Evolutionary Surrogate Optimization of an Industrial Sintering Process

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.