Eric S. Fraga

Mass exchange network synthesis using genetic algorithms

Abstract Mass Exchange Networks (MENs) are used in the chemical industry to reduce the waste generated by a plant to an acceptable level at the cheapest cost. Finding the optimal network, however, is often difficult due to the non-convexity of the mathematical representation of the problem. This paper describes a novel approach for the synthesis of MENs and MENs with regeneration using Genetic Algorithms (GA), a stochastic optimisation technique based on the concepts of natural evolution. We present an encoding for a genetic algorithm which describes a rich search space, considering both stream splitting and in-series exchangers. For a certain class of problems, all encoded solutions are feasible and require a simple evaluation to yield a cost, resulting in an efficient genetic algorithm. For other problems, the number of infeasible solutions is small, having little effect on the convergence of the genetic algorithm. In comparison with other methods, the GA presented herein is able to find better networks than have been reported elsewhere.

Multicriteria process synthesis for generating sustainable and economic bioprocesses

Abstract Minimising the environmental impact of a process is an important objective for plant design engineers. It is a goal, however, which is closely linked to the flowsheet structure and, therefore, must be considered at the conceptual stage of the design process. At the same time economic criteria are important for the selection of good designs. Frequently, these goals may result in conflicting designs and compromises are required. In this paper, we describe a multiobjective process synthesis methodology, for the early stages of design, suitable for identifying processes with minimal environmental impact and the desired economic performance. The algorithm is particularly suitable for synthesising bioprocesses and has been implemented in the Jacaranda system, which has been enhanced to consider multiple simultaneous criteria. The synthesis algorithm is demonstrated and evaluated by applying it to the synthesis of a penicillin manufacturing process. The procedure is able to identify a set of flowsheets which satisfy both environmental and economic objectives demonstrating that a multicriteria synthesis tool can be invaluable for design with multiple objectives.

Synthesis of bioprocesses using physical properties data

The aim of this article is to illustrate and evaluate a synthesis procedure which has been extended to tackle bioprocesses. Physical property information is used to screen candidate units thereby reducing the size of the synthesis problem. In this way, only units which exploit large property differences between components in a stream are selected. This is important for bioprocesses because of the large number of components and wide range of unit operations which are available. The screening technique and bioprocess-unit-design methodologies have been incorporated within an implicit enumeration algorithm which was developed for chemical process synthesis and is implemented in Java programming language. An important advantage is the ability of the bioprocess synthesis software to generate a ranked list of flowsheets which may subsequently be analyzed in more detail. Two case studies are used to evaluate the bioprocess-synthesis technique. The first system involves a product which is secreted from the host organism. The second has significantly different characteristics in that the product is intracellular and forms inclusion bodies. The latter case study, in particular, is a large synthesis problem with 12 unit operations and 20 contaminant compounds. The results show that the synthesis methodology identifies a set of economically optimal flowsheets in a reasonable computational time which demonstrates its ability to deal with large synthesis problems. Using the synthesis methodology we can generate bioprocesses which are optimal in a system-wide, rather than unit-by-unit, sense.

Synthesis and optimization of a nonideal distillation system using a parallel genetic algorithm

Abstract The automated synthesis of separation sequences for nonideal mixtures has only recently been attempted. The need for rigorous physical property estimation procedures along with the possibly complex recycle structures leads to the specification of a nonlinear programming problem, one in which both the objective function and the feasibility region are nonconvex. This paper describes the use of a stochastic optimization procedure, genetic algorithms, for the optimization of a preselected sequence of distillation units for the separation of a three component azeotropic mixture. By fixing the structure in terms of the units desired, the optimization problem is reduced to one of designing each of the relevant units, choosing the appropriate operating conditions, and finding the optimal heat exchange network. To tackle the high computational effort required, the implementation uses a distributed memory multicomputer in the form of a network of workstations.

