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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

Postal Subscription Code 80-973

2018 Impact Factor: 3.883

Front. Environ. Sci. Eng.    2023, Vol. 17 Issue (11) : 135    https://doi.org/10.1007/s11783-023-1735-8
RESEARCH ARTICLE
Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated sludge fermentation
Weishuai Li1, Jingang Huang1,2(), Zhuoer Shi1, Wei Han1,2, Ting Lü1, Yuanyuan Lin3, Jianfang Meng4, Xiaobing Xu2, Pingzhi Hou2
1. College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
2. China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing, Hangzhou Dianzi University, Hangzhou 310018, China
3. Zhejiang Province Environmental Engineering Co. Ltd., Hangzhou 310012, China
4. M-U-T Maschinen-Umwelttechnik-Transportanlagen GmbH, Stockerau 2000, Austria
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Abstract

● Data-driven approach was used to simulate VFA production from WAS fermentation.

● Three machine learning models were developed and evaluated.

● XGBoost showed best prediction performance and excellent generalization ability.

● pH and protein were the top two input features for the modeling.

● The maximal VFA production was predicted to be 650 mg COD/g VSS.

Riboflavin is a redox mediator that promotes volatile fatty acids (VFAs) production from waste activated sludge (WAS) and is a promising method for WAS reuse. However, time- and labor-consuming experiments challenge obtaining optimal operating conditions for maximal VFA production. In this study, three machine learning (ML) models were developed to predict the VFAs production from riboflavin-mediated WAS fermentation systems. Among the three tested ML algorithms, eXtreme Gradient Boosting (XGBoost) presented the best prediction performance and excellent generalization ability, with the highest testing coefficient of determination (R2 of 0.93) and lowest root mean square error (RMSE of 0.070). Feature importance analysis and their interactions using the Shepley Additive Explanations (SHAP) method indicated that pH and soluble protein were the top two input features for the modeling. The intrinsic correlations between input features and microbial communities corroborated this deduction. On the optimized ML model, genetic algorithm (GA) and particle swarm optimization (PSO) solved the optimal solution of VFA output, predicting the maximum VFA output as 650 mg COD/g VSS. This study provided a data-driven approach to predict and optimize VFA production from riboflavin-mediated WAS fermentation.

Keywords Machine learning      Volatile fatty acids      Riboflavin      Waste activated sludge      eXtreme Gradient Boosting     
Corresponding Author(s): Jingang Huang   
About author:

* Both are co-first authors.

Issue Date: 15 November 2023
 Cite this article:   
Weishuai Li,Jingang Huang,Zhuoer Shi, et al. Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated sludge fermentation[J]. Front. Environ. Sci. Eng., 2023, 17(11): 135.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1735-8
https://academic.hep.com.cn/fese/EN/Y2023/V17/I11/135
Fig.1  Pearson’s correlation coefficients among input features and those between input features and output variable (VFA production) associated with the training dataset.
ModelHyperparametersRangeOptimized value
RFn_estimators10–8010
Max depth1–96
Min samples leaf1–91
Min samples split2–93
XGBoostMax depth1–103
Min child weight1–81
Subsample0.1–0.90.8
Colsample bytree0.1–0.90.8
Learning rate0.1–0.90.7
Gamma0.0–0.50.00133
Reg_alpha0–0.50.23
Reg_lambda0–0.10.09
ANNHidden layers1–52
Neurons8–25664
Learning rate0.0001–0.10.001
Dropout rate0.0–0.50.1
Tab.1  Well-tuned hyper-parameters of ML models for predicting VFA production
Fig.2  Comparison of predicted VFA production with actual experimental data. The predictions were simulated by optimal ML models at the 95% confidence level. (a) XGBoost modeling, (b) ANN modeling and (c) RF modeling.
ModelSubstratePrediction targetTesting R2Reference
MathematicalCattle manureAcetic acid production0.82Arudchelvam et al. (2010)
Butyric acid production0.72
GBRBiowasteVFAs production0.92Li et al. (2022)
RFWastewaterH2 production0.90Hosseinzadeh et al. (2022)
RFOrganic wastesCH4 production0.82Long et al. (2021)
XGBoostOrganic wastesCH4 production0.88De Clercq et al. (2020)
RFSludgeVFAs production0.85This study
XGBoostVFAs production0.93
ANNVFAs production0.93
Tab.2  Comparison of prediction accuracy between this study and previous publications
Fig.3  Feature importance ranking (a, c) and individual correlation (b, d) of various variables to predicting VFA production. The analyses were conducted by SHAP method based on the well-tuned XGBoost model (a, b) and ANN model (c, d).
Fig.4  The interactive effects between key input features pH and other input features (protein, total carbohydrate, reducing sugar and NH4+–N) on predicting VFA production using the optimal XGBoost model.
Fig.5  (a) Compressed principal components (PCs) of high-dimensional taxonomic composition at the phylum level; (b) the correlation between various PCs and input features, including operating conditions and intermediates; and (c) the contributions of various phyla on individual PCs.
SubstrateMaximal VFA yield (mg COD /g VSS)Reference
Sewage WAS261Liu et al. (2018)
Sewage WAS423Chen et al. (2017)
Sewage WAS595Yuan et al. (2011)
Sewage WAS192–520Fang et al. (2020)
WAS + Riboflavin650This study
Tab.3  Comparison of predicted VFA production from riboflavin-mediated WAS fermentation in this study with previous experimental results from riboflavin-free WAS fermentation
Fig.6  Optimal input features for predicting maximal VFA production based on the optimized XGBoost model after 10 runs by GA and PSO optimization algorithms (Reducing sugar value was enlarged by 10,000 times to make the results visible).
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