Enhanced meta-heuristic methods for industrial winding process modelling

Nonlinear industrial system modelling entails two critical phases: The first is selecting a method in order to estimate the parameter list values, and the second is selecting a proper model structure with a relatively short parameter list. Developing a comprehensive model for an industrial design process is critical for the model-based control system. This article presents a model-based strategy that aims to develop three linear and three nonlinear dynamic models using three well-known meta-heuristic optimization algorithms to simulate a challenging plant-wide process. As a case study, an industrial real winding process (WP) is targeted to accomplish the aim of this study. The algorithms have been optimized to find the best weights of the inputs of the WP with a key issue to effectively describe the behaviour aspects of the process. To test the validity of the developed models, a series of experiments were carried out on each of the developed linear and nonlinear models. Several relevant evaluation metric measures are used to demonstrate the models' performance level. The experimental results for training and test sets of 1250 independent samples for each set based upon the proposed modelling schemes show that the mean square error to correctly model the WP occurred in less than 0.001. A comparison of the developed intelligent linear and nonlinear models with the Auto-Regressive Integrated Moving Average (ARIMA) and Multiple Linear Regression (MLR) models obtained through the evaluation criteria asserts the effectiveness of the proposed models-based approaches.