Optimizing coagulation–flocculation processes with aluminium coagulation using response surface methods

Mensah-Akutteh, Hanson; Buamah, R.; Wiafe, Samuel; Nyarko, Kwabena Biritwum

doi:10.1007/s13201-022-01708-1

Cited by 20 publications

(2 citation statements)

References 32 publications

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“…Despite research evidence that proves the benefits of NBS to both humans and the environment, insufficient investments are being made toward their implementation [47][48][49][50][51]. For example, researchers have shown how modified plant-based natural coagulants could serve as good alternatives to chemical-based coagulants [52]. They are capable of working 70% more than conventional coagulants.…”

Section: Optimisation Processmentioning

confidence: 99%

Evaluation of Annona diversifolia Seed Extract as A Natural Coagulant for Water Treatment

Usman

Lee

et al. 2023

Sustainability

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The ever-present environmental crises are current research hotspots. Nature-based solutions have been shown to have multiple co-benefits toward solving these crises. Plant-based coagulants are known to be a cost-effective and environmentally friendly approach for coagulation and flocculation processes for drinking-water treatment. In this study, a natural coagulant was extracted from Annona diversifolia seed, and its effectiveness was investigated for turbidity reduction using jar test in kaolin suspension, river water, and evaluation of factors for sludge dewatering. The characterisation studies of Annona diversifolia seed extract were carried out using techniques including Fourier-transform infrared spectroscopy (FTIR), zeta potential analyser, and transmission electron microscopy (TEM). Response surface methodology (RSM) was also performed for the optimisation study. The results from FTIR showed that Annona diversifolia seed extract contains carboxyl and hydroxyl functional groups. The charge density was found to be negative. A web-like structure surface morphology was observed from TEM. The optimum treatment settings were found to be at pH 3 and a dosage of 25 mg/L for water treatment, and 50 mg/L for sludge dewatering, which were comparable to the metal–salts coagulants. Annona diversifolia seed extract has been shown to be a good natural coagulant. Further research can be conducted to modify and enhance its performance.

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Section: Optimisation Processmentioning

confidence: 99%

Evaluation of Annona diversifolia Seed Extract as A Natural Coagulant for Water Treatment

Usman

Lee

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…The use of chemical coagulants has been shown to leave trace amounts of chemicals in water filtrate, which has been associated with neurodegenerative diseases due to long-term ingestion of these traces of chemicals. This negates the essence of water treatment; hence, an alternative to using these chemicals is necessary [8,9].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Review of Modification, Optimisation, and Characterisation Methods Applied to Plant-Based Natural Coagulants (PBNCs) for Water and Wastewater Treatment

Usman

Baloo

et al. 2023

Sustainability

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This review investigates the modification, optimisation, and characterisation of plant-based natural coagulants applied to water treatment. The disadvantages of plant-based materials hinder their application as alternatives to chemical coagulants, necessitating their modification to alter and enhance their physicochemical properties, making them suitable for application. Modification via microwave-assisted grafting copolymerisation has been found to be the most preferred approach compared to conventional methods. Optimisation of the coagulation process using response surface methodology has been shown to be practical. Different techniques are used in determining the physicochemical properties of plant-based natural coagulants. Some of these techniques have been tabulated, describing the properties each technique is capable of investigating. In conclusion, modification and optimisation of plant-based natural coagulants will result in the production of new materials that are stable and suitable for application as coagulants in water treatment.

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Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms

Kumar,

Singh,

Abed

et al. 2023

Appl Water Sci

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The present research work focused on predicting the electrical conductivity (EC) of surface water in the Upper Ganga basin using four machine learning algorithms: multilayer perceptron (MLP), co-adaptive neuro-fuzzy inference system (CANFIS), random forest (RF), and decision tree (DT). The study also utilized the gamma test for selecting appropriate input and output combinations. The results of the gamma test revealed that total hardness (TH), magnesium (Mg), and chloride (Cl) parameters were suitable input variables for EC prediction. The performance of the models was evaluated using statistical indices such as Percent Bias (PBIAS), correlation coefficient (R), Willmott’s index of agreement (WI), Index of Agreement (PI), root mean square error (RMSE) and Legate-McCabe Index (LMI). Comparing the results of the EC models using these statistical indices, it was observed that the RF model outperformed the other algorithms. During the training period, the RF algorithm has a small positive bias (PBIAS = 0.11) and achieves a high correlation with the observed values (R = 0.956). Additionally, it shows a low RMSE value (360.42), a relatively good coefficient of efficiency (CE = 0.932), PI (0.083), WI (0.908) and LMI (0.083). However, during the testing period, the algorithm’s performance shows a small negative bias (PBIAS = − 0.46) and a good correlation (R = 0.929). The RMSE value decreases significantly (26.57), indicating better accuracy, the coefficient of efficiency remains high (CE = 0.915), PI (0.033), WI (0.965) and LMI (− 0.028). Similarly, the performance of the RF algorithm during the training and testing periods in Prayagraj. During the training period, the RF algorithm shows a PBIAS of 0.50, indicating a small positive bias. It achieves an RMSE of 368.3, R of 0.909, CE of 0.872, PI of 0.015, WI of 0.921, and LMI of 0.083. During the testing period, the RF algorithm demonstrates a slight negative bias with a PBIAS of − 0.06. The RMSE reduces significantly to 24.1, indicating improved accuracy. The algorithm maintains a high correlation (R = 0.903) and a good coefficient of efficiency (CE = 0.878). The index of agreement (PI) increases to 0.035, suggesting a better fit. The WI is 0.960, indicating high accuracy compared to the mean value, while the LMI decreases slightly to − 0.038. Based on the comparative results of the machine learning algorithms, it was concluded that RF performed better than DT, CANFIS, and MLP. The study recommended using the current month’s total hardness (TH), magnesium (Mg), and chloride (Cl) parameters as input variables for multi-ahead forecasting of electrical conductivity (ECt+1, ECt+2, and ECt+3) in future studies in the Upper Ganga basin. The findings also indicated that RF and DT models had superior performance compared to MLP and CANFIS models. These models can be applied for multi-ahead forecasting of monthly electrical conductivity at both Varanasi and Prayagraj stations in the Upper Ganga basin.

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Optimizing coagulation–flocculation processes with aluminium coagulation using response surface methods

Cited by 20 publications

References 32 publications

Evaluation of Annona diversifolia Seed Extract as A Natural Coagulant for Water Treatment

Evaluation of Annona diversifolia Seed Extract as A Natural Coagulant for Water Treatment

Comprehensive Review of Modification, Optimisation, and Characterisation Methods Applied to Plant-Based Natural Coagulants (PBNCs) for Water and Wastewater Treatment

Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms

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