Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

Full text
Author(s):
Franzoi, Robert E. [1] ; Kelly, Jeffrey D. [2] ; Menezes, Brenno C. [3] ; Swartz, Christopher L. E. [4]
Total Authors: 4
Affiliation:
[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
Total Affiliations: 4
Document type: Journal article
Source: Computers & Chemical Engineering; v. 152, SEP 2021.
Web of Science Citations: 0
Abstract

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)

FAPESP's process: 17/03310-1 - Integrated scheduling optimization in the crude-oil refinery industry: from crude-oil unloading to fuel deliveries
Grantee:Robert Eduard Franzoi Junior
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)