Enhancing BNN structure learning of hybrid modeling strategy for free formulated mechanism complex systems. (December 2022)

• Record Type: Journal Article
• Authors: Liang, Mingyu; Li, Shaoyuan
• Is Part Of: Journal of process control. Volume 120(2022)
• Page Start: 44
• Page End: 67
• Publication Date: 2022-12
• Subjects: Hybrid modeling -- Deep learning -- BNN structure learning -- Data-augmented graph; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.10.006
• Languages: English
• ISSNs: 0959-1524

Abstract

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. Highlights: This paper proposes an enhanced Bayesian neural network structure learning strategy to address the hybrid modeling of complex systems without formalized mechanistic information. In order to solve the problem of representing the non-formulated mechanism information of complex systems, directed graph networks are introduced to represent the mechanism information of the system. Different from the fixed model structure in the previous hybrid modeling strategy, the model structure in the strategy proposed in this paper can be adjusted according to actual requirements, so as to obtain a reasonable balance of model complexity and accuracy. A real chemical plant is used to verify the method proposed in this paper. The results show that the method can guarantee a certain accuracy and reduce the computational burden.