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Global Optimization of Chemical Processes using Stochastic Algorithms
 IN &QUOT;STATE OF THE ART IN GLOBAL OPTIMIZATION&QUOT;, CA FLOUDAS AND PM PARDALOS (EDS
, 1996
"... Many systems in chemical engineering are difficult to optimize using gradientbased algorithms. These include process models with multimodalobjective functions and discontinuities. Herein, a stochastic algorithm is applied for the optimal design of a fermentation process, to determine multiphase equ ..."
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Cited by 9 (2 self)
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Many systems in chemical engineering are difficult to optimize using gradientbased algorithms. These include process models with multimodalobjective functions and discontinuities. Herein, a stochastic algorithm is applied for the optimal design of a fermentation process, to determine multiphase equilibria, for the optimal control of a penicillin reactor, for the optimal control of a nondifferentiable system, and for the optimization of a catalyst blend in a tubular reactor. The advantages of the algorithm for the efficient and reliable location of global optima are examined. The properties of these algorithms, as applied to chemical processes, are considered, with emphasis on the ease of handling constraints and the ease of implementation and interpretation of results. For the five processes, the efficiency of computation is improved compared with selected stochastic and deterministic algorithms. Results closer to the global optimum are reported for the optimal control of the penicillin reactor and the nondifferentiable system.
Optimization Strategies for Dynamic Systems
 In C. Floudas, P. Pardalos (Eds), Encyclopedia of Optimization
, 1999
"... Introduction and Problem Statement Interest in dynamic simulation and optimization of chemical processes has increased significantly during the last two decades. Common problems include control and scheduling of batch processes; startup, upset, shutdown and transient analysis; safety studies and th ..."
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Cited by 3 (0 self)
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Introduction and Problem Statement Interest in dynamic simulation and optimization of chemical processes has increased significantly during the last two decades. Common problems include control and scheduling of batch processes; startup, upset, shutdown and transient analysis; safety studies and the evaluation of control schemes. Chemical processes are modeled dynamically using differentialalgebraic equations (DAEs). The DAE formulation consists of differential equations that describe the dynamic behavior of the system, such as mass and energy balances, and algebraic equations that ensure physical and thermodynamic relations. The general dynamic optimization problem can be stated as follows: min z(t);y(t);u(t);t f ;p '(z(t f ); y(t f ); u(t<F8
Optimization Framework for the Synthesis of Chemical Reactor Networks
, 1998
"... The reactor network synthesis problem involves determining the type, size, and interconnections of the reactor units, optimal concentration and temperature profiles, and the heat load requirements of the process. A general framework is presented for the synthesis of optimal chemical reactor networks ..."
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Cited by 2 (1 self)
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The reactor network synthesis problem involves determining the type, size, and interconnections of the reactor units, optimal concentration and temperature profiles, and the heat load requirements of the process. A general framework is presented for the synthesis of optimal chemical reactor networks via an optimization approach. The possible design alternatives are represented via a process superstructure which includes continuous stirred tank reactors and cross flow reactors along with mixers and splitters that connect the units. The superstructure is mathematically modeled using differential and algebraic constraints and the resulting problem is formulated as an optimal control problem. The solution methodology for addressing the optimal control formulation involves the application of a control parameterization approach where the selected control variables are discretized in terms of time invariant parameters. The dynamic system is decoupled from the optimization and solved as a func...
Hybrid Dynamic Modeling and Identification
"... The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. iii ..."
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The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. iii
Numerical Computation of Optimal Feed Rates for a FedBatch Fermentation Model
, 1997
"... . In this paper we consider a model for a fedbatch fermentation process which describes the biosynthesis of penicillin. Firstly, we solve the problem numerically by using a direct method. By discretization of the control variable, we transform the basic optimal control problem to a finite dimension ..."
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. In this paper we consider a model for a fedbatch fermentation process which describes the biosynthesis of penicillin. Firstly, we solve the problem numerically by using a direct method. By discretization of the control variable, we transform the basic optimal control problem to a finite dimensional nonlinear programming problem, which is solved numerically by a standard SQPmethod. Contrary to an earlier paper by Luus (1993) we consider the problem as a free final time problem, thus obtaining an improved value of the penicillin output. The results indicate that the assumption of a continuous control which underlies the discretization scheme seems not to be valid. In a second step, we apply classical optimal control theory to the fedbatch fermentation problem. We derive a boundaryvalue problem (bvp) with switching conditions, which can be solved numerically by multiple shooting technique. It turns out, however, that this bvp is sensitive, which is due to the rigid behaviour of the ...
Computers and Chemical Engineering 28 (2004) 1169–1192 Retrospective on optimization
"... In this paper, we provide a general classification of mathematical optimization problems, followed by a matrix of applications that shows the areas in which these problems have been typically applied in process systems engineering. We then provide a review of solution methods of the major types of o ..."
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In this paper, we provide a general classification of mathematical optimization problems, followed by a matrix of applications that shows the areas in which these problems have been typically applied in process systems engineering. We then provide a review of solution methods of the major types of optimization problems for continuous and discrete variable optimization, particularly nonlinear and mixedinteger nonlinear programming (MINLP). We also review their extensions to dynamic optimization and optimization under uncertainty. While these areas are