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

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 scale was set to 6.93, while the box constraint (C) was optimized using the CV method. Results demonstrated that R-2 for the k-NN model (k = 2) was 0.9830 at 35 degrees C and 0.9957 at 55 degrees C. The Gaussian-based SVM model (C = 1200) provided an R-2 of 0.9973 at 35 degrees C and 0.9989 at 55 degrees C, which are slightly better than those achieved by k-NN. The Gaussian-based SVM model produced RMSE of 0.598 at 35 degrees C and 0.4183 at 55 degrees 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.

Automated summary

In this study, two data-driven models, k-nearest neighbours (k-NN) and support vector machine (SVM), were proposed to predict biogas production from anaerobic digestion. The results showed that the Gaussian-based SVM model performed slightly better than the k-NN model in predicting biogas production. These findings suggest that SVM modeling is a reliable technique for predicting biogas production in anaerobic digestion processes.