Intelligent modeling and optimization of process operations using neural networks and genetic algorithms: Recent advances and industrial validation

Artificial Neural Networks (ANN) have been used as black-box models for many systems during the past years. Specifically, neural networks have been used advantageously in the Chemical Processing Industries (CPI) in a number of ways. Successful applications reported range from enhanced productivity by kinetic modeling, to improved product quality, and the development of models for market forecasting. Typically, a main objective in ANN modeling is to accurately predict steady-state or dynamic process behavior to monitor and improve process performance. Furthermore, they also can help in process fault diagnosis. The black-box character of neural net models can be enriched by available mathematical knowledge. This approach has been extended to consider nonlinear time-variant processes. The potential of neural network technology faces rewarding challenges in two key areas: evolutionary modeling and process optimization including qualitative analysis and reasoning. Recent work indicates that evolutionary optimization of non-linear time-dependent processes can be satisfactorily achieved by combining neural network models with genetic algorithms. Industrial validation studies indicate that present solutions point to the right direction, but additional effort is required to consolidate and generalize the results obtained.