Reducing the energy consumption and CO2 emissions of energy intensive industries through decision support systems – An example of application to the steel industry

Giacomo Filippo Porzio, Barbara Fornai, Alessandro Amato, Nicola Matarese, Marco Vannucci, Lisa Chiappelli and Valentina Colla

Applied Energy, 2013, vol. 112, issue C, 818-833

Abstract: The management of process industries is becoming in the recent years more and more challenging, given the stringent environmental policies as well as raising energy costs and the always-present drive for profit. A way to help plant decision makers in their daily choices is to refer to decision-support tools, which can give advice on the best practices on how to operate a plant in order to reduce the energy consumption and the CO2 emissions keeping at the same time the costs under control. Such an approach can be useful in a variety of industries, particularly the most energy-intensive ones such as iron and steel industries. In this paper, an approach to the realisation of a software system, which allows to generate internal reports on the plant performances, as well as to simulate the plant behaviour in different scenarios, is described. The main production processes (coke plant, blast furnace, steel shop, hot rolling mill) are described and simulated focusing on the prediction of products flow rates and composition, energy consumption and GHGs (Greenhouse Gases) emissions in different operating conditions. The importance of a correct management of the CO2 within the plant is underlined, particularly with regard to the new EU Emission Trading System, which will be based on European benchmarks. The software tool is illustrated and a case study is included, which focuses on the simultaneous minimisation of the CO2 emissions and maximisation of the profit through an optimised management of the by-product gases. The results from the case study show a good potential for process improvement, by a reduction in the cost and environmental impact.

Keywords: Simulation; Energy consumption reduction; CO2 reduction; Decision support; Process optimisation (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (31)

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DOI: 10.1016/j.apenergy.2013.05.005

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