Nicolas Tchertchian

Dominique Millet

Olivier Pialot

Modular Grouping Exploration to design Remanufacturable Products

Keywords: Lifecycle design, Modular design, Remanufacturing

One of the challenges of this decade is to rationalize the consumption of global resources while increasing economic activity. Remanufacturing is an option to this challenge; however this end of life strategy must be optimized. The information collected from a franking machine manufacturer, which has set up a refurbishment activity of its machines, has allowed us to qualify remanufacturing as a home-made process in many cases. This paper focuses on aspects of product Architecture. The purpose of the approach proposed in this paper is to reorganize the classical architectures towards modular architectures allowing a much more industrialized remanufacturing.

INTRODUCTION

The environmental problems caused by the emergence of new markets and the fast development of the goods consumption require a rationalization of the world resources consumption while maintaining an economic activity increasing. To reach this objective, the remanufacturing is a process of restoring a discarded product (at the end of the lifetime) in an equivalent functional state to a new product. [START_REF] Lund | The Remanufacturing Industry: Hidden Giant[END_REF] [START_REF] Ijomah | A model-based definition of the generic remanufacturing business process[END_REF] This definition constrained the architecture of the new product to take into account the operations relating to the remanufacturing defined by Steinhilper [START_REF] Steinhilper | Remanufacturing -The Ultimate Form of Recycling[END_REF] or Sundin [START_REF] Sundin | Product and Process Design for Successful Remanufacturing[END_REF] such as dismantling, cleaning, the inspection, the sorting, the reconditioning and the reassembly. The modularization of the product is a way to allow the optimization of the operations related to the product end-of-life like the reutilisability, recycling, maintainability and the technological update [START_REF] Umeda | Product modularity for life cycle design[END_REF]. In the literature, the modularization of the products forms part of the DfX approaches. "Design for Modularity" is an approach aiming at subdividing a system in smaller parts which can be created in an independent way and then used in various systems. In addition to the reduction of the costs, the modularity offers flexibility in the design (the addition of new technical solutions).

The goal of this paper is to propose to the designer potential Product Architectures (pPA) containing modules remanufacturables, recyclables and upgradable. Three levels of improvement of architecture are distinguished: (Optimization, improvement and innovation) correspondent with various evolutions of use: necessary functions, Short term upgradable functions and Long term upgradable functions). This article is structured in the following way. In section 2, we introduce requirements of the remanufacturing on product architecture. In section 3, we define contours of the method. In section 4, we explain the operation of MGE tool. Section 5 examines validity of the approach with a case study. Finally section 6 concludes this study and positions the tool inside a more global methodology.

PRODUCT ARCHITECTURES REQUIREMENTS FOR

REMANUFACTURING

Design For Remanufacturing

The literature shows a growing interest for the remanufacturing [START_REF] Lund | The Remanufacturing Industry: Hidden Giant[END_REF], [START_REF] Steinhilper | Remanufacturing -The Ultimate Form of Recycling[END_REF]. The definition of the remanufacturing [START_REF] Ijomah | A model-based definition of the generic remanufacturing business process[END_REF] implies several design fields such as the evaluation of remanufacturability, re-design of product to facilitate the remanufacturing processes [START_REF] Van Der Laan | Production planning and inventory control with remanufacturing and disposal[END_REF], [START_REF] Ijomah | Development of design for remanufacturing guidelines to support sustainable manufacturing[END_REF], [START_REF] Duflou | Efficiency and feasibility of product disassembly: A case-based study[END_REF], the research on the necessary operation to refurbish product (dismantling, cleaning, inspection and sorting, reconditioning and re-assembly) [START_REF] Lund | The Remanufacturing Industry: Hidden Giant[END_REF], [START_REF] Steinhilper | Remanufacturing -The Ultimate Form of Recycling[END_REF], environmental [START_REF] Gehin | Développement d'une Méthodologie de Conception de Produits Durables[END_REF] and economic [START_REF] Kerr | Remanufacturing and eco-efficiency: A case study of photocopier remanufacturing at Fuji Xerox Australia[END_REF], [START_REF] Mont | A new business model for baby prams based on leasing and product remanufacturing[END_REF] assessment.

The majority of these publications treat often one aspect of this very vast field. Many research works [START_REF] Gungor | An evaluation methodology for disassembly process[END_REF], [START_REF] Lambert | Determining Optimum Disassembly Sequences In Electronic Equipment[END_REF], [START_REF] Desai | Evaluation of disassemblability to enable design for disassembly in mass production[END_REF] relate to the means to set up to optimize the dismantling of the products for their valorization. Complexity increase with the number of components and time necessary for dismantling depends on many parameters (interconnection between the components, types of fixing, direction of dismantling, etc.)

The complete dismantling of a product quickly would be not easily justifiable from an economic point of view. In fact the remanufacturing must consider several criteria because, as Kara underlines it [START_REF] Kara | Selective Disassembly Sequencing: A Methodology for the Disassembly of End-of-Life Products[END_REF], «full disassembly of product tends to be unproductive due to technical and cost constraint». Zuidwijk insists « a product recovery strategy determines the degree of disassembly of a product and the assignment of recovery options» [START_REF] Zuidwijk | Strategic Response to EEE Returns: Product Eco-Design Or New Recovery Process[END_REF]. For him four options of valorization coexist:

•

The remanufacturing on a component level (requiring a complete disassembling of the product)

•

Recycling after complete disassembling of the product • Recycling after partial disassembling of the product (to respect quotas)

•

And the setting in discharge

It will be noted that the option which would privilege an optimized dismantling of the product to re-use modules having an addedvalue is not mentioned. In 2002, Lambert introduces the concept of incomplete dismantling [START_REF] Lambert | Determining Optimum Disassembly Sequences In Electronic Equipment[END_REF] justified by certain technical constraints: irreversible connections, economic constraints (since the costs of dismantling are inversely proportional to the profit generated by the re-use of the components extracted). By using CAD software, it is possible to determine if the disassembling of a part is blocked by another.

