Novel prosperous computational estimations for greenhouse gas adsorptive control by zeolites using machine learning methods.

To predict CO2 adsorptive capture, as a vital environmental issue, using different zeolites including 5A, 13X, T-Type, SSZ-13, and SAPO-34, different models have been developed by implementing artificial intelligence algorithms. Hybrid adaptive neuro-fuzzy inference system (Hybrid-ANFIS), particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS) and the least-squares support vector machine (LSSVM) modeling optimized with the coupled simulated annealing (CSA) optimization have been employed for the models. The developed models, validated by utilizing various graphical and statistical methods exhibited that the Hybrid-ANFIS model estimations for the gas adsorption on 5A, T-Type, SSZ-13, and SAPO-34 zeolites with average absolute relative deviation (AARD) % of 8.21, 1.92, 4.99 and 2.26, and PSO ANFIS model estimations for the gas adsorption on zeolite 13X with an AARD of 4.85% were in good agreement with corresponding experimental data. It could be deduced that the proposed models were more prosperous and efficient in favor of the design and analysis of adsorption processes than previous ones.

Rigorous prognostication and modeling of gas adsorption on activated carbon and Zeolite-5A.

Gas adsorption on various adsorbents is of highly important issue for the separation of gas mixtures in many industrial processes. In this work, estimation of pure gases (CH4, N2, CO2, H2, C2H4) adsorption on activated carbon (AC) and CO2, CH4, N2 on Zeolite-5A adsorbent were studied by developing four different computing techniques, namely MLP-ANN, ANFIS, LSSVM, and PSO-ANFIS for a broad range of experimental data found in the literature. Temperature, pressure, pore size (only for AC) and kinetic diameter of adsorbed gases are considered as the inputs and the gas adsorption as the output parameters of the developed models. We also used several statistical and graphical tools to assess the accuracy and applicability of the proposed models. The results of the study suggest the reliability and validity of all the models developed for estimating the equilibrium adsorption of gases on the adsorbents. Also, it is found that of all the models developed, the ANN model estimates experimental data of the gas adsorption on AC more accurately due to its values of R2 and AARD%, 0.9865 and 0.8948, respectively. Besides, PSO-ANFIS is the best model to prognosticate gas adsorption on zeolite 5A with R2 and AARD%, 0.9897 and 0.9551, respectively.