Ecodesign of Batch Processes: Optimal Design Strategies for Economic and Ecological Bioprocesses

This work deals with the multicriteria cost-environment design of multiproduct batch plants, where the design variables are the equipment item sizes as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a Genetic Algorithm (GA) with a Discrete Event Simulator (DES). To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a Multicriteria Genetic Algorithm (MUGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design

Strategies for multiobjective genetic algorithm development: Application to optimal batch plant design in process systems engineering

This work deals with multiobjective optimization problems using Genetic Algorithms (GA). A MultiObjective GA (MOGA) is proposed to solve multiobjective problems combining both continuous and discrete variables. This kind of problem is commonly found in chemical engineering since process design and operability involve structural and decisional choices as well as the determination of operating conditions. In this paper, a design of a basic MOGA which copes successfully with a range of typical chemical engineering optimization problems is considered and the key points of its architecture described in detail. Several performance tests are presented, based on the influence of bit ranging encoding in a chromosome. Four mathematical functions were used as a test bench. The MOGA was able to find the optimal solution for each objective function, as well as an important number of Pareto optimal solutions. Then, the results of two multiobjective case studies in batch plant design and retrofit were presented, showing the flexibility and adaptability of the MOGA to deal with various engineering problems

Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations

This work deals with the multicriteria cost–environment design of multiproduct batch plants, where the design variables are the size of the equipment items as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a genetic algorithm (GA) with a discrete-event simulator (DES). Another incentive to use this kind of optimization method is that, there is no easy way of calculating derivatives of the objective functions, which then discards gradient optimization methods. To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a multiobjective genetic algorithm (MOGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design

Optimal design of batch plants under economic and ecological considerations: Application to a biochemical batch plant

This work deals with the multicriteria cost-environment design of multiproduct batch plants, where the design variables are the equipment item sizes as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a Genetic Algorithm (GA) with a Discrete Event Simulator (DES). To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental considerations, a Multicriteria Genetic Algorithm (MUGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design

Selective hydrogenation in trickle-bed reactor. Experimental and modelling including partial wetting.

A steady state model of a trickle bed reactor is developed for the consecutive hydrogenation of 1,5,9-cyclododecatriene on a Pd/Al2O3 catalyst. Various experiments have shown that the selectivity of this reaction towards the product of interest is much lower in co-current down-flow (trickle-bed) than in up-flow. This is due to uneven liquid distribution and to partial wetting of the catalyst surface at low liquid flow rates. The non-isothermal heterogeneous model proposed here takes into account the partial wetting of the catalyst, as well as the resistances to heat and mass transfer at the gas-liquid, liquid-solid and solid-gas interfaces. It assumes that the catalyst particles can be divided into two distinct concentration zones corresponding to the wetted and dry catalyst surfaces; mass transfer between these two zones is described by a simplified diffusion mechanism. Compared to previous models assuming a uniform concentration of liquid-phase components inside the catalyst particles, this model improves the prediction of the outlet concentrations of hydrogenation products

Multiobjective Multiproduct Batch Plant Design Under Uncertainty

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 fuzzy concepts. For this purpose, we extended a multiobjective genetic algorithm developed in previous works, 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 former is computed by comparing the fuzzy computed production time to a given fuzzy production time horizon and the latter is based on the additional fuzzy demand that the plant is able to produce. 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

A fuzzy multiobjective algorithm for multiproduct batch plant: Application to protein production

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 fuzzy concepts. For this purpose, we extended a multiobjective genetic algorithm (MOGA) developed in previousworks, 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 former is computed by comparing the fuzzy computed production time to a given fuzzy production time horizon and the latter is based on the additional fuzzy demand that the plant is able to produce. 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

Comparison of model predictive control strategies for the simulated moving bed

International audienceThis work compares three ways of controlling the simulated moving bed (SMB), an efficient process for chromatographic separation. Linear model predictive control (MPC) is considered in this work. A comparison of two different sets of manipulated inputs is carried out: on one hand, the classical one often presented in the literature, which consists in manipulating directly different flow rates involved in the process and, on the other hand, an approach coming from other counter-current separation processes which consists in manipulating the ratios of flow rates of each SMB zone. A hybrid method using the inputs calculated for a true moving bed (TMB) and implemented on the SMB is also compared. The advantages and drawbacks of each control strategy are discussed. In all cases, results show clearly the interest of applying MPC to high complexity systems such as the SMB

Optimisation multicritère pour la conception d'ateliers discontinus multiproduits : aspects économique et environnemental.

