In Bioresource technology
Machine learning models were developed in this study to predict and optimize the medium-chain carbolic acids (MCCAs) production from food waste. All three selected prediction algorithms achieved decent performance (accuracy>0.85, R2>0.707). Three optimization algorithms were applied for MCCA production optimization based on the prediction algorithms. The maximum MCCA production rate (0.68g chemical oxygen demand per liter per day) was achieved by simulated annealing coupled with random forest under the optimal conditions of pH 8.3, temperature 50 °C, retention time 4 days, loading rate 15.8 gram volatile solid per liter per day, and inoculum to food waste ratio 70:30 with semi-continuous mode. Further experiments validated (18% error) that the MCCA production rate was 113% higher than the highest production rate of current lab experiments and 60% higher than the statistical optimization using response surface methodology. This study demonstrates the potential of using machine learning for MCCA production prediction and optimization.
Long Fei, Fan Joshua, Liu Hong
2022-Dec-24
Carboxylate Platform, Optimization Models, Prediction Models, Response Surface Methodology
