Abstract

Stirred tank reactors are widely used in chemical process industries due to their flexibility in design and reliability. Stirred tanks are used for reactions, leaching, crystallization, extraction, evaporation, etc. operations. Most of the processes in chemical engineering involve heat effects, and it is necessary to provide or extract heat from the system. The stirred tanks often employ jackets and single/multiple internal helical coils for the heat transfer. The overall heat transfer rate depends upon the area available, temperature driving force, and overall heat transfer coefficient. The overall heat transfer coefficient is strongly dependent upon the hydrodynamics in the system. The accurate estimation of overall heat transfer coefficient involves understanding and quantification of process side and utility side heat transfer coefficient, fouling effects, and the wall conduction resistance. Many empirical/semi-empirical correlations are available in the published literature. These correlations account for impeller Reynolds number, fluid Prandtl number, viscosity ratio, dimensionless geometric factors, etc. Machine learning-based techniques can provide more accurate and unified correlation for heat transfer prediction by accounting the complex interaction between process and geometric factors. Computational fluid dynamics is also a useful tool to get detailed flow patterns and temperature profile in the reactor. In case of gas-liquid dispersion in stirred tanks, heat transfer rates are enhanced due to gas sparging. Heat transfer coefficient is also affected by particle properties and concentration in solid-liquid slurries. Heat transfer in glass lined reactors is controlled by glass thermal conductivity, and intensification is achieved using specialized design on jacket side.

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