267006 Optimization of Reactive Absorption Using an Evolutionary Algorithm
Optimization of reactive absorption using an evolutionary algorithm
Chinmay Kale, Philip Lutze, Andrzej Górak
TU Dortmund University, Department of Biochemical and Chemical Engineering, Laboratory of Fluid Separations, Emil-Figge-Strasse 70, D-44227 Dortmund, Germany; phone: +49 (0) 231 755-3034, e-mail: email@example.com
Keywords: Column design, operating conditions, non-equilibrium stage model, operating cost
Suggested theme: Separations division: Distillation and absorption - Advances in Absorption (02a02)
Increasing amount of CO2 is considered as the main reason for the global warming. Hence, carbon capture and storage has received a lot of attention over the past decade. Post combustion capture of CO2 by reactive absorption using aqueous amines is an attractive solution to mitigate the CO2 emissions from coal-fired power plants. This process has been extensively studied in terms of thermodynamics, reaction kinetics and the selection of amine(s). Nevertheless, for economic design of the absorption columns, rigorous analysis of the process based on non-equilibrium stage model and process optimization is necessary. In this contribution, results of detailed model validation of the reactive absorption together with the results of the sensitivity analysis and optimization are presented.
The process optimization is based on a generic non-equilibrium stage model which considers multicomponent mass and heat transfer, chemical reactions, non-ideal behaviour of the phases and hydrodynamics in the column. The model has been validated using the experimental results from the pilot scale absorption column and it predicts the experimental results with acceptable accuracy .
The optimization study was done by assuming 90% removal of CO2 from feed gas as the design criterion for absorption column. The optimization problem is solved by applying an evolutionary algorithm  to minimize the annualized operating costs for absorption of CO2 as objective function. The optimization variables are mainly column dimensions, flow rates and concentration of absorption solvent. The optimization method mentioned above allows the simultaneous determination of the dimensions of the column and operating conditions required to achieve the design criteria with minimum possible operating cost.
 C. Kale, I. Tönnies, H. Hasse, A. Górak, Computer aided chemical engineering 2011, 29.
 R. Angira, B. Babu, Chemical Engineering Science 2006, 61 (14), 4707.