A multi-objective genetic algorithm for the design of pressure swing adsorption

Pressure Swing Adsorption (PSA) is a cyclic separation process, with advantages over other separation options for middle-scale processes. Automated tools for the design of PSA processes would be beneficial for the development of the technology, but their development is a difficult task due to the complexity of the simulation of PSA cycles and the computational effort needed to detect the performance in the cyclic steady state. A preliminary investigation is presented of the performance of a custom multi-objective genetic algorithm (MOGA) for the optimization of a fast cycle PSA operation – the separation of air for N2 production. The simulation requires a detailed diffusion model, which involves coupled nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA to handle this complex problem has been assessed by comparison with direct search methods. An analysis of the effect of MOGA parameters on the performance is also presented.

CAPE Web Services: The COGents way †

The COGents approach to dynamic CAPE service composition uses the paradigm of multi-agent systems, where a number of software agents collaborate to configure a process model, according to a users requirements defines using the OntoCAPE ontology. Our agents are “CAPE web service choreographers”, building and running suites of CAPE-OPEN compliant process modelling components.

Evaluation of hybrid optimization methods for the optimal design of heat integrated distillation sequences

Optimal process design often requires the solution of mixed integer non-linear programming problems. Optimization procedures must be robust and efficient if they are to be incorporated in automated design systems. For heat integrated separation process design, a natural hybrid evolutionary/local search method with these properties is possible. The method is based on the use of local search methods for the continuous design parameters for the units in the process and the use of an evolutionary optimization procedure for the design of the heat exchanger network. The use of a stochastic method for the heat exchanger network aspect introduces noise in the evaluation of the objective function used by the local search methods. A smoothing procedure has been designed and implemented to improve the efficacy of the hybrid approach.This paper presents the evaluation of a variety of local search methods. It is shown that the Hooke and Jeeves algorithm, combined with a simple genetic algorithm, provides a robust, efficient and effective solution procedure for optimizing heat integrated distillation sequences.

The generation and use of partial solutions in process synthesis

The use of automated tools can be of great benefit to an engineer involved in a large, complex task such as the design of a chemical plant. Such tools, however, must provide the engineer with more than just a solution to the problem. It is vital that the engineer be given an insight into the reasons for the choice of a given solution. This is particularly important in optimization of complex problems. In these cases, the advantages of the solution (other than a better value of the objective function) are often not immediately obvious and the user is asked to take the results on faith. A process synthesis procedure which provides some indication of why a particular design alternative was chosen can be invaluable, especially during the early stages of design when many decisions are still to be made. This paper describes a novel implicit enumeration method, using dynamic programming, which enables and encourages an engineer to easily explore the solution space for new problems. When completely feasible solutions are not available, the synthesis tool provides information in the form of partial solutions which can lead to a better understanding of or a different perspective on the problem. Using partial solutions, the method can itself generate better solutions, dynamically increasing the search space for solutions that include complex recycle structures.

Global sensitivity analysis of the impact of impurities on CO2 pipeline failure

This paper describes the testing, comparison and application of global sensitivity techniques for the study of the impact of the stream impurities on CO2 pipeline failure. Global sensitivity analysis through non-intrusive generalised polynomial chaos expansion with sparse grids is compared to more common techniques and is found to achieve superior convergence rate to crude Monte Carlo, quasi-Monte Carlo and EFAST for functions with up to a moderate level of “roughness”. This methodology is then applied to the hypothetical full bore rupture of a 1 km CO2 pipeline at 150 bara and 283.15 K. The sensitivity of the ensuing outflow to the composition of a quaternary mixture of CO2 with N2, CH4 and O2 as representative stream impurities. The results indicate that the outflow rate is highly sensitive to the composition during the early stages of depressurisation, where the effect of the impurities on phase equilibria has a significant impact on the outflow.

Data analysis and visualisation for robust multi-criteria process optimisation

Process optimisation is often a multi-criteria problem. Combined with the use of nonlinear models, generating a Pareto front can be difficult to achieve reliably. This paper describes the use of high-dimensional data analysis and visualisation techniques as the basis for a multi-step procedure for generating a Pareto front for a two criteria problem. A case study in process design is used to illustrate the procedure. The results show that the use of data analysis and visualisation can help gain insight into the Pareto optimal solutions or confirm the insight the engineer already has.