He, X., Asada, H., 1993. A New Method for Identifying Orders of Input-Output Models for Nonlinear Dynamic Systems. In: 1993 American Control Conference. IEEE, pp. 2520–2523.
Helton, J.C., Davis, F.J., 2003. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab. Eng. Syst. Saf. 81, 23–69.
Nogueira, I., Fontes, C., Sartori, I., Pontes, K., Embiruçu, M., 2017. A model-based approach to quality monitoring of a polymerization process without online measurement of product specifications. Comput. Ind. Eng. 106.
Nogueira, I.B.R., Ribeiro, A.M., Requião, R., Pontes, K. V, Koivisto, H., Rodrigues, A.E., Loureiro, J.M., 2018. A quasi-virtual online analyser based on an artificial neural networks and offline measurements to predict purities of raffinate / extract in simulated moving bed processes. Appl. Soft Comput. J. 67, 29–47.
Oliveira, L.M.C., Koivisto, H., Iwakiri, I.G.I., Loureiro, J.M., Ribeiro, A.M., Nogueira, I.B.R., 2020. Modelling of a pressure swing adsorption unit by deep learning and artificial Intelligence tools. Chem. Eng. Sci. 224.
Ongari, D., Talirz, L., Smit, B., 2020. Too many materials and too many applications , an experimental problem waiting for a computational solution.
Rajagopalan, A.K., Avila, A.M., Rajendran, A., 2016. Do adsorbent screening metrics predict process performance? A process optimisation based study for post-combustion capture of CO2. Int. J. Greenh. Gas Control 46, 76–85.
Regufe, M.J., Ferreira, A.F.P., Loureiro, J.M., Rodrigues, A., Ribeiro, A.M., 2020. Development of Hybrid Materials with Activated Carbon and Zeolite 13X for CO2Capture from Flue Gases by Electric Swing Adsorption. Ind. Eng. Chem. Res. 59, 12197–12211.
Ribeiro, R.P.P.L., Grande, C.A., Rodrigues, A.E., 2012. Electrothermal performance of an activated carbon honeycomb monolith. Chem. Eng. Res. Des. 90, 2013–2022.
Sant Anna, H.R., Barreto, A.G., Tavares, F.W., de Souza, M.B., 2017. Machine learning model and optimization of a PSA unit for methane-nitrogen separation. Comput. Chem. Eng. 104, 377–391.
Wang, C., Klatt, K., D, G., Engell, S., Hanisch, F., 2003. Neural network-based identification of SMB chromatographic processes. Control Eng. Pract. 11, 949–959.