A Multiprocess Joint Modeling Method for Performance Prediction of Nonlinear Industrial Processes Based on Multitask Least Squares Support Vector Machine

Abstract

Developing the models of multiple processes rapidly and accurately is very important for process control and optimization in industrial production. This paper proposes a multiprocess joint modeling method to establish the models of multiple industrial processes simultaneously. Under the assumption that the related processes shared common information, this method views the process model as the combination of the common feature model and the special feature model. Through the full mining and using the shared information, the proposed method can establish models of multiple processes simultaneously and improve the accuracy of each process model. In particular, an improved multitask least squares support vector machine algorithm, which assumes that all the learning processes share the common feature model, is proposed to train multiprocess models simultaneously. Experiments developing the models of multiple centrifugal compressors are conducted, and the simulation experiment results show that the proposed method can realize joint modeling of multiple similar processes, improve modeling efficiency, and reduce modeling costs.

Distributed online multi‐task sparse identification for multiple systems with asynchronous updates

“…For example, multi-grade processes, [30][31][32] multiple distillation columns, 33 multiple compressors. 34 These systems often have the same model structure with different parameters due to the similarity of physical principles. That is, they have the same parameter sparsity properties.…”

“…In practical industrial processes, there are often multiple similar systems that need to be identified simultaneously. For example, multi‐grade processes, 30‐32 multiple distillation columns, 33 multiple compressors 34 . These systems often have the same model structure with different parameters due to the similarity of physical principles.…”