An adaptive sampling surrogate model building framework for the optimization of reaction systems

Autor(es)

Franzoi, Robert E. ^[1] ; Kelly, Jeffrey D. ^[2] ; Menezes, Brenno C. ^[3] ; Swartz, Christopher L. E. ^[4]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Dept Chem Engn, Av Prof Lineu Prestes 580, BR-05508000 Sao Paulo - Brazil

^[2] Ind Algorithms Ltd, 15 St Andrews Rd, Toronto, ON M1P 4C3 - Canada

^[3] Hamad Bin Khalifa Univ, Qatar Fdn, Coll Sci & Engn, Div Engn Management & Decis Sci, Doha - Qatar

^[4] McMaster Univ, Dept Chem Engn, 1280 Main St W, Hamilton, ON L8S 4L7 - Canada

• Tipo de documento: Artigo Científico
• Fonte: Computers & Chemical Engineering; v. 152, SEP 2021.
• Citações Web of Science: 0

Resumo

Many industrial engineering problems involve complex formulations and are assisted by simulation tools. Although these tools provide highly accurate solutions, they may not be suitable for large scale problems and for optimization applications. Looking for alternatives to complex formulations that often lead to convergence issues and to time consuming solutions, the use of surrogate modeling for reaction systems is addressed herein. We propose a novel adaptive sampling algorithm that iteratively explores the solution space and incorporates ideas from adaptive sampling, trust region methods, and successive linear programming approaches. The surrogates are iteratively embedded into optimization problems to check feasibility and to collect insights to the following adaptive sampling iteration. The methodology is applied to a reaction system network and the surrogates are built to predict the reactor outputs. The adaptive sampling algorithm builds highly accurate surrogates that can be embedded into the reaction system optimization leading to near optimal solutions. (c) 2021 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 17/03310-1 - Otimização integrada da programação de produção no refino de petróleo: da descarga de óleo até a entrega de combustíveis
• Beneficiário: Robert Eduard Franzoi Junior
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto