Enhanced PH Neutralization Process Control Using Firefly-Optimized Artificial Neural Network Predictive Controller

The pH neutralization is a challenging process, and it plays a significant role in the chemical process industry. Neutralization is the process of eradicating alkalinity or acidity by combining acids and bases to generate a neutral solution. To have the least amount of environmental impact, acidic or basic wastewater must be neutralized before discharge. Specifically, due to their inherent characteristics, the processes are difficult to control for high nonlinearity and sensitivity near the equilibrium point. To manage the difficult process of pH control, the traditional control techniques are utilized, including Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers. Here, to standardize the pH process Artificial Neural Network (ANN) is employed and is optimized with the utilization of the Fire Fly Optimization (FFO) algorithm for tuning its input parameters. This paper aims to deploy a smoother control signal to achieve efficient load regulation and convenient set-point tracking. The suggested FF-ANN controller outperforms other existing control approaches with improved disturbance rejection, set-point tracking, and excellent sensitivity to changes in model parameters, according to the results of the determined simulation.