Design For Modularity

A new field emerges in answer to the need to carry out an intelligent and advantageous dismantling: "Design for Modularity". The modularization of the products is the first step towards a sustainable design [START_REF] Seliger | Modularization as an enabler for cycle economy[END_REF]. The modular products make it possible to improve valorization of materials by differentiating the modules which are potentially recyclable and which are not recoverable [START_REF] Kimura | Product modularization for parts Reuse in inverse manufacturing[END_REF].

The design of the future products will have to take into account the definition of the modules and Product Architecture. Today most of product on the market does not have a clearly defined module. So the evaluation of the end-of-life scenarios is more complicated. In the products having a high number of components, sub-units can be defined as functional group of the product (example of the washing machine [START_REF] Kimura | Product modularization for parts Reuse in inverse manufacturing[END_REF]).

A modular decomposition is a means to optimize dismantling and to make more profits on the economic and environmental aspects.

The remanufacturing makes it possible to re-use products or modules on several cycles according to the requirements of the consumer or the market evolution (update of the computers) [START_REF] Seliger | Modularization as an enabler for cycle economy[END_REF].

Tomiyama [START_REF] Tomiyama | The Post Mass Production Paradigm, Knowledge Intensive Engineering, and Soft Machine[END_REF] proposes the concept of "Post Mass Paradigm Production" to reduce the consumption of the natural resources as well as the production of waste by maintaining the standard of living or by improving it.

The satisfaction of this new model passes by an increase of the modules lifespan but the consequences are the functional obsolescence of the product. The increase of product lifetime as well as the limitation of obsolescence requires to establish a strategy adapted to the value of product (repair, update, re-use, etc.): "Longer-life products should have functional upgradability besides reliability and fault-tolerance" [START_REF] Kondoh | Development of Upgradable Cellular Machines for Environmentally Conscious Products[END_REF] [5].

In the literature the benefits of the adoption of a modular approach in the product life cycle are less discussed. The product is not any more the result of a material assembly and a manufacturing process but a sum of inter-connected modules playing the role of central unit in the product modeling [START_REF] Tchertchian | The Influence of the Multiple Life Cycles on the Environmental Impact of a Product[END_REF]. Thus in the model developed by Gehin [START_REF] Gehin | Développement d'une Méthodologie de Conception de Produits Durables[END_REF] the product is composed of modules following the strategies of valorization on several cycles of use. Its approach allows an environmental evaluation of the product from these modules but by considering the operational costs on several cycles (supply chain, refurbishing…).

Kimura [START_REF] Kimura | Product modularization for parts Reuse in inverse manufacturing[END_REF] and Umeda [START_REF] Umeda | Product modularity for life cycle design[END_REF] propose methods to design modular product depending on modules characteristics as life cycle options and geometrical information. Considering the product life cycle, the components which undergo the same life cycle should be gathered in a module. These modules can then undergo refurbishing and recycling processes without disassembling. Then, the management of the components throughout the life cycle is optimized and to the environmental impact and the costs of logistics and recovery are reduced. According to established criteria, impacting on the life cycle of the product, various modules can be defined. The result of modular grouping is a variety of product architectures.

It is the choice among this variety which poses problem; selected architecture must answer to impact reduction and costs controls requirements while the technical criteria are satisfied.

METHOD TO DEFINE POTENTIAL PRODUCT ARCHITECTURE OF REMANUFACTURABLE SYSTEM

The general principles of the methods

The general principles of our approach relate to the definition of Product Architecture concept, the integration of new uses/upgradability, the calculation of the environmental/economic evaluation and the concept of affinity.

What is the definition of product architecture?

«Product architecture is the scheme by which the function of a product is allocated to physical components» [START_REF] Ulrich | The role of product architecture in the manufacturing firm[END_REF] «Each level in the product hierarchy has its architecture. Depending on the type of components, we speak about a functional, technology or physical architecture» [START_REF] Erens | Architectures for product families[END_REF] «The Product architecture is « a comprehensive description of a bundle of product characteristics, including number and type of components and number and type of interface of those components » [START_REF] Fixson | Product architecture assessment: a tool to link product, process, and supply chain design decisions[END_REF] By convention, we define Product Architecture as:

Current Product Architecture = ∑ Current Module i With Current Module = ∑ Component j
In the current product, the modules defined are "virtual" and depend on the function fulfilled.

Architecture Nouveau Produit = ∑ Module Actuel i -∑ Composant j + ∑ Nouveau Composant k

The goal of the methodology is to suggest a more evolutive Product Architectures and taking account of new uses, new technologies, the new regulations…

Integration of new usages associated with new functionalities -Upgradability

Criteria

Economic and environmental assessment

Environmental evaluation: The Environmental Impact (EI) uses to evaluate the modules corresponds to the Extraction/Manufacture Impact.

Economic evaluation: The economic Costs (C) taking into account are the price of the modules.

Concept of affinity

Affinity is a Potential of interaction between two modules (or submodules) according to certain criteria.

According to the literature to improve modular architecture of a product it is necessary to gather modules (or components).

