During crystallization processes the control and effective distribution of heat transfer from the heat exchanger to the solution plays an important role. The turbulent flow field and the temperature variations in the solution determine the local supersaturation profiles and the spatial particle distribution. Thus they have a strong impact on the final product quality, production capacity and efficiency of the process. In this sense, a sensor able to determine in situ the local process variables to get better insight in the process behavior, would be required. The recently proposed Smart moving Process Environment Actuators and Sensors (Smart PEAS) is a promising initiative to achieve a more efficient process control. In this PEAS system a network of floating sensors is integrated using Ultra Wide Band (UWB) wireless technology to form a monitoring and control system. As a first phase in the development of the Smart PEAS, the research is focused on the accurate measurements of the flow field and local temperature distribution in a reactor. The hydrodynamics of the Smart PEAS are very important to achieve the necessary accurate process information. In order to determine the hydrodynamic characteristics of the Smart PEAS and to validate and compare the obtained results, laternative non intrusive techniques are used to investigate them in this work. Microencapsulated liquid crystals are used as a measurement technique to study the local temperature and flow field in a heat exchanger crystallizer geometry. To measure the 3D flow and temperature fields the microencapsulated liquid crystals are recorded in a sheet of light plane inside the crystallizer by two digital color cameras in a stereoscopic position. The images of the liquid crystals are correlated to obtain the three velocity components (3C), while from the colors of the microencapsulated liquid crystals the local temperature can be deduced after appropriate calibration. Parallel experiments are done to investigate the three dimensional trajectories of different sensor geometries in the equipment by direct visualization of the sensors. Computational fluid dynamics simulations are performed to calculate the flow field, temperature distribution and sensor trajectories. The results are compared with the experimental results for validation. The validation of the computational simulation results with the experiments gave the necessary confidence to predict flow fields in new crystallizer designs. On the basis of the analysis the Smart PEAS sensor design and the reliability of the measurements obtained can be compared and implemented.

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