An integrated machine learning framework for effluent quality prediction in Sewage Treatment Units

Srungavarapu, Chandra Sainadh; Sheik, Abdul Gaffar; Tejaswini, E. S. S.; Yousuf, Sheik Mohammed; Rao, A. Seshagiri

doi:10.1080/1573062x.2023.2183137

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…It is particularly suitable for dealing with complex tasks and situations with high data noise. Srungavarapu et al applied integrated learning to predict the water quality of wastewater treatment plant effluent [23], and Tsai et al optimized the chemical reaction rate [24]. Traditional data-driven modeling and multivariate linear analysis can be a time-consuming, energyintensive process.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Biochar Yield and Specific Surface Area Based on Integrated Learning Algorithm

Zhou,

Liu,

Sun

et al. 2024

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Biochar is a biomaterial obtained by pyrolysis with high porosity and high specific surface area (SSA), which is widely used in several fields. The yield of biochar has an important effect on production cost and utilization efficiency, while SSA plays a key role in adsorption, catalysis, and pollutant removal. The preparation of biochar materials with better SSA is currently one of the frontiers in this research field. However, traditional methods are time consuming and laborious, so this paper developed a machine learning model to predict and study the properties of biochar efficiently for engineering through cross-validation and hyper parameter tuning. This paper used 622 data samples to predict the yield and SSA of biochar and selected eXtreme Gradient Boosting (XGBoost) as the model due to its excellent performance in terms of performance (yield correlation coefficient R2 = 0.79 and SSA correlation coefficient R2 = 0.92) and analyzed it using Shapley Additive Explanation. Using the Pearson correlation coefficient matrix revealed the correlations between the input parameters and the biochar yield and SSA. Results showed the important features affecting biochar yield were temperature and biomass feedstock, while the important features affecting SSA were ash and retention time. The XGBoost model developed provides new application scenarios and ideas for predicting biochar yield and SSA in response to the characteristic input parameters of biochar.

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Section: Introductionmentioning

confidence: 99%

Prediction of Biochar Yield and Specific Surface Area Based on Integrated Learning Algorithm

Zhou,

Liu,

Sun

et al. 2024

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Machine learning-based monitoring and design of managed aquifer rechargers for sustainable groundwater management: scope and challenges

Sheik,

Kumar,

Sharanya

et al. 2024

Environ Sci Pollut Res

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Managed aquifer recharge (MAR) replenishes groundwater by artificially entering water into subsurface aquifers. This technology improves water storage, reduces over-extraction, and ensures water security in water-scarce or variable environments. MAR systems are complex, encompassing various components such as water storage, soil, meteorological factors, groundwater management (GWM), and receiving bodies. Over the past decade, the utilization of machine learning (ML) methodologies for MAR modeling and prediction has increased significantly. This review evaluates all supervised, semi-supervised, unsupervised, and ensemble ML models employed to predict MAR factors and parameters, rendering it the most comprehensive contemporary review on this subject. This study presents a concise and integrated overview of MAR’s most effective ML approaches, focusing on design, suitability for water quality (WQ) applications, and GWM. The paper examines performance measures, input specifications, and the variety of ML functions employed in GWM, and highlights prospects. It also offers suggestions for utilizing ML in MAR, addressing issues related to physical aspects, technical advancements, and case studies. Additionally, previous research on ML-based data-driven and soft sensing techniques for MAR is critically evaluated. The study concludes that integrating ML into MAR systems holds significant promise for optimizing WQ management and enhancing the efficiency of groundwater replenishment strategies.

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Enhancing Water treatment predictions: a Machine Learning Approach with CNN and Water Wave optimization

Alkhadrawi,

Alzboon

2024

Asian J Civ Eng

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An integrated machine learning framework for effluent quality prediction in Sewage Treatment Units

Cited by 6 publications

References 36 publications

Prediction of Biochar Yield and Specific Surface Area Based on Integrated Learning Algorithm

Prediction of Biochar Yield and Specific Surface Area Based on Integrated Learning Algorithm

Machine learning-based monitoring and design of managed aquifer rechargers for sustainable groundwater management: scope and challenges

Enhancing Water treatment predictions: a Machine Learning Approach with CNN and Water Wave optimization

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