"Considering a whole life cycle of a product, components that undergo the same life cycle processes should be grouped into one module for improving, e.g., disassemblability, maintainability, upgradability, reusability, and recyclability" [START_REF] Umeda | Product modularity for life cycle design[END_REF] Types of grouping proposed in the paper:

•

Grouping between modules recyclables • Grouping between modules remanufacturables • Grouping between modules upgradables MACPMR Methodology [START_REF] Tchertchian | A method helping to define eco-innovative systems (product architecture + RSC structure + scenario usage[END_REF] is based on criteria which make it possible to gather modules having the same properties. Among these criteria:

• Reliability / Maintainability • Technical and visual obsolescence • Product range

Preliminary definition of the Modules

The method depends on an algorithm (Figure 1) allowing a characterization of the modules in pMR (i.e. Module potentially Remanufacturable), in pMr (i.e. Module potentially recyclable) and in intermediate modules pMU (i.e. module potentially Undefined which characterizes remanufacturables and/or recyclables modules). These modules can contain components which can be recycled and others remanufacturables (these modules pMU will be broken up in the stage of optimization into Sub-Modules SM so that there remain only pSMR and pSMr).

In order to classify each module identified, the environmental and economic criteria were selected because they make it possible to explain, qualitatively in first time, the true concerns in industry: decrease the environmental impacts (to respect the regulations more and more severe) and increase the profits. The characterization of the modules is done according to the distribution of the costs and the impacts of the product. The cost price was used to characterize the economic costs of the module, while the environmental impact is characterized by impact in the extractionmanufacture phases; this methodological choice (focusing on the Pre-life and not on the whole life cycle) makes it possible on the one hand to simplify the problem and on the other hand to identify objectively the strongest economico-environmental improvement source.

Modules organization

This stage makes it possible to determine quantitatively the main "potential" modules of the product. These potential modules (pMR or pMr) are groupings of evaluated modules then identified like remanufacturables or recyclable with the sub-modules resulting from the decomposition of the modules pMU (in homogeneous submodules r or R) in preliminary definition of modules. The modular grouping is based on calculation of affinity coefficient between modules (pMR or pMr), between sub-modules or modules and submodules. This affinity (eq. 1) contains 3 components presented in 3.1 like "Product criteria" influencing the remanufacturing: affinity of reliability (eq. 2), affinity of obsolescence (eq. 3) and affinity of commonality (eq.4). The purpose of this affinity is to privilege the groupings between modules whose coefficients are strongest.

com obs rel A A A Affinity ⋅ + ⋅ + ⋅ = γ β α (1)
The state of the art as of the applications carried out on an industrial case allow to privilege three criteria [START_REF] Umeda | Product modularity for life cycle design[END_REF], [START_REF] Tseng | Modular design to support green life-cycle engineering[END_REF]. Decomposition of affinity in several components at two strong points:

•

An aggregate score allows an evaluation and an interpretation of the results more adapted for a use of the tool by a multidisciplinary team.

•

The other advantage is the possibility to add weighting coefficients for each criteria, thus making it possible to better take into account particular strategies (for example: to support the grouping of the unreliable components in order to simplify the replacement of the module).

Characterization of grouping criteria: (2)

• Obsolescence: Obsolescence is evaluated qualitatively according technologies evolution [START_REF] Umeda | Product modularity for life cycle design[END_REF] and usages of the consumers.

Data input: VLT of existing Component in Modules. VLT is defined by Umeda [START_REF] Umeda | Product modularity for life cycle design[END_REF] and is calculated by Tool [START_REF] Pialot | Definition of potential upgrade scenario in early design phases of remanufacturable system[END_REF] in MacPMR [START_REF] Tchertchian | A method helping to define eco-innovative systems (product architecture + RSC structure + scenario usage[END_REF]. (3)

Figure 1 : Flowchart of method.

• Commonality: Data input: The number of product in the range and the contents of each product in the range.

Acom is the affinity of commonality between modules i and j, and p is the number of product in the range. The last stage of methodology is the validation of the pMR and pMr groupings. This stage is carried out by a control of functional compatibility of all the modules between them (materials, energy and information flow). This control must be carried out by the means of a block function functional diagram (functional analysis) because one does not need only the presence of a component blocks the operation of the module and/or the product.

When they are an incompatibility (low affinity), the grouping is not validated. It is necessary to make iteration in eliminating the component (or Sub-module) from the grouping and to compose a new grouping by a new affinity calculation.

For more flexibility, design team has liberty of freedom to modify the Sub-modules (by optimizing materials, or connections…) and to make them compatible with the pMR or pMr.

Assessment of Product Architectures

The early phases of the tool allow the designer to have a large panel of modules, according to the selected sensitivity for each criterion: reliability, obsolescence and commonality. In the last phase the designer is assisted in his choice of one or more architectures. The choice of architectures depends on two criteria. First is economic, it takes into account the cost of the disassembly operations compared to the retail prices of the modules (or of the constitutive components). And second is environmental, the choice of architectures takes into account the impact of the modules in Extraction and Manufacture phase. The designer establishes, for each list of modules, various organizations helped by function block diagram (Figure 2) with information flows between modules. The objective is to chart the sequence of dismantling simply so as to withdraw in priority the pMR without disassembling the pMr. To calculate the time of dismantling it is necessary to provide information: type of connections, orientation of dismantling, accessibility, ect. The means used to estimate times of dismantling is the Prodtect software. The disassembly cost of the pMR is given by:

∑ × = = n i pMRi pMRi dis C t C 1 (5)
For the environmental criteria, the designer calculates the impact generated during materials extraction and manufacture of modules. Avoided impact defines environmental impact for pMR and generated impact defines environmental impact for pMr. Solutions are compared with their Environmental Gain.

MODULAR GROUPING EXPLORER ON CASE STUDY

The case study considered the strategy improvement.

