Evolutionary Surrogate Optimization of an Industrial Sintering Process

Mitra, Kishalay (2013) Evolutionary Surrogate Optimization of an Industrial Sintering Process. Materials and Manufacturing Processes, 28 (7). pp. 768-778. ISSN 1042-6914

Full text not available from this repository. (Request a copy)

Abstract

Despite showing immense potential as an optimization technique for solving complex industrial problems, the use of evolutionary algorithms, especially genetic algorithms (GAs), is restricted to offline applications in many industrial cases due to their computationally expensive nature. This problem becomes even more severe when the underlying function as well as constraint evaluation is computationally expensive. To reduce the overall application time under this kind of scenario, a combined usage of the original expensive model and a relatively less expensive surrogate model built around the data provided by the original model in the course of optimization has been proposed in this work. Use of surrogates provides the quickness in the application, thereby saving the execution time, and the use of original model allows the optimization tool to be in the right path of the search process. Switching to the surrogate model happens if predictability of the model is of acceptable accuracy (to be decided by the decision maker), and thereby the optimization time is saved without compromising the solution quality. This concept of successive use of surrogate (artificial neural network [ANN]) and original expensive model is applied on an industrial two-layer sintering process where optimization decides the individual thickness and coke content of each layer to maximize sinter quality and minimize coke consumption simultaneously. The use of surrogate could reduce the execution time by 60% and thereby improve the decision support system utilization without compromising the solution quality.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Mitra, Kishalayhttp://orcid.org/0000-0001-5660-6878
Item Type: Article
Uncontrolled Keywords: Neural network, NSGA II, Pareto, Sintering, Surrogate modeling
Subjects: Chemical Engineering > Biochemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Mr. Siva Shankar K
Date Deposited: 19 Sep 2014 08:16
Last Modified: 10 Nov 2017 05:04
URI: http://raiith.iith.ac.in/id/eprint/22
Publisher URL: https://doi.org/10.1080/10426914.2012.736668
OA policy: http://www.sherpa.ac.uk/romeo/issn/1042-6914/
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 22 Statistics for this ePrint Item