A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

The success of anaerobic digestion (AD) process for biogas production is contingent upon complex mix of operating factors, process conditions, and feedstock types, which could be affected by inadequate understanding of microbial, kinetic, and physicochemical processes. To address these limitations, efforts have been directed toward developing mathematical and intelligent models. Although mathematical models provide near-optimal solutions, they are time consuming, highly expensive, and demanding. Intelligent standalone models are also limited by their low predictive capability and inability to guarantee global optimal solution for the prediction of cumulative biogas yield for FFV waste. However, hyperparameter optimization of such models is essential to improve the prediction performance for cumulative biogas yield for FFV waste. Therefore, this study applies a genetic algorithm (GA) to optimize an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of cumulative biogas production. Seven (7) input variables, organic loading rate (OLR), volatile solids (VS), pH, hydraulic retention time (HRT), temperature, retention time, and reaction volume, were considered with cumulative biogas production as the output. The effect of varying clustering techniques was evaluated. The three (3) clustering techniques evaluated are fuzzy c-means and subtractive clustering and grid partitioning. The hybrid model was evaluated based on some verified statistical performance metrics. Optimal root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and standard deviation error (error STD) of 0.0529, 0.0326,7.6742, and 0.0474, respectively, were reported at the model testing phase for the subtractive clustering technique being the best-performing model. The results confirm the capacity of hybrid evolutionary (genetic) algorithm based on subtractive clustering technique to predict the biogas yield from FFV and serve as an effective tool for the upscaling of anaerobic digestion units as well as in techno-economic studies toward more efficient energy utilization. Graphical abstract

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Evolutionary optimization of biogas production from food, fruit, and vegetable (FFV) waste

Olatunji

Adedeji

Madushele

et al. 2023

Biomass Conv. Bioref.

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Modelling Biomass Elemental Composition: a Neurofuzzy Approach

Olatunji

Adedeji

Madushele

et al. 2022

Procedia Computer Science

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Performance Evaluation of the Impact of Clustering Methods and Parameters on Adaptive Neuro-Fuzzy Inference System Models for Electricity Consumption Prediction during COVID-19

2022

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Increasing economic and population growth has led to a rise in electricity consumption. Consequently, electrical utility firms must have a proper energy management strategy in place to improve citizens’ quality of life and ensure an organization’s seamless operation, particularly amid unanticipated circumstances such as coronavirus disease (COVID-19). There is a growing interest in the application of artificial intelligence models to electricity prediction during the COVID-19 pandemic, but the impacts of clustering methods and parameter selection have not been explored. Consequently, this study investigates the impacts of clustering techniques and different significant parameters of the adaptive neuro-fuzzy inference systems (ANFIS) model for predicting electricity consumption during the COVID-19 pandemic using districts of Lagos, Nigeria as a case study. The energy prediction of the dataset was examined in relation to three clustering techniques: grid partitioning (GP), subtractive clustering (SC), fuzzy c-means (FCM), and other key parameters such as clustering radius (CR), input and output membership functions, and the number of clusters. Using renowned statistical metrics, the best sub-models for each clustering technique were selected. The outcome showed that the ANFIS-based FCM technique produced the best results with five clusters, with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Variation (RCoV), Coefficient of Variation of the Root Mean Square Error (CVRMSE), and Mean Absolute Percentage Error (MAPE) being 1137.6024, 898.5070, 0.0586, 11.5727, and 9.3122, respectively. The FCM clustering technique is recommended for usage in ANFIS models that employ similar time series data due to its accuracy and speed.

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A GA-ANFIS Model for the Prediction of Biomass Elemental Properties

Cited by 3 publications

References 37 publications

Evolutionary optimization of biogas production from food, fruit, and vegetable (FFV) waste

Evolutionary optimization of biogas production from food, fruit, and vegetable (FFV) waste

Modelling Biomass Elemental Composition: a Neurofuzzy Approach

Performance Evaluation of the Impact of Clustering Methods and Parameters on Adaptive Neuro-Fuzzy Inference System Models for Electricity Consumption Prediction during COVID-19

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