Skip to main content

Advertisement

SpringerLink
Log in

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

  • Research Article
  • Published:
Frontiers of Environmental Science & Engineering Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  • Arudchelvam Y, Perinpanayagam M, Nirmalakhandan N (2010). Predicting VFA formation by dark fermentation of particulate substrates. Bioresource Technology, 101(19): 7492–7499

    Article  CAS  Google Scholar 

  • Battista F, Strazzera G, Valentino F, Gottardo M, Villano M, Matos M, Silva F, Reis M A, Mata-Alvarez J, Astals S (2022). New insights in food waste, sewage sludge and green waste anaerobic fermentation for short-chain volatile fatty acids production: a review. Journal of Environmental Chemical Engineering, 10(5): 108319

    Article  CAS  Google Scholar 

  • Bevilacqua R, Regueira A, Mauricio-Iglesias M, Lema J M, Carballa M (2020). Protein composition determines the preferential consumption of amino acids during anaerobic mixed-culture fermentation. Water Research, 183: 115958

    Article  CAS  Google Scholar 

  • Bhandari V, Gupta R S (2012). Molecular signatures for the phylum Synergistetes and some of its subclades. Antonie van Leeuwenhoek, 102(4): 517–540

    Article  CAS  Google Scholar 

  • Breiman L (2001). Random forests. Machine Learning, 45(1): 5–32

    Article  Google Scholar 

  • Chen T, Guestrin C (2016). XGBoost: a scalable tree boosting system. San Francisco, California, USA: Association for Computing Machinery, 785–794

    Google Scholar 

  • Chen Y, Jiang S, Yuan H, Zhou Q, Gu G (2007). Hydrolysis and acidification of waste activated sludge at different pHs. Water Research, 41(3): 683–689

    Article  CAS  Google Scholar 

  • Chen Y, Jiang X, Xiao K, Shen N, Zeng R, Zhou Y (2017). Enhanced volatile fatty acids (VFAs) production in a thermophilic fermenter with stepwise pH increase-Investigation on dissolved organic matter transformation and microbial community shift. Water Research, 112: 261–268

    Article  CAS  Google Scholar 

  • Andrade Cruz I, Chuenchart W, Long F, Surendra K C, Renata Santos Andrade L, Bilal M, Liu H, Tavares Figueiredo R, Khanal S K, Fernando Romanholo Ferreira L (2022). Application of machine learning in anaerobic digestion: perspectives and challenges. Bioresource Technology, 345: 126433

    Article  CAS  Google Scholar 

  • D’Silva T C, Isha A, Chandra R, Vijay V K, Subbarao P M V, Kumar R, Chaudhary V P, Singh H, Khan A A, Tyagi V K, et al. (2021). Enhancing methane production in anaerobic digestion through hydrogen assisted pathways: a state-of-the-art review. Renewable & Sustainable Energy Reviews, 151: 111536

    Article  Google Scholar 

  • De Clercq D, Wen Z, Fei F, Caicedo L, Yuan K, Shang R (2020). Interpretable machine learning for predicting biomethane production in industrial-scale anaerobic co-digestion. Science of the Total Environment, 712: 134574

    Article  CAS  Google Scholar 

  • Du Y, Wang M, Yang L, Tong L, Guo D, Ji X (2022). Optimization and scale-up of fermentation processes driven by models. Bioengineering (Basel, Switzerland), 9(9): 473

    CAS  Google Scholar 

  • Fang W, Zhang X, Zhang P, Wan J, Guo H, Ghasimi D S, Morera X C, Zhang T (2020). Overview of key operation factors and strategies for improving fermentative volatile fatty acid production and product regulation from sewage sludge. Journal of Environmental Sciences (China), 87: 93–111

    Article  CAS  Google Scholar 

  • Gahlot P, Balasundaram G, Tyagi V K, Atabani A, Suthar S, Kazmi A, Štěpanec L, Juchelková D, Kumar A (2022). Principles and potential of thermal hydrolysis of sewage sludge to enhance anaerobic digestion. Environmental Research, 214(2): 113856

    Article  CAS  Google Scholar 

  • Guo Q, Yang Z, Zhang B, Hua M, Liu C, Pan B (2022). Enhanced methane production during long-term UASB operation at high organic loads as enabled by the immobilized Fungi. Frontiers of Environmental Science & Engineering, 16(6): 71

