In industry process control, the model identification and predictive control of nonlinear systems are always difficult problems. This necessitates the development of empirical nonlinear model from dynamic plant data. This process is known as ‘Nonlinear System Identification’. Artificial neural networks are the most popular frame-work for empirical model development. The model is implemented by training a Multi-Layer Perceptron Artificial Neural network (MLP-ANN) with input output experimental data. Satisfactory agreement between identified and experimental data is found and results shown that the neural model successfully predicts the evolution of the product composition. Trained data available from nonlinear system used for process control using Model Predictive Control (MPC) algorithm. The Simulation result illustrates the validity and feasibility of the MPC algorithm.