Comparative Assessment of Performance of Aluminium Sulphate (Alum) and Ferrous Sulphate as Coagulants in Water Treatment
CHAPTER ONE
Aim and Objectives of the Study
The aim of this study is to compare the performance of two selected coagulants (alum and ferrous sulphate) in water treatment. Consequently, the following specific objectives are applicable.
- To determine coagulation efficiency of each coagulant with respect to each of selected water quality parameter set of parameters.
- To assess the coagulant dosage in relation to optimum coagulation efficiency.
CHAPTER TWO
LITERATURE REVIEW
Coagulation/flocculation processes are of great importance in solid-liquid separation practice16. Findings on various coagulation processes have been reported in literature. Some of these include, studying the effect of dosage and mixing conditions on the flocculation of concentrated suspensions using polymeric coagulants 17,18, coagulation of synthetic water by plant seeds 19 and coagulation of low turbidity water using bentonite 20. Guida et al. (2007) used alum as coagulant to remove Chemical Oxygen Demand (COD) and Total Suspended Solids (TSS) from municipal wastewater samples. The coagulation experiments indicated that alum effectively removed COD (65%) and TSS (>75%) using 150mgL-1 aluminium sulphate at a pH range of 5 – 821.
Coagulation targets the colloid particles of size 10-7 to 10-14cm in diameter. The colloid particles exhibit Brownian movement through the water; their surface is negatively charged so they repel one another, and they form a stable dispersed suspension 22. If colloid particles or ions of positive electric charge are added, it neutralizes the electric negative charge. Flocculation refers to the successful collision that occurs when destabilized particles are driven toward each other by the hydraulic shear force in the rapid mix and flocculation basin. It agglomerates of a few colloids then quickly bridge together to form microflocs which is turned into visible floc masses23, which is shown in figure 1.
The separation of particular matter from the liquid phase is one of the important steps in most wastewater treatment processes. All waters, especially surface waters, contain both dissolved and suspended particles. Coagulation processes are used to separate the suspended solids portion from the water. The suspended particles vary considerably in source, composition charge, particles size, shape, and density. Correct application of coagulation processes and selection of the coagulants depend upon understanding the interaction between these factors. Coagulant chemicals come in two main types, primary coagulants and coagulant aids. Primary coagulants neutralize the electrical charges of particles into the water which causes the particles to clump together 24. Chemically, coagulants are either metallic salts (such as alum) or polymers. Polymers are man – made organic compounds made up of a long chain of smaller molecules. Polymers can be either cationic (positively charged), anionic (negatively charged), or nonionic (neutral charged). Chemical coagulation has been in practice for several decades to precipitate the soluble heavy metals present in the waste water, as hydroxide and facilitate their removal by physical separations through the sedimentation process. The process of coagulation separation consist of four steps, which is shown in figure 2.
CHAPTER THREE
Experimental
Apparatus
- pH – meter
- Magnetic stirrer
- Sedimentation beaker
- Stop watch
- 20 liters white gallon
- 1 liter gallon
- 5 liters gallon
- Conical flask (250ml)
- 50ml beurette
- Heating mantle
- Whaltman filter paper
- Electronic weighing balance
- Spectrophotometer
- Incubator
- Oven and minifurnance
Materials/Reagents
- Buffer solutions of pH 6.8 and distilled water
- Humus soil
- H2S04
- Distilled water
- Clean tap water/Base water
- Aluminum sulphate Al2(S04)3 18H20 Octadecahydrate (Alum)
- Ferrous sulphate crystalline: FeS047H20 Heptadydrate
Coagulant Processing
The two coagulants used for this experiment which are Aluminum sulphate and iron (II) sulphate with chemical formulae Al2(S04)3.18H20 and FeS04.7H20 respectively were purchased from Ogige Main Market Nsukka. The caked coagulants were crushed with ceramics pestle and mortar.
Soil Sample Processing
The humus soil which was collected from University of Nigeria, Nsukka Agricultural Farm was first destoned. It was ground into powder using ceramics pestle and mortar in the laboratory. The powder was sieved with a laboratory sieve of known mesh size to obtain smooth texture of the material.
