Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model
One of the major challenges confronting researchers is how to predict biogas yield because it is a herculean task since research in the field of modeling and optimization of biogas yield is still limited, especially with the adaptive neuro-fuzzy inference system (ANFIS). This study used ANFIS to mod...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Limited
2023-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/9335814 |
| _version_ | 1797903027755024384 |
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| author | Moses Oluwatobi Fajobi Olumuyiwa Ajani Lasode Adekunle Akanni Adeleke Peter Pelumi Ikubanni Ayokunle Olubusayo Balogun Prabhu Paramasivam |
| author_facet | Moses Oluwatobi Fajobi Olumuyiwa Ajani Lasode Adekunle Akanni Adeleke Peter Pelumi Ikubanni Ayokunle Olubusayo Balogun Prabhu Paramasivam |
| author_sort | Moses Oluwatobi Fajobi |
| collection | DOAJ |
| description | One of the major challenges confronting researchers is how to predict biogas yield because it is a herculean task since research in the field of modeling and optimization of biogas yield is still limited, especially with the adaptive neuro-fuzzy inference system (ANFIS). This study used ANFIS to model and predict biogas yield from anaerobic codigestion of cow dung, mango pulp, and Chromolaena odorata. Asides from the controls, 13 experiments using various agglomerates of the selected substrates were carried out. Cumulatively (for 40 days), the agglomerate that comprised 50% cow dung, 25% mango pulp, and 25% Chromolaena odorata produced the highest volume of biogas, 4750 m3/kg, while the one with 50% cow dung, 12.5% mango pulp, and 37.5% Chromolaena odorata produced the lowest volume of biogas, 630 m3/kg. The data articulated for modeling were those of the optimum biogas yield. Data implemented for modeling comprised two inputs (temperature in Kelvin and pressure in kN/m2) and one output (biogas yield). The Gaussian membership function (Gauss-mf) was implemented for the fuzzification of input variables, while the hybrid algorithm was selected for the learning and mapping of the input-output dataset. The developed ANFIS architecture was simulated at varied membership functions, MFs, and epoch numbers to determine the minimum root mean square error, RMSE, and maximum R-squared R2 values. The one that fulfilled the conditions was considered to be the optimized model. The minimum RMSE and maximum R2 values recorded for the developed model are 14.37 and 0.99784, respectively. The implication is that the model was able to efficiently predict not less than 99.78% of the experimental data. These results prove that the ANFIS model is a reliable tool for modeling data and predicting biogas yield in the biomass anaerobic digestion process. Therefore, the use of the developed ANFIS model is recommended for biogas producers and other allies for predicting biogas yield adequately. |
| first_indexed | 2024-04-10T09:26:28Z |
| format | Article |
| id | doaj.art-5cbd8db018714063b17f3e471836144b |
| institution | Directory Open Access Journal |
| issn | 2314-4912 |
| language | English |
| last_indexed | 2024-04-10T09:26:28Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj.art-5cbd8db018714063b17f3e471836144b2023-02-20T01:58:11ZengHindawi LimitedJournal of Engineering2314-49122023-01-01202310.1155/2023/9335814Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) ModelMoses Oluwatobi Fajobi0Olumuyiwa Ajani Lasode1Adekunle Akanni Adeleke2Peter Pelumi Ikubanni3Ayokunle Olubusayo Balogun4Prabhu Paramasivam5Department of Mechanical EngineeringDepartment of Mechanical EngineeringNile University of NigeriaDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringOne of the major challenges confronting researchers is how to predict biogas yield because it is a herculean task since research in the field of modeling and optimization of biogas yield is still limited, especially with the adaptive neuro-fuzzy inference system (ANFIS). This study used ANFIS to model and predict biogas yield from anaerobic codigestion of cow dung, mango pulp, and Chromolaena odorata. Asides from the controls, 13 experiments using various agglomerates of the selected substrates were carried out. Cumulatively (for 40 days), the agglomerate that comprised 50% cow dung, 25% mango pulp, and 25% Chromolaena odorata produced the highest volume of biogas, 4750 m3/kg, while the one with 50% cow dung, 12.5% mango pulp, and 37.5% Chromolaena odorata produced the lowest volume of biogas, 630 m3/kg. The data articulated for modeling were those of the optimum biogas yield. Data implemented for modeling comprised two inputs (temperature in Kelvin and pressure in kN/m2) and one output (biogas yield). The Gaussian membership function (Gauss-mf) was implemented for the fuzzification of input variables, while the hybrid algorithm was selected for the learning and mapping of the input-output dataset. The developed ANFIS architecture was simulated at varied membership functions, MFs, and epoch numbers to determine the minimum root mean square error, RMSE, and maximum R-squared R2 values. The one that fulfilled the conditions was considered to be the optimized model. The minimum RMSE and maximum R2 values recorded for the developed model are 14.37 and 0.99784, respectively. The implication is that the model was able to efficiently predict not less than 99.78% of the experimental data. These results prove that the ANFIS model is a reliable tool for modeling data and predicting biogas yield in the biomass anaerobic digestion process. Therefore, the use of the developed ANFIS model is recommended for biogas producers and other allies for predicting biogas yield adequately.http://dx.doi.org/10.1155/2023/9335814 |
| spellingShingle | Moses Oluwatobi Fajobi Olumuyiwa Ajani Lasode Adekunle Akanni Adeleke Peter Pelumi Ikubanni Ayokunle Olubusayo Balogun Prabhu Paramasivam Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model Journal of Engineering |
| title | Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model |
| title_full | Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model |
| title_fullStr | Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model |
| title_full_unstemmed | Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model |
| title_short | Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model |
| title_sort | prediction of biogas yield from codigestion of lignocellulosic biomass using adaptive neuro fuzzy inference system anfis model |
| url | http://dx.doi.org/10.1155/2023/9335814 |
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