Energy Efficiency Prediction Based on PCA-FRBF Model: A Case Study of Ethylene Industries

Abstract

Energy conservation and emission reduction in the ethylene industry is the main way to attain sustainable development, which can be achieved if the energy efficiency of petrochemical industries can be accurately analyzed and predicted. This paper proposes an improved radial basis function neural network based on fuzzy ${C}$ -means (FCM) algorithm integrated with principal component analysis (PCA) technology (PCA-FRBF). The PCA is used to denoise and reduce dimensions of data to decrease the training time and errors of the modeling process. The FCM is used to separate every fuzzy class in input space and decide the number of neurons in hidden layer to overcome the shortcoming of setting them by experience subjectively. Meanwhile, the robustness and effectiveness of the PCA-FRBF model are validated through the standard data set from the University of California Irvine repository. Moreover, to predict the energy efficiency of ethylene plants, a multi-inputs and single-output model of energy efficiency is established based on the PCA-FRBF for monthly data of ethylene production process. We obtain a rational allocation of crude oil, fuel, steam, water, and electricity, and the greatest benefit of ethylene plants under different technologies. Finally, the empirical results show the effectiveness and practicability of the PCA-FRBF model applied to predict and guide the ethylene production in the petrochemical industry.