Energy Efficient Control and Optimisation of Distillation Column Using Artificial Neural Network

This paper presents a neural network based strategy for the modelling and optimisation of distillation columns incorporating the second law of thermodynamics. Real time optimisation of distillation columns based on mechanistic models is often infeasible due to the effort in model development and the large computation effort associated with mechanistic model computation. This issue can be addressed by using neural network models which can be quickly developed from process operation data. The computation time in neural network model evaluation is very short making them ideal for real-time optimisation. Bootstrap aggregated neural networks are used in this study for enhanced model accuracy and reliability. Aspen HYSYS was used for the simulation of the distillation systems. Neural network models for exergy efficiency and product compositions are developed from simulated process operation data and are used to maximise exergy efficiency while satisfying product quality constraints. Applications to Methanol-Water and Benzene-Toluene separation columns demonstrate the effectiveness of the proposed method.