Impeller Design Optimization

The choice of a turboexpander load may be influenced by the desire to optimize refrigeration. In other words, a dynamometer load may be chosen over a generator load due to speed considerations. Additionally, there are other constraints imposed on optimal design. Factors such as impeller peripheral velocity (tip speed), bearing design, axial load balance, material selection, and manufacturing methods (which have greatly improved in the recent decades) all have an influence. 

The models presented correctly predict blend time and reaction product distribution. The reaction model correctly predicts the effects of scale, impeller speed, and feed location. This shows that such models can provide valuable tools for designing chemical reactors. Process problems may be avoided by using CFM early in the design stage. When designing an industrial chemical reactor it is recommended that the values of the model constants are determined on a laboratory scale. The reaction model constants can then be used to optimize the product conversion on the production scale varying agitator speed and feed position. 

Among the factors impacting binder performance in high shear equipment are the binder quantity, binder addition method (wet vs dry), solvent quantity, solvent addition rate and method (spray vs open tube), wet massing time, impeller speed, chopper speed, and equipment design and these parameters need to be optimized during the development process, typically with the aid of a statistically designed set of experiments. 

Initial process development elforts usually begin with evaluation of a standard process that is well characterized. During this evaluation, various indicators of bioreactor performance, such as cell mass and productivity, are monitored and then analyzed in order to design further process development studies. Bioreactor parameters optimized often include inoculum cell density, impeller speed, medium pH, nutrient levels, temperature, and so forth. 

The traditional study of suspension crystallization has been carried out using the MSMPR crystallization model. It has been found that uniform mixing in a commercial-size crystallizer, as required by the MSMPR model, is impossible to achieve. Therefore, the understanding of industrial crystallization is hampered by the use of the MSMPR model. Also, it is difficult to experimentally study the effects of mixing on crystallization, as described earlier in Section 64.2.5. Therefore, the CFD presents the means for local simulation in the tank. Furthermore, CFD simulation enables the tank to be designed so that the shape and the positioning of the impellers and the liquid velocity create the optimal level of supersaturation and mass transferrate in all locations. This is likely to result in a narrowing of the particle size distribution. 

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