Catherine Azzaro-Pantel

A thermochemical approach for calcium phosphate precipitation modeling in a pellet reactor

Calcium phosphate precipitation is studied in this article. The P-recovery process is carried out in a fluidized sand bed, the so-called pellet reactor which presents major advantages from the hydrodynamical viewpoint. The associated chemistry is yet relatively complex, due to pH gradient along the column and to the residence time of the various precipitates. The experimental observations showed three different phenomena: first, an agglomeration of fines around the sand grains is observed, second, a stagnation of fines in the bed occurs while a significant amount of fines also leaves the bed with the liquid effluent. The purpose of this work is to validate the thermodynamical model developed in our previous works on a semi-industrial sized pilot. Additional experimental runs carried out for various operating conditions showed the robustness of the model. These results open some interesting perspectives for the determination of optimized operating conditions at industrial scale.

A two-stage methodology for short-term batch plant scheduling: discrete-event simulation and genetic algorithm

Abstract In this paper, a two-stage methodology for solving jobshop scheduling problems is proposed. The first step involves the development of a discrete-event simulation (DES) model to represent dynamically the production system behavior, taking into account the main features inherent to the application field. Since most scheduling problems in batch processing belong to the family of problems classified as NP-complete, probabilistic optimization algorithms (such as simulated annealing, evolutionary algorithms) represent a good alternative for solving large-scale combinatorial problems (for instance, the traveling salesman problem). In the second step of our approach, we thus investigate genetic algorithms (GAs) for solving batch process scheduling problems: a GA has been developed for minimizing the average residence time to produce a set of batches in function of batch order in a multipurpose-multiobjective plant with unlimited storage. The evaluation of the objective function is provided by its coupling with the DES model embedded in the optimization loop. Computational results show that the use of this approach can significantly help to improve the efficiency of the production system. This paper is focused on semiconductor application, which is the first example treated in our laboratory, although the general approach adopted in this study is now extended to other fields of applications (e.g. fine chemistry with finite intermediate storage and unstable intermediates).

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.

Design and retrofit of multiobjective batch plants via a multicriteria genetic algorithm

This paper addresses the development of a two-stage methodology for multiobjective batch plant design and retrofit, according to multiple criteria. At the upper level (master problem), a multiobjective genetic algorithm (MOGA) is implemented for managing the problem of design or retrofit and proposes several plant structures. At the inner level (slave problem), a discrete event simulator (DES) evaluates the technical feasibility of the proposed configurations. The basic principles of the DES are first recalled; then the following section develops a MOGA based on the combination of a single objective genetic algorithm (SOGA) and a Pareto sort (PS) procedure. Finally, a didactic example, related to the manufacturing of four products by using three types of equipment of discrete sizes, illustrates the approach. First, two criteria (investment cost and number of different sizes for units of the plant) are considered for designing the workshop. Then starting from the best solution with regard to investment cost found in the design phase, the plant is retrofitted for manufacturing a double production. Finally, assuming a double production at the design phase, the workshop is designed again. In terms on investment cost, this new solution yields a significant saving compared with the retrofitted plant. In fact, redesigning a new plant, may challenge the retrofitting choice. Secondly, an additional criterion concerning the number of production campaigns for reaching the steady-state or oscillatory regime is introduced, and the same approach (designing, retrofitting and redesigning) is carried out, leading to the same conclusion as in the bicriteria case.

Economic and environmental strategies for process design

Abstract This paper first addresses the definition of various objectives involved in eco-efficient processes, taking simultaneously into account ecological and economic considerations. The environmental aspect at the preliminary design phase of chemical processes is quantified by using a set of metrics or indicators following the guidelines of sustainability concepts proposed by IChemE (2001) . The resulting multiobjective problem is solved by a genetic algorithm following an improved variant of the so-called NSGA II algorithm. A key point for evaluating environmental burdens is the use of the package ARIANE™, a decision support tool dedicated to the management of plants utilities (steam, electricity, hot water, etc.) and pollutants (CO2, SO2, NO, etc.), implemented here both to compute the primary energy requirements of the process and to quantify its pollutant emissions. The well-known benchmark process for hydrodealkylation (HDA) of toluene to produce benzene, revisited here in a multiobjective optimization way, is used to illustrate the approach for finding eco-friendly and cost-effective designs. Preliminary biobjective studies are carried out for eliminating redundant environmental objectives. The trade-off between economic and environmental objectives is illustrated through Pareto curves. In order to aid decision making among the various alternatives that can be generated after this step, a synthetic evaluation method, based on the so-called Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) ( Opricovic & Tzeng, 2004 ), has been first used. Another simple procedure named FUCA has also been implemented and shown its efficiency vs. TOPSIS. Two scenarios are studied; in the former, the goal is to find the best trade-off between economic and ecological aspects while the latter case aims at defining the best compromise between economic and more strict environmental impacts.

A multiobjective optimization framework for multicontaminant industrial water network design

The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view.

Optimization of preventive maintenance strategies in a multipurpose batch plant: application to semiconductor manufacturing

Abstract This paper addresses the problem of preventive maintenance (PM) strategy optimization in a semiconductor manufacturing environment, with the objective of minimizing maintenance costs. The approach developed takes into account the interaction of production and maintenance aspects. For this purpose, a discrete-event production-oriented simulator (MELISSA-C++) has been extended to incorporate equipment failures and maintenance operations, thus modeling residual breakdowns, occurring in a combined corrective/PM context. The usefulness of the simulation tool has also been demonstrated for the estimation of both direct and indirect maintenance costs, which are impossible to determine empirically due to the reentrant nature of product flows in a semiconductor manufacturing facility. The results obtained have confirmed the marked effect of equipment characteristics (bottleneck or non-limiting step) on maintenance cost evaluation. Following a tutorial example, typical results are presented and analyzed.

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.

Multiobjective scheduling for semiconductor manufacturing plants

Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment.

Total Cost Minimization of a High-Pressure Natural Gas Network

This paper deals with a high-pressure gas pipeline optimization, where the problem is to find the design properties of the pipelines and necessary compressor stations to satisfy customer requirements, using available supply gas and storage capacities. The considered objective function is the total annualized cost, including the investment and operating costs. The binary variables used to represent the flow direction of pipelines lead to a mixed integer nonlinear programming problem, solved by using the standard branch and bound solver in GAMS. The optimization strategy provides the main design parameters of the pipelines (diameters, pressures, and flow rates) and the characteristics of compressor stations (location, suction pressure, pressure ratio, station throughput, fuel consumption, and station power consumption) to satisfy customer requirements.