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International audience; Hydrogen is currently considered one of the most promising sustainable energy carriers for mobility ap- plications. A model of the hydrogen supply chain (HSC) based on MILP formulation (mixed integer linear programming) in a multi-objective, multi-period formulation, implemented via the ε-constraint method to generate the Pareto front, was conducted in a previous work and applied to the Occitania region of France. Three objective functions have been considered, i.e., the levelized hydrogen cost, the global warm- ing potential, and a safety risk index. However, the size of the problem mainly induced by the number of binary variables often leads to difficulties in problem solution. The first innovative part of this work explores the potential of genetic algorithms (GAs) via a variant of the non-dominated sorting genetic al- gorithm (NSGA-II) to manage multi-objective formulation to produce compromise solutions automatically. The values of the objective functions obtained by the GAs in the mono-objective formulation exhibit the same order of magnitude as those obtained with MILP, and the multi-objective GA yields a Pareto front of better quality with well-distributed compromise solutions. The differences observed between the GA and the MILP approaches can be explained by way of managing the constraints and their different logics. The second innovative contribution is the modelling of demand uncertainty using fuzzy concepts for HSC design. The solutions are compared with the original crisp models based on either MILP or GA, giving more robustness to the proposed approach.

International audience; This study performed a Life Cycle Assessment (LCA) to evaluate the environmental impact of chicken meat production from a Mexican case study, with a “cradle-to-slaughterhouse gate” approach. To overcome the LCA's limitations and provide a more holistic picture of the system, simulation and artificial intelligence techniques were integrated. First, raw material/energy requirements were obtained from the case study and simulated using Process simulation (PS) and Monte Carlo (MC) simulation to estimate the emissions and quantify their uncertainty. Then, IMPACT 2002 + was used to calculate the overall impact using Ecoinvent and LCA Food databases. The results highlight that chicken farms are the main factors responsible for the environmental impacts assessed, where feed production (use of chemicals and energy requirements) and on-farm emissions (organic waste decomposition) are the main contributors. Concerning the slaughterhouse, the energy production (electricity and steam) and the cooling-related activities present a significant impact. Afterwards, three impact allocation procedures (mass method, neural networks, and stepwise regression) were tested, showing similar results. Finally, a multiobjective optimization model based on a Genetic Algorithm was applied looking to minimize the environmental impacts and maximize the economic benefits. The selected alternative achieved a reduction of 15.14% per functional unit at the environmental indicators. The results encourage the use of support techniques for LCA to perform a reliable assessment and an environmental/economic optimization of the system.

International audience; This article proposes a novel approach that uses a mathematical model optimized by Genetic Algorithms harmonized with the Russian theory of problem solving and invention (TRIZ) to design an export packing of Persian Lime. The mathematical model (with functional elements of non-spatial type) optimizes the spaces of the Persian Lime Packing, maximizes the Resistance to Vertical Compression and minimizes the Amount of Material Used, according to the operation restrictions of the packing during the transport of the merchandise. This approach is developed in four phases: the identification of the solution space; the optimization of the conceptual design; the application of TRIZ; and the generation of the final proposal solution. The results show the proposed packing (with 28% less cardboard) supports at least the same vertical load with respect to the nearest competitor packing. However, with the same number of packings per pallet and pallets per container, the space used by the packing assembled and deployed in the container is greater by 10% and 38% respectively. Besides, TRIZ includes innovative non-spatial elements such as the airflow and the friction of the product inside the packing. The contribution of this approach can be replicable for the packing design of other horticultural products of the agri-food chain

International audience; A lot of recent studies have concluded that hydrogen could gradually become a much more significant component of the European energy mix for mobility and stationary fuel cell system applications. Yet, the challenge of developing a future commercial hydrogen economy still remains through the deployment of a viable hydrogen supply chain and an increasing fuel cell vehicle market share, which allows to nar- row the existing cost difference regarding the conventional fossil fuel vehicle market. In this paper, the market penetration of hydrogen fuel cell vehicles, as substitutes for internal combustion engine vehicles has been evaluated from a social and a subsidy-policy perspective from 2020 to 2050. For this purpose, the best compromise hydrogen supply chain network configuration after the sequential application of an optimization strategy and a multi-criteria decision-making tool has been assessed through a Social Cost-Benefit Analysis (SCBA) to determine whether the hydrogen mobility deployment increases enough the social welfare. The scientific objective of this work is essentially based on the development of a method- ological framework to quantify potential societal benefits of hydrogen fuel cell vehicles. The case study of the Occitania Region in France supports the analysis. The externality costs involve the abatement cost of CO2 , noise and local pollution as well as platinum depletion. A subsidy policy scenario has also been im- plemented. For the case study considered, the results obtained that are not intended to be general, show that CO2 abatement dominates the externalities, platinum is the second largest externality, yet reduc- ing the benefits obtained by the CO2 abatement. The positive externalities from air pollution and noise abatement almost reach to compensate for the negative costs caused by platinum depletion. The exter- nalities have a positive effect from 2025. Using a societal cost accounting framework with externalities and subsidies, hydrogen transition timing is reduced by four years for the example considered.

