Improving the accuracy of mechanistic models for dynamic batch distillation enabled by neural network: An industrial plant case

Abstract

Neural networks are often viewed as pure ‘black box’ models, lacking interpretability and extrapolation capabilities of pure mechanistic models. This work proposes a new approach that, with the help of neural networks, improves the conformity of the first-principal model to the actual plant. The final result is still a first-principal model rather than a hybrid model, which maintains the advantage of the high interpretability of first-principal model. This work better simulates industrial batch distillation which separates four components: water, ethylene glycol, diethylene glycol, and triethylene glycol. GRU (gated recurrent neural network) and LSTM (long short-term memory) were used to obtain empirical parameters of mechanistic model that are difficult to measure directly. These were used to improve the empirical processes in mechanistic model, thus correcting unreasonable model assumptions and achieving better predictability for batch distillation. The proposed method was verified using a case study from one industrial plant case, and the results show its advancement in improving model predictions and the potential to extend to other similar systems.