A novel nonlinear virtual sample generation approach integrating extreme learning machine with noise injection for enhancing energy modeling and analysis on small data: Application to petrochemical industries

Building a robust and accurate energy analysis model is considered as an important issue in the field of petrochemical industries. Under the circumstance of small samples, the accuracy of the energy analysis model is unacceptable. In order to solve this problem, a novel noise injection integrated with extreme learning machine based nonlinear virtual sample generation method is proposed. Through injecting noise in the output matrix of the hidden layer of ELM, a virtual information matrix that is different the original one generated using the original small dataset can be obtained. Then the newly generated information matrix is adopted to produce good-quality virtual samples for supplement knowledge for small samples. To authenticate the effectiveness of the proposed method, a standard trigonometric function is first selected; and then the proposed method is developed as an energy analysis model for an ethylene production process. Simulation results indicate that good virtual samples can be generated using the proposed method, and the accuracy of the energy analysis model is much improved with the aid of the newly generated virtual samples. The proposed method will effectively help production departments of petrochemical industries set more suitable targets of energy consumption and make better use of available resources.