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    Evaluation of landfill leachate treatment system using multivariate analysis
    (Desalination Publ, 2021) Ayash, Muneer M. A.; Abu Amr, Salem S.; Alkarkhi, Abbas F. M.; Zulkifli, Muzafar; Mahmud, M. N.
    In this study, selected physicochemical and heavy metal concentrations were identified and analyzed in leachate samples. The leachate samples were collected at four different stages namely; raw equalization pond (EqP), dissolved air floatation combined with coagulation ( DAF1/coagulation), sequencing batch reactor (SBR), and dissolved air floatation combined with coagulation (DAF2/coagulation). For each stage, 19 parameters were tested covering 12 physiochemical parameters including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD5), chemical oxygen demand (COD), color, ammonical nitrogen (NH3-N), total suspended solids (TSS), total dissolved solids (TDS), electrical conductivity (EC), total concentrations of sodium (Na), magnesium (Mg), and calcium (Ca) as well as the total concentrations of seven heavy metals involving iron (Fe), copper (Cu), cadmium (Cd), lead (Pb), manganese (Mn), nickel (Ni), and zinc (Zn). Identifying the characteristics of the four leachate samples from each stage was aided with three different statistical methods consisting of descriptive, factor, and cluster analyses. The results of factor analysis showed that 95.34% of the total variation in the selected parameters was explained by two factors and identified as the responsible factors. Cluster analysis exhibited that the four ponds entirely have different properties (EqP, DAF1, SBR, and DAF2). This study helps to evaluate and comprehend the behavior of the designated parameters and better understand their relationships with one another for more efficient, practical, and productive landfill leachate treatment and management.
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    Statistical model for comparing the performance of two coagulants using response surface model
    (Desalination Publ, 2022) Ayash, Muneer M. A.; Alkarkhi, Abbas F. M.; Abu Amr, Salem S.; Mahmud, M. N.; Zulkifli, Muzafar
    In this research, the performances of modified tannin and aluminium sulfate (alum) for stabilized leachate treatment were investigated and compared using coagulant dosage, pH, and rapid mixing speed as the input variables. Four different responses were used to compare the treatment performances; the responses are, chemical oxygen demand (COD), color, ammoniacal nitrogen (NH3??? N) and total suspended solids (TSS). The results of the analysis for 36 experiments showed that the optimum operating conditions for 1% modified tannin and 10% alum are a coagulant dosage of 6 mL, a pH of 9 and a rapid mixing speed of 100 rpm. The optimum removal efficiencies of COD, color, NH3???N and TSS using 1% modified tannin were 42.86%, 54.38%, 39.39% and 60.33% respectively, and using 10% alum were 60.71%, 63.09%, 42.42% and 60.33%, respectively. The findings revealed that the effectiveness of modified tannin for the treatment of landfill leachate was significant using a ten-time lower dosage concentration than alum. This study will help better understanding the behaviour of organic and inorganic coagulants for wastewater treatments using the same polynomial model.
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    Treatment of rubber wastewater using zinc sulphate as coagulants-data collection on removal efficiency for physicochemical and heavy metal parameters
    (Elsevier, 2021) Alkarkhi, Abbas F. M.; Abu Amr, Salem S.; Alqaraghuli, Wasin A. A.; Ozdemir, Yahya; Zulkifli, Muzafar; Mahmud, M. N.
    This article provides data regarding the performance of zinc sulphate as a coagulant for treating rubber industry wastewater. The effect of four factors on removal efficiency of nine parameters is investigated, namely: pH, mixing speed, dosage of coagulant (zinc sulphate) and retention time. Response surface methodology was used to investigate the effect of selected variables. The data obtained from face centered composite design (FCCD) were analyzed by using analysis of variance (ANOVA) and regression model to find the optimum operating conditions for the selected factors. (c) 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)