International audience; Hydrogen and fuel cell technologies are one solution foraddressing the challenges that major airports are facing today, such asupward price trends of liquid hydrocarbon fuels, greenhouse gasemission regulations, and stricter noise and air pollutant emissionregulations, especially for on-ground pollution. An airport can also beviewed as the center of a hydrogen ecosystem, around which multiplehydrogen users could be clustered, with cost sharing of hydrogenproduction and storage occurring among users. The main novelty of thepresent work is the design of a hydrogen infrastructure irrigated by theairport ecosystem that satisfies the airport ecosystem energy needs. Forthis purpose, the model development is based on a multiobjectiveoptimization framework designed to consider four echelons: energysources, hydrogen production, transportation, and storage. Themultiperiod problem is then solved using theε-constraint method.Two objective functions are involved, that is, the total daily cost (TDC) of the network and an environmental indicator basedon the global warming potential. The second innovative contribution is to model the demand uncertainty using fuzzy conceptsfor a hydrogen supply chain design. Because hydrogen demand is one the most significant parameters, the uncertainty of thedemand has been considered using a proposed fuzzy linear programming strategy. The solutions are compared with the originalcrisp model, giving more robustness to the proposed approach. This work has been performed in the framework of the Hyportmeta-project and, in particular, within the“H2modeling”project. This paper focuses on a hydrogen airport ecosystem located inthe department of Hautes-Pyrénées (France). However, the developed methodology could be extended to other hydrogenecosystems for which deployment involves a multiperiod multi-objective formulation under an uncertain demand

International audience; The pressure of the market, the exigencies of the society, and the environmental restrictions ask for new problem-solving approaches. In this context, the Theory of Inventive Problem Solving (TRIZ) offers several advantages: it is a knowledge-based approach for problem-solving that links the problem requirements with some engineering models to guide the solving process. However, the learning process of TRIZ and its use with a practical purpose reveal many drawbacks. A significant problem, while using TRIZ, emerges when the user needs to analyze and formulate an inventive problem. To deal with this issue, a combination of TRIZ with other tools seems the best strategy. The use of the Functional Analysis (FA) is one of the best examples. Despite the usefulness of the FA technique, a difficulty remains: it is a complex task to model the causal relationship between several parameters or conditions within a system. However, a tool used in the System Dynamics Modeling deals well with this situation. The System Dynamics (SD) analyzes the nonlinear behavior of complex systems over time. Congruent with recent TRIZ advances, the SD is a computer aided-approach with an extended application domain, practically in any complex system-social, managerial, economic or natural system defined by some relationships, a flow of information, and some effects of causality. Hence, SD can produce useful information when there are several conflicts in a system, also called a problem network. SD uses a graphical tool to model the variables and states of a system: The Causal-Loop Diagram. This tool is helpful to explain a conflict, the change of a system, or merely the interactions that take place to obtain an effect. This article presents a comparison between the Functional Analysis and the Causal-Loop Diagram to model inventive problems.