    Article  CAS  Google Scholar 

  • Hecht J S, Zia A, Clemins P J, Schroth A W, Winter J M, Oikonomou P D, Rizzo D M (2022). Modeling the sensitivity of cyanobacteria blooms to plausible changes in precipitation and air temperature variability. Science of the Total Environment, 812: 151586

    Article  CAS  Google Scholar 

  • Hosseinzadeh A, Zhou J L, Altaee A, Li D (2022). Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process. Bioresource Technology, 343: 126111

    Article  CAS  Google Scholar 

  • Huang J, Chen S, Wu W, Chen H, Guo K, Tang J, Li J (2019). Insights into redox mediator supplementation on enhanced volatile fatty acids production from waste activated sludge. Environmental Science and Pollution Research International, 26(26): 27052–27062

    Article  CAS  Google Scholar 

  • Kazemi P, Bengoa C, Steyer J P, Giralt J (2021). Data-driven techniques for fault detection in anaerobic digestion process. Process Safety and Environmental Protection, 146: 905–915

    Article  CAS  Google Scholar 

  • Kazemi P, Steyer J P, Bengoa C, Font J, Giralt J (2020). Robust data-driven soft sensors for online monitoring of volatile fatty acids in anaerobic digestion processes. Processes (Basel, Switzerland), 8(1): 67

    CAS  Google Scholar 

  • Kennedy J, Eberhart R (1995). Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks. IEEE. 4: 1942–1948

  • LeCun Y, Bengio Y, Hinton G (2015). Deep learning. Nature, 521(7553): 436–444

    Article  CAS  Google Scholar 

  • Li J, Pan L, Suvarna M, Wang X (2021). Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening. Chemical Engineering Journal, 426: 131285

    Article  CAS  Google Scholar 

  • Li J, Zhang L, Li C, Tian H, Ning J, Zhang J, Tong Y W, Wang X (2022). Data-driven based in-depth interpretation and inverse design of anaerobic digestion for CH4-rich biogas production. ACS ES&T Engineering, 2(4): 642–652

    Article  CAS  Google Scholar 

  • Liang T, Elmaadawy K, Liu B, Hu J, Hou H, Yang J (2021). Anaerobic fermentation of waste activated sludge for volatile fatty acid production: recent updates of pretreatment methods and the potential effect of humic and nutrients substances. Process Safety and Environmental Protection, 145: 321–339

    Article  CAS  Google Scholar 

  • Liu H, Han P, Liu H, Zhou G, Fu B, Zheng Z (2018). Full-scale production of VFAs from sewage sludge by anaerobic alkaline fermentation to improve biological nutrients removal in domestic wastewater. Bioresource Technology, 260: 105–114

    Article  CAS  Google Scholar 

  • Liu J, Huang J, Li H, Shi B, Xu Y, Liu J, Zhang D, Tang J, Hou P (2022). Effect of temperature on fermentative VFAs production from waste sludge stimulated by riboflavin and the shifts of microbial community. Water Science and Technology, 85(4): 1191–1201

    Article  CAS  Google Scholar 

  • Long F, Wang L, Cai W, Lesnik K, Liu H (2021). Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data. Water Research, 199: 117182

    Article  CAS  Google Scholar 

  • Lu W, Huo W, Gulina H, Pan C (2022). Development of machine learning multi-city model for municipal solid waste generation prediction. Frontiers of Environmental Science & Engineering, 16(9): 119

    Article  Google Scholar 

  • Lundberg S M, Lee S I (2017). A unified approach to interpreting model predictions. International Conference on Neural Information Processing Systems, 30: 4768–4777

    Google Scholar 

  • Luo J, Fang S, Huang W, Wang F, Zhang L, Fang F, Cao J, Wu Y, Wang, D (2022). New insights into different surfactants’ impacts on sludge fermentation: focusing on the particular metabolic processes and microbial genetic traits. Frontiers of Environmental Science & Engineering, 16(8): 106

    Article  CAS  Google Scholar 

  • Malhotra M, Garg A (2021). Characterization of value-added chemicals derived from the thermal hydrolysis and wet oxidation of sewage sludge. Frontiers of Environmental Science & Engineering, 15(1): 13

