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

Reza Salehi, Qiuyan Yuan and Sumate Chaiprapat
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Reza Salehi: Department of Civil and Environmental Engineering, Prince of Songkla University, Hat Yai 90110, Thailand
Qiuyan Yuan: Department of Civil Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Sumate Chaiprapat: Department of Civil and Environmental Engineering, Prince of Songkla University, Hat Yai 90110, Thailand

Agriculture, 2022, vol. 12, issue 8, 1-20

Abstract: 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 °C and 0.9957 at 55 °C. The Gaussian-based SVM model ( C = 1200) provided an R 2 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.

Keywords: anaerobic digestion; biogas production; k -nearest neighbours; support vector machine (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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