[
Aha, D.W. (1997). Lazy Learning, Kluwer Academic Publisher, Dordrecht.10.1007/978-94-017-2053-3
]Search in Google Scholar
[
Aha, D.W., Kibler, D. and Albert, M.K. (1991). Instance-based learning algorithms, Machine Learning 6(1): 37–66.10.1007/BF00153759
]Search in Google Scholar
[
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 Management 44(17): 2763–2777.10.1016/S0196-8904(03)00025-6
]Search in Google Scholar
[
Atkeson, C.G., Moore, A.W. and Schaal, S. (1997). Locally weighted learning, in D.A. Kidwell (Ed.), Lazy Learning, Springer, Dordrecht, pp. 11–73.10.1007/978-94-017-2053-3_2
]Search in Google Scholar
[
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 Management 47(15–16): 2602–2611.10.1016/j.enconman.2005.10.032
]Search in Google Scholar
[
Bontempi, G., Birattari, M. and Bersini, H. (1999). Lazy learning for local modelling and control design, International Journal of Control 72(7–8): 643–658.10.1080/002071799220830
]Search in Google Scholar
[
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, Fuel 93: 44–51.10.1016/j.fuel.2011.11.039
]Search in Google Scholar
[
Chen, S., Gunn, S.R. and Harris, C.J. (2001). The relevance vector machine technique for channel equalization application, IEEE Transactions on Neural