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Our research focuses on the application of airlift contactors (ALRs) for the decontamination of CO 2 -containing gas streams, such as biogas. To assess the performance of ALRs during CO 2  absorption, a complex experimental programme was applied in a laboratory-scale rectangular pneumatic contactor, able to operate either as a bubble column or as an airlift reactor. Using the experimental data, a model based on  artificial neural network (ANN)  was developed. The algorithm for determining the optimal neural network model and for reactor optimization is  clonal selection (CS) , belonging to  artificial immune system  class, which is a new computational intelligence paradigm based on the principles of the vertebrate immune system. To improve its capabilities and the probability for highly suitable models and input combinations, addressing maximum efficiency, a  Back-Propagation (BK)  algorithm – a supervised learning method based on the delta rule – is used as a local search procedure. It is applied in a greedy manner for the best antibody found in each generation. Since the highest affinity antibodies are cloned in the next generation, the effect of  BK  on the suitability of the individuals propagates into a large proportion of the population. In parallel with the  BK  hybridization of the basic  CS–ANN  combination, a series of normalization procedures are included for improving the overall results provided by the new algorithm called  nCS-MBK  (normalized  Clonal Selection-Multilayer Perceptron Neural Network  and  Back-Propagation  algorithm). The optimization allowed for achieving the optimal reactor configuration, which leads to a maximum amount of CO 2  dissolved in water.

international journal of nonlinear sciences and numerical simulation

Modelling and Optimization of CO 2  Absorption in Pneumatic Contactors Using Artificial Neural Networks Developed with Clonal Selection-Based Algorithm