Nonlinear Learning Predictive Control of Fed-Batch Hydrogenation Reactor Based on Gaussian Process Regression

A large body of hydrogenation reactions is frequently running in fed-batch reactors now, which is very important in the industrial field, as we know. However, in the face of such a complex reaction system, relying only on traditional control algorithms will cause many problems in getting stable control. With the rise of intelligent control algorithms, a more effective control method is introduced based on the gaussian process(GP) to rebuild a model and combined with a predictive model method to achieve a stable control during operation and learning with more minor data set, compared to the neural network. The proposed technique’s effectiveness is verified on the simulink platform. The results show that this method can control the corresponding state to an acceptable range and has a more effective control result.