Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated sludge fermentation

doi:10.1007/s11783-023-1735-8

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 Huang^1,²(

), Zhuoer Shi¹, Wei Han^1,², 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.

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.

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.

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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