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Optimal operational adjustment in multi-functional energy systems in response to process inoperability

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  • Kasivisvanathan, Harresh
  • Barilea, Ivan Dale U.
  • Ng, Denny K.S.
  • Tan, Raymond R.

Abstract

Multi-functional energy systems such as biorefineries and polygeneration plants are able to efficiently produce product portfolios by taking advantage of inherent opportunities for optimal integration among process units. Thus, such systems provide valuable solutions towards efficient, sustainable and economically viable production of energy, fuels, utilities and commodity chemicals. On the other hand, the high level of interdependency among process units within such industrial plants is also a potential disadvantage, since the inoperability of one process unit propagates upstream and downstream through the process network to affect other operations. This paper presents a simple mixed-integer linear programming (MILP) model for determining optimal process adjustments in multi-functional energy systems as a result of partial inoperability. The methodology assumes that partial or complete inoperability of some of the process units within the plant forces it to operate away from the baseline state that it was originally designed for. For example, such emergency conditions may result from damaged process equipment. The MILP model determines the optimal reallocation of process streams and operating levels of the process units in order to maximise operating profits from the product portfolio.

Suggested Citation

  • Kasivisvanathan, Harresh & Barilea, Ivan Dale U. & Ng, Denny K.S. & Tan, Raymond R., 2013. "Optimal operational adjustment in multi-functional energy systems in response to process inoperability," Applied Energy, Elsevier, vol. 102(C), pages 492-500.
    • Handle: RePEc:eee:appene:v:102:y:2013:i:c:p:492-500
    DOI: 10.1016/j.apenergy.2012.07.052
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    6. Tan, Raymond R. & Cayamanda, Christina D. & Aviso, Kathleen B., 2014. "P-graph approach to optimal operational adjustment in polygeneration plants under conditions of process inoperability," Applied Energy, Elsevier, vol. 135(C), pages 402-406.
    7. Tan, R.R. & Aviso, K.B. & Cayamanda, C.D. & Chiu, A.S.F. & Promentilla, M.A.B. & Ubando, A.T. & Yu, K.D.S., 2016. "A fuzzy linear programming enterprise input–output model for optimal crisis operations in industrial complexes," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 410-418.
    8. Mikulčić, Hrvoje & Ridjan Skov, Iva & Dominković, Dominik Franjo & Wan Alwi, Sharifah Rafidah & Manan, Zainuddin Abdul & Tan, Raymond & Duić, Neven & Hidayah Mohamad, Siti Nur & Wang, Xuebin, 2019. "Flexible Carbon Capture and Utilization technologies in future energy systems and the utilization pathways of captured CO2," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    9. Aristotle T. Ubando & Isidro Antonio V. Marfori & Kathleen B. Aviso & Raymond R. Tan, 2019. "Optimal Operational Adjustment of a Community-Based Off-Grid Polygeneration Plant using a Fuzzy Mixed Integer Linear Programming Model," Energies, MDPI, vol. 12(4), pages 1-17, February.
    10. Andiappan, Viknesh & Ng, Denny K.S. & Tan, Raymond R., 2017. "Design Operability and Retrofit Analysis (DORA) framework for energy systems," Energy, Elsevier, vol. 134(C), pages 1038-1052.

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