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

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 (R2) 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 R2 (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.

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Correspondence to D. R. E. Ewim.

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Appendices

Appendix 1

Digester A (cattle dung 0.25 of 0.3 kg, poultry waste 0.75 of 0.3 kg).

Digester B (cattle dung 0.5 of 0.30 kg, poultry waste 0.5 of 0.3 kg).

Digester C (cattle dung 0.75 of 0.30 kg, poultry waste 0.25 of 0.30 kg).

Digester D (cattle dung 1 of 0.30 kg).

Digester E (poultry waste 1 of 0.3 kg).

Digester F (cattle dung 0.2 of 0.30 kg, poultry waste 0.8 of 0.30 kg).

Digester G (cattle dung 0.8 of 0.30 kg, poultry waste 0.20 of 0.30 kg).

Digester H (cattle dung 0.4 of 0.30 kg, poultry waste 0.6 of 0.30 kg).

Digester I (cattle dung 0.6 of 0.30 kg, poultry waste 0.40 of 0.30 kg).

Appendix 2

Digester volume = 0.50 l, Volume of slurry = 0.450 l, Headspace = 0.050 l.

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Okwu, M.O., Samuel, O.D., Ewim, D.R.E. et al. Estimation of biogas yields produced from combination of waste by implementing response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). Int J Energy Environ Eng (2021). https://doi.org/10.1007/s40095-021-00381-5

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Keywords

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