Coagulation/flocculation process for textile mill effluent treatment: experimental and numerical perspectives

Figures & data

Table 1. Range of variables in ANN

Variables	Ranges
Effect of dose	0.25–1.50 g/L
Effect of rapid mixing speed	50–250 rpm
Effect of slow mixing speed	10–50 rpm
Effect of rapid mixing time	1–5 min
Effect of slow mixing time	10–30 min
Effect of settling time	10–50 min
pH is constant	9

Figure 1. Architecture of ANN model

Figure 2. pH effect on all studied coagulants for the removal of a) COD, b) TSS, c) Colour, d) TN and e) Turbidity (dose 1 g/L, 150 rpm rapid stirring rate)

Figure 3. Operating conditions of Fecl₃ for the removal of COD, TSS, Colour, TN and turbidity a) effect of dose, b) effect of rapid mixing speed, c) effect of rapid mixing time, d) effect of slow mixing speed, e) effect of slow mixing time and f) effect of settling time

Figure 4. Raw and final effluent treated sample with optimum operating conditions by using 1 g/L FeCl₃ at pH 9

Table 2. Optimum operating conditions for the treatment of real textile effluents using different coagulants in various literatures

S. No	Coagulant	Contaminant	pH	Coagulant dose, mg/L	Rapid mixing conditions	Slow mixing conditions	Settling time, min	Removal rate	Ref.
1	FeCl3	COD	4	4000	2 min at 200 rpm	15 min at 40 rpm	120	54%	(Rana and Suresh 2017)
2	Al2(SO4)3	COD, TSS and colour	7	550	1 min at 90 rpm	3 min at 30 rpm	15	36%, 19% and 68.8%	(Naghan et al. 2015)
3	Magnesium chloride aided with lime (MgCl2/CaO)	COD and colour	11	120	rapid mixing for 1 min	20 min at 24 rpm	60	40% and 100%	(El-Gohary and Tawfik 2009)
4	Al2(SO4)3 aided with Cytec cationic polymer	COD and colour	6.6	200	rapid mixing for 1 min	20 min at 24 rpm	60	44% and 94%	(El-Gohary and Tawfik 2009)
5	Lime (Cao)	COD and colour	11.7	600	rapid mixing for 1 min	20 min at 24 rpm	60	50% and 100%	(El-Gohary and Tawfik 2009)
6	FeCl3	COD, TSS, colour, TN and turbidity	9	1000	1 min at 150 rpm	20 min at 30 rpm	30	62.2%, 68.6%, 84.7%, 10.9% and 54.7%	This study

Figure 5. Predicted vs. target values for the removal of COD, TSS, colour, TN and turbidity based on the effect of: A1- dose, A2- rapid mixing speed, A3- slow mixing speed

Figure 6. Predicted vs. target values for the removal of COD, TSS, colour, TN and turbidity based on the effect of: A4- rapid mixing time, A5- slow mixing time, A6- settling time

Figure 7. Predicted vs. target (actual) values for the removal of COD, TSS, colour, TN and turbidity based on the effect of all variables (Model B)

Figure 8. Linear regression between predicted (network output) & target for the removal of COD, TSS, colour, TN and turbidity based on the effect of: A1- dose, A2- rapid mixing speed, A3- slow mixing speed (Model A)

Figure 9. Linear regression between predicted (network output) & target for the removal of COD, TSS, colour, TN and turbidity based on the effect of: A4- rapid mixing time, A5- slow mixing time, A6- settling time (Model A)

Figure 10. Linear regression between predicted (network output) & target for the removal of COD, TSS, colour, TN and turbidity based on the effect of all variables (Model B)

Rana, S., and S. Suresh. 2017. “Comparison of Different Coagulants for Reduction of COD from Textile Industry Wastewater.” Materials Today: Proceedings 4 (2): 567–574.

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Naghan, D. J., M. D. Motevalli, N. Mirzaei, et al. 2015. “Efficiency Comparison of Alum and Ferric Chloride Coagulants in Removal of Dye and Organic Material from Industrial Wastewater-a Case Study.” Bulgarian Chemical Communications 47 (Specia): 206–210.

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El-Gohary, F., and A. Tawfik. 2009. “Decolorization and COD Reduction of Disperse and Reactive Dyes Wastewater Using Chemical-coagulation Followed by Sequential Batch Reactor (SBR) Process.” Desalination 249 (3): 1159–1164. doi:https://doi.org/10.1016/j.desal.2009.05.010.

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