Prediction of biological nutrients removal in full-scale wastewater treatment plants using H2O automated machine learning and back propagation artificial neural network model: Optimization and comparison

Luo, Jingyang; Luo, Yuting; Cheng, Xiaoshi; Liu, Xinyi; Wang, Feng; Fang, Fang; Cao, Jiashun; Liu, Weijing; Xu, Runze

doi:10.1016/j.biortech.2023.129842

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…Considering the complexity of the experimental conditions and the contingency in the process of experimental operation, researchers typically require significant time and resources in order to obtain effective experimental results. ML is capable of using and processing large amounts of data generated by experiments and can be used for operational forecasting purposes . For example, an automatic framework of feature engineering based on variation sliding layer (VSL) was developed to control the air demand in wastewater treatment plants and reduce 16.1% energy consumption .…”

Section: Introductionmentioning

confidence: 99%

“…ML is capable of using and processing large amounts of data generated by experiments and can be used for operational forecasting purposes. 15 For example, an automatic framework of feature engineering based on variation sliding layer (VSL) was developed to control the air demand in wastewater treatment plants and reduce 16.1% energy consumption. 16 Adaboost algorithm successfully predicts COD removal under different salinities in an anaerobic membrane bioreactor (AnMBR).…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Accelerating the Condition Screening of Ceftriaxone Sodium Anaerobic Co-Metabolic Degradation

Chen,

Cao,

Wang

et al. 2024

ACS EST Eng.

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Anaerobic co-metabolism is a biotechnological process that improves biodegradation efficiency of refractory organics. By adding cosubstrates, it provides additional carbon sources and energy for the microbial metabolic degradation of organic matter. However, a large number of repeated experiments are required to screen out suitable conditions. It is typically lengthy and carries significant uncertainty. In this study, machine learning (ML) was used to drive the screening of anaerobic degradation conditions for ceftriaxone sodium (CTX) in wastewater treatment. The results showed that the XGBoost algorithm was able to effectively predict the decomposition efficiency with an accuracy of up to 95%. A Shapley additive explanation (SHAP) analysis showed that temperature, pH, and CTX/glucose ratio had the greatest impacts on the removal efficiency of CTX, thus highlighting the remarkable ability of ML to accelerate the screening of optimal degradation conditions. A high-throughput analysis proved that the dominant genera and microbial structures presented under the two environmental conditions with the largest significant difference (temperature, CTX/glucose ratio) were significantly different and that bacterial genera such as Fastidiosipila, norank_f_Prolixibacteraceae, norank_f_Bacteroidetes_vadinHA17, and Georgenia significantly affected the hydrolysis and acidification process. This work stands out by integrating advanced ML techniques into environmental engineering, thereby enhancing efficiency and providing richer analytical insights compared to traditional methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Accelerating the Condition Screening of Ceftriaxone Sodium Anaerobic Co-Metabolic Degradation

Chen,

Cao,

Wang

et al. 2024

ACS EST Eng.

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Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

Ganthavee,

Trzcinski

2024

Environ Chem Lett

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The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in the context of rapid industrialization and urbanization. Moreover, most pollutants such as antibiotics escape conventional wastewater treatments and are thus discharged in ecosystems, requiring advanced techniques for wastewater treatment. Here we review the use of artificial intelligence and machine learning to optimize pharmaceutical wastewater treatment systems, with focus on water quality, disinfection, renewable energy, biological treatment, blockchain technology, machine learning algorithms, big data, cyber-physical systems, and automated smart grid power distribution networks. Artificial intelligence allows for monitoring contaminants, facilitating data analysis, diagnosing water quality, easing autonomous decision-making, and predicting process parameters. We discuss advances in technical reliability, energy resources and wastewater management, cyber-resilience, security functionalities, and robust multidimensional performance of automated platform and distributed consortium, and stabilization of abnormal fluctuations in water quality parameters.

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Machine learning framework for wastewater circular economy — Towards smarter nutrient recoveries

Soo,

Gao,

Shon

2024

Desalination

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Prediction of biological nutrients removal in full-scale wastewater treatment plants using H2O automated machine learning and back propagation artificial neural network model: Optimization and comparison

Cited by 6 publications

References 49 publications

Machine Learning Accelerating the Condition Screening of Ceftriaxone Sodium Anaerobic Co-Metabolic Degradation

Machine Learning Accelerating the Condition Screening of Ceftriaxone Sodium Anaerobic Co-Metabolic Degradation

Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

Machine learning framework for wastewater circular economy — Towards smarter nutrient recoveries

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