Initial classification of the Modules

In order to show the various steps of the proposed approach, a mechatronic product is considered (Figure 3), made up of 11 modules, marketed in the leasing form.

According to the strategy of change wished by the user, the input data table is updated (Table 1). In improvement strategy only the two criteria Reliability and obsolescence, are used.

Validation of remanufacturability

Then the reliability and obsolescence of the pMR are verified. The class of the 11 modules of the product is defined in Table 2.

Modules

Modular grouping by Affinity calculation

The following stage consists in establishing pMR/pMR, pMr/pMr, pMU/pMR, pMU/pMr grouping according to reliability and obsolescence affinity. The table below is a sample of the affinity calculation between module UI and the 10 other modules. The modules with same class and having an affinity higher or equal to 80% are likely to be gathered within the same module.

The designer validates or not in regarding technical and functional compatibility. For the modules whose affinity is lower than 80%, tool MGE calculates a potential affinity using BEST MTBF and BEST VLT introduced in INPUT. Finally when a couple pMU/pMX or pMX/pMX (with X=R or R) does not fill the criteria, there are a stage of iteration with decomposition of the module pMU and/or pMX Submodules when it's possible. Afiab Aobs Table 3 : Affinity calculation.

Evaluation Of Product Architectures

Five architectures are evaluated, corresponding to different strategies: (strategy economic, environmental, mixed and different levels from reliability and obsolescence).

The procedure of evaluation will be detailed for the first list.

1. The designer defines the modules organization within the product and defines connections between modules. An economic gain is considered when pMR are extracted, whereas for the pMr the profits generated by the resale of materials to the recyclers are neglected. In the case study the resale of pMR compensates for the transport costs [START_REF] El Korchi | Conditions stratégiques d'émergence d'une reverse supply chain fondée sur le remanufacturing chez le fabricant d'origine[END_REF]. When a pMr is found at the end of the dismantling sequence the disassembly cost is null (Cdis=0).

5. Calculation of the avoided impacts and the pollution generated during materials extraction and manufacture of a new machine (Table 6).

Choice of the best Product Architectures

The current scenario is modeled by taking the current Product Architecture with the dismantling process carried out in experiments. The end-of-life scenario for modules is defined table 3, i.e. M1, M4 and M6 are remanufactured and other modules are recycled or discarded. Economically there are more or less important disparities between the solutions ( Figure 5). The dismantling of the current product cost approximately 40€. The best solution is solution 2 with a profit of 38€.

From an environmental point of view the results are contrasted ( Figure 6), indeed the performances of solutions from 1 to 4 are overall identical because the same modules are recovered for remanufacturing. The environmental performance of current product is less good because only three modules are remanufactured. The approach suggested in this article and materialized by a tool MGE allows to pre-define Modules in early phases of design process. These modules are made up in order to optimize the endof-life, by gathering the modules according to the characteristics of their life cycle. The groupings take into account the physical characteristics (reliability) of the components as well as the obsolescence of the components throughout life cycle (VLT). This approach was tested on a B2B product "A Franking Machine" with the goal to design a modular architecture remanufacturable. The tool determines a variety of solutions classified economically and environmentally.

This tool is part of a more comprehensive methodology integrating the definition of Upgrade cycles, structure of Reverse Supply Chain and allowing the activities of remanufacturing economically viable while taking into account environmental and social considerations.

  input: MTBF of existing Component in Modules.

  i and j = k then α = 1 else α = 0

Figure 2 :

 2 Figure 2 : Dismantling sequence.

Figure 3 :

 3 Figure 3 : Franking machine's Modules

Figure 5 :

 5 Figure 5 : Economic Assessment for each Solution.

Figure 6 :

 6 Figure 6 : Environmental Assessment for each Solution.

Table 1 :

 1 Functionalities extension.

		Parametric Upgrade	Functional Upgrade
	Integration Planned,	No planned
		By substitution of module	By externalisation of module
	Cost	Low	High
	Incertitude Low	High
	Innovation Enhanced Functions	Enhanced Functions
		New functions	Innovative Functions
	…

Table 1 :

 1 Input data table for Modular Grouping Explorer.

	INPUT DATA
	N° Modules' listing	Environmental Impact	Production Cost	MTBF MTBF + VLT VLT +
	M1	User Interface	28,30	12,00	25	30	4	8
	M2	ALD	0,20	5,00	20	25	8	8
	M3	Service Station	7,20	9,00	18	20	8	10
	M4	Power Supply	62,70	10,00	7	15	7	10
	M5	Modem	5,80	6,00	9	14	4	6
	M6	Main Board	22,10	5,00	7	11	3	6
	M7	Main Motor	0,20	14,00	21	30	10	15
	M8	Top Module	0,7	7	15	15	6	10
	M9	Lower Module	1,1	5,5	17	20	9	9
	M10	Lower Housing	0,2	4	50	50	4	8
	M11	Cover	0,7	5	50	50	4	6

Class Reliability fraction Value fraction Validation Class

	User Interface	pMR	66,67	40	pMR
	ALD	pMU	75,00	80	pMU
	Service Station pMU	58,33	80	pMU
	Power Supply	pMR	66,67	70	pMR
	Modem	pMU	50,00	40	pMU
	Main Board	pMR	41,67	30	pMR
	Main Motor	pMU	83,33	100	pMU
	Top Module	pM_r	58,33	60	pM_r
	Lower Module	pM_r	66,67	90	pM_r
	Lower Housing pM_r	100,00	40	pM_r
	Cover	pM_r	100,00	40	pM_r

Table 2 :

 2 Definition of Modules End of Life.

Table 4 :

