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AI type | Objectives | Input parameters | Optimum models | Output | Ref. |
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ANN | To develop a model to forecast the effects of process parameters | COD, BOD, flow rate, recirculated sludge flow, and feed rate | ANN-MLP | Biogas yield | [96] |
ANN | To develop a numerical simulation model to estimate the optimum biogas production | Feed type, volatile solid (VS), pH, OLR, HRT, temperature, and reactor volume | | Cumulative biogas yield | [97] |
ANFIS and ANN | To estimate the biogas yield | C/N ratio, reactor temperature, and RT | ANFIS | Biogas | [24] |
ANN | To simulate and model the performances of the codigestion process | Mix ratios and RT | ANN-Bayesian regularization algorithm | Methane yield | [98] |
ANN | To model the relationship among the physicochemical parameters of a blend of codigestion of cattle dung and poultry droppings to estimate biogas production | Mix ratios, pH, TDS, temperature, slurry, BOD, and DO | | The volume of biogas yield | [94] |
ANN-GA and ACO | An integrated model to predict and evaluate the biogas production rate | TSs, VSs, acid detergent fiber, acid detergent lignin, NH3-N, VFA, HRT, and OLR | ANN with GA and ACO | Biogas production rate | [99] |
ANN-GA | Optimization and prediction of the amount of biogas generation from codigestion of selected substrates | pH, C/N ratios, and SRT | ANN optimized with GA | Biogas generation | [92] |
ANN-GA | To model and optimize mixing ratios in the codigestion process | Substrate-to-inoculum ratios, mix ratios, temperature, SRT, and feed type | ANN-GA | CH4 production | [100] |
RNN | To estimate the biogas production rate | SRT, soluble COD, total VFA, free ammonia, and total ammonia | | Biogas production rate | [101] |
ANN-PSO | To optimize and forecast the biogas generation from codigestion of cattle manure (CM) and palm oil mill effluent (POME) | Mixture ratio, oxidation by hydrogen peroxide, and ammonium bicarbonate | | Biogas yields | [102] |
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