An adaptive sequential wavelet-based algorithm developed for dynamic optimization problems

作     者：Santos, Lizandro de Sousa Secchi, Argimiro Resende Prata, Diego Martinez Biscaia Jr, Evaristo Chalbaud 

作者机构：Univ Fed Fluminense Sch Engn Dept Chem Engn & Petr Rua Passo Patria 156 BR-24210240 Niteroi RJ Brazil Univ Fed Rio de Janeiro PEQ COPPE Chem Engn Program Ctr Tecnol Av Horcicio Macedo 2030Bloco GSala G-116 BR-21941914 Rio De Janeiro RJ Brazil 

出 版 物：《COMPUTERS & CHEMICAL ENGINEERING》 (计算机与化工)

年 卷 期：2019年第121卷

页      面：465-482页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Brazilian Agency CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico) FAPERJ (Fundacao de Amparo a Pesquisa do Rio de Janeiro) 

主　　题：Dynamic optimization Nonlinear programming Wavelets Thresholding Control vector parameterization 

摘      要：In this paper we present an adaptive wavelet algorithm (WAA) tailored for dynamic optimization problems (DOP). The main feature of the WAA is the automatic computation of time-domain discretization, generating a self-adapting control parameterization, which depends on the nonlinear characteristics of the mathematical model. For this, the control variables are analyzed and treated at different wavelet levels. First, we have demonstrated the advantages of WAA over heuristic adaptive procedures, proposed in the last years. Second, the results of the proposed strategy are illustrated through the solution of ten case studies. According to the results, the computation cost could be reduced by about 56% on average. Besides, the average NLP size reduction was approximately 49.94%, showing that one of the most considerable advantages of the algorithm is the adaptive discretization without prior information of the control profile. (C) 2018 Elsevier Ltd. All rights reserved.