International audience; Food production has put enormous strain on the environment. Supply chain network design provides a means to frame this issue in terms of strategic decision making. It has matured from a field that addressed only operational and economic concerns to one that comprehensively considers the broader environmental and social issues that face industrial organizations of today. Adding the term “green” to supply chain activities seeks to incorporate environmentally conscious thinking in all processes in the supply chain. The methodology is based on the use of Life Cycle Assessment, Multi-objective Optimization via Genetic Algorithms and Multiple-criteria Decision Making tools (TOPSIS type). The approach is illustrated and validated through the development and analysis of an Orange Juice Supply Chain case study modelled as a three echelon GrSC composed of the supplier, manufacturing and market levels that in turn are decomposed into more detailed subcomponents. Methodologically, the work has shown the development of the modelling and optimization GrSCM framework is useful in the context of eco-labelled agro food supply chain and feasible in particular for the orange juice cluster. The proposed framework can help decision makers handle the complexity that characterizes agro food supply chain design decision and that is brought on by the multi-objective nature of the problem as well as by the multiple stakeholders, thus preventing to make the decision in a segmented empirical manner. Experimentally, under the assumptions used in the case study, the work highlights that by focusing only on the “organic” eco-label to improve the agricultural aspect, low to no improvement on overall supply chain environmental performance is reached in relative terms. In contrast, the environmental criteria resulting from a full lifecycle approach is a better option for future public and private policies to reach more sustainable agro food supply chains.

Display Omitted The behavior patterns in the Persian lime production process are studied.An expert system (ES) based on fuzzy logic is developed.The interrelationship of the agricultural practices is highlighted.The productivity of the orchard increases when the "pruning" of the tree is performed prior to fertilization.The producer decisions in the production of Persian lime can be improved. Production of the Persian lime (Citrus latifolia tanaka) has been the main objective of several studies related to the problem of low performance of yield and fruit quality in the orchard, attributed among different technological factors to the minimal application of Good Agricultural Practices (GAP) and to the cultural aspects of the producer. This paper contributes to the recognition of the behavior patterns of GAP for seasonal orchard (SO), to allow the Persian lime producers to make the right decisions assessing and improving the management of their orchards. To identify the behavior patterns in the Persian lime production process an expert system (ES) based on fuzzy logic proposed by Fernandez et al. (2014) has been used, in which a set of inference rules based on the knowledge of experts in this field is encoded to explain the interrelationship of the agricultural practices and uncertainties in the production of Persian lime: Pruning, Soil nutrition, Pests Control, Planting density, Tree production, Wind, Rainfall. The ES simulates from agricultural practices and uncertainties, the Persian lime production system in three stages of fruit growth, which represent the fuzzy models of the ES: flowering, bud, and fruit. The manipulation of the agricultural practices in the ES allowed to model production scenarios for SO of Persian lime, and helped to identify behavior patterns in these practices with production yield and fruit quality. The results demonstrate that if prior to fertilization, the practice of "pruning" the tree is performed, orchard productivity increases. However, when the "pruning" (aesthetics or stressful) is performed less than 50mmmonth-1 of rain, even in optimal conditions of application of nutrients and pest control, the production yield is similar. The modeling scenarios of the ES provide information regarding behavior patterns to the producer, and the interrelation of agricultural practices in uncertain environments of rain and wind in order to improve the decision-making process in Persian lime production.

International audience; Despite various publications in the area during the last few years, the adaptation step is still a crucial phase for a relevant and reasonable Case Based Reasoning system. Furthermore, the online acquisition of the new adaptation knowledge is of particular interest as it enables the progressive improvement of the system while reducing the knowledge engineering effort without constraints for the expert. Therefore this paper presents a new interactive method for adaptation knowledge elicitation, acquisition and reuse, thanks to a modification of the traditional CBR cycle. Moreover to improve adaptation knowledge reuse, a test procedure is also implemented to help the user in the adaptation step and its diagnosis during adaptation failure. A study on the quality and usefulness of the new knowledge acquired is also driven. As our Knowledge Based Systems (KBS) is more focused on preliminary design, and more particularly in the field of process engineering, we need to unify in the same method two types of knowledge: contextual and general. To realize this, this article proposes the integration of the Constraint Satisfaction Problem (based on general knowledge) approach into the Case Based Reasoning (based on contextual knowledge) process to improve the case representation and the adaptation of past experiences. To highlight its capability, the proposed approach is illustrated through a case study dedicated to the design of an industrial mixing device.

