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Comparison of response surface methods for the optimization of an upflow anaerobic sludge blanket for the treatment of slaughterhouse wastewater

  • Chollom, Martha Noro (Faculty of Engineering and the Built Environment, Department of Chemical Engineering, Durban University of Technology) ;
  • Rathilal, Sudesh (Faculty of Engineering and the Built Environment, Department of Chemical Engineering, Durban University of Technology) ;
  • Swalaha, Feroz Mohammed (Faculty of Applied science, Department of Biotechnology and Food Technology, Durban University of Technology) ;
  • Bakare, Babatunde Femi (Faculty of Engineering, Department of Chemical Engineering, Mangosuthu University of Technology) ;
  • Tetteh, Emmanuel Kweinor (Faculty of Engineering and the Built Environment, Department of Chemical Engineering, Durban University of Technology)
  • Received : 2018.10.18
  • Accepted : 2019.02.16
  • Published : 2020.02.28

Abstract

This study was aimed at using the Central Composite Design (CCD) and Box-Behnken Design (BBD) to compare the efficiency and to elucidate the main interacting parameters in the upflow anaerobic sludge blanket (UASB) reactor, namely: Organic Loading Rate (OLR), Hydraulic Retention Times (HRT) and pH at a constant temperature of 35℃. Optimum HRT (15 h), OLR (3.5 kg.m-3.d-1) and pH (7) resulted in biogas production of 5,800 mL/d and COD removal of 80.8%. BBD produced a higher desirability efficiency of 94% as compared to the CCD which was 92%. The regression quadratic models developed with high R2 values of 0.961 and 0.978 for both CCD and BBD, respectively, demonstrated that the interaction models could be used to pilot the design space. BBD model developed was more reliable with a higher prediction of biogas production (5,955.4 ± 225.3 mL/d) and COD removal (81.5 ± 1.014%), much close to the experimental results at a 95% confidence level. CCD model predictions was greater in terms of COD removal (82.6 ± 1.06% > 80.8%) and biogas production (4,636.31 mL/d ± 439.81 < 5,800 mL/d) which was less than the experimental results. Therefore, RSM can be adapted for optimizing various wastewater treatment processes.

Keywords

References

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