Optimisation of the coagulation–flocculation process of raw water by optimal design method

Franceschi, M.; Girou, A.; Carro-Diaz, A.M; Maurette, Marie-Thérèse; Puech-Costes, E.

doi:10.1016/s0043-1354(02)00066-0

Cited by 67 publications

(40 citation statements)

References 20 publications

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“…Recently, many methods have been designed and have proven their effectiveness in treating raw water such as coagulation and flocculation (Franceschi et al, 2002;Liu et al, 2011;Syafalni et al, 2012a), absorption (Ćurković et al, 1997), filtration (Paune et al, 1998) and combining (Hidaka et al, 2003). Careful consideration of the most suitable method is important to ensure that the adsorption process is the most beneficial, economically feasible method as well as easy to operate for producing high quality of water in a particular location.…”

Section: Introductionmentioning

confidence: 99%

Peat Water Treatment Using Combination of Cationic Surfactant Modified Zeolite, Granular Activated Carbon, and Limestone

Syafalni¹,

Abustan²,

Brahmana³

et al. 2013

MAS

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This research was conducted essentially to treat fresh peat water using a series of adsorbents. Initially, the characterization of peat water was determined and five parameters, including pH, colour, COD, turbidity, and iron ion exhibited values that exceeded the water standard limit. There were two factors influencing the adsorption capacity such as pH, and adsorbent dosages that were observed in the batch study. The results obtained indicated that the majority of the adsorbents were very efficient in removing colour, COD, turbidity at pH range 2-4 and Fe at pH range 6-8. The optimum dosage of cationic surfactant modified zeolite (CSMZ) was found around 2 g while granular activated carbon (GAC) was exhibited at 2.5 g. In column study, serial sequence of CSMZ, GAC, and limestone showed that the optimal reduction on the 48 hours treatment were found pH = 7.78, colour = 12 TCU, turbidity = 0.23 NTU, COD = 0 mg/L, and Fe= 0.11 mg/L. Freundlich isotherm model was obtained for the best description on the adsorption mechanisms of all adsorbents.

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Section: Introductionmentioning

confidence: 99%

Peat Water Treatment Using Combination of Cationic Surfactant Modified Zeolite, Granular Activated Carbon, and Limestone

Syafalni¹,

Abustan²,

Brahmana³

et al. 2013

MAS

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“…A thorough analysis of jar test data obtained with synthetic raw water containing both particles and NOM that was coagulated with alum was conducted by Franceschi et al [67]. Eight water quality parameters-type of particles, initial turbidity, initial UV absorbance, temperature, pH, conductivity, calcium concentration, and sulfate concentration-were accounted for.…”

Section: Multivariate Regression Analysismentioning

confidence: 99%

State of the Art of Online Monitoring and Control of the Coagulation Process

Ratnaweera

Fettig

2015

Water

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Abstract:Coagulation is an essential process for the removal of suspended and colloidal material from water and wastewater. However, no comprehensive or universally accepted mathematical description of the process has been developed so far. Therefore, process optimization and control is usually based on data from jar tests and simple flow-proportional dosing concepts, while more accurate concepts based on water quality parameters that can be measured online are emerging. In addition, there have been attempts to develop software sensors and control schemes that involve advanced mathematical analyses of these parameters. The paper presents an overview of the parameters and physical sensors that are used for feed-forward and feed-backward control schemes and the experiences that have been made with their implementation. Moreover, the development and use of software sensors is described. Finally, the practical applications of different control techniques are given in order to illustrate the state of the art of coagulation control. Some thoughts about research needs conclude this review.

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“…These are all hydrophilic and tend to not settled. Bacteria are also colloidal particles and are not separated in the basic coagulation phase (Franceschi et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Optimum coagulant forecasting by modeling jar test experiments using ANNs

Haghiri

Daghighi

Moharramzadeh

2018

Drink. Water Eng. Sci.

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Abstract. Currently, the proper utilization of water treatment plants and optimizing their use is of particular importance. Coagulation and flocculation in water treatment are the common ways through which the use of coagulants leads to instability of particles and the formation of larger and heavier particles, resulting in improvement of sedimentation and filtration processes. Determination of the optimum dose of such a coagulant is of particular significance. A high dose, in addition to adding costs, can cause the sediment to remain in the filtrate, a dangerous condition according to the standards, while a sub-adequate dose of coagulants can result in the reducing the required quality and acceptable performance of the coagulation process. Although jar tests are used for testing coagulants, such experiments face many constraints with respect to evaluating the results produced by sudden changes in input water because of their significant costs, long time requirements, and complex relationships among the many factors (turbidity, temperature, pH, alkalinity, etc.) that can influence the efficiency of coagulant and test results. Modeling can be used to overcome these limitations; in this research study, an artificial neural network (ANN) multi-layer perceptron (MLP) with one hidden layer has been used for modeling the jar test to determine the dosage level of used coagulant in water treatment processes. The data contained in this research have been obtained from the drinking water treatment plant located in Ardabil province in Iran. To evaluate the performance of the model, the mean squared error (MSE) and correlation coefficient (R 2 ) parameters have been used. The obtained values are within an acceptable range that demonstrates the high accuracy of the models with respect to the estimation of water-quality characteristics and the optimal dosages of coagulants; so using these models will allow operators to not only reduce costs and time taken to perform experimental jar tests but also to predict a proper dosage for coagulant amounts and to project the quality of the output water under real conditions.

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Optimisation of the coagulation–flocculation process of raw water by optimal design method

Cited by 67 publications

References 20 publications

Peat Water Treatment Using Combination of Cationic Surfactant Modified Zeolite, Granular Activated Carbon, and Limestone

Peat Water Treatment Using Combination of Cationic Surfactant Modified Zeolite, Granular Activated Carbon, and Limestone

State of the Art of Online Monitoring and Control of the Coagulation Process

Optimum coagulant forecasting by modeling jar test experiments using ANNs

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