The Solution of Time Optimal Control Problems by Simulated Annealing

Sun, Daim-Yuang; Lin, Pi-Min

doi:10.1252/jcej.39.753

Cited by 5 publications

(7 citation statements)

References 31 publications

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“…Moreover, Carrasco and Banga (1997) and Aziz and Mujtaba (2002) report that applying the input in variable time interval is good for the numerical qualities. The same conclusion is also reached in previous investigations (Sun and Chen, 2004;Sun and Lin, 2006). Thus, this policy is still adopted in this study.…”

Section: Problem Formulationsupporting

confidence: 83%

“…Since studies (Carrasco and Banga, 1997;Sun and Chen, 2004;Sun and Lin, 2006) have pointed that the piecewise linear inputs may give better results in most cases, therefore, Equation (10) is further adopted in this study. Thus, from practical point of view, the continuous input in Equations (6) and Equations (7) are approximated by a series of ramp inputs,…”

Section: The Treatment Of Inequality Constraintsmentioning

confidence: 99%

“…By applying the algorithm based on simulated annealing (SA), the authors have successfully solved some typical optimal control problems in the framework of CVP. Using piecewise linear input with variable time interval to rebuild the input policy, our numerical strategy (Sun and Chen, 2004;Sun and Lin, 2006) provides the solutions with very nice qualities, even if SA is just a convenient but old-fashioned technique. In this paper, we further apply the algorithm to explore the batch reactor operations.…”

Section: Introductionmentioning

confidence: 99%

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Using Dynamic Optimization Technique to Study the Operation of Batch Reactors

Sun

Lin

2007

Can J Chem Eng

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2. Reach the specifi ed yield for the desired product(s) in the minimum operating period under some constraints. The operating constraints in the above expressions usually arise from the physical bounds on the control variable(s), safety limitations, and environmental regulations. In optimal control studies, the fi rst expression can be formulated as a maximum yield (or conversion) problem, while the second one as a minimum operating time problem. By comparing with the solutions from the above optimal control problems (OCPs), the operators can easily assess whether or not the real production qualities are satisfactory and further consider how to modify the practical operating policies to make the processes more better.Basically, to solve the above OCPs is a challenging task due to the highly non-linear, non-convex and multimodal nature derived from chemical systems. In the past decades, most developed solution techniques are based on one of the following theories:How to apply the global optimization technique, simulated annealing, and to explore the operation of batch reactors is addressed in this study. Based on the operating purposes and the imposed constraints, the batch reactor operations are fi rst formulated as two optimal control problems: the maximal yield (or conversion) problem and the minimal operating time problem. The problems are then converted into nonlinear programming problems by the concept of control vector parameterization. The converted problems are solved by the algorithm derived from simulated annealing to determine the optimal operating policy and the performance index. These results are useful in assessing design and operation of batch reactors. In this article, the CSTR model is used to demonstrate the convenience and robustness of the proposed algorithm. Two typical reaction models are used to discuss the operations based on the optimal solutions.On s'intéresse dans cette étude à la façon d'appliquer la technique d'optimisation globale, dite de recuit simulé, pour explorer le fonctionnement de réacteurs discontinus. À partir des objectifs de fonctionnement et des contraintes imposées, le fonctionnement des réacteurs discontinus est d'abord formulé sous forme de deux problèmes de contrôle optimums: le problème du rendement (ou la conversion) maximal et le problème du temps de fonctionnement minimal. Ces problèmes sont ensuite convertis en problèmes de programmation non linéaires par le concept de paramé-trisation des vecteurs de contrôle. Les problèmes convertis sont résolus par l'algorithme établi à partir du recuit circulé afi n de déterminer la politique de fonctionnement optimale des réacteurs et l'indice de performance. Ces résultats sont utiles dans l'évaluation de la conception et du fonctionnement des réacteurs discontinus. Dans cet article, le modèle CSTR permet de démontrer le côté pratique et la robustesse de l'algorithme proposé. Deux modèles de réaction typique sont utilisés pour analyser le fonctionnement des réacteurs d'après des solutions optimales.

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Section: Problem Formulationsupporting

confidence: 83%

Section: The Treatment Of Inequality Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Dynamic Optimization Technique to Study the Operation of Batch Reactors

Sun

Lin

2007

Can J Chem Eng

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“…These perturbations depend on a temperature index, , and rate at which it is lowered ( = [0.5, 0.99]). More details on this temperature control parameter for simulated annealing are presented in [32]. Figure 1 represents the temperature index decreasing per iteration of the algorithm.…”

Section: Hybrid Incremental Modeling Based On the Optimal Settingmentioning

confidence: 99%

Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization

et al. 2017

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The complexity of machining processes relies on the inherent physical mechanisms governing these processes including nonlinear, emergent, and time-variant behavior. The measurement of surface roughness is a critical step done offline by expensive quality control procedures. The surface roughness prediction using an online efficient computational method is a difficult task due to the complexity of machining processes. The paradigm of hybrid incremental modeling makes it possible to address the complexity and nonlinear behavior of machining processes. Parametrization of models is, however, one bottleneck for full deployment of solutions, and the optimal setting of model parameters becomes an essential task. This paper presents a method based on simulated annealing for optimal parameters tuning of the hybrid incremental model. The hybrid incremental modeling plus simulated annealing is applied for predicting the surface roughness in milling processes. Two comparative studies to assess the accuracy and overall quality of the proposed strategy are carried out. The first comparative demonstrates that the proposed strategy is more accurate than theoretical, energy-based, and Taguchi models for predicting surface roughness. The second study also corroborates that hybrid incremental model plus simulated annealing is better than a Bayesian network and a multilayer perceptron for correctly predicting the surface roughness.

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“…SA is quite a popular algorithm within the optimization community. Thus, there are several studies in different fields in which we can find guidelines to set the search parameters, including process engineering (Kookos, 2004;Faber et al, 2005;Sun and Lin, 2006).…”

Section: Simulated Annealing (Sa)mentioning

confidence: 99%