Pascal Floquet

Separation sequence synthesis how to use simulated annealing procedure

Abstract In order to apply the simulated annealing (SA) algorithm, a separation-based coding procedure for complex columns sequence synthesis is presented in this paper. After the recall of the combinatorial aspect of separation sequence synthesis, the main steps of SA procedure are proposed. Then we have focused our attention on the definition of a local solution, that is an acceptable sequence structure, and on the evolution of this one to another acceptable solution. Due to the necessary feasible aspect of a reachable structure solution, we chose a structure-based coding for sequences. It consists in defining, for each structure, a list of elements describing it in a depth first manner and in transforming it by three elementary rules: creation of a complex separator, removal of a complex separator and transformation without changing the type or the number of separators. Many examples are explained, and the implementation into SA procedure is studied in detail.

Process optimization by simulated annealing and NLP procedures. Application to heat exchanger network synthesis

This paper addresses the problem of automatically synthesize heat exchanger network that features minimum global cost. We present a new approach including HEN configuration choice by simulated annealing algorithm and NLP formulation to systematically optimize their operating conditions (temperatures and split rates). Particular attention is made to the size reduction of the NLP problem which has been the subject of a precedent paper (Athier et al., 1996). In this paper, the global methodology used to solve this problem is presented. The proposed approach is illustrated by two examples, the 5SP1 problem with forbidden matches and a more large scale problem, the 14SP1 one.

A mixed method for retrofiting heat-exchanger networks

Abstract This paper addresses the problem of automatically determining the optimal retrofit of an existing heat exchanger network, considering the placement/reassignment of existing exchangers to different process stream matches, their need for additional area, the placement of anew heat exchanger and the cost of stream repiping. A two level procedure, derived from a grassroot design model is proposed. The master problem, related to structural optimization, is carried out by a simulated annealing (SA) procedure. For each generated network, the required additional area for existing exchangers and the size of the new exchangers are optimized by a NonLinear Programming (NLP) algorithm. The purpose of this paper is to present the methodology and the resulting modifications to a previously developed Heat Exchanger Network Synthesis (HENS) model to treat this new aim. This approach is illustrated by two examples.

Optimization of heat exchanger networks by coupled simulated annealing and NLP procedures

This paper addresses the problem of systematic generation of heat exchanger networks for minimizing the global cost. To an upper level, the approach implements simulated annealing to propose HEN configurations. This information is then used in a lower level within a nonlinear programming (NLP) frame to optimize the operating variables. In this paper, the automatic generation, initialization and optimization of these nonlinear problems are only presented. The proposed method provides minimum size problems subject to a minimum number of variables and constraints.

Synthesis of optimal reactor networks using mathematical programming and simulated annealing

Abstract The combination of a nonlinear programming method with the simulated annealing procedure allows the automatic synthesis and optimization of reactor networks. This requires the search of the optimal solution within a superstructure containing the totality of all potential and feasible configurations to provide the optimal solution. Due to the complexity and combinatorial aspect of this type of process, the definition and the mathematical formulation of a feasible scheme of the superstructure is not a trivial task. The proposed synthesis strategy is based on the mathematical representation of any structure through an incidence matrix. The presented procedure is based on this incidence matrix and is illustrated by some significant examples.

Mathematical programming tools for chemical engineering process design synthesis

Abstract The objective of this paper is to review different optimization techniques used in chemical process synthesis. The paper first defines the process synthesis problem as a mixed-integer programming problem and then discusses the three approaches which have been used to solve it. In the discrete approach, where the operating conditions of unit operations are fixed, combined heuristic and algorithmic procedures are promising tools. In the continuous approach, the synthesis problem is a nonlinear programming problem, where the design and structural variables are optimized simultaneously. Since efficient large-scale nonlinear programming codes are now available, this approach will be a great success. A recent alternative consists in merging the two previous approaches to implement large-scale mixed-integer nonlinear programming codes. This method will have wide applications in complex chemical process synthesis.

Synthesis of RTD models VIA stochastic procedures: simulated annealing and genetic algorithm

Abstract The general strategy adopted in the development of a computational tool performing structural and parametric identifications of models based on the residence time distribution (RTD) theory is exposed. After a brief presentation of the global methodology, the main aspect of the procedure constituted by the master problem performing the structural identification is detailed. This problem of discrimination of the various schemes is solved by two stochastic procedures based on Simulated Annealing (SA) algorithm or on Genetic Algorithm (GA). The implementation of the last algorithm is exposed. An industrial approach with experimental data is proposed for the qualification step and for the description and quantification of air flows patterns and the associated pollutant transfers observed in ventilated indoor spaces, and particularly in nuclear ventilation networks.

Simulation and data validation in multistage flash desalination plants

Abstract This paper describes the main concepts of a specific Fortran program tackling steady-state simulation and data validation (gross error detection and data reconciliation) for multistage flash desalination plants. The process simulation was carried out using an equation-oriented approach in which the decomposition of the system leads to a sensitivity matrix which is diagonal by blocks with extra diagonal blocks. The topology of this matrix is conserved according to a procedure which can be extended to an unsteady-state simulation of the process, justifying the interest of the adopted methodology. The validation of the raw process data was achieved by the implementation of a simultaneous strategy of gross error detection and data reconciliation, based on the bivariate method. In this approach we minimize an objective function using the maximum likelihood principle, which takes into account both contributions from random and gross errors. The complete mathematical model describing the process behaviour at steady state was considered as constraints in the process optimization. Results corresponding to the structural and operating conditions of the distiller No 5 of Umm Al Nar East plant which was put into service in 1987/1988 in the Emirate of Abu Dhabi are presented. This package was developed at the School of Chemical Engineers in Toulouse (France), and the project was sponsored by the International Centre for Water and Energy Systems (ICWES) located in Abu Dhabi (United Arabs Emirates).

Strategies for slop cut recycling in multicomponent batch distillation

Abstract In multicomponent batch distillation, several main cuts and slop cuts are collected into tanks. A general model (based on a superstructure) is proposed which allows the calculation of the proportion of each slop cut to be added to each initial load and to each new batch in order to optimise the global operation. The formulation of this problem leads to a non-linear programming (NLP) problem, where the set of decision variables are the proportions of each slop cut to be added to each batch and the reflux ratios. Two type of criteria and of reflux ratio policies as well as the number of batches and the number of trays, are explored. Results show the importance of the criterion choice, the little difference between an optimal constant reflux policy for a batch and a variable reflux policy, and the increase of the production rate (for problems without liquid holdup) with the number of trays.

A fuzzy approach for performance modeling in a batch plant: application to semiconductor manufacturing

In the current literature dealing with job shop scheduling, most of the approaches have developed models based on the assumption that the problem domain does not contain any imprecision. However, this hypothesis is strongly challenged in the implementation phase of such models-imprecision is inherent to production systems involving human intervention. The aim of this paper is to demonstrate the advantages of possibilistic production data modeling in a real-world application, i.e., semiconductor manufacturing. In this work, a discrete-event simulation model (MELISSA) for performance evaluation of a batch-manufacturing facility previously developed in our laboratory has been extended to treat uncertainties modeled by fuzzy numbers. Due to the confidential nature of industrial data, an illustrative example, presenting the same typical features as a real problem, is treated and analyzed using fuzzy concepts. Inclusion of fuzzy techniques provides the decision-maker with a range of possible values for completion times, average storage times, and operator workload instead of a unique value (which has little significance due to the variety of human operators). In addition, the negative portion of average waiting times yields useful information for the manager to detect deficient resources in the production system.