A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry. (15th March 2018) - He, Yan-Lin; Wang, Ping-Jiang; Zhang, Ming-Qing; Zhu, Qun-Xiong; Xu, Yuan
Abstract: An accurate energy prediction and optimization model plays a very important role in the petrochemical industries. Due to the imbalanced and uncompleted characteristics of complex petrochemical small data, it is a big challenge to build accurate prediction and optimization models for energy analysis. In order to solve this problem, a nonlinear interpolation virtual sample generation method integrated with extreme learning machine is proposed. Well virtual input and output variables can be generated through interpolation of the hidden layer outputs of extreme learning machine. The generated virtual samples are put together with the original samples to train models for enhancing accuracy performance. To validate the effectiveness of the proposed nonlinear interpolation virtual sample generation method, a standard function is firstly selected, and then the proposed nonlinear interpolation virtual sample generation method is applied to developing a model of energy analysis for ethylene production systems. Simulation results showed that the prediction accuracy could be significantly improved, which provided helpful guidance for production departments and government to achieve the goal of energy management of petrochemical industries. Highlights: Energy modeling and analysis under small sample circumstance is investigated. A novel nonlinear interpolation based virtual sample generation method is proposed. The proposed method is easy to construct and effective to generate virtual data. Accuracy of energy modeling can be much improved by adding generated virtual data. A wide range of energy modeling application with the small data problem is enjoyed.