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Process optimization of chemical looping combustion of solid waste/biomass using machine learning algorithm

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  • Yaqub, Z.T.
  • Oboirien, B.O.
  • Leion, H.

Abstract

Chemical Looping Combustion (CLC) is a carbon capture technology that uses an oxygen carrier to transfer the oxidizing agent to the fuel for combustion. This study used different machine learning algorithms, Artificial neural network and Response surface methodology to estimate the surface region process performance and optimize the process condition for the CLC of different solid fuels waste paper, plastic waste, and sugarcane bagasse blends. Based on the combustion efficiency, CO2 yield and CO2 capture efficiency responses, A high performance correlation (R2 > 0.8) was obtained for all the combustion parameters analyzed. The perturbation plot derived from the RSM analysis indicated that the most significant input parameters include the steam to fixed carbon, blend ratio and the fuel reaction temperature.

Suggested Citation

  • Yaqub, Z.T. & Oboirien, B.O. & Leion, H., 2024. "Process optimization of chemical looping combustion of solid waste/biomass using machine learning algorithm," Renewable Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:renene:v:225:y:2024:i:c:s096014812400363x
    DOI: 10.1016/j.renene.2024.120298
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    References listed on IDEAS

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