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Application of Statistical Design to an Industrial‐Scale Dead‐End Ultrafiltration Process
Author(s) -
Shams Mohamed Bin,
Mumtaz Faisal,
Murali Ashwin,
Akbar Waseem,
AlBastaki Nader
Publication year - 2015
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.201400535
Subject(s) - ultrafiltration (renal) , factorial experiment , filtration (mathematics) , central composite design , design of experiments , process engineering , engineering , scale (ratio) , residual , dead end , fractional factorial design , linear regression , mathematics , statistics , chemistry , chromatography , response surface methodology , physics , flow (mathematics) , geometry , quantum mechanics , algorithm
An industrial‐scale dead‐end ultrafiltration system was optimized using statistically designed experiments. Given a certain level of pollutant, a two‐level full factorial design and a central composite design were used to optimize the filtrate production of a single 8‐inch industrial ultrafiltration membrane while manipulating the levels of four factors: feed pressure, backwash pressure, forward filtration time, and backwash time. Analysis of variance and residual analysis were used to validate and check the adequacy of the developed regression models. The optimal levels were later validated experimentally. The predicted filtrate production was in reasonable agreement with the experimental data.