Les politiques environnementales et énergétiques imposent de plus en plus la prise en compte, dès la phase de conception d'un procédé, la limitation de la génération d'effluents et de la consommation énergétique. Il importe donc de considérer les impacts sur l'environnement émanant du cycle de vie complet du procédé. Le cas d'ateliers multiproduits de chimie fine avec l'objectif de minimiser à la source la génération des effluents et d'intervenir à titre préventif dès les stades de conception et de développement du procédé est examiné plus particulièrement. Le but de cette thèse est la conception multicritère coût - impact environnemental d'un atelier multiproduit, où les variables de décision sont la configuration de l'atelier, la taille et le nombre des équipements à chaque étape du traitement et les conditions opératoires ayant un impact majeur sur les critères d'optimisation. La méthodologie retenue associe un algorithme génétique multicritère, qui est la procédure maître d'optimisation, à un simulateur à événements discrets chargé de la vérification des contraintes et de l'évaluation des critères de performance de l'atelier. Le simulateur est couplé à des modèles d'opérations unitaires représentant les procédés étudiés notamment afin de quantifier l'impact environnemental. L'aspect multicritère est pris en compte à travers une procédure de tri de Pareto. Un atelier multiproduit pour la production de quatre protéines, comportant huit étapes de traitement sert de support à la validation de l'approche. Celle-ci est cependant suffisamment générique pour être facilement réutilisable et adaptable à d'autres contextes. Les critères pris en compte dans l'exemple concernent le coût d'investissement, la biomasse rejetée et la quantité de solvant utilisée. La méthode propose un ensemble suffisamment large de solutions de compromis permettant au décideur d'aborder le problème du choix final. Deux stratégies de production sont envisagées, modes mono et multiproduit. Pour tous les essais réalisés, la solution multiproduit s'est avérée plus performante en terme de coût ou de flexibilité. La méthodologie multicritère est ensuite appliquée à un atelier industriel à des fins de conception et de remodelage. Elle a permis de retrouver le comportement industriel et met en évidence l'intérêt d'une stratégie multiproduit. ABSTRACT : Because of more and more stringent regulations, pollution prevention and limitation on energy consumption have become important objectives at the earliest stages of process design. It is thus necessary to take into account environmental impacts which may occur during life cycle assessment of a given process. This work is focused more particularly on multiproduct batch plant design for the manufacturing of specialty chemicals, with the objective of preventing the generation of environmentally offensive wastes. This work deals with the multi-criteria cost-environment design of multi-product batch plants, where the design variables are the plant configuration with equipment item sizes and parallel equipment number as well as the operating conditions. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a Genetic Algorithm (GA) with a Discrete Event Simulator (DES). The management of production constraints and the computation of the objective functions are carried out by the DES embedding unit operation models in order to quantify environmental impact. The master Multicriteria Genetic Algorithm (MUGA) involves a Pareto sort procedure. The problem of optimal design of a multi-product batch plant for the production of four recombinant proteins is first treated for validation purpose. It must be said that the approach is generic enough to be easily reused and adapted to other production contexts. The criteria involved are based on investment cost, biomass released and solvent amount. The methodology leads to a set of compromise solutions which may be useful for final decision making. Two production strategies have been tested, either mono or multiproduct. For all the simulations runs, the multiproduct case finds out to be slightly more efficient from cost and flexibility viewpoints. The multicriteria approach has then be applied to an industrial batch plant, for both design and retrofit purposes. The results obtained fit with the industrial practice, thus validating the approach, and showing the interest of a multiproduct strategy