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Performance evaluation of gasification system efficiency using artificial neural network

Author

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  • Ozonoh, M.
  • Oboirien, B.O.
  • Higginson, A.
  • Daramola, M.O.

Abstract

Gasification is one of the thermo-chemical energy conversion processes with energy self-sufficiency, recoverability and controllable efficiency when compared to combustion and pyrolysis. Gasification process is carried out using different systems (gasifiers), different types of fuels and process conditions, and these factors determine the efficiency of the overall process. The performance of a gasifier is evaluated by some parameters such as Cold Gas Efficiency (CGE), Carbon Conversion Efficiency (CCE), gas yield, gas composition, and lower heating value (LHV) of gas. To understand how efficient a gasifier is, several experiments are needed, but conducting these experiments is time consuming and capital intensive, and the information is vital in energy production plants. Meanwhile, a model that could accurately predict the aforementioned parameters irrespective of the type of gasifier, fuel, and operating conditions is imperative for some time conditions. In this study, Levenberg-Marquardt (LM) back-propagation and Bayesian Regularisation (BR) training algorithms for an Artificial Neural Networks (ANN) were used to study a dataset containing 315 experimental data of biomass, coal, and blends of biomass and coal from various gasifiers and process conditions. Eleven input variables were used in the study, and the result shows that the Mean Square Error (MSE) of the BR algorithm was higher than that of the L-M algorithm. To reduce the MSE, techniques called Input Variables Representation Technique with Visual Inspection method (IVRT-VIM) and Output Variables Representation Technique with Visual Inspection Method (OVRT-VIM) were developed, and their applications produced smaller MSE and an R2 of between 79 – 98% and 95–96%, respectively. Further, the results of the sensitivity analysis revealed that carbon (% amount) is the most important input parameter affecting the outputs, and with sum of the Squares of the Partial Derivatives (SSD) value of 1.18 for the CGE prediction.

Suggested Citation

  • Ozonoh, M. & Oboirien, B.O. & Higginson, A. & Daramola, M.O., 2020. "Performance evaluation of gasification system efficiency using artificial neural network," Renewable Energy, Elsevier, vol. 145(C), pages 2253-2270.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:2253-2270
    DOI: 10.1016/j.renene.2019.07.136
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    Cited by:

    1. Khondker Mohammad Zobair & Louis Sanzogni & Luke Houghton & Md Zahidul Islam, 2021. "Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-31, September.
    2. Zhao, Lu-Tao & Liu, Zhao-Ting & Cheng, Lei, 2021. "How will China's coal industry develop in the future? A quantitative analysis with policy implications," Energy, Elsevier, vol. 235(C).
    3. Kartal, Furkan & Özveren, Uğur, 2020. "A deep learning approach for prediction of syngas lower heating value from CFB gasifier in Aspen plus®," Energy, Elsevier, vol. 209(C).
    4. Owen Sedej & Eric Mbonimpa & Trevor Sleight & Jeremy Slagley, 2022. "Application of Machine Learning to Predict the Performance of an EMIPG Reactor Using Data from Numerical Simulations," Energies, MDPI, vol. 15(7), pages 1-22, March.
    5. Zhang, Jinchun & Hou, Jinxiu & Zhang, Zichuan, 2022. "Real-time identification of out-of-control and instability in process parameter for gasification process: Integrated application of control chart and kalman filter," Energy, Elsevier, vol. 238(PB).
    6. Liu, Shanke & Yang, Yan & Yu, Lijun & Cao, Yu & Liu, Xinyi & Yao, Anqi & Cao, Yaping, 2023. "Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation," Energy, Elsevier, vol. 272(C).
    7. Kartal, Furkan & Özveren, Uğur, 2022. "Prediction of torrefied biomass properties from raw biomass," Renewable Energy, Elsevier, vol. 182(C), pages 578-591.

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