Article ID Journal Published Year Pages File Type
8072148 Energy 2018 10 Pages PDF
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.
Related Topics
Physical Sciences and Engineering Energy Energy (General)
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