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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Energy and Environmental Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.