Energy Efficiency Recognition and Diagnosis of Complex Industrial Processes using Multivariate Nonlinear Regression Method

Due to the characteristics of multi-dimensional, strong coupling and noise of complex industrial data, this paper establishes an energy efficiency recognition and diagnosis model using the multivariate nonlinear regression method to analyze the objective mathematical relationship between raw material consumption and the corresponding output in complex industrial processes. The curve estimation between raw materials and production of complex industrial processes are carried out. Then the raw materials are replaced by the calculation results of the equation with higher fitting degree. Finally, the linear regression and energy efficiency diagnosis of the substituted raw materials are carried out. The proposed model is tested and analyzed using actual industrial data of ethylene production plants. The results show the validity and applicability of the proposed model. Meanwhile, the objective mathematical relationship between the ethylene production capacity and the energy consumption of various raw materials can be recognized to adjust the proportion of raw materials in ethylene production plants, which can improve the energy efficiency and reduce raw materials waste.