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Modeling and Optimization of the Coagulation/Flocculation Process in Turbidity Removal From Water Using Poly Aluminum Chloride and Rice Starch As a Natural Coagulant Aid Publisher Pubmed



Asadighalhari M1 ; Usefi S2 ; Ghafouri N3 ; Kishipour A4 ; Mostafaloo R5 ; Tabatabaei F1
Authors
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Authors Affiliations
  1. 1. Department of Environmental Health Engineering, Faculty of Health, Research Center for Environmental Pollutants, Qom University of Medical Sciences, Qom, Iran
  2. 2. Student Research Committee, Qom University of Medical Sciences, Qom, Iran
  3. 3. Department of Environmental Health Engineering, Alborz University of Medical Sciences, Alborz, Iran
  4. 4. Department of Environmental Health Engineering, Isfahan University of Medical Sciences, Isfahan, Iran
  5. 5. Department of Environmental Health Engineering, School of Public Health and Research Center for Health Sciences, Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran

Source: Environmental Monitoring and Assessment Published:2023


Abstract

The application of the coagulation/flocculation process is very important due to its simplicity in removing turbidity. Due to the disadvantages of using chemical coagulants in water and the lack of sufficient effect of natural materials alone in removing turbidity for proper performance, the simultaneous use of chemical and natural coagulants is the best way to reduce the harmful effects of chemical coagulants in water. In this study, the application of poly aluminum chloride (PAC) as a chemical coagulant and rice starch as a natural coagulant aid to remove turbidity from aqueous solutions was investigated. Effects of the above coagulants on the four main factors, coagulant dose (0–10 mg/L), coagulant adjuvant dose (0–0.1 mg/L), pH (5–9), turbidity (NTU 0–50), and each five levels were assessed using a central composite design (CCD). Under the optimized conditions, the maximum turbidity elimination efficiency was found to be 96.6%. The validity and adequacy of the proposed model (quadratic model) were confirmed by the corresponding statistics (i.e., F-value of 23.3, p-values of 0.0001, and lack of fit of 0.877 for the model, respectively, R2 = 0.88, R2adj. = 0.84, R2pred = 0.79, AP = 22.04). Graphical Abstract: [Figure not available: see fulltext. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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