Altafini, C.R., Wander, P.R. and Barreto, R.M. (2003). Prediction of the working parameters of a wood waste gasifier through an equilibrium model, Energy Conversion and Management44(17): 2763–2777.10.1016/S0196-8904(03)00025-6
Babu, B. and Sheth, P.N. (2006). Modeling and simulation of reduction zone of downdraft biomass gasifier: Effect of char reactivity factor, Energy Conversion and Management47(15–16): 2602–2611.10.1016/j.enconman.2005.10.032
Bontempi, G., Birattari, M. and Bersini, H. (1999). Lazy learning for local modelling and control design, International Journal of Control72(7–8): 643–658.10.1080/002071799220830
Chavan, P., Sharma, T., Mall, B., Rajurkar, B., Tambe, S., Sharma, B. and Kulkarni, B. (2012). Development of data-driven models for fluidized-bed coal gasification process, Fuel93: 44–51.10.1016/j.fuel.2011.11.039
Cheng, C. and Chiu, M.-S. (2004a). A new data-based methodology for nonlinear process modeling, Chemical Engineering Science59(13): 2801–2810.10.1016/j.ces.2004.04.020
Cheng, C. and Chiu, M.-S. (2004b). A new data-based methodology for nonlinear process modeling, Chemical Engineering Science59(13): 2801–2810.10.1016/j.ces.2004.04.020
Corella, J. and Sanz, A. (2005). Modeling circulating fluidized bed biomass gasifiers. A pseudo-rigorous model for stationary state, Fuel Processing Technology86(9): 1021–1053.
Demir, B. and Erturk, S. (2007). Hyperspectral image classification using relevance vector machines, IEEE Geoscience and Remote Sensing Letters4(4): 586–590.10.1109/LGRS.2007.903069
Fiaschi, D. and Michelini, M. (2001). A two-phase one-dimensional biomass gasification kinetics model, Biomass and Bioenergy21(2): 121–132.10.1016/S0961-9534(01)00018-6
Garcia, E.K., Feldman, S., Gupta, M.R. and Srivastava, S. (2010). Completely lazy learning, IEEE Transactions on Knowledge and Data Engineering22(9): 1274–1285.10.1109/TKDE.2009.159
Gordillo, E. and Belghit, A. (2011). A two phase model of high temperature steam-only gasification of biomass char in bubbling fluidized bed reactors using nuclear heat, International Journal of Hydrogen Energy36(1): 374–381.10.1016/j.ijhydene.2010.09.088
Han, P., Li, D.-Z. and Wang, Z. (2008). A study on the biomass gasification process model based on least squares SVM, Energy Conservation Technology1(147): 3–7.
Hou, Z.-S. and Wang, Z. (2013). From model-based control to data-driven control: Survey, classification and perspective, Information Sciences235: 3–35.10.1016/j.ins.2012.07.014
Hou, Z.-S. and Xu, J.-X. (2009). On data-driven control theory: The state of the art and perspective, Acta Automatica Sinica35(6): 650–667.10.3724/SP.J.1004.2009.00650
Huang, C.-E., Li, D. and Xue, Y. (2013). Active disturbance rejection control for the ALSTOM gasifier benchmark problem, Control Engineering Practice21(4): 556–564.10.1016/j.conengprac.2012.11.014
Kalita, P., Clifford, M., Jiamjiroch, K., Kalita, K., Mahanta, P. and Saha, U. (2013). Characterization and analysis of thermal response of rice husk for gasification applications, Journal of Renewable and Sustainable Energy5(1): 013119.10.1063/1.4792487
Liu, S., Sun, J., Ji, H. and Hou, Z. (2020). Model free adaptive control for the temperature adjustment of UGI coal gasification processes in synthetic ammonia industry, IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS), Guangxi, China, pp. 1–6.
Mondal, P., Dang, G. and Garg, M. (2011). Syngas production through gasification and cleanup for downstream applications: Recent developments, Fuel Processing Technology92(8): 1395–1410.10.1016/j.fuproc.2011.03.021
Nelles, O. and Isermann, R. (1996). Basis function networks for interpolation of local linear models, Proceedings of the 35th IEEE Conference on Decision and Control, Kobe, Japan, pp. 470–475.
