Modeling the Biogas and Methane Yield from Anaerobic Digestion of Arachis hypogea Shells with Combined Pretreatment Techniques Using Machine Learning ... View Full Text


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Article Info

DATE

2022-09-26

AUTHORS

Kehinde O. Olatunji, Daniel M. Madyira, Noor A. Ahmed, Oluwatobi Adeleke, Oyetola Ogunkunle

ABSTRACT

This study experimented biogas production from Arachis hypogea shells subjected to combined pretreatments, namely particle size reduction and Fe3O4 additives. Further to this, the biogas production based on organic dry matter biogas (oDMBY), fresh mass biogas (FMBY), organic dry matter methane (oDMMY), and fresh mass methane (FMMY) were modeled using machine learning algorithms. A fuzzy c-means (FCM)-clustered Adaptive neuro-fuzzy inference systems (ANFIS) and optimized artificial neural network (ANN) model were developed using significant operating parameters of temperature, retention time, and pretreatment methods as input variables. The maximum daily gas yield of 100.4 lN/kgoDM, 18.4 lN/kgFM, 75.7 lNCH4oDM, and 16.1 lNCH4FM were recorded when different particle sizes were combined with Fe3O4 additives. Single pretreatment with Fe3O4 improves the oDMBY, FMBY, oDMMY, and FMMY by 150, 20.7, 79.39, and 176.19%, while the combination with particle size improves the yields by 256.03, 138.96, 155.74, and 283.33%, respectively. The models’ performances were evaluated using relevant statistical metrics. The ANN model gave Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Correlation Coefficient (R2) values of 1.9354, 6.3213, 1.0234, and 0.9496. The FCM-ANFIS model with ten clusters outperformed the ANN model with RMSE, MAPE, MAD, and Coefficient (R2) values of 1.2343, 5.2343, 1.2463, and 0.9850. The result shows that the pretreatment applied enhanced the biogas and methane yields of Arachis hypogea shells. This study showed that FCM-clustered ANFIS can predict biogas yield of pretreated Arachis hypogea shells satisfactorily, and it is recommended for other similar studies.Graphical Abstract More... »

PAGES

1-19

References to SciGraph publications

  • 2015-06-17. An Artificial Neural Network and Genetic Algorithm Optimized Model for Biogas Production from Co-digestion of Seed Cake of Karanja and Cattle Dung in WASTE AND BIOMASS VALORIZATION
  • 2019-11-11. Application of Fuzzy Regression Analysis in Predicting the Performance of the Anaerobic Reactor Co-digesting Spent Tea Waste with Cow Manure in WASTE AND BIOMASS VALORIZATION
  • 2021-01-12. Experimental determination of the effects of pretreatment on selected Nigerian lignocellulosic biomass in bioethanol production in SCIENTIFIC REPORTS
  • 2009-05-01. Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting in WATER RESOURCES MANAGEMENT
  • 2021-01-20. Estimation of biogas yields produced from combination of waste by implementing response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) in INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING
  • 2012-05-09. Particle Size Reduction during Harvesting of Crop Feedstock for Biogas Production I: Effects on Ensiling Process and Methane Yields in BIOENERGY RESEARCH
  • 2021-09-20. Optimizing the process parameters to maximize biogas yield from anaerobic co-digestion of alkali-treated corn stover and poultry manure using artificial neural network and response surface methodology in BIOMASS CONVERSION AND BIOREFINERY
  • 2018-11-15. Impact of Harvest Date and Cutting Length of Grass Ley and Whole-Crop Cereals on Methane Yield and Economic Viability as Feedstock for Biogas Vehicle Fuel Production in BIOENERGY RESEARCH
  • 2021-07-19. Optimization of biogas yield from lignocellulosic materials with different pretreatment methods: a review in BIOTECHNOLOGY FOR BIOFUELS AND BIOPRODUCTS
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