10.1007/s40095-021-00381-5

Estimation of biogas yields produced from combination of waste by implementing response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS)

  • M. O. Okwu1,
  • O. D. Samuel1,
  • D. R. E. Ewim*2,
  • Z. Huan2,
  1. Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, Warri, Delta State, NG
  2. Department of Mechanical Engineering, Mechatronics and Industrial Design, Tshwane University of Technology, Pretoria, 0001, ZA

Published in Issue 2021-01-20

How to Cite

Okwu, M. O., Samuel, O. D., Ewim, D. R. E., & Huan, Z. (2021). Estimation of biogas yields produced from combination of waste by implementing response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). International Journal of Energy and Environmental Engineering, 12(2 (June 2021). https://doi.org/10.1007/s40095-021-00381-5
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Abstract

Abstract A creative algorithm, response surface methodology (RSM) and hybrid algorithm, adaptive neuro-fuzzy inference system (ANFIS) have been implemented to optimize biogas production from 2 different biodegradable animal waste (substrates of poultry wastes (PW) and cow dung (CW)) in a lightweight biodigester system. A maximum biogas yield of 51.3% was achieved with 38:23 CD/PW within the retention time of nine (9) days. The computed coefficient of determination ( R 2 ) of 0.9998, root-mean-square-error (RMSE) of 0.0055, standard error of prediction (SEP) of 0.00011092, mean average error (MAE) of 0.0015, and average absolute deviation (AAD) of 0.0030 was estimated by implementing the RSM model. This was compared with the ANFIS result with R 2 (1.0), RMSE (1.0), SEP (0), MAE (0), and AAD (− 0.00022483). From the analysis of the RSM and ANFIS results, the result obtained from the ANFIS prediction is statistically marginal and gave a faster and better prediction compared to the RSM model.

Keywords

  • ANN,
  • RSM,
  • Anaerobic digestion,
  • Feedstock,
  • Biogas yield

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