查看更多>>摘要：The fractional distillation process of methyl chlorosilanes composed of multiple distillation units plays a vital role in the silicones industry. It consumes energy intensively because of the high demand for separating capacity. Therefore, it is crucial to establish the energy consumption models for better forecasting. A multi-task learning approach is presented in this paper to improve the model accuracy for each unit while saving the modeling cost. Firstly, the simplified white-box model of each distillation unit is established according to the heat and material balance. Then, the multi-task least square support vector machine algorithm is proposed to identify the model parameters by employing the similarity between multiple distillation units. Finally, the actual industrial data is used in the simulation section to verify the validity, practicability, and advantages of the multi-task models over single-task ones. It shows that the proposed models can enhance the understanding of the distillation process significantly and forecast the energy consumption more accurately than the existing single-task models.
NSTL
Elsevier