Modelling of Biogas Yield from Anaerobic Co-digestion of Food Waste and Animal Manure using Artificial Neural Networks

Ejiroghene Kelly Orhorhoro, Joel O. Oyejide

Abstract

The anaerobic digestion process is a technology that recovers energy in form of biogas and nutrients from biodegradable waste streams in useable forms in the absence of oxygen. It is sustainable, renewable and a zero-carbon form of energy supply. In this research work, modelling of biogas yield from co-digestion of food waste and animal manure using artificial neural networks was carried out. An experimental three stage continuous anaerobic digestion plant was used to co-digest food waste and animal manure. The composition of food waste and animal manure used include fufu, eba, starch, rice, beans, yam, fish, meat, moi moi, pig and cow dung. The feedstock was ground into fine particles to increase its surface area, and then mixed with water in a ratio of 1:2. The actual biogas yield was compared to the predicted biogas yield using artificial neural networks model. The performance of the developed artificial neural networks model was validated, and the results obtained from the research work revealed the effectiveness of the model to predict biogas yield with a mean squared error (MSE) of best validation performance of 5.1115e-4. Also, the coefficient of determination (R2) values of the training set, the testing set, the validation set, and the all data set were found to be high and close to 1, the values being 0.97193, 0.96510, 0.98378 and 0.97229 respectively. The high values R2 demonstrates the appropriateness of the artificial neural networks model for accurate estimation of anaerobic co-digestion of food waste and animal manure.  Besides, there was a good correlation between the actual and the predicted values of biogas yields. Therefore, the artificial neural networks model learned the relation between the input to the anaerobic digestion plant and the output in the biogas stream very well, thus the correct prediction of biogas yield.

Keywords

Anaerobic co-digestion; Animal manure; Artificial neural networks model; Biogas yield; Food waste.

Article Metrics

Abstract view : 316 times
PDF - 205 times

Full Text:

PDF

References

Human Development Ranking. Work for Human Development, Human Development Report 2016, Retrieved from http:// http://hdr.undp.org/en/2016-report.

C. Zhang, H. Su, J. Baeyens and T. Tan, Reviewing the anaerobic digestion of food waste for biogas production. Renewable and Sustainable Energy Reviews, 38, 2014, 383–392.

E. K. Orhorhoro, P. O. B. Ebunilo, and E. G. Sadjere, experimental determination of effect of total solid (TS) and volatile solid (VS on biogas yield, American Journal of Modern Energy, 3(6), 2017, 131-135.

E. K. Orhorhoro and A. A. Erameh, Performance evaluation of the effect of temperature and pH on biogas yields from anaerobic co-digestion of food waste and pig dung, Igbinedion University Research Day and Conference, Nigeria, 2019.

P. O. Ebunilo, E. K. Orhorhoro and O. A. Adegbayi, Investigation of the purification of biogas from domestic wastes using local materials in Nigeria, International Journal of Scientific & Engineering Research, 7(2), 2016, 505-515.

E. K. Orhorhoro, P. O. Ebunilo, R. I. Tamuno and I. A. Essienubong, The study of anaerobic co-digestion of non-uniform multiple feed stock availability and composition in Nigeria, European Journal of Engineering Research and Science, 1(1), 2016, 1-4.

M. K. Peter, M. I. Alfa, G. Datau, K. C. Aluwong and K. C. Hadi, Design, development and performance evaluation of an anaerobic plant, American Journal of Engineering Research, 6(4), 2017, 28-33.

E. K. Orhorhoro, P. O. B. Ebunilo and E. G. Sadjere, design of bio-waste grinding machine for anaerobic digestion (AD) system, European Journal of Advances in Engineering and Technology, 4 (7), 2017, 560-568.

D. Edmond, J. Etleva, V. Valdete, D. Erta and S. Enkeleida, temperature and stirring effect of biogas production from two different systems, American Journal of Energy Engineering, 5(2), 2017, 6-10.

B. B. Sajeena, P. P. Jose and G. Madhu, Effect of total solid concentration on anaerobic digestion of the organic fraction of municipal solid waste, International Journal of Scientific and Research Publications, 3(8), 2013, 1-5.

P. O. Ebunilo, S. I. Ukwuaba, I. B. Owunna, E. G. Sadgere and E.K. Orhorhoro, Evaluation of cow dung and talinum triangulare as a seeding agent for the production of biogas from domestic wastes in Warri metropolis, International Journal of Scientific and Engineering Research, 7(3), 2016, 633-641.

E. K. Orhorhoro, O. W. Orhorhoro and P. O. Ebunilo, Analysis of the effect of Carbon/Nitrogen (C/N) ratio on the performance of biogas yields for non-uniform multiple feed stock availability and composition in Nigeria, International Journal of Innovative Science, and Engineering & Technology, 3(5), 2016, 119-126.

E. K. Orhorhoro, P. O. Ebunilo and A. E. Ikpe, Effect of pH on anaerobic digestion (AD) of organic municipal solid waste in Benin City, Nigeria, Journal of the Nigerian Association of Mathematical Physics, 36(1), 2016, 369-374.

G. Paramagurua, M. Kannanb and P. Lawrencec, Effect of pH on biogas production through anaerobic digestion of food waste, Journal of Advanced Engineering Research, 4(1), 2017, 59-62.

P. Mullai, M. K. Yogeswari and K. Sridevi, Artificial Neural Network (ANN) modeling for hydrogen production in continuous anaerobic sludge blanket filters (ASBF), Singapore Journal of Scientific Research, 5(1), 2013, 1-7.

G. K. Latinwo and S. E. Agarry, Modeling the kinetics of biogas generation from mesophilic anaerobic co-digestion of sewage sludge with municipal organic waste, Chemical and Process Engineering Research, 31, 2015, 43-53.

L. Aigars and V. Revik, Mathematical modelling of biogas dehumidification by using of counter flow heat exchanger, Journal of Clean Energy Technologies, 5(2), 2017, 170-173.

G. Esposito, L. Frunzo, F. Liotta, A. Panico, and F. Pirozzi, BMP tests to measure the biogas production from the digestion and co-digestion of Complex organic substrates, Open Journal of Environmental Engineering, 5, 2012, 1-8.

P. Karthic, S. Joseph and N. Arun, Optimization of biohydrogen production by Enterobacter species using artificial neural network and response surface methodology, Journal of Renewable and Sustainable Energy, 5(3), 2013, 33-104.

I. M. Santos-Dueñas, J. E. J. Hornero, A. M. Cañete-Rodriguez and I. Garcia-Garcia, Modeling and optimization of acetic acid fermentation: A polynomial-based approach, Biochemical Engineering Journal, 99, 2015, 35-43.

J. K. Whiteman and K. E. B. Gueguim, Comparative assessment of the artificial neural network and response surface modelling efficiencies for biohydrogen production on sugar cane molasses, Bioenergy Research,7(1), 2015, 295–305.

A. Tardast, M. Rahimnejad and G. Najafpour, Use of artificial neural network for the prediction of bioelectricity production in a membrane less microbial fuel cell, Fuel, 117, 2014, 697–703.

E. Betiku and A. E. Taiwo, Modeling and optimization of bioethanol production from breadfruit starch hydrolysate vis-a-vis response surface methodology and artificial neural network, Renewable Energy, 74, 2015, 87-94.

M. Nasr, A. Tawfik and S. Ookawara, Prediction of hydrogen production from starch wastewater using artificial neural networks, International Water Technology Journal, 4(1), 2014, 36–44.

R. S. El-Emam, I. Dincer and G. F. Naterer, Energy and exergy analyses of an integrated SOFC and coal gasification system, International Journal of Hydro Energy, 37, 2012, 1689-1697.

B. Mahanty, M. Zafar and H. S. Park, Characterization of co-digestion of industrial sludges for biogas production by artificial neural network and statistical regression models, Environment Technology, 34, 2013, 2145–2153.

P. O. Ebunilo, S. A. Aliu and E. K. Orhorhoro, Performance study of a biogaspilot plant using domestic wastes from Benin Metropolis, International Journal of Thermal & Environmental Engineering,10(2), 2015, 135-141.

S. K. Behera, S. K. Meher and H. S. Park, Artificial neural network model for predicting methane percentage in biogas recovered from a landfill upon injection of liquid organic waste, Clean Technologies and Environmental Policy, 17, 2015, 443-453.

K. Yetilmezsoy, F. I. Turkdogan, I. Temizel and A. Gunay, Development of ANN-based models to predict biogas and methane productions in anaerobic treatment of molasses wastewater, International Journal of Green Energy, 10(9), 2012, 885-907.

B. Ozkaya, A. Demir and M. S. Bilgili, Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors,

Environmental Modelling and Software, 22(6), 2007, 815-822.

Refbacks

  • There are currently no refbacks.