Nougués, J., Pan, Y., Velo, E. and Puigjaner, L. (2000). Identification of a pilot scale fluidised-bed coal gasification unit by using neural networks, Applied Thermal Engineering20(15–16): 1561–1575.10.1016/S1359-4311(00)00023-5
Papole, G., Focke, W.W. and Manyala, N. (2012). Characterization of medium-temperature Sasol–Lurgi gasifier coal tar pitch, Fuel98: 243–248.10.1016/j.fuel.2012.04.002
Petersen, I. and Werther, J. (2005). Experimental investigation and modeling of gasification of sewage sludge in the circulating fluidized bed, Chemical Engineering and Processing: Process Intensification44(7): 717–736.10.1016/j.cep.2004.09.001
Puig-Arnavat, M., Hernández, J.A., Bruno, J.C. and Coronas, A. (2013). Artificial neural network models for biomass gasification in fluidized bed gasifiers, Biomass and Bioenergy49: 279–289.10.1016/j.biombioe.2012.12.012
Raman, P., Walawender, W.P., Fan, L. and Chang, C.-C. (1981). Mathematical model for the fluid-bed gasification of biomass materials: Application to feedlot manure, Industrial & Engineering Chemistry Process Design and Development20(4): 686–692.10.1021/i200015a019
Ren, Y.-Q., Xu, S.-S. and Gao, S.-W. (2004). Development status and tendency of coal gasification technology with dry coal feed in china, Electric Power37(6): 49–52.
Ruggiero, M. and Manfrida, G. (1999). An equilibrium model for biomass gasification processes, Renewable Energy16(1–4): 1106–1109.10.1016/S0960-1481(98)00429-7
Sadaka, S.S., Ghaly, A. and Sabbah, M. (2002). Two phase biomass, air-steam gasification model for fluidized bed reactors. Part I: Model development, Biomass and Bioenergy22(6): 439–462.
Shabbir, Z., Tay, D.H. and Ng, D.K. (2012). A hybrid optimisation model for the synthesis of sustainable gasification-based integrated biorefinery, Chemical Engineering Research and Design90(10): 1568–1581.10.1016/j.cherd.2012.02.015
Simani, S., Farsoni, S. and Castaldi, P. (2018). Data-driven techniques for the fault diagnosis of a wind turbine benchmark, International Journal of Applied Mathematics and Computer Science28(2): 247–268, DOI: 10.2478/amcs-2018-0018.10.2478/amcs-2018-0018
Simone, M., Barontini, F., Nicolella, C. and Tognotti, L. (2013). Assessment of syngas composition variability in a pilot-scale downdraft biomass gasifier by an extended equilibrium model, Bioresource Technology140: 43–52.10.1016/j.biortech.2013.04.05223672938
Srinivas, T., Gupta, A. and Reddy, B. (2009). Thermodynamic equilibrium model and exergy analysis of a biomass gasifier, Journal of Energy Resources Technology131(3): 98–107.10.1115/1.3185354
Sun, B., Liu, Y., Chen, X., Zhou, Q. and Su, M. (2011). Dynamic modeling and simulation of shell gasifier in IGCC, Fuel Processing Technology92(8): 1418–1425.10.1016/j.fuproc.2011.02.017
Taieb, S.B., Sorjamaa, A. and Bontempi, G. (2010). Multiple-output modeling for multi-step-ahead time series forecasting, Neurocomputing73(10–12): 1950–1957.10.1016/j.neucom.2009.11.030
Tipping, M.E. and Faul, A.C. (2003). Fast marginal likelihood maximisation for sparse Bayesian models, Proceedings of the 9th International Workshop on Artificial Intelligence and Statistics, Key West, USA, pp. 1–14.
Wei, L., Yang, Y., Nishikawa, R.M., Wernick, M.N. and Edwards, A. (2005). Relevance vector machine for automatic detection of clustered microcalcifications, IEEE Transactions on Medical Imaging24(10): 1278–1285.10.1109/TMI.2005.85543516229415
Wei, Q. and Liu, D. (2013). Adaptive dynamic programming for optimal tracking control of unknown nonlinear systems with application to coal gasification, IEEE Transactions on Automation Science and Engineering11(4): 1020–1036.10.1109/TASE.2013.2284545
Wei, Q. and Liu, D. (2014). Data-driven neuro-optimal temperature control of water–gas shift reaction using stable iterative adaptive dynamic programming, IEEE Transactions on Industrial Electronics61(11): 6399–6408.10.1109/TIE.2014.2301770
Yu, G.W., Wang, Y.M. and Xu, Y.Y. (2013). Modeling analysis of Shell, Texaco gasification technology’s effects on water gas shift for Fischer–Tropsch process, Advanced Materials Research608–609: 1446–1453.10.4028/www.scientific.net/AMR.608-609.1446
Zanoli, S., Astolfi, G. and Barboni, L. (2012). Application of a new dataset selection procedure for the prediction of the syngas composition of a gasification plant, IFAC Proceedings Volumes45(15): 868–873.10.3182/20120710-4-SG-2026.00065
Zhao, D., Liu, J., Wu, R., Cheng, D. and Tang, X. (2019). An active exploration method for data efficient reinforcement learning, International Journal of Applied Mathematics and Computer Science29(2): 351–362, DOI: 10.2478/amcs-2019-0026.10.2478/amcs-2019-0026