A comprehensive review on comparison among effluent treatment methods and modern methods of treatment of industrial wastewater effluent from different sources

Sathya, K.; Nagarajan, K; Malar, G Carlin Geor; Rajalakshmi, S.; Lakshmi, P. Raja

doi:10.1007/s13201-022-01594-7

Cited by 133 publications

(37 citation statements)

References 132 publications

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“…Extreme suspended loads in streams may endanger aquatic life and have a negative impact on the ecosystem (Sathya et al 2022). After all, the suspended solids can clog the fish gills and impede light penetration into the stream bed, limiting the rate of photosynthesis of aquatic plants and resulting in a decrease in the dissolved oxygen level of the streams, which can be lethal to aquatic life.…”

Section: Introductionmentioning

confidence: 99%

Sediment load prediction in Johor river: deep learning versus machine learning models

et al. 2023

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Sediment transport is a normal phenomenon in rivers and streams, contributing significantly to ecosystem production and preservation by replenishing vital nutrients and preserving aquatic life’s natural habitats. Thus, sediment transport prediction through modeling is crucial for predicting flood events, tracking coastal erosion, planning for water supplies, and managing irrigation. The predictability of process-driven models may encounter various restrictions throughout the validation process. Given that data-driven models work on the assumption that the underlying physical process is not requisite, this opens up the avenue for AI-based model as alternative modeling. However, AI-based models, such as ANN and SVM, face problems, such as long-term dependency, which require alternative dynamic procedures. Since their performance as universal function approximation depends on their compatibility with the nature of the problem itself, this study investigated several distinct AI-based models, such as long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM), in predicting sediment transport in the Johor river. The collected historical daily sediment transport data from January 1, 2008, to December 01, 2018, through autocorrelation function, were used as input for the model. The statistical results showed that, despite their ability (deep learning and machine learning) to provide sediment predictions based on historical input datasets, machine learning, such as ANN, might be more prone to overfitting or being trapped in a local optimum than deep learning, evidenced by the worse in all metrics score. With RMSE = 11.395, MAE = 18.094, and R2 = 0.914, LSTM outperformed other models in the comparison.

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Section: Introductionmentioning

confidence: 99%

Sediment load prediction in Johor river: deep learning versus machine learning models

et al. 2023

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“…The adsorption having a high capacity of adsorption, low cost, and compatibility are indispensable. Traditional methods are prone to generate toxic sludge and maintenance is expensive so research has focussed majorly on the use of microorganisms and plants for the removal of pollutants ( Sathya et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Perspective on the heavy metal pollution and recent remediation strategies

Sodhi

Mishra

Singh

et al. 2022

Current Research in Microbial Sciences

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“…The high pollutant levels in dairy wastewater can aggravate eutrophication and degradation of natural water bodies if discharged directly into the environment [ 11 ]. Therefore, effective methods are needed to remove pollutants and prevent negative impacts on receiving water bodies [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Performance of an Innovative Low-Cost Recycled Filling (LCRF) in Anaerobic Treatment of Dairy Effluent—A Pilot-Scale Study

2022

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The rapid growth in dairy production leads to increasing outputs of high-load effluent, necessitating new methods of treating such waste. Anaerobic processes have been increasingly popular but are hamstrung by limited nutrient removal efficiency. The aim of the present study was to investigate whether low-cost recycled filling (LCRF) improves the anaerobic treatment of dairy effluent. The addition of LCRF was found to increase both COD removal (86.1 ± 2.6%–92.8 ± 1.6%) and Ptot. removal (22.1 ± 3.5% to 36.9 ± 4.6%) from the wastewater. The LCRF ensured near-neutral pH and stabilized the structure of the anaerobic microbe community (including Archaea) across all pollutant loads tested. This translated to efficient biogas production and high methane content in the LCRF reactors, peaking at 0.35 ± 0.01 m3/kg CODremoved and 68.2 ± 0.6% (respectively) in the best-performing variant.

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A comprehensive review on comparison among effluent treatment methods and modern methods of treatment of industrial wastewater effluent from different sources

Cited by 133 publications

References 132 publications

Sediment load prediction in Johor river: deep learning versus machine learning models

Sediment load prediction in Johor river: deep learning versus machine learning models

Perspective on the heavy metal pollution and recent remediation strategies

Performance of an Innovative Low-Cost Recycled Filling (LCRF) in Anaerobic Treatment of Dairy Effluent—A Pilot-Scale Study

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