Modelling of chemical processes using artificial neural network

Abstract

ABSTRACT In this paper, a methodical strategy is put forth for the development of models for chemical processes by using an Artificial Neural Network (ANN). To show the effectiveness of the proposed methodology, three industrial chemical processes were considered. The intended work is to introduce the guideline for designing any ANN model by identifying the optimum number of neurons and hidden layers and coming up with the most suitable architecture for the neural network. The effect of viable parameters on predicting the specified parameter was evaluated using an ANN model. A systematic procedure adopted, ANN networks were designed to predict the boiler efficiency, frost thermal conductivity, and gas holdup under different operating conditions. At last, a comparative study on the experimental output and ANN approximated output for the three chemical processes was done. For developing the ANN model for boiler and frost conductivity, it was found that employing two layers provided a superior fit. The R² values for these fits were found to be 0.98877 and 0.9891, respectively. On the other hand, when configuring the column flotation model, a single layer with nine nodes was identified as the optimal ANN structure, yielding an excellent R² fit value of 0.99337.