An engineering control design approach based on high-dimensional data analysis in closed-loop systems for industrial networks

Industrial processes are constantly developing towards large-scale and automation, and the smooth, safe, high-quality, and efficient operation of industrial processes has become a hot spot of concern, and higher requirements have been put forward for the control of production processes. This study analyzes the high-dimensional data in the closed-loop system of industrial network based on the HOPLS-SVM algorithm with higher-order singular value decomposition method. A BP neural network model is constructed with the processed data as the input set to realize the real-time prediction of the main data in the project, and the error of the prediction model is corrected by using linear regression method, and then the project prediction control system is constructed by piggybacking on the model. The results show that the prediction performance of the model in this paper is better than that of the comparison model, and the average absolute error is only 0.0347. At the same time, it is found that the control system in the decomposition project of CHP is able to regulate and optimize the temperature and flow rate of the crude product, which ensures the balance between the product temperature and yield, and the safety of the project operation. The engineering control method designed in this study has strong adaptability and effectiveness, and can provide solutions to engineering control problems in complex industrial processes.