Process modeling and optimization

We are interested in the application of mathematical modelling and process optimization tools in analyzing and optimizing the performance of chemical engineering processes. These include, design of experiment for response surface methodology, machine learning methods like artificial neural networks, adaptive neuro-fuzzy inference system, support vector machine, extreme learning machine, etc, as well as equation-oriented and sequential modular modelling and simulation.

Some papers published already under this theme are listed as follows.