Abstract
The price of electric energy depends on additional factors since the introduction of renewable energy sources, which has changed the basics of electricity production and the determination of its price. Iron and steel industries strongly require forecasting procedures for the energy amount of their production cycles: today production planning is performed without taking into account that the difference in electricity price between night and day can overcome 500%. The aim of this work is to create a model allowing to estimate energy requirements for steel industry; the model correctness is assessed, for both energy and power analysis, by comparison with real data. A planning tool is employed to provide data to a computer platform able to assess, on the basis of required energy, the best market on which power can be purchased ensuring money saving for the steelworks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
E. Briano, C. Caballini, R. Revetria, A. Testa, M. De Leo, F. Belgrano, A. Bertolotto, Anticipation models for on-line control in steel industry: methodologies and case study, CP1303, in CASYS09 9th International Conference on Computing Anticipatory Systems (2009)
G. Fiorani, L. Damiani, R. Revetria, P. Giribone, M. Schenone, Models to estimate energy requirements for iron and steel industry: application case for electric steelworks, in Lecture Notes in Engineering and Computer Science: Proceedings of the World Congress on Engineering and Computer Science 2017, 25–27 Oct, San Francisco, USA, pp. 920–924 (2017)
L. Damiani, P. Giribone, R. Revetria, Simulink study of a smart node for domestic applications equipped with PV panel, Energy Storage and Home Automation, in IAENG Transactions on Engineering Sciences, Special Issue for the International Association of Engineers Conferences 2016, vol. II (2016). ISBN 978-9813230-76-7
L. Damiani, P. Giribone, R. Revetria, A. Testa, An innovative model for supporting energy-based cost reduction in steel manufacturing industry using online real-time simulation, in Lecture Notes in Engineering and Computer Science: Proceedings of the World Congress on Engineering and Computer Science 2014, San Francisco, USA, 22–24 October, 2014, pp. 1–7
E. Briano, C. Caballini, P. Giribone, R. Revetria, Using system dynamics for short life cycle supply chains evaluation. Proc. Winter Simul. Conf. Art. 5678887, 1820–1832 (2010)
L. Cassettari, R. Mosca, R. Revetria, Monte Carlo simulation models evolving in replicated runs: a methodology to choose the optimal experimental sample size. Math. Prob. Eng. (2012); in The Technical Writers Handbook, ed. by M. Young (University Science, Mill Valley, CA, 1989)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Damiani, L., Revetria, R., Giribone, P., Schenone, M. (2019). Energy Requirements Estimation Models for Iron and Steel Industry Applied to Electric Steelworks. In: Ao, SI., Kim, H., Amouzegar, M. (eds) Transactions on Engineering Technologies. WCECS 2017. Springer, Singapore. https://doi.org/10.1007/978-981-13-2191-7_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-2191-7_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2190-0
Online ISBN: 978-981-13-2191-7
eBook Packages: EngineeringEngineering (R0)