International audience; Optimization approaches for PV grid-connected system (PVGCS) have focused on optimizing the technical and economic performances. The main objective of this study is thus to propose an integrated framework that manages simultaneously technical, economic and environmental criteria. Life Cycle Assessment (LCA) is applied for the evaluation of environmental impacts of PVGCS. The proposed framework involves a PVGCS sizing simulator involving the computation of solar irradiance coupled to an outer optimization loop, based on a Genetic Algorithm. The objective is to maximize the annual energy generated by the facility. The analysis was carried out for different types of solar panel technologies: monocrystalline silicon (m-Si), polycrystalline silicon (p-Si), amorphous silicon (a-Si), cadmium telluride (CdTe) and copper indium diselenide (CIS). The environmental impact assessment was achieved by use of the IMPACT 2002+ method embedded in the SimaPro software tool with Ecoinvent database. The other chosen criteria based on technical and economic aspects concern the payback time of investment (PBT) and energy payback time (EPBT). To select the best option among the five choices under study, a weighted evaluation is performed on all criteria in order to obtain a score for each technology. The technology with the lowest total score is the a-Si technology. A more relevant analysis is then performed taking into account the environmental impacts per kWh produced, as new criteria. In this case, the CIS PV module technology best meets the objectives.

International audience; The supply chain is a major issue in a global market. The efficient relationship between supplier-producer-retailer conforms the perfect combination to achieve a high level of competitiveness according to the needs of increasingly demanding and changing markets. The difficulty of synchronizing the members within the SC is related to the inherent uncertain factors. This paper proposes a methodology for integrating the supply chain that can be applied in any type of business. To demonstrate its relevance, a case study is performed at a handmade shoe company. An improved demand forecasting, an optimal balanced production line, a proper inventory management of raw materials, and an efficient calculation of the delivery time to the customer represent some of the main results.

International audience; Due to the environmental issues, innovation is one way to challenge eco-friendly technologies, create new process options which are needed to meet the increasing demands for sustainable production. To accelerate and improve eco-innovative design, there is a need for the computer aided eco-innovation tools to support engineers in the preliminary design phase. Currently, several computer aided innovation tools with a clear focus on specific innovation tasks exist but very few of them deal with the eco-innovation issues. Therefore the purpose of this paper is to present the development of a computer aided model based preliminary design methodology focused on technological eco-innovation for chemical engineering. This methodology is based on modified tools of the structured TRIZ theory. The general systematic framework gives the same level of importance, to the technological and environmental requirements during the conceptual design phase. Integrating environment oriented design approach at the earliest, in the design phase, is essential for product effectiveness and future development. The methodology employs a decomposition based solution approach in hierarchical steps by analysing the problem faced, formulation of the problem and the generation of possible and feasible ideas. At each step, various methods and tools will be needed. In this paper some existing tools are adapted to chemical engineering and some tools of the structured TRIZ theory are modified and improved to build a specific methodology oriented towards the increasing technological complexity and environmental issues of current designs. Undoubtedly, the selection of materials and substances for a particular generated concept, mainly affects the structure, mechanical factors (processability and dimensions) and the environmental impact. In order to deal with these environmental criteria, the resources and their impacts are considered in the upstream phase of the design process and are introduced as constraints in our model. To highlight its capabilities, the methodology is illustrated through a case study dedicated to tars and ashes issues in biomass gasification.

International audience; Chemical industries have the potential to become a driving force to introduce efficient production practices for reducing the negative impact on the environment. In order to meet these environmental challenges, innovation is a key factor in turning the concept of green growth into a reality through the development of eco-friendly technologies and sustainable production. Therefore, to accelerate and improve the design of eco-inventive solutions, new approaches must be created and adapted to integrate the constraints of eco invention in the preliminary design. The purpose of this paper is to present the first elements of a computer aided eco-innovation system to support the engineers in preliminary design. This research paper proposes a method based on a synergy between the Theory of Inventive Problem Solving (TRIZ) and the Case Based Reasoning. However, the typical level of abstraction of the TRIZ tools is modified. Indeed, TRIZ only gives way or guidelines to explore in order to find an inventive solution, which are often too abstract and hard to traduce into an inventive concept. To reduce this level of abstraction, this work proposes to apply the physical, chemical, biological, geometrical effects or phenomenon as solutions as they are more concrete. This is done thanks to a resources oriented search in order to better exploit the resources encompassed in the system. A case study on a new production process in chemical engineering illustrates the effectiveness of the proposed approach.

