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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; 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.

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.

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.