behavior.

	Time4Sys Class	Before Transformation	After Transformation
		Alternative precedence relations context C A
	PrecedenceRelation	Connector kind set to Merge	Connector kind set to Sequence
	Step	Single step with two (or more) precedences	As much steps as there were precedences
	SoftwareSchedulableResource	One task	As much tasks as there were precedences
	MutualExclusion	None	A mutual exclusion for each instance of the task(s)
		Sliding Windows activation pattern context C B
	SlidingWindowPattern	n i maximum occurences
	(inherited from ActivationPattern)	in any window of size w i	Non-existent
	BurstPattern	Non-existent	Burst of n i occurences every w i
		Probabilistic values context C C
	ProbabilisticDuration
	(inherited from NFP_Duration
	& NFP_TimeInterval)	Probabilistic distribution of time	Non-existent
	Duration
	(for WCET values)	Probabilistic	Equals to maximum duration value
	Duration
	(for inter-arrival time)	Probabilistic	Equals to minimum duration value
		Watchdog context C D
	Alarm	Designed as a watchdog	Non-existent
	SoftwareSchedulableResource	Taskset	Creation of task Watchdog
			Creation of a sporadic activation
	ActivationPattern	N/A	associated to the task Watchdog

Table 2 :

 2 Summary of the system

Table 3 :

 3 MAST Results -Sliding Window

		WCRT
		Case 1 Case 2
	Task1	285ms	545ms
	Task2	10ms	15ms
	Task3 (activation by T 1 ) 190ms	345ms
	Task3 (activation by T 2 ) 110ms	210ms
	Watchdog	-	5ms

https://www.polarsys.org/projects/polarsys.time4sys

https://github.com/polarsys/time4sys

ACKNOWLEDGMENTS

The authors would like to thank all the partners of the project.

This work is funded through the Waruna project by the French Ministry of the Economy, Finances and Industry. This project is cofunded by the following Regions: Ile-de-France, Occitanie, Grand-Est, and Grand-Nancy. This project is co-funded by the European Union. Europe is committed in France through the European Funds for Regional Development and the European Social Fund.