Comparison between response surface methodology and artificial neural network: Application in three‐product hydrocyclones

Abstract

Modelling a process or equipment is a profitable strategy to build better control strategies, predict fault conditions, and optimize the processes. Different approaches could be explored to achieve the development of better models. This paper investigates the use of experimental data generated by a central composite rotatable design (CCRD) to develop models capable of predicting the performance of a three‐product hydrocyclone for several setups with different dimensional parameters values. Two different modelling strategies are explored: response surface methodology (RSM) and artificial neural networks (ANN). With the RSM models, it was possible to evaluate the statistical importance of the input variables to each output variable. The ANN models showed improved coefficients of determinations (R2) compared to the RSM models, presenting values higher than 97% for all cases, while the RSM models ranged from 79.07%–88.83%. The ANN was demonstrated to be the most effective method to model the physical problem of three‐product hydrocyclones, and it captured its non‐linearities. It was shown that the combination of the design of experiments and ANN to analyze this physical problem is successful and may also be applied to other problems. As far as we have knowledge, a work regarding the comparison of both RSM and ANN methods applied to three‐product hydrocyclones was not found in the literature; this absence was the motivation for this work.

Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

“…Backpropagation (BP) neural networks are popular data-driven models, which have good approximation abilities for complex nonlinear systems, [19][20][21] and their accuracy and generalization ability have been proven through applications in various fields. [22] Considering the highly nonlinear mapping ability of the BP neural network model, the historical data of a cobalt removal process and the error data between the predicted and detected values were used in this study to establish an error compensation model based on an improved genetic algorithm (GA)-based BP neural network to compensate for the errors of the SCSTR model.…”