Global Optimization of Chemical Processes using Stochastic Algorithms

Banga, Julio R.; Seider, Warren D.

doi:10.1007/978-1-4613-3437-8_33

Cited by 83 publications

(77 citation statements)

References 46 publications

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“…Basically, ICRS is a sequential (one trial vector at a time), adaptive random search method that can handle inequality constraints via penalty functions, and which has been successfully applied to a number of dynamic optimization problems (Banga and Seider 1996;Banga et al 1997). …”

Section: Icrsmentioning

confidence: 99%

Parameter Estimation in Biochemical Pathways: A Comparison of Global Optimization Methods

Moles

Mendes²,

Banga³

2003

Genome Res.

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742

597

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Here we address the problem of parameter estimation (inverse problem) of nonlinear dynamic biochemical pathways. This problem is stated as a nonlinear programming (NLP) problem subject to nonlinear differential-algebraic constraints. These problems are known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based) local optimization methods fail to arrive at satisfactory solutions. To surmount this limitation, the use of several state-of-the-art deterministic and stochastic global optimization methods is explored. A case study considering the estimation of 36 parameters of a nonlinear biochemical dynamic model is taken as a benchmark. Only a certain type of stochastic algorithm, evolution strategies (ES), is able to solve this problem successfully. Although these stochastic methods cannot guarantee global optimality with certainty, their robustness, plus the fact that in inverse problems they have a known lower bound for the cost function, make them the best available candidates.Mathematical optimization can be used as a computational engine to arrive at the best solution for a given problem in a systematic and efficient way. In the context of biochemical systems, coupling optimization with suitable simulation modules opens a whole new avenue of possibilities. Mendes and Kell (1998) highlight two types of important applications:1. Design problems: How to rationally design improved metabolic pathways to maximize the flux of interesting products and minimize the production of undesired by-products (metabolic engineering and biochemical evolution studies); 2. Parameter estimation: Given a set of experimental data, calibrate the model so as to reproduce the experimental results in the best possible way.This contribution considers the latter case, that is, the socalled inverse problem. The correct solution of inverse problems plays a key role in the development of dynamic models, which, in turn, can promote functional understanding at the systems level, as shown by, for example, Swameye et al. (2003) and Cho et al. (2003) for signaling pathways.The paper is structured as follows: In the next section, we state the mathematical problem, highlighting its main characteristics, and very especially its challenging nature for traditional local optimization methods. Next, global optimization (GO) is presented as an alternative to surmount those difficulties. A brief review of GO methods is given, and a selection of the presently most promising alternatives is presented. The following section outlines a case study considering the estimation of 36 parameters of a three-step pathway, which will be used as a benchmark to compare the different GO methods selected. A Results and Discussion section follows, ending with a set of Conclusions. METHODS Statement of the Inverse ProblemParameter estimation problems of nonlinear dynamic systems are stated as minimizing a cost function that measures the goodness of the fit of the model with respect to a given experimental data set, subject to the dynamics of the ...

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Section: Icrsmentioning

confidence: 99%

Parameter Estimation in Biochemical Pathways: A Comparison of Global Optimization Methods

Moles

Mendes²,

Banga³

2003

Genome Res.

Self Cite

742

597

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“…We have followed the published recommendations for each method (see references [2], [4], [5], [8], [9], [21] and [27]), together with the feedback obtained after a few preliminary runs. All the computation times reported here were obtained using a low cost platform, PC Pentium III/866 MHz.…”

Section: Resultsmentioning

confidence: 99%

Global optimization of climate control problems using evolutionary and stochastic algorithms

Moles

Lieber

Banga

et al. 2003

Advances in Soft Computing

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Summary. Global optimization can be used as the main component for reliable decision support systems. In this contribution, we explore numerical solution techniques for nonconvex and nondifferentiable economic optimal growth models. As an illustrative example, we consider the optimal control problem of choosing the optimal greenhouse gas emissions abatement to avoid or delay abrupt and irreversible climate damages. We analyze a number of selected global optimization methods, including adaptive stochastic methods, evolutionary computation methods and deterministic/hybrid techniques. Differential evolution (DE) and one type of evolution strategy (SRES) arrived to the best results in terms of objective function, with SRES showing the best convergence speed. Other simple adaptive stochastic techniques were faster than those methods in obtaining a local optimum close to the global solution, but misconverged ultimately.

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“…Therefore, methods with global convergence properties should be used. Several deterministic and non-deterministic approaches for global optimization have been proposed 19,20 .…”

Section: Solution Of the Optimal Control Problemmentioning

confidence: 99%

On the Optimal Control of Contact-Cooking Processes

Banga

Pan²,

Singh

2001

Food and Bioproducts Processing

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SUMMARYIn this study, we have considered the design of optimised thermal processes for the preparation of cooked hamburger patties. For this purpose, advanced dynamic optimisation (optimal control) techniques have been used. These techniques make use of predictive mathematical models of heat and mass transfer, previously validated with experimental studies. The generic optimal control problem considered was formulated as the determination of the optimal cooking conditions to obtain the highest quality product with an ensured final level of safety in a minimum time. The obtained optimal policies (dynamic heating temperatures) show significant advantages over nominal constant temperature processes. These optimisation studies provide new information to design the next generation of grills with dynamic controls to improve the product quality and safety

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Global Optimization of Chemical Processes using Stochastic Algorithms

Cited by 83 publications

References 46 publications

Parameter Estimation in Biochemical Pathways: A Comparison of Global Optimization Methods

Parameter Estimation in Biochemical Pathways: A Comparison of Global Optimization Methods

Global optimization of climate control problems using evolutionary and stochastic algorithms

On the Optimal Control of Contact-Cooking Processes

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