    Article  CAS  Google Scholar 

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12: 2825–2830

    Google Scholar 

  • Piri J, Pirzadeh B, Keshtegar B, Givehchi M (2021). Reliability analysis of pumping station for sewage network using hybrid neural networks-genetic algorithm and method of moment. Process Safety and Environmental Protection, 145: 39–51

    Article  CAS  Google Scholar 

  • Qin S, Wainaina S, Liu H, Soufiani A M, Pandey A, Zhang Z, Awasthi M K, Taherzadeh M J (2021). Microbial dynamics during anaerobic digestion of sewage sludge combined with food waste at high organic loading rates in immersed membrane bioreactors. Fuel, 303: 121276

    Article  CAS  Google Scholar 

  • Ramsay I R, Pullammanappallil P C (2001). Protein degradation during anaerobic wastewater treatment: derivation of stoichiometry. Biodegradation, 12(4): 247–257

    Article  CAS  Google Scholar 

  • Regueira A, Lema J M, Carballa M, Mauricio-Iglesias M (2020). Metabolic modeling for predicting VFA production from protein-rich substrates by mixed-culture fermentation. Biotechnology and Bioengineering, 117(1): 73–84

    Article  CAS  Google Scholar 

  • Shehadeh A, Alshboul O, Al Mamlook R E, Hamedat O (2021). Machine learning models for predicting the residual value of heavy construction equipment: an evaluation of modified decision tree, LightGBM, and XGBoost regression. Automation in Construction, 129: 103827

    Article  Google Scholar 

  • Shi B, Huang J, Lin Y, Han W, Qiu S, Zhang D, Tang J, Hou P (2023). Towards valeric acid production from riboflavin-assisted waste sludge: pH-dependent fermentation and microbial community. Waste and Biomass Valorization, 14: 833–845

    CAS  Google Scholar 

  • Shi B, Huang J, Yin Z, Han W, Qiu S, Tang J, Hou P (2020). Riboflavin boosts fermentative valeric acid generation from waste activated sludge. BioResources, 15(2): 3962–3969

    Article  CAS  Google Scholar 

  • Strazzera G, Battista F, Garcia N H, Frison N, Bolzonella D (2018). Volatile fatty acids production from food wastes for biorefinery platforms: a review. Journal of Environmental Management, 226: 278–288

    Article  CAS  Google Scholar 

  • Sun C, Xia A, Liao Q, Fu Q, Huang Y, Zhu X, Wei P, Lin R, Murphy J D (2018). Improving production of volatile fatty acids and hydrogen from microalgae and rice residue: effects of physicochemical characteristics and mix ratios. Applied Energy, 230: 1082–1092

    Article  CAS  Google Scholar 

  • Tang C, Yao X, Jin H, Sun Q, Zou Z, Yang W, He Z, Zhou A, Chen F, Ren Y, et al. (2022). Stepwise freezing-thawing treatment promotes short-chain fatty acids production from waste activated sludge. Science of the Total Environment, 818: 151694

    Article  CAS  Google Scholar 

  • Vasudevan M, Natarajan N (2022). Towards achieving sustainable bioplastics production and nutrient recovery from wastewater: a comprehensive overview on polyhydroxybutyrate. Biomass Conversion and Biorefinery, DOI:https://doi.org/10.1007/s13399-022-02399-z

  • Vigneron A, Alsop E B, Lomans B P, Kyrpides N C, Head I M, Tsesmetzis N (2017). Succession in the petroleum reservoir microbiome through an oil field production lifecycle. ISME Journal, 11(9): 2141–2154

    Article  CAS  Google Scholar 

  • Wang J, Cao L, Liu Y, Huang Z, Li C, Wu D, Ruan R (2022a). Multiple hydrolyses of rice straw by domesticated paddy soil microbes for methane production via liquid anaerobic digestion. Bioresource Technology, 354: 127184

    Article  CAS  Google Scholar 

  • Wang L, Lei Z, Yang X, Zhang C, Liu C, Shimizu K, Zhang Z, Yuan T (2022b). Fe3O4 enhanced efficiency of volatile fatty acids production in anaerobic fermentation of food waste at high loading. Bioresource Technology, 364: 128097

