Multifactor optimization for treatment of textile wastewater using complex salt–Luffa cylindrica seed extract (CS-LCSE) as coagulant: response surface methodology (RSM) and artificial intelligence algorithm (ANN–ANFIS)

Nnaji, P.C.; Anadebe, Valentine Chikaodili; Onukwuli, Okechukwu Dominic; Okoye, Chukwunonso Chukwuzuloke; Ude, Chiamaka Joan

doi:10.1007/s11696-021-01971-7

Cited by 19 publications

(4 citation statements)

References 47 publications

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“…The interaction of extraction time ( X 1 ) and ethanol concentration ( X 3 ) of TV and TG had a significant effect on the yields (0.01 < P < 0.05). The value of F was positively correlated with the effects of three factors on the yields of SB, TV and TG [24] . On the basis of the F -value of the models, the factors influencing the yields of SB and TV were: ethanol concentration ( X 3 ) > extraction temperature ( X 2 ) > extraction time ( X 1 ); while the yield of TG was: ethanol concentration ( X 3 ) > extraction time ( X 1 ) > extraction temperature ( X 2 ).…”

Section: Resultsmentioning

confidence: 89%

Ultrasonic-assisted extraction and adsorption separation: Large-scale preparation of trans-ε-Viniferin, suffruficosol B and trans-Gnetin H for the first time

Deng

Wang²,

Hou³

et al. 2022

Ultrasonics Sonochemistry

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Section: Resultsmentioning

confidence: 89%

Ultrasonic-assisted extraction and adsorption separation: Large-scale preparation of trans-ε-Viniferin, suffruficosol B and trans-Gnetin H for the first time

Deng

Wang²,

Hou³

et al. 2022

Ultrasonics Sonochemistry

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“…where Y represents the dependent variable (turbidity and SVI), X i and X j are the independent variables influencing y; and β o , β i , β ii , and β ij are the offset terms, the i th linear coefficient, the ii th quadratic coefficient, the ij th interaction coefficient, and ε the error term, respectively (Onukwuli et al, 2021;Nnaji et al, 2022). The experimental design of this study and the responses are presented in Table 2.…”

Section: X X Xmentioning

confidence: 99%

Application of biopolymer in turbidity removal and sludge settling behaviour of travertine-processing wastewater: Performance optimization using response surface methodology (RSM)

Taş

Ugwu

Sabah

et al. 2023

WSA

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A flocculation process was performed to treat travertine-processing effluents with a high concentration of suspended solids using an eco-friendly biopolymer. The experiments were conducted through a standard jar test procedure to optimize the process parameters for sludge volume index (SVI) and turbidity removal. The effects of mixing time, suspension pH, and polymer dosage on treatment efficiency were investigated using central composite design, a standard technique in response surface methodology. The constructed response model was tested using the analysis of variance (ANOVA). Using the Design-Expert tool, the coefficients of regression models were computed. The Fischer value (F-value) was used to evaluate the significance and validity of the predicted model, while the coefficient of determination (R2) was applied to estimate the model significance by comparing the predicted data with the measured data. The optimized parameters obtained were polymer dose of 276.20 mg/L, suspension pH of 8.60, and mixing time of 4.20 min. The optimal SVI and turbidity values obtained were 1.36 mL/g and 2.99 NTU, respectively. Additionally, R2 values for SVI and turbidity were determined as 0.9337 and 0.8654, respectively. Also, the difference between adjusted R2 values and predicted R2 was less than 0.2. Validation tests showed that the response surface methodology is an effective method for optimizing the flocculation mechanism.

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“…The results show that the dynamic model has better performance than static ANN models in modeling effluent quality. Nnaji et al [113] predicted COD and CTSS removal efficiencies from textile wastewater using complex salt-Luffa cylindrica seed extract (CS-LCSE) as a coagulant based on RSM, ANN and ANFIS models. The results demonstrate that the ANFIS model has the best predictive performance with a higher R 2 value (0.9997 and 0.9996 for CTSS and COD removals) and a lower MSE value (0.0002643 and 0.0038472 for CTSS and COD removals).…”

Section: Process Performance Predictionmentioning

confidence: 99%

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Wang,

Cheng,

Liu

et al. 2023

Sustainability

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In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.

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Multifactor optimization for treatment of textile wastewater using complex salt–Luffa cylindrica seed extract (CS-LCSE) as coagulant: response surface methodology (RSM) and artificial intelligence algorithm (ANN–ANFIS)

Cited by 19 publications

References 47 publications

Ultrasonic-assisted extraction and adsorption separation: Large-scale preparation of trans-ε-Viniferin, suffruficosol B and trans-Gnetin H for the first time

Ultrasonic-assisted extraction and adsorption separation: Large-scale preparation of trans-ε-Viniferin, suffruficosol B and trans-Gnetin H for the first time

Application of biopolymer in turbidity removal and sludge settling behaviour of travertine-processing wastewater: Performance optimization using response surface methodology (RSM)

A Review on Applications of Artificial Intelligence in Wastewater Treatment

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