Mutual benefits of two multicriteria analysis methodologies: A case study for batch plant design

This paper presents a MultiObjective Genetic Algorithm (MOGA) optimization framework for batch plant design. For this purpose, two approaches are implemented and compared with respect to three criteria, i.e., investment cost, equipment number and a flexibility indicator based on work in process (the so-called WIP) computed by use of a discrete-event simulation model. The first approach involves a genetic algorithm in order to generate acceptable solutions, from which the best ones are chosen by using a Pareto Sort algorithm. The second approach combines the previous Genetic Algorithm with a multicriteria analysis methodology, i.e., the Electre method in order to find the best solutions. The performances of the two procedures are studied for a large-size problem and a comparison between the procedures is then made

Design of sustainable energy systems : a new challenge for Engineering Education

This paper presents the main features of the master-level programme in “EcoEnergy” offered as a full-time one year course at “Institut National Polytechnique of Toulouse” in order to provide engineers with a state-of-the-art education in the area of advanced energy technologies and systems. It is based on an original and equilibrated combination of process systems engineering and electrical engineering disciplines, with an interdisciplinary problem-solving approach necessary for identifying sustainable solutions in the energy sector. More precisely, the students learn how to design, develop and implement energy systems and technologies in various industrial sectors for which efficient management of energy issues is vital to remain competitive

New Product Development and Supply Chains in the Pharmaceutical Industry

The concept of chemical supply chain has received increased attention in the process systems engineering community in the last decade. This chapter discusses the methods, tools, and applications, which are relevant to the pharmaceutical industry. Among the various supply chains that have been studied, pharmaceutical supply chains turn out to be very complex, due to several factors such as long development timelines, high attrition rates in drug development, resource-intensive operations, involvement of multiple stakeholders, among others. A fundamental challenge in managing a pharmaceutical company is identifying the optimal allocation of finite resources across the infinite constellation of available investment opportunities. Specific attention is given here to the modeling and optimization of three key phases in the life cycle of an innovative drug product, namely, product development pipeline management, capacity planning, and supply chain management. The state of the art in these domains is reviewed, some challenges are identified, and opportunities for further research effort are highlighted

Economic and environmental impacts of the energy source for the utility production system in the HDA process

The well-known benchmark process for hydrodealkylation of toluene (HDA) to produce benzene is revisited in a multi-objective approach for identifying environmentally friendly and cost-effective operation solutions. The paper begins with the presentation of the numerical tools used in this work, i.e., a multi-objective genetic algorithm and a Multiple Choice Decision Making procedure. Then, two studies related to the energy source involved in the utility production system (UPS), either fuel oil or natural gas, of the HDA process are carried out. In each case, a multi-objective optimization problem based on the minimization of the total annual cost of the process and of five environmental burdens, that are Global Warming Potential, Acidification Potential, Photochemical Ozone Creation Potential, Human Toxicity Potential and Eutrophication Potential, is solved and the best solution is identified by use of Multiple Choice Decision Making procedures. An assessment of the respective contribution of the HDA process and the UPS towards environmental impacts on the one hand, and of the environmental impacts generated by the main equipment items of the HDA process on the other hand is then performed to compare both solutions. This ‘‘gate-to-gate’’ environmental study is then enlarged by implementing a ‘‘cradle-togate’’ Life Cycle Assessment (LCA), for accounting of emission inventory and extraction. The use of a natural gas turbine, less economically efficient, turns out to be a more attractive alternative to meet the societal expectations concerning environment preservation and sustainable development

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

A MINLP Solution for Pellet Reactor Modeling

A fluidized bed reactor for phosphate precipitation and removal from wastewater is modeled according to a two-step procedure. The first modeling phase, based on the development of a thermodynamic model for the computation of phosphate conversion, previously presented elsewhere is not reported here. The second step is related to the reactor modeling in the core of this paper. The pellet reactor is modeled as a reactor network involving a set of elementary cells representing ideal flow patterns. All the potential solutions are imbedded into a superstructure and the modeling problem is expressed as a MINLP problem. The MINLP problem is solved by means of the GAMS package, first for two flow rate values corresponding to two experimental fluidized bed behaviours, and then for the two flow rates considered simultaneously. In each case, the problem consists in finding an output concentration as close as possible to the experimental output concentration. Three objective functions are studied. The results are compared with those of Montastruc et al. (2004) who used a different numerical procedure. Whatever the considered case, the solutions found are structurally simpler than the ones of Montastruc et al. (2004). A major assessment of this study is that the reactor efficiency can easily be deduced, without any precise knowledge of some key parameters such as the density and thickness of the calcium phosphate layer. Finally a last numerical study concerning the superstructure definition shows that too complex a superstructure does not provide significant refinements on the solution

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Calcium phosphate precipitation modeling in a pellet reactor

The calcium phosphate precipitation in a pellet reactor can be evaluated by two main parameters: the phosphate conversion ratio and the phosphate removal efficiency. The conversion ratio depends mainly on the pH. The pellet reactor efficiency depends not only on pH but also on the hydrodynamical conditions. An efficiency model based on a thermochemical precipitation approach and an orthokinetic aggregation model is presented. In this paper, the results show that optimal conditions for pellet reactor efficiency can be obtained

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