Learning from a carbon dioxide capture system dataset: Application of the piecewise neural network algorithm

Svendsen, 2011, Carbon dioxide capture by absorption, challenges and possibilities, Chem. Eng. J., 171, 718, 10.1016/j.cej.2011.01.014 Merkel, 2010, Power plant post-combustion carbon dioxide capture : an opportunity for membranes, J. Membr. Sci., 1, 126, 10.1016/j.memsci.2009.10.041 Zhou, 2012, Part 4b: application of data modeling and analysis techniques to the CO2 capture process system, J. Carbon Manag., 3, 81 Zhou, 2009, A statistical analysis of the carbon dioxide capture process, Greenh. Gas. Control, 3, 535, 10.1016/j.ijggc.2009.02.007 Zhou, 2010, Application of three data analysis techniques for modeling the carbon dioxide capture process, vol. 23, 1 Zhou, 2013, Application of neuro-fuzzy modeling technique for operational problem solving in a CO2 capture process system, Int. J. Greenh. Gas Control, 15, 32, 10.1016/j.ijggc.2013.01.031 Rao, 2002, A technical, economic and environmental assessment of amine-based CO2 capture technology for power plant greenhouse gas control, Env. Sci. Technol., 36, 4467, 10.1021/es0158861 Rao, 2006, Evaluation of potential cost reductions from improved amine-based CO2 capture system, Energy Policy, 34, 3765, 10.1016/j.enpol.2005.08.004 Idem, 2006, Pilot plant studies of the CO2 capture performance of aqueous MEA and mixed MEA/MDEA solvents at the University of Regina CO2 capture technology development plant and the boundary dam CO2 capture demonstration plant, Ind. Eng. Chem. Res., 45, 2414, 10.1021/ie050569e T. Yokoyama, Japanese R&D on large-scale CO2 capture, Presented at: 2004 ECI Conference on Separations Technology VI: New Perspectives on Very Large-Scale Operations, Fraser Island, Queensland, Australia, 3–8 October 2004. Sakwattanapong, 2005, Behavior of reboiler heat duty for CO2 capture plants using regenerable single and blended alkanolamines, Ind. Eng. Chem. Res., 44, 4465, 10.1021/ie050063w Aroonwilas, 2004, Characterization and comparison of the CO2 absorption performance into single and blended alkanolamines in a packed column, Ind. Eng. Chem. Res., 43, 2228, 10.1021/ie0306067 Aroonwilas, 2007, Integration of CO2 capture unit using single-and blended-amines into supercritical coal-fired power plants: implications for emission and energy management, Ind. Eng. Chem. Res., 1, 143 Meireles, 2003, A comprehensive review for industrial applicability of artificial neural networks, IEEE Trans. Ind. Electron., 50, 585, 10.1109/TIE.2003.812470 Andrews, 1995, Survey and critique of techniques for extracting rules from trained artificial neural networks, Knowledge-Based Syst., 8, 373, 10.1016/0950-7051(96)81920-4 Towell, 1993, Extracting refined rules from knowledge-based neural networks, Mach. Learn., 13, 71, 10.1007/BF00993103 Setiono, 1995, Understanding neural netowrks via rule extraction, 480 Setiono, 2007, Neurolinear: from neural networks to oblique decision rules, Neurocomputing, 17, 1, 10.1016/S0925-2312(97)00038-6 Krishnan, 1999, A search technique for rule extraction from trained neural networks, Pattern Recognit. Lett., 20, 273, 10.1016/S0167-8655(98)00145-7 Kim, 2000, Handling continuous-valued attributes in decision tree with neural network modelling, 211 Setiono, 2002, Extraction of rules from artificial neural networks for nonlinear regression, IEEE Trans. Neural Netw., 13, 564, 10.1109/TNN.2002.1000125 Setiono, 2004, An approach to generate rules from neural networks for regression problems, Eur. J. Oper. Res., 155, 239, 10.1016/S0377-2217(02)00792-0 Odajim, 2008, Greedy rule generation from discrete data and its use in neural network rule extraction, Neural Netw., 21, 1020, 10.1016/j.neunet.2008.01.003 Wang, 2011, Regression rules extraction from artificial neural network based on least squares, 203 Thrun, 1993 Craven, 1996, Extracting tree-structured representations of trained neural networks, Adv. Neural Inf. Process. Syst., 8, 24 Saad, 2007, Neural network explanation using inversion, Neural Netw., 20, 78, 10.1016/j.neunet.2006.07.005 de Fortuny, 2012, Active learning based rule extraction for regression, 926 Augasta, 2012, Rule extraction from neural networks – a comparative study Hruschka, 2006, Extracting rules from multilayer perceptrons in classification problems: a clustering-based approach, Neurocomputing, 70, 384, 10.1016/j.neucom.2005.12.127 Mohamed, 2011, Rules extraction from constructively trained neural networks based on genetic algorithms, Neurocomputing, 74, 3180, 10.1016/j.neucom.2011.04.009 Al Iqbal, 2011, Eclectic extraction of propositional rules from neural networks, 234 Lichman, 2013 Wu, 2009