Currently, chemical engineering researchers have to propose tools to support design in order to be consistent with today's economic cycles and industrial constraints. One way to accelerate the design is to use past experiences as starting point (because some choices or decisions are not to do or to question) and then to adapt and enhance them further. For this adaptation step new experts' knowledge is often mandatory to modify the past solution. In this paper we propose a coupling between two knowledge based methods: Case Based Reasoning and Constraint Satisfaction Problem to accelerate preliminary design by on line knowledge acquisition during problem solving episodes

Today innovation is recognised as the main driving force in the market. This complex process involves several intangible dimensions, such as creativity, knowledge and social interactions among others. Creativity is the starting point of the process, and knowledge is the force that transforms and materialises creativity in new products, services and processes. In this paper a synergy that aims to assists the innovation process is presented. The synergy combines several concepts and tools of the theory of inventive problem solving (TRIZ) and the case-based reasoning (CBR) process. The main objective of this synergy is to support creative engineering design and problem solving. This synergy is based on the strong link between knowledge and action. In this link, TRIZ offers several concepts and tools to facilitate concept creation and to solve problems, and the CBR process offers a framework capable of storing and reusing knowledge with the aim of accelerating the innovation process.

This paper is devoted to the presentation of a discrete-event (DES) simulation model to represent the drug development process and to help decision-making. The presentation is mainly focused on the development of the simulator initially developed for scheduling, planning and design purposes. A typical example involving 6 drugs is used as a test bench to illustrate the proposed methodology. Monte Carlo trials are used for each portfolio of drugs. Each trial can be viewed as a simulated path of the potential product along the probabilistic network. Different scenarios are analyzed, reflecting the pessimist or optimistic viewpoint of the decision’s maker.

This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must constitute a basic part of the design problem. Rather than resorting to a traditional probabilistic approach for modeling the imprecision on product demands, this work proposes an alternative treatment by using fuzzy concepts. The design problem is tackled by introducing a new approach based on a multiobjective genetic algorithm, combined wit the fuzzy set theory for computing the objectives as fuzzy quantities. The problem takes into account simultaneous maximization of the fuzzy net present value [email protected]?V and of two other performance criteria, i.e. the production delay/advance and a flexibility index. The delay/advance objective is computed by comparing the fuzzy production time for the products to a given fuzzy time horizon, and the flexibility index represents the additional fuzzy production that the plant would be able to produce. The multiobjective optimization provides the Pareto's front which is a set of scenarios that are helpful for guiding the decision's maker in its final choices. About the solution procedure, a genetic algorithm was implemented since it is particularly well-suited to take into account the arithmetic of fuzzy numbers. Furthermore because a genetic algorithm is working on populations of potential solutions, this type of procedure is well adapted for multiobjective optimization.

The design of batch plants necessary involves how equipment may be utilized, which means that plant scheduling and production must form an integral part of the design problem. The market demand for such products is usually changeable, and at the stage of conceptual design of a batch plant, it is almost impossible to obtain the precise information on the future product demand over the lifetime of the plant. This paper addresses the problem of the optimal design of batch plants with imprecise demands and proposes an alternative treatment of the imprecision by using concepts of fuzzy logic. For this purpose, we extended a Multi-Objective Genetic Algorithm (MOGA) developed in previous works, taking into account simultaneously three criteria, i.e. minimization of the investment cost, the operation cost and the total production time. The case of study is a multiproduct batch plant for the production of proteins taken from the literature. The methodology provides a set of scenarios that are helpful to the decision's maker and constitutes a very promising framework for taken imprecision into account in new product development stage.

This paper presents a discrete-event simulation environment that models the underlying structure of a pharmaceutical enterprise portfolio. An object-oriented model structure previously developed for batch plant scheduling and design is extended to embed the case of product management, which is particularly adequate for re-use of both structure and logic. By wrapping such a simulation with advanced optimization approaches, such as Genetic Algorithms, it becomes possible to evaluate a large set of scenarios for the pharmaceutical enterprise, with a holistic fashion that avoids local optima that can be detrimental to the enterprise.

The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must form an integral part of the design problem. This work proposes an alternative treatment of the imprecision (demands) by using fuzzy concepts. In this study, we introduce a new approach to the design problem based on a multiobjective genetic algorithm, taking into account simultaneously maximization of the net present value NPV ~ and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The methodology provides a set of scenarios that are helpful to the decision’s maker and constitutes a very promising framework for taken imprecision into account in new product development stage. Besides, a hybrid selection method Pareto rank-tournament was proposed and showed a better performance than the classical Goldberg’s wheel, systematically leading to a higher number of non-dominated solutions.