(140c) Multiobjective Optimization of a Spouted Bed Reactor

Alwan, G. M. - Presenter, University of Technology
Nedeltchev, S. - Presenter, Missouri University of Science and Technology
Aradhya, S. - Presenter, Missouri University of Science and Technology
Al-Dahhan, M. - Presenter, Missouri University of Science and Technology

Gas-solid spouted beds are either cylindrical bed with cone base or the whole bed is in a cone shape where the gas enters as a jet. The gas forms a spout region that carries the solids upward in a diluted phase which form a fountain at the top of the bed where the solids fall down and move downward in the annular region. These beds have found wide and efficient applications as reactors, dryers, coaters, etc. Various design and operating variables affect the performance of these beds. It seems that the performance of the gas-solid spouted bed benefit from solids uniformity structure along with lower pressure drop (PD). Therefore, the focus of this work is to perform multiobjective optimization on gas-solid spouted bed, to maximize the uniformity index (UI) of the solid particles across the spouted bed and to minimize the PD along the bed. Hence, UI and PD are considered as the objective functions. Four decision variables were selected which affect the objective functions of the process. These decision variables are: gas velocity, pressure, particle density and particle diameter. The dynamics of the spouted bed reactor are  highly interactive nonlinear processes. Thus, the optimization technique is a powerful tool to obtain the desired design parameters and set of operating conditions. This would guide the experimental work and reduce the risk and cost of the design and operation. It also yields the best range of the operating conditions which could improve the efficiency of the spouted bed reactor. Various optimization algorithms are available. However, the reliability of the optimized solutions depends on the formulation of the objective functions and the selected optimization technique.

In this work, genetic algorithm has been used which is considered the best stochastic search technique for highly nonlinear objective functions. The accuracy and the reliability of the optimization search could be enhanced by using adaption operators. It has been found that maximum UI could be obtained with high density particles, while minimum PD could be obtained with lower particle density. The results of the optimization have been compared with the experimental data using sophisticated optical probe and high accuracy pressure transducers. In this presentation, the results and the findings will be discussed.