Uncertainty analysis of industrial energy conservation management in China's iron and steel industry

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)205-214
Journal / PublicationJournal of Environmental Management
Volume225
Early online date4 Aug 2018
StatePublished - 1 Nov 2018

Abstract

There are remarkable uncertainty factors in the industrial sector that enhance the difficulties of setting energy conservation strategies, such as the macro economy, industrial structures, and technical uncertainties. However, current studies simply predict the possible trends or conduct scenario analyses, and neglect uncertainty factors in the management of industrial energy conservation. In response, this article considers China's iron and steel industry as an example and builds the Industrial Energy Conservation Uncertainty Analysis (IECUA) model to recognize and analyze the uncertainty factors via a 200-thousand-time Latin hypercube sampling. Then, we propose some management measures, including setting energy conservation targets and energy conservation strategies. The results show that energy conservation targets should be more flexible than just the predicted values, to enhance the feasibility of their realization. In addition, energy conservation strategies are set at industrial and technique levels. On the one hand, such key parameters as production output, the coke/steel ratio, and pig iron/steel ratio, should be strictly controlled to avoid non-compliance risks. On the other hand, energy conservation technologies can be considered under four quadrants depending on their sensitivity to energy conservation and economic efficiency. Finally, some differentiated technologies promotion suggestions are made, such as economic stimulation, market entry standards and technical application guidelines.

Research Area(s)

  • Energy conservation, Iron and steel industry, Latin hypercube sampling, Uncertainty analysis