The use of response surface methodology for modelling and analysis of water and wastewater treatment processes: a review

Nair, Abhilash T.; Makwana, Abhipsa R.; Ahammed, M. Mansoor

doi:10.2166/wst.2013.733

Cited by 185 publications

(87 citation statements)

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“…The 3D surface plots provide exact geometrical representation and give the appropriate statistics about the performance of the system within the experimental design . The optimum range for different values of the test variables can be predicted from these plots . Figure and a–c show the three‐dimensional response surface plot of the color, COD, and turbidity removal with the interactive effect of X 1 : pH, X 2 : coagulant dose (g), X 3 : contact time (min), X 4 : agitation speed (rpm).…”

Section: Resultsmentioning

confidence: 99%

“…[33,34] The optimum range for different values of the test variables can be predicted from these plots. [35,36] Figure 7 and 8a-c show the three-dimensional response surface plot of the color, COD, and turbidity removal with the interactive effect of X 1 : pH, X 2 : coagulant dose (g), X 3 : contact time (min), X 4 : agitation speed (rpm). Figure 7a-c shows the effect of pH and coagulant dosage on color, COD and turbidity removal, while other variables were fixed at central coded level (0) for GHC coagulant.…”

Section: Model Analysismentioning

confidence: 99%

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Treatment of Textile Wastewater by Coagulation–Flocculation Process Using Gossypium herbaceum and Polyaniline Coagulants

Arulmathi¹,

Jeyaprabha²,

Sivasankar³

et al. 2019

CLEAN Soil Air Water

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The present study is carried out to evaluate the performance efficiency of biopolymeric and synthetic polymeric coagulants for the removal of color, chemical oxygen demand (COD), and turbidity from textile industrial wastewater. Shells of Gossypium herbaceum (GHC), a bio‐polymeric coagulant from bio‐waste, and polyaniline coagulants (PAC) (a synthetic polymeric) are chosen for the present study. A Box‐Behnken design (BBD) is employed for the optimization of the effects of the four process variables, such as pH, coagulation dose, contact time, and agitation speed, on the removal of color, COD, and turbidity of the wastewater. Fourier‐transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) are carried out to understand the functional characteristics and surface morphology of the coagulants. The maximum removal of color, COD, and turbidity with GHC is found to be 90.13, 72.96, and 85.48%, respectively, at pH 5.5, coagulant dose 2.2 g L−1, and contact time 35 min, while the maximum removal of color, COD, and turbidity with PAC is found to be 94.38, 91.45, and 89.36%, respectively, at pH 8.0, coagulant dose 1.6 g L−1, and contact time 35 min. The experimental results show that pH and coagulant dose influence the treatment efficiency of GHC and PAC. From the experimental observations, it is concluded that the performance of the biopolymeric coagulant is highly compatible in the treatment of textile industrial wastewater.

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Section: Resultsmentioning

confidence: 99%

Section: Model Analysismentioning

confidence: 99%

Treatment of Textile Wastewater by Coagulation–Flocculation Process Using Gossypium herbaceum and Polyaniline Coagulants

Arulmathi¹,

Jeyaprabha²,

Sivasankar³

et al. 2019

CLEAN Soil Air Water

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“…, ) , ( ) is a vector function of elements, is a vector of unknown constant coefficients referred to as parameters, and is a random experimental error assumed to have a zero mean. RSM has been widely applied in optimizing various processes in environmental studies for modeling and analysis of water and wastewater treatment processes [39].…”

Section: Response Surface Methodologymentioning

confidence: 99%

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

et al. 2018

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The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers' artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit ( = 1.0000) against the response surface methodology ( = 0.9996). Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

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“…Most of these studies were conducted using one-factor-at-a-time approach which estimates the influence of a single variable while keeping all other variables at a fixed condition. The major disadvantage of this technique is that it cannot estimate interactive effects among the variables and thus cannot depict the complete effects of the parameters on the process [19,20]. It also requires large number of tests to be conducted.…”

Section: Introductionmentioning

confidence: 99%

Effect of water quality parameters on solar water disinfection: a statistical experiment design approach

Ahammed

Dave

Nair

2015

Desalination and Water Treatment

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A B S T R A C TWith a view to evaluating the effects and interactions of three influential factors, turbidity, pH and dissolved oxygen (DO), on the efficiency of solar water disinfection process, response surface methodology (RSM) based on Box-Behnken design was employed. Total coliform (TC) removal and heterotrophic plate count (HPC) removal were used as response factors. The RSM models were developed based on the experimental results of four-hour solar exposure in polyethylene terephthalate bottles. The measured and predicted removals were close to each other. Though the HPC removal and TC removal showed similar trends, the HPC removal was much lower (40-66%) compared to TC removal (68-97%) for the conditions studied. The results showed that turbidity up to certain levels (10-20 NTU) did not adversely impact the bacterial removal. In the range studied (6.0-9.5), pH showed little influence on the removals. While bacterial inactivation showed significant reduction at low DO levels, increasing DO beyond a certain level (5-6 mg/L) did not show any beneficial effect on bacterial inactivation. The study thus demonstrated the usefulness of RSM in modelling and analysing solar disinfection process.

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The use of response surface methodology for modelling and analysis of water and wastewater treatment processes: a review

Cited by 185 publications

References 0 publications

Treatment of Textile Wastewater by Coagulation–Flocculation Process Using Gossypium herbaceum and Polyaniline Coagulants

Treatment of Textile Wastewater by Coagulation–Flocculation Process Using Gossypium herbaceum and Polyaniline Coagulants

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

Effect of water quality parameters on solar water disinfection: a statistical experiment design approach

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