IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v189y2019ics0360544219319486.html
   My bibliography  Save this article

A data-driven approach towards finding closer estimates of optimal solutions under uncertainty for an energy efficient steel casting process

Author

Listed:
  • Pantula, Priyanka D.
  • Mitra, Kishalay

Abstract

The process of steel casting involves several energy-intensive tasks such as heat transfer, solidification process, etc. Though the evolution of continuous casting of steel from the conventional ingot casting enabled a high amount of energy savings, operational parameter optimization considering various avenues of uncertainty is the key for next level of improvement in energy efficiency and process sustainability. To achieve this, a multi-objective optimization formulation under uncertainty has been proposed that can lead to simultaneous maximization of productivity and minimization of energy consumption. Among various uncertainty handling techniques, Chance Constrained Programming (CCP) is considered as an efficient approach. However, the requirement of uncertain parameters to follow some well-behaved probability distribution for having a closed form analytical solution in CCP is a bottleneck for most of the practical situations due to the unknown nature of uncertain data. This paper proposes a novel methodology called DDCCP (Data-Driven CCP), to amalgamate machine learning algorithms with CCP, thereby making the approach data-driven. A novel fuzzy clustering mechanism is implemented to transcript uncertain space such that the specific regions of uncertainty are identified accurately based on given uncertain data for more realistic sampling and thereby impacting the optimal solution accuracy. Implementing DDCCP on the casting model, ∼20–70% improvement in the objectives of energy calculations and 50–100% improvement in the metrics of Pareto optimal solutions are observed as compared to the existing box sampling approach showing efficacy of the proposed methodology.

Suggested Citation

  • Pantula, Priyanka D. & Mitra, Kishalay, 2019. "A data-driven approach towards finding closer estimates of optimal solutions under uncertainty for an energy efficient steel casting process," Energy, Elsevier, vol. 189(C).
  • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319486
    DOI: 10.1016/j.energy.2019.116253
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219319486
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2019.116253?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tajeddini, Mohammad Amin & Rahimi-Kian, Ashkan & Soroudi, Alireza, 2014. "Risk averse optimal operation of a virtual power plant using two stage stochastic programming," Energy, Elsevier, vol. 73(C), pages 958-967.
    2. Feng, Huijun & Chen, Lingen & Xie, Zhihui & Ding, Zemin & Sun, Fengrui, 2014. "Generalized constructal optimization for solidification heat transfer process of slab continuous casting based on heat loss rate," Energy, Elsevier, vol. 66(C), pages 991-998.
    3. Ahmadian Behrooz, Hesam & Boozarjomehry, R. Bozorgmehry, 2017. "Dynamic optimization of natural gas networks under customer demand uncertainties," Energy, Elsevier, vol. 134(C), pages 968-983.
    4. Crestaux, Thierry & Le Maıˆtre, Olivier & Martinez, Jean-Marc, 2009. "Polynomial chaos expansion for sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1161-1172.
    5. He, Kun & Wang, Li, 2017. "A review of energy use and energy-efficient technologies for the iron and steel industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1022-1039.
    6. Cunico, Maria Laura & Flores, Julio Rolando & Vecchietti, Aldo, 2017. "Investment in the energy sector: An optimization model that contemplates several uncertain parameters," Energy, Elsevier, vol. 138(C), pages 831-845.
    7. Miriyala, Srinivas Soumitri & Subramanian, Venkat & Mitra, Kishalay, 2018. "TRANSFORM-ANN for online optimization of complex industrial processes: Casting process as case study," European Journal of Operational Research, Elsevier, vol. 264(1), pages 294-309.
    8. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haider, Md Alquma & Chaturvedi, Nitin Dutt, 2023. "A mathematical formulation for robust targeting in heat integrated water allocation network," Energy, Elsevier, vol. 264(C).
    2. Xue, Jian & Zhang, Wenjing & Zhao, Laijun & Zhu, Di & Li, Lei & Gong, Ruifeng, 2022. "A cooperative inter-provincial model for energy conservation that accounts for employment and social energy costs," Energy, Elsevier, vol. 239(PB).
    3. Kumar, Dinesh & Bahauddin Alam, Syed & Ridwan, Tuhfatur & Goodwin, Cameron S., 2021. "Quantitative risk assessment of a high power density small modular reactor (SMR) core using uncertainty and sensitivity analyses," Energy, Elsevier, vol. 227(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Changxin & Xie, Zhihui & Sun, Fengrui & Chen, Lingen, 2017. "Exergy analysis and optimization of coking process," Energy, Elsevier, vol. 139(C), pages 694-705.
    2. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    3. Pierluigi Morano & Francesco Tajani & Felicia Di Liddo & Paola Amoruso, 2024. "A Feasibility Analysis of Energy Retrofit Initiatives Aimed at the Existing Property Assets Decarbonisation," Sustainability, MDPI, vol. 16(8), pages 1-19, April.
    4. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    5. Scott, James & Ho, William & Dey, Prasanta K. & Talluri, Srinivas, 2015. "A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments," International Journal of Production Economics, Elsevier, vol. 166(C), pages 226-237.
    6. Hu, Xueyue & Wang, Chunying & Elshkaki, Ayman, 2024. "Material-energy Nexus: A systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    7. Wang, Zihan & Daeipour, Mohamad & Xu, Hongyi, 2023. "Quantification and propagation of Aleatoric uncertainties in topological structures," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    8. Minjiao Zhang & Simge Küçükyavuz & Saumya Goel, 2014. "A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints," Management Science, INFORMS, vol. 60(5), pages 1317-1333, May.
    9. Hermann Held, 2019. "Cost Risk Analysis: Dynamically Consistent Decision-Making under Climate Targets," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(1), pages 247-261, January.
    10. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    11. Wu, Desheng (Dash) & Lee, Chi-Guhn, 2010. "Stochastic DEA with ordinal data applied to a multi-attribute pricing problem," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1679-1688, December.
    12. Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
    13. Odetayo, Babatunde & MacCormack, John & Rosehart, William D. & Zareipour, Hamidreza, 2017. "A sequential planning approach for Distributed generation and natural gas networks," Energy, Elsevier, vol. 127(C), pages 428-437.
    14. Wang, S. & Huang, G.H., 2014. "An integrated approach for water resources decision making under interactive and compound uncertainties," Omega, Elsevier, vol. 44(C), pages 32-40.
    15. Ghazale Kordi & Parsa Hasanzadeh-Moghimi & Mohammad Mahdi Paydar & Ebrahim Asadi-Gangraj, 2023. "A multi-objective location-routing model for dental waste considering environmental factors," Annals of Operations Research, Springer, vol. 328(1), pages 755-792, September.
    16. Kamjoo, Azadeh & Maheri, Alireza & Putrus, Ghanim A., 2014. "Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 66(C), pages 677-688.
    17. Gayathri, P. & Umesh, K. & Ganguli, R., 2010. "Effect of matrix cracking and material uncertainty on composite plates," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 716-728.
    18. Yuancheng Lin & Honghua Yang & Linwei Ma & Zheng Li & Weidou Ni, 2021. "Low-Carbon Development for the Iron and Steel Industry in China and the World: Status Quo, Future Vision, and Key Actions," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
    19. Giada Spaccapanico Proietti & Mariagiulia Matteucci & Stefania Mignani & Bernard P. Veldkamp, 2024. "Chance-Constrained Automated Test Assembly," Journal of Educational and Behavioral Statistics, , vol. 49(1), pages 92-120, February.
    20. Jana, R.K. & Sharma, Dinesh K. & Chakraborty, B., 2016. "A hybrid probabilistic fuzzy goal programming approach for agricultural decision-making," International Journal of Production Economics, Elsevier, vol. 173(C), pages 134-141.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319486. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.