Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes

Yongming Han, Shuang Liu, Di Cong, Zhiqiang Geng, Jinzhen Fan, Jingyang Gao and Tingrui Pan

Energy, 2021, vol. 225, issue C

Abstract: Energy-saving and emission reduction are crucial to modern society, but the previous resource optimization methods based on traditional neural networks are complex and low accuracy. Therefore, this paper presented a novel extreme learning machine (ELM) method based on t-distributed stochastic neighbor embedding (t-SNE) to optimize the energy and reduce the carbon emission. In terms of mapping high-dimensional production data to low-dimensional space, the t-SNE can deal with the major factors affecting the energy efficiency, which are taken as inputs of the ELM. Then the resource optimization model for energy-saving is obtained based on the ELM to predict the output and achieve the optimal configuration. Finally, the proposed method is used to establish the resource optimization model for the ethylene and purified terephthalic acid (PTA) production processes in complex industrial processes. The experimental results demonstrate that the proposed model can improve the prediction accuracy of resource optimization models of complex industrial processes, and realize energy-saving and carbon emissions reduction.

Keywords: Resource optimization; Energy saving; Energy efficiency improvement; Extreme learning machine; T-distributed stochastic neighbor embedding; Complex industrial processes (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:225:y:2021:i:c:s0360544221005041

DOI: 10.1016/j.energy.2021.120255

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