    Article  CAS  Google Scholar 

  • Wang Y, Wang S (2021). Soft sensor for VFA concentration in anaerobic digestion process for treating kitchen waste based on SSAE-KELM. IEEE Access: Practical Innovations, Open Solutions, 9: 36466–36474

    Article  Google Scholar 

  • Xu R, Cao J, Wu Y, Wang S, Luo J, Chen X, Fang F (2020). An integrated approach based on virtual data augmentation and deep neural networks modeling for VFA production prediction in anaerobic fermentation process. Water Research, 184: 116103

    Article  CAS  Google Scholar 

  • Yang X, Du M, Lee D J, Wan C, Zheng L, Wan F (2012). Improved volatile fatty acids production from proteins of sewage sludge with anthraquinone-2,6-disulfonate (AQDS) under anaerobic condition. Bioresource Technology, 103(1): 494–497

    Article  CAS  Google Scholar 

  • Yang Z, Sun H, Kurbonova M, Zhou L, Arhin S G, Papadakis V G, Goula M, Liu G, Zhang Y, Wang W (2022). Simultaneous supplementation of magnetite and polyurethane foam carrier can reach a Pareto-optimal point to alleviate ammonia inhibition during anaerobic digestion. Renewable Energy, 189: 104–116

    Article  CAS  Google Scholar 

  • Yuan Q, Sparling R, Oleszkiewicz J A (2011). VFA generation from waste activated sludge: effect of temperature and mixing. Chemosphere, 82(4): 603–607

    Article  CAS  Google Scholar 

  • Zhang L, Mou A, Sun H, Zhang Y, Zhou Y, Liu Y (2021). Calcium phosphate granules formation: key to high rate of mesophilic UASB treatment of toilet wastewater. Science of the Total Environment, 773: 144972

    Article  CAS  Google Scholar 

  • Zhang Y, Li L, Ren Z, Yu Y, Li Y, Pan J, Lu Y, Feng L, Zhang W, Han Y (2022). Plant-scale biogas production prediction based on multiple hybrid machine learning technique. Bioresource Technology, 363: 127899

    Article  CAS  Google Scholar 

  • Zhao J, Wang D, Liu Y, Ngo H H, Guo W, Yang Q, Li X (2018). Novel stepwise pH control strategy to improve short chain fatty acid production from sludge anaerobic fermentation. Bioresource Technology, 249: 431–438

    Article  CAS  Google Scholar 

  • Zhong S, Zhang K, Bagheri M, Burken J G, Gu A, Li B, Ma X, Marrone B L, Ren Z J, Schrier J, et al. (2021). Machine learning: new ideas and tools in environmental science and engineering. Environmental Science & Technology, 55(19): 12741–12754

    CAS  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Key R & D Program of China (No. 2022YFE0210700), the National College Students Innovation and Entrepreneurship Training Program in China (No. 202210336059), the Key Research and Development Program of Zhejiang Province, China (No. 2023C03134), and the Zhejiang Provincial Ecological & Environmental Research Project and Application (No. 2021HT0028).

Author information

Authors and Affiliations

  1. College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Weishuai Li, Jingang Huang, Zhuoer Shi, Wei Han & Ting Lü

  2. China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing, Hangzhou Dianzi University, Hangzhou, 310018, China

    Jingang Huang, Wei Han, Xiaobing Xu & Pingzhi Hou

  3. Zhejiang Province Environmental Engineering Co. Ltd., Hangzhou, 310012, China

    Yuanyuan Lin

  4. M-U-T Maschinen-Umwelttechnik-Transportanlagen GmbH, Stockerau, 2000, Austria

    Jianfang Meng

Authors
  1. Weishuai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhuoer Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ting Lü
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuanyuan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jianfang Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaobing Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Pingzhi Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jingang Huang.

Additional information

Conflict of Interest

The authors declare no competing interests.

Highlights

• 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.

Supplemental Materials

11783_2023_1735_MOESM1_ESM.pdf

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, W., Huang, J., Shi, Z. et al. Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated sludge fermentation. Front. Environ. Sci. Eng. 17, 135 (2023). https://doi.org/10.1007/s11783-023-1735-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11783-023-1735-8

Keywords

Search

Navigation