A CSTR State Observer Based on Residual Neural Network

Continuous stirred tank reactor (CSTR) is one of the most common industrial equipment in petroleum and chemical industry, and is widely used in regrouping, fermentation engineering and additive preparation. In general, CSTR is used to prepare a fixed concentration of the output product. Accurate and fast monitoring of the changes in the state quantities of the CSTR chemical reaction process becomes the most important aspect before implementing excellent control. This paper presents a neural network observer with a residual network as the core component. In addition, the operations of the neural network are also matrixed to isolate the nonlinearities as much as possible. Finally we conduct numerical experiments in MATLAB R2018b based on SIMULINK framework to verify the feasibility of our strategy.