 4 Potential solutions of Product Architecture defined by MGE.

	2. Modeling of the dismantling sequence on Prodtect.
		Figure 4 : Dismantling sequence.
	3. Time and cost for pMR extraction
	From	To	Type de	Nombre de	Direction de
	Module 1	Module 2	fixation	fixation	désassemblage	t (s)
	M'1	M'2	Snap fit	4 +X	8,42
	M'2	M'3	Screw	4 +X
	M'2	M'3	Screw	2 +Y	64,46
	M'2	M'3	Screw	2 -Y
	M'2	M'4	Snap fit	1 +X	1,84
	M'3	M'4	Snap fit	1 +Y	4,86
	M'3	M'5	Screw	4 -X	35
	M'3	M'6	Snap fit	1 +Y	4,86
	M'4	M'5	Screw	1 +Y	1,84
	M'5	M'6	Screw	2 +X	1,84

Table 5 :

 5 Potential solutions of Product Architecture defined.The calculation of disassembly cost is based on an hourly wage with 49€/h (tax included).4. Calculation of the recovered value and the losses for the manufacture of a new machine (Table6) GpMR=Pr -Cdis and LpMr=Pr + Cdis

								Avoided	Generated
			tdis	Cdis	Pr	Recovered	Loss	Impact	impacts
	Modules EoL	(s)	(€)	(€)	Value (€)	Value (€)	(Pts)	(Pts)
	M'1	pMr 8,4	0,11	9		9,11		0,9
	M'2	pMR 74,7 1,02 33	31,98		118,9
	M'3	pMr 119,4 1,63 12		13,63		0,9
	M'4	pMR 76,6 1,04	9	7,96		7,2
	M'5	pMr 123,1	0 5,5		5,5		1,1
	M'6	pMR 123,1 1,68	7	5,32		0,2

Table 6 :

 6 Economic and Environmental data per Modules for solution1.

ACKNOWLEDGMENTS

This work was financially supported by ADEME and CETIM foundation in the framework of MacPMR research program. We thank them.


Nicolas Tchertchian
email: nicolas.tchertchian@supmeca.fr

Dominique Millet
email: dominique.millet@supmeca.fr

Eco-Maintenance for Complex Systems: Application on System of Renewable energy production

Keywords: Eco-Maintenance, Ecodesign, Wind energy, Wind turbines. I

The marine energy sector is experiencing a growing interest; large offshore wind farms continue to emerge, particularly in northern Europe. The electrical energy produced by these turbines is intended as clean and renewable. Operations and Maintenance (O&M) of offshore wind farms is however costly and generating environmental impacts. Indeed in view of the circumstances on the high seas the accessibility to Wind Turbine (WT) is greatly complicated causing long periods of unavailability compared to onshore wind farm. To remedy this, operators will mobilize significant and specific resources (Fast Crew Boat, large boat, helicopters).

The use of these vehicles and systems of maintenance can improve the availability of WTs but also degrade the environmental performance of offshore wind farm. The optimization of O&M requires rethinking the concept of maintenance of renewable energy systems in mobilizing the concept of Eco-Maintenance. Eco-Maintenance aims to minimize the environmental losses due to maintenance while maximizing the environmental benefits generated assuming the energy recovered due to better maintenance / availability equivalent to an environmental gain corresponding to the environmental impacts of a thermal power plant should have been put into operation to produce this energy.

This approach is tested on an offshore wind farm. In this context, modeling the complex system (Offshore Wind Farm + Maintenance Systems) was developed. The various models show that the environmental performance of Systems of Energy Production and System of Maintenance (SM) are interconnected.

INTRODUCTION

The development of offshore wind energy has become a strategic issue of energy independence. The northern European countries (UK, Belgium, the Netherlands and the Scandinavian countries) play a major role and are motivated by the need to find new sources of electrical energy by avoiding the use of fossils fuel. Knowing that the wind is available over large areas along the coasts and the impact of offshore wind farms on populations is lower than onshore.

However, there are problems to be solved for offshore wind energy grows on a larger scale and this requires the need to make this energy more competitive. Offshore work is Between 5 to 10 times more expensive than work on land [START_REF] Bussel | Estimation of turbine reliability figures within the DOWEC project[END_REF]. Indeed the combination of several factors: distance from the shore, the extreme weather conditions in high sea and the construction of more powerful wind turbine (WT), with higher production capacity needed to develop logistic means adapted to build, operate and maintain the turbines. The implementation of these means causes Operations and Maintenance (O&M) cost increasingly high.

The choice of the composition of the vehicles of maintenance fleet, and investment in platforms at sea will have a great impact on O&M costs of an offshore wind farm [START_REF] Halvorsen-Weare | Vessel fleet analysis for maintenance operations at offshore wind farms[END_REF].

In order to develop the wind industry in the future, optimization of maintenance of wind farms is essential to increase the availability and longevity of turbine and ultimately reduce the cost of the life cycle of offshore wind farm.

The unavailability of a WT is the result of two contributors [START_REF] Christensen | Availability of wind turbines in remote places. A statistical and a real-time view[END_REF]:

• The largest contributor to the unavailability of a WT is the downtime of the turbine during preventive maintenance.

• The second cause of unavailability of a WT is the unforeseen faults of the technical equipment requiring immediate stop of the turbine.

While the failure causes an immediate stop of the turbine until the maintenance crew can fix it; preventive maintenance causes the shutdown of the turbine during part of the maintenance activities. During the period of unavailability of the wind, the energy that is not produced must be elsewhere. This creates economic costs of operating losses; In addition the non-production of this energy also generates additional environmental impacts associated with the production of non-renewable energy (nuclear power plant, thermal power plant…).

