Development of Data-Driven Models to Predict Biogas Production from Spent Mushroom Compost

Reza Salehi, Qiuyan Yuan, Sumate Chaiprapat
2022 Agriculture  
In this study, two types of data-driven models were proposed to predict biogas production from anaerobic digestion of spent mushroom compost supplemented with wheat straw as a nutrient source. First, a k-nearest neighbours (k-NN) model (k = 1–10) was constructed. The optimal k value was determined using the cross-validation (CV) method. Second, a support vector machine (SVM) model was developed. The linear, quadratic, cubic, and Gaussian models were examined as kernel functions. The kernel
more » ... was set to 6.93, while the box constraint (C) was optimized using the CV method. Results demonstrated that R2 for the k-NN model (k = 2) was 0.9830 at 35 °C and 0.9957 at 55 °C. The Gaussian-based SVM model (C = 1200) provided an R2 of 0.9973 at 35 °C and 0.9989 at 55 °C, which are slightly better than those achieved by k-NN. The Gaussian-based SVM model produced RMSE of 0.598 at 35 °C and 0.4183 at 55 °C, which are 58.4% and 49.5% smaller, respectively, than those produced by the k-NN. These findings imply that SVM modeling can be considered a robust technique in predicting biogas production from AD processes as they can be implemented without requiring prior knowledge of biogas production kinetics.
doi:10.3390/agriculture12081090 fatcat:cb2pexigbzaftelladada7nlca