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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Ojolo, S., Oke, S., Animasahun, K., Adesuyi, B.: Utilization of poultry, cow and kitchen wastes for biogas production: a comparative analysis. J. Environ. Health Sci. Eng. 4(4), 223–228 (2007)
E.A. Diagi, M.L. Akinyemi, M.E. Emetere, I.E. Ogunrinola, A.O. Ndubuisi: Comparative analysis of biogas produced from cow dung and poultry droppings. IOP Conf. Series: Earth and Environ. Sci. IOP Publishing, 331: 012064 (2019)
Ejike Ewim, D.R., Mehrabi, M., Meyer, J.P.: Modeling of heat transfer coefficients during condensation at low mass fluxes inside horizontal and inclined smooth tubes. Heat Trans. Eng. 10(1080/01457632), 1723844 (2020)
Ewim, D.R.E., Adelaja, A.O., Onyiriuka, E.J., Meyer, J.P., Huan, Z.: Modelling of heat transfer coefficients during condensation inside an enhanced inclined tube. J. Thermal Anal Calor (2020). https://doi.org/10.1007/s10973-020-09930-2
Diaz, G., Sen, M., Yang, K., McClain, R.L.: Simulation of heat exchanger performance by artificial neural networks. Hvac&R Research 5(3), 195–208 (1999)
Zareei, S., Khodaei, J.: Modeling and optimization of biogas production from cow manure and maize straw using an adaptive neuro-fuzzy inference system. Renew. Energy 114, 423–427 (2017)
Djatkov, D., Effenberger, M., Martinov, M.: Method for assessing and improving the efficiency of agricultural biogas plants based on fuzzy logic and expert systems. Appl. Energy 134, 163–175 (2014)
Najafi, B., Faizollahzadeh Ardabili, S.: Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC). Resour, Conserv Recycl 133, 169–178 (2018)
Begum, S., Ahuja, S., Anupoju, G.R., Kuruti, K., Juntupally, S., Gandu, B., Ahuja, D.K.: Process intensification with inline pre and post processing mechanism for valorization of poultry litter through high rate biomethanation technology: A full scale experience. Renew. Energy 114, 428–436 (2017)
Nair, V.V., Dhar, H., Kumar, S., Thalla, A.K., Mukherjee, S., Wong, J.W.: Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor. Biores. Technol. 217, 90–99 (2016)
Jacob, S., Banerjee, R.: Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm. Biores. Technol. 214, 386–395 (2016)
Vanti, C.V., Leite, L.C., Batista, E.A.: Monitoring and control of the processes involved in the capture and filtering of biogas using FPGA embedded fuzzy logic. IEEE Latin Am. Trans. 13(7), 2232–2238 (2015)
Olojede, M.A., Ogunkunle, O., Ahmed, N.A.: Quality of optimized biogas yields from co-digestion of cattle dung with fresh mass of sunflower leaves, pawpaw and potato peels. Cogent Eng. 5(1), 1538491 (2018)
Caruso, M.C., Braghieri, A., Capece, A., Napolitano, F., Romano, P., Galgano, F., Altieri, G., Genovese, F.: Recent updates on the use of agro-food waste for biogas production. Appl. Sci. 9(6), 1217 (2019)
Safari, M., Abdi, R., Adl, M., Kafashan, J.: Optimization of biogas productivity in lab-scale by response surface methodology. Renew. Energy 118, 368–375 (2018)
Wang, L., Long, F., Liao, W., Liu, H.: Prediction of anaerobic digestion performance and identification of critical operational parameters using machine learning algorithms. Biores. Technol. 298, 122495 (2020)
Yetilmezsoy, K., Turkdogan, F.I., Temizel, I., Gunay, A.: Development of ann-based models to predict biogas and methane productions in anaerobic treatment of molasses wastewater. Int. J. Green Energy 10(9), 885–907 (2013)
Khayum, N., Rout, A., Deepak, B., Anbarasu, S., Murugan, S.: Application of fuzzy regression analysis in predicting the performance of the anaerobic reactor co-digesting spent tea waste with cow manure. Waste and Biomass Valorization 11(11), 1–14 (2019)
Mendes, C., da Silva Magalhes, R., Esquerre, K., Queiroz, L.M.: Artificial neural network modeling for predicting organic matter in a full-scale up-flow anaerobic sludge blanket (UASB) reactor. Environ. Model. Assess 20(6), 625–635 (2015)
Saghouri, M., Abdi, R., Ebrahimi-Nik, M., Rohani, A., Maysami, M.: (2020) Modeling and optimization of biomethane production from solid-state anaerobic co-digestion of organic fraction municipal solid waste and other co-substrates. Energy Sour., Part A: Recover. Util Environ. Effects 10(1080/15567036), 1767728 (2020)
Tufaner, F., Avşar, Y., Gönüllü, M.T.: Modeling of biogas production from cattle manure with co-digestion of different organic wastes using an artificial neural network. Clean Technol. Environ. Policy 19(9), 2255–2264 (2017)
Kenasa, G., Kena, E.: Optimization of biogas production from avocado fruit peel wastes co-digestion with animal manure collected from juice vending House in Gimbi Town, Ethiopia. Ferment Technol. 8(153), 2 (2019)
Ramachandran, A., Rustum, R., Adeloye, A.J.: Review of anaerobic digestion modeling and optimization using nature-inspired techniques. Process. 7(12), 953 (2019)
Okwu, M.O., Samuel, O.D., Otanocha, O.B., Balogun, P.P., Tega, O.J., Ojo, E.: Design and development of a bio-digester for production of biogas from dual waste. World J. Eng. 17(2), 247–260 (2020)
Okwu, M.O., Otanocha, O.B., Balogun, P., Tega, O.: Comparative analysis and performance of load bearing characteristics of biogas and gasoline-fuelled electric generator. Int. J. Am. Energy 41(12), 1377–1386 (2018)
Eze, J., Onwuka, N.: Biodegradation of poultry wastes in batch operated plastic bodigesters. Niger. J. Solar Energy 18(8), 63–67 (2007)
Conflict of interest
We hereby declare that we have no competing financial interests or personal relationships that could have appeared to influence the results reported in this paper.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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).
Digester volume = 0.50 l, Volume of slurry = 0.450 l, Headspace = 0.050 l.
About this article
Cite this article
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
- Anaerobic digestion
- Biogas yield