CHAPTER FOUR
RESULTS AND DISCUSSION
Results
The results of various levels of efficiency achieved by different doses of each of the coagulants are shown in tables 3-12. Furthermore, the comparative variations in coagulant performance efficiency have been represented graphically in figure 15-41.
CHAPTER FIVE
CONCLUSION AND RECOMMENDATIONS
Conclusion
The comparative assessment of the performance of Aluminium Sulphate (Alum) and Ferrous Sulphate as Coagulants in water Treatment was carried out on turbid water using sedimentation beaker experiments. The results showed that pH, DO, BOD5, Fluoride, phosphate and COD mean % efficiency were higher for iron (II) sulphate compared to aluminum sulphate at dosage of 10g of coagulant per 3 litres of water. The aluminum sulphate revealed a better performance in TSS, Turbidity and chloride mean % efficiency when compared with iron (II) sulphate at the same coagulant dosage. The overall results of the coagulation studies showed that coagulation efficiency of the coagulant is parameter dependent.
- Recommendations
- It is suggested that a combination of lime as a coagulant aid with the used coagulants could improve efficiency in some parameters.
- Application and the use of alternative coagulants like moringa oleifera, prosopis affricanapeel powder need to be evaluated at the industrial scale and possibly commercialized in order to guarantee a large range of coagulants available in treatment of all waters in all conditions. This is to ensure safety, efficacy and quality of treated water and protection of our water sources is advocated to minimize the need and intensity of water treatment.
- Finally, the outcome of this work can be an important guide to water treatment operators.
REFERENCES
- Leiknes, T., Odegaard, H., Myklebust, (2004). Removal of Natural Organic Matter (NOM) in drinking water treatment by coagulation-microfiltration using metal membranes. Journal of membrane science, 242.
- Matilainen A., Lindovist N., Tuhkanen T., (2005). Comparison of the efficiency of Aluminium and Ferric sulphate in the Removal of (2005) Natural Organic Matter During Drinking Water Treatment Process. Experimental Technology, vol. 26pp., 867-875. 2005
- Chowdhury, S., Champagne, P., and Mclelland, P.J. (2009). Models for predicting disinfection by product (DBP) formation in drinking waters: A Chronological Review. Science of the Total Environment, 407, 4189-4206.
- Hsu, Ch. H., Jeng, W.L. Chang, R.M., Chien L. Ch., Han, B. Ch., (2001). Estimation of potential lifetime cancer risk for THM from consuming chlorinated drinking water in Taiwan. J. Environmental Research, A.(85), 77-82.
- USEPA, National Primary Drinking water regulations: stage 2 Disinfectants and Disinfection By-product Rule: proposed Rule, Fed. Reg., 49547-49681, August 18, (2003).
- Wu, G., Lo S., (2010). Effects of data normalization and inherent-factor on decision of optimal coagulant dosage in water treatment by artificial neural network. Expert systems with applications, 37, 4974-4983.
- M. Zainal-Abideen, A. Aris, F. Yusof, Z. Abdul-Majid, A. Selamat and S.I. Omar, Optimizing the Coagulation process in a drinking water treatment plant comparison between traditional and statistical experiment as design jar test, water science and technology, 65.3 (2012).
- Joo, D.S., Choi D.J., Park, H., (2000). The effects of data processing in the determination coagulant dosing rate water research, 34(13), 3295-3302.
- Yu, R.F., Kang S.F., Liaw S.L., Chen M.C., (2000). Application of Artificial neural network to control the coagulant dosing in water treatment plant. Water science and technology, 42, 403-408.
- Maier, H.R., Morgan, N, and Chow, C, W, K., (2004). Use of Artificial Neural networks for predicting optimal alum doses and treated water quality parameters. Environmental modeling and software, 19(5), 485-494.
- Gholikandi G.B., Delnavaz M., Riahi R., (2011). Use of Artificial Neural Network for prediction of Coagulation/Flocculation process by Pac in water Treatment Plant. Environmental Engineering and Management Journal, November. Vol. 10, No. 11, 1719-1725.
