Energy Consumption Prediction Method for Process Industry Based on VMD-HHO-ELM

The cement industry is one of the most energy-intensive process industries in the world, cement production is a complex industrial process consisting of several processes with time-varying delay characteristics. uncertainty and nonlinear characteristics. Cement clinker firing is the most energy consuming part in cement production process. It is one of the research focuses of cement industry to predict the multi-energy consumption index of cement clinker firing while solving the timevarying delay problem. Based on the above problems, an ELM (extreme learning machine) model optimized by VMD(variational mode decomposition) and HHO(Harris Eagle Optimization algorithm) is proposed to predict the energy consumption of cement calcination. First, the main variables affecting the quality and energy consumption of cement clinker calcined process were selected and classified, and the data were preprocessed. Then, the input layer of the ELM model was designed by VMD and sliding window algorithm (SW), and the structure and parameters of the ELM model were optimized by HHO algorithm. Improve the predictive performance of the model. Finally, the validity and prediction accuracy of the proposed model are verified by simulation analysis.