Design, construction and commisioning of bioreactor experimental rig to measure kLa of oxigen in newtonian fluid
In this research, a bioreactor experimental rig was designed, constructed and finally commissioned in order to measure the kLa and kap of oxygen in Newtonian fluid. Distilled water was used as a sample of Newtonian fluid. The design of the rig was based on the standard geometry of a stirred tank rea...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2010
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| Online Access: | http://umpir.ump.edu.my/id/eprint/3378/ http://umpir.ump.edu.my/id/eprint/3378/ http://umpir.ump.edu.my/id/eprint/3378/1/CD5825_MUHAMMAD_AFIQ_BIN_ARIFFIN.pdf |
| Summary: | In this research, a bioreactor experimental rig was designed, constructed and finally commissioned in order to measure the kLa and kap of oxygen in Newtonian fluid. Distilled water was used as a sample of Newtonian fluid. The design of the rig was based on the standard geometry of a stirred tank reactor. Standard geometrical ratios of a stirred tank reactor with two Rushton disc turbine (RDT) impellers was used to design a system that can provide good mixing for a gas-liquid system. Two sets of RDT impellers were used to disperse the gas sparged by a ring sparger into the liquid content in the tank. The construction of this rig was done with the help of the Assistant Training Vocational Officer in the engineering workshop. The tank was built using transparent Polyvinyl chloride (PVC) while the two impellers and four equally spaced baffles were constructed using stainless steel. The effect of two variables, namely impeller speed and air flow rate on the volumetric transfer coefficient of oxygen from gas to the bulk liquid, kLa and on the mass transfer coefficient of oxygen from bulk liquid to the oxygen electrode, kap were studied. From the result, the increase in impeller speed and air flow rate will both increase the value of kLa and kap. The values of kLa and kap were predicted by fitting the mass transfer equation of oxygen to the experimental data by using sum of squared error minimization of the difference between the actual and predicted data using MATLAB programming. |
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