Enhancing BNN structure learning of hybrid modeling strategy for free formulated mechanism complex systems

Abstract

Modeling of large and complex industrial processes such as chemical processes has always been an ongoing research topic. Hybrid modeling approaches draw more and more attentions due to its ability to combine the benefits of both mechanism(first-principle) models and data-based models in the term of the balance between accuracy and cost. However, there is an insurmountable problem in the application of hybrid modeling, that is, when the accurate formulated mechanism information of the system is difficult to obtain, the hybrid modeling strategy is often difficult to obtain the desired results in existing hybrid modeling strategies. In order to solve the contradiction between the need for accurate formulated mechanism and the reality that obtaining them in a large actual process is difficult or even impossible, we utilized the directed graph and the method of Bayesian Neural Network(BNN)-Structure Learning to combine the mechanism information and data information into a hybrid information graph, which is called BNN-Data Augmented Graph(BNN-DAG) to form a hybrid model without accurate first-principles formulas. On the basis of the hybrid information graph, the system is first partitioned into small blocks according to the demand of the target variables, then an appropriate modeling method is utilized based on the requirements of the object. Then, the selected modeling method is implemented into blocks and contributes to a complete predictive model, so that the complex system can still be modeled with a certain accuracy without the mechanism information expressed by accurate formulas. The method in this manuscript is validated on a Tennessee Eastman(TE) benchmark process and a Hydrogenation Fractionation Unit(HFU) in a real plant. The prediction results are compared with the currently popular deep learning methods. It is proved that under the framework of the method in this manuscript, it is possible to obtain a system prediction model with the same or higher accuracy as the existing deep learning methods but with a relatively a low cost. Under the framework, the interpretability of the prediction model could be obtained clearly. Also, the improvement is beneficial to the application in the actual industrial process.