The concept of Eco-Maintenance aims to optimize the lifetime of the system to maintain while minimizing environmental losses (impact) due to maintenance ; it takes here not only the impacts generated by the use of ships and machinery used in maintenance but also the impacts caused by their manufacture and end of life, in proportion to the use that can be assigned to this maintenance and maximizing the environmental gains from maintenance operations across the life cycle (these gains can be evaluated by considering that the energy recovered due to better availability of turbines is equivalent to an environmental gain corresponding to the opposite -in the mathematical sense of the termenvironmental impacts generated by a thermal power plant should have been put into operation to generate the same power).

Our goal is to define, for the System of Maintenance (SM), the optimum to achieve to ensure that the gains generated by a better maintenance are higher than the impacts generated by the sophistication of the system. To illustrate this problem, do we need a "light" SM (low environmentally impacting) but generating downtime or a more sophisticated and "heavy" system but reducing downtime?

As a first step we provide a brief overview of the work to optimize operational resources for the maintenance of wind farms. In the following, based on data extracted from the library we model an offshore wind farm. In the third part of this article we simulate various scenarios and for each assess the environmental impact generated by the maintenance of system through EI 99 (end-point) environmental assessment method. In the last part of this article we give an interpretation of these results of different simulations and determine the parameters influencing the overall system performance of renewable energy production.

II. STATE OF THE ART

A. Background

The maintenance optimization is the process that seeks to balance the constraints of maintenance (legislative, economic, technical, and environmental) and the resources used to run the maintenance program (human resources, spare parts, equipment, arrangements, etc.) [4]. Basically, the main objective of the maintenance optimization is to determine the strategy with the least costly economically with the least danger to operators and, to a lesser one whose environmental impacts are lower strategy. This strategy should provide the best balance between direct cost service (labor, materials, administration ...) and the impact on the system if maintenance is not performed as expected (loss of production and profits) while avoiding the consequences on health, safety and environment by mobilizing the concept of "Green" Maintenance [START_REF] Ajukumar | Evaluation of green maintenance initiatives in design and development of mechanical systems using an integrated approach[END_REF].

To optimize the maintenance of wind farm two types of action are possible; first by acting on internal factors (organizational improvement of the maintenance process: fault detection as early as possible [START_REF] Tavner | Reliability of different wind turbine concepts with relevance to offshore application[END_REF], automatic detection of deviations performance of WTs [START_REF] Sainz | Robust filtering for the characterization of wind turbines: Improving its operation and maintenance[END_REF], planning maintenance [START_REF] Nielsen | Planning of O&M for Offfshore Wind Turbines using Bayesian Graphical Models[END_REF] ...); then acting on the external factors in improving the performance of maintenance systems (optimization of logistics scenarios [START_REF] Scheu | Maintenance Strategies for Large Offshore Wind Farms[END_REF], fleet composition, type of vessel or helicopter [START_REF] Halvorsen-Weare | Vessel fleet analysis for maintenance operations at offshore wind farms[END_REF]).

In practice, two strategies are confronting: • Identify the risks of failure to intervene earlier • Or repair as soon as possible in difficult weather conditions to avoid extended downtime of turbines. In this paper we deal with the second option.

The achievement of maintenance activities such as inspection, preventive maintenance, and replacement of wear components increases the direct costs of maintenance. In return, the risk of incidents or consequences of failure to complete the required maintenance decreased [START_REF]BS3811 Glossary of maintenance terms interotechnology[END_REF].

B. Optimization of operational resources for the maintenance of wind farms.

In the remainder of this article we assess the impact of external factors on the environmental performance of maintenance of wind farms.

The performance and therefore the choice of a logistics concept are influenced amongst others by the following factors [START_REF] Münsterberg | Design and Evaluation Tool for Operations and Maintenance Logistics Concepts for Offshore Wind Power Plants[END_REF]: the distance to shore; the weather conditions; the failure behavior of WTs in the farm; the number of supplied WTs/farms and; the number/types of supply vessels.

The distance to shore will lead to two types of maintenance strategies [START_REF] Tavner | Reliability of different wind turbine concepts with relevance to offshore application[END_REF]; the first based on maintenance made from shore: technicians return to port after each intervention; the second strategy for wind farm away from the coast, technicians are permanently close to the farm. Münsterberg [START_REF] Münsterberg | Design and Evaluation Tool for Operations and Maintenance Logistics Concepts for Offshore Wind Power Plants[END_REF] characterizes transfer system according to weather conditions and the distance to shore. In the case of the first strategy the technicians are brought to the farm by small Crew Transfer Vessels (CTV). Unfortunately the range of use of a "CTV" is limited by sea conditions and the significant wave height to be able to intervene (The significant wave height limitations of CTV are usually assumed with 1.5 m). Beyond operators of wind farm have to mobilize helicopters. The preventive maintenance is usually scheduled during the summer season, when the wind speed and wave height is low enough to intervene. When a failure occurs during the winter season, sometimes access to turbines is inaccessible for long periods, resulting in a long periods of downtime and production losses [START_REF] Obdam | O&M Cost Estimation & Feedback of Operational Data[END_REF]. For example, a transfer system limited by a significant wave height <1.5m, the wait for better conditions of sea is about four days in the summer, 10-15 days in spring and autumn and between 2 and 6 weeks of winter [START_REF] Dowell | Analysis of wind and wave data to assess maintenance access for offshore wind farms[END_REF].

In this paper we consider only vehicles allowing the transfer of maintenance technicians to the WTs as well as the supply of small parts and tools. Heavy lifting equipment and transportation necessary for large components (e.g. blades) are excluded from the study.

Two systems of maintenance are considered in the following of this study: the current system (Crew Transfer Vessel) allows daylight intervention to a significant wave < 1.5 m and an alternative system capable of transferring technicians through a SWATH hull coupled to a dynamic stabilization system on a 24 hour/7 day basis and up to 3m of significant wave height.

III. OFFSHORE WIND FARM AND MAINTENANCE SCENARIO MODELLING

A. Principles of an Eco-Maintenance simulator

The model is built around an event generator. The principle of this generator is to randomly assign failures on all WTs over their lifetime and deduct the planned maintenance affecting the wind farm. The generator is built around a global optimization method of the maintenance system, formalized by the SADT diagram (Figure 1) and composed by eight activity box:

The reference wind farm is first characterized (A1) through its lifetime, the number and type of WTs erected (Max power output), the location of the Farm (weather condition), availability of WTs is characterized (A2) by the reliability of the components expressed as Mean Time Between Failure (MTBF), and the 4 levels of preventive maintenance, identified by the operator. The wind farm location is assigned by sea conditions (A3) (significant wave height) and wind (average wind). The logistics scenarios of maintenance are then modeled: in first time reference scenario (A4), which consists of the use of fast vessels (Crew Transfer Vessels) and limited by significant wave height of 1.5 m, then the alternative scenario (A5) which consists of the use of a heavier ship more sophisticated and able to transfer on-site technicians to significant wave heights of 3m. Generic modeling of nonrenewable energy production system (A6) is established for losses of production when WT is unavailable. Finally, the environmental impact of the maintenance system is evaluated (A7) and areas for improvements are identified (A8).

In this model, the event generation can identify resources to implement to perform the corrective and preventive maintenance of the WT and environmental impact generated. By combining meteorological data (wind speed and significant wave height) with the moment of occurrence of a failure enable to estimate the energy lost during the downtime of the turbine and thus the loss of revenue (and environmental impacts due to the production non-renewable energy).

B. Definition of logistics scenarios of maintenance

A1: Characteristics of wind farm

The lifetime of the offshore wind farm is often estimated at 20 years in the literature, despite the feedback is limited on last generation WT. There are eighty turbines, each with three blades and capable of generating 6 MW with a lifetime of 20 years. The rated power of WT is calculated by the power curve as a function of wind (m / s) [14]. Reliability of WT is a strategic data that is not usually provided by the operators. The only values currently available are those provided by the scientific community [START_REF] Bussel | Estimation of turbine reliability figures within the DOWEC project[END_REF], [START_REF] Richardson | Relating onshore wind turbine reliability to offshore application[END_REF], [START_REF] Arwadel | Probalistic Models for Wind Turbine and Wind Farm Performance[END_REF]. In our model, failures are randomly distributed from the failure rate defined by a group of experts [START_REF] Ribrant | Survey of Failures in Wind Power Systems With Focus on Swedish Wind Power Plants During 1997-2005[END_REF].

A3: Weather conditions assignement

Climatic conditions (average wind speed and significant wave height) are defined for a given wind farm location. These data are needed to evaluate the energy production of the wind farm and to plan the schedule of preventive maintenance. Climate data over long periods are generally not available; in our case, however, the wind farm for this study is located in south of the UK, whose data is known about the last 5 years [18]. For a sample of 20 years we will repeat several times the known data.

A4: Operations and Maintenance baseline scenario

In baseline O&M scenario, the preventive and corrective maintenance are achieving by fast crew boat (known as Crew Transfer Vessels, CTV). The CTV transports technicians to the wind farm then remain on standby till the maintenance crew needs to return [START_REF] Tavner | Offshore Wind Turbine -Reliability, availability and maintenance[END_REF]. CTV allows daylight intervention (we assume from 8AM to 8PM throughout the year) to a significant wave < 1.5 m.

A5: Operations and Maintenance alternative scenario

In this scenario, CTV is replaced by an alternative vessel, able of responding on a 24 hour/7 day basis and up to 3m of significant wave height. This alternative vessel stays on wind farm for a 3 months mission; at the end of each mission it returns to port to refuel.

A6: Nonrenewable energy mix

During WT downtime, wind farm suffered losses in energy production. These losses must be compensated. In environmental performance assessment, the impacts generated by electricity production from non-renewable energy sources have to be taken into account. A7: Definition of the environmental impact (economic cost) of the wind energy production system A wind energy production system is a complex system. The environmental (and economic) assessment of this system consists of assessing the impact of wind energy (life cycle of WT), its maintenance system (reference system or alternative system) and the production of non-renewable energy when the WTs are unavailable.

The Environmental Impact (EI) of the System of Energy Production (SEP) is described in Equations 1-7. For the sake of clarity, we note first (1), ( 2) and (3):

 EI (System of Energy Production) = EI (SEP).   EI (Wind energy Production) = EI (P_Wind energy).   EI (Nonrenewable energy mix Production) = EI (P_NRE mix).

Environmental Impact of SEP is defined by (4), ( 5), ( 6) and ( 7):

 EI (SEP) = EI (P_Wind energy) + EI (P_NRE mix). 

With

 EI (P_Wind energy) = EI (Raw + Manufacture WT) + EI (Use WT) SM + EI (Maintenance WT) + EI (End of Life WT).   EI (Maintenance WT) = EI (System of Maintenance) = EI (Raw SM) + IE (Man SM ref ) + EI (Use SM ref ) + EI (EoL SM ref ). 

And

 EI (P_NRE mix) = α SM × EI (1kWh P_NRE mix). 

Where α SM = non generated energy when WT is unavailable A8: Optimisation of system of maintenance As environmental impact of WTs Manufacturing and End-of-Life stage are the same whatever the SM, the second term in equation [START_REF] Nielsen | Planning of O&M for Offfshore Wind Turbines using Bayesian Graphical Models[END_REF] has to be minimized.

 EI (SM ref ) + EI (Use WT) SMref + α SMref x EI (1kWh NRE mix) > EI (SM opt ) + EI (Use WT)SM opt + α SMopt x EI (1kWh NRE mix). 

In this article we will not discuss optimization methods to minimize the environmental impact (or economic cost). The aim here is rather to identify the constraints of wind energy production system and the parameters of SM that will influence the overall performance of complex system.

IV. CASE STUDY : ENVIRONMENTAL ASSESSMENT OF O&M SCENARIOS

A. Assumptions and definition of O&M scenarios

Corrective maintenance is based on the Annual Failure Rate (AFR) due to 10 failures with the highest occurrence (see Table 1). Failures generated by the simulator follow a random distribution according AFR. Each failure event requires a repair time of 6h while maintenance time is estimated at 4 hours per turbine. Preventive maintenance is interrupted in case of failure on another WT (case 1 Figure 2) maintenance resumes when the repair is complete or shifted if the failure occurs prior to maintenance (case 2 Figure 2).

Preventive maintenance is planned. Two scenarios of preventive maintenance are evaluated to test the influence of the season on the performance of SM:

• Summer scenario: Each WT is visited 1 times during the month of July.

• Winter scenario: Each WT is visited 1 times during the month of January.

It is during the winter season that energy production is the most important, while during summer the weather conditions are more favorable to maintenance operations.

Thus Table IV shows the loss of energy production during the lifetime of the wind farm, due to planned maintenance; losses are much smaller when these operations are carried out during the summer season. Systems of Maintenance evaluated carry the same number of technicians, three groups of 4 technicians. The reference system is a fast crew boat (CTV) which drops 12 technicians on-site early in the day (8AM) and returns to port with technicians in the evening (8PM). During the workday the CTV remains on standby till the maintenance crew to return.

The alternative system is constantly on the wind farm, a team of 4 technicians working 8 hours then they are replaced by another, ect.

The data used for evaluating the use cycle of the two systems of maintenance are presented in Table II

B. Results

The environmental assessment of the System of Energy Production shows that the alternative SM saves 500kPt (Figure 3), corresponding to the production of 15 GWh (Table IV) of additional renewable energy. In return for the use phase the alternative SM is more fuel consumer compared to reference SM even if the CTV return to port each day and the alternative SM is present on the site constantly. Furthermore, more the wind farm is far from shore more the alternative SM is competitive with CTV. Uncertainty remains about the impact of alternative system when it remains standby on the site. In fact it acts as a "boat hotel" for the crew. When the alternative SM uses its auxiliaries for operations and life on board more than 3 hours CTV is less impacting even from 100km from land to wind farms. However, when the alternative SM uses its auxiliary for life on board less than 2 hours, it becomes more advantageous, from 50km offshore. V. DISCUSSIONS This approach should be applied at the same time as designing WT. It would be interesting to assess the size of the wind farm coupled with the power of WT. For example, is it better to design a 500 MW wind farm composed by 80 * 6MW, or composed by 62* 8 MW WTs. The failure rate of these turbines is assumed to be different (because the technologies used are different). Unavailability of 8 MW WTs may be more important generating more environmental impact with production of non-renewable energy. In exchange, the maintenance of a smaller wind farm may induce fewer resources in terms of maintenance. In conclusion, the environmental performance of Systems of Energy Production and system of maintenance are interdependent.

Figure 1 .

 1 Figure 1. General framework of optimization method of the maintenance system

  Three wind farm configurations are considered: • 10km from land to wind farms • 50km from land to wind farms • 100km from land to wind farms A2: Characterization of Wind Turbines

Figure 2 .

 2 Figure 2. Case of interruption or postponement of preventive maintenance.

TABLE I

 I

	.	GENERIRIC RELIABILITY OF WIND TURBINES [17]
			Annual Failure rate/
	Component	WT
	Gears		0.045
	Control system	0.050
	Electric System	0.067
	Yaw system	0.026
	Blades and pitchsystem 0.052
	Generator	0.021
	Sensors		0.054
	Hydraulics	0.061
	Drive Train	0.004
	Mechanical Brake	0.005

  and TableIII.

TABLE IV .

 IV NONRENEWABLE ENERGY PRODUCTION (MWH)

			ACCORDING DIFFERENT SYSTÈM OF MAINTENANCE
				SMref Nonrenewable energy SMopt production (MWh)	Gains beetween SMref and SMopt (GWh)
			corrective maintenance	86 730	73 400	13,3
	renewable Non	energy	Preventive maintenance (Winter) Preventive maintenance (Summer) Total Winter	11 500 5 340 98 230	7 860 4 075 81 260	3,6 1,3 17,0
			Total Summer	92 070	77 475	14,6

TABLE V .

 V COMPARISON OF ENVIRONMENTAL IMPACT (EI99) BETWEEN SMREF AND SMOPT 120 min Waiting time / wind farm 180 min Waiting time/ wind farm

	Impact	Raw	WT unavailability	WT		Use/	Use/	Use/	Use/	Use/	Use/
		materials/	due to corrective	unavailability	10km	50km	100km	10km	50km	100km
		Manufacturing	maintenance	due	to
		(Pt)	(Pt/20ans)	summer
				preventive
				maintenance
				(Pt/20ans)
	SMref	38 294	3 313 001	203 865		50 710	248 380	495 468	50 710	248 380	495 468
	SMopt	343 229	2 803 941	155 680		429 594	491 045	567 859	621 306	682 757	759 571

Figure 3. Environmental Impact of System of Renewable Energy production