@inproceedings{93af55cd7c344972899e51206b68caa3,
title = "Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester",
abstract = "This study indicates the modelling and optimization of biogas production on assorted substrates of poultry wastes (PW) and cow dung using RSM and ANN. Three-layered ANN feedforward BP and RSM models were developed to estimate the yield of biogas produced via mixture of CD and PW droppings produced from a bio-digester system in the ratio 1:2. At the first run, maximum biogas yield of 51.3% was achieved with 38:23 CD/PW within the retention time of 9 days. The results showed that the coefficient of determination (R2) of the RSM and ANN models were 0.9998 and 1.0. The root-mean-square-error (RMSE) for best RSM and ANN were obtained at 0.0055 and 0.00022188. The study showed that ANN result seems marginally better than the RSM model. This is a confirmation that biomass could be harnessed in solving the current global energy crisis.",
keywords = "ANN, Anaerobic digestion, Biogas yield, Feedstock, RSM",
author = "Okwu, {Modestus O.} and Tartibu, {Lagouge K.} and Samuel, {Olusegun D.} and Omoregbee, {Henry O.} and Ivbanikaro, {Anna E.}",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 16th International Work-Conference on Artificial Neural Networks, IWANN 2021 ; Conference date: 16-06-2021 Through 18-06-2021",
year = "2021",
doi = "10.1007/978-3-030-85030-2_17",
language = "English",
isbn = "9783030850296",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "202--215",
editor = "Ignacio Rojas and Gonzalo Joya and Andreu Catala",
booktitle = "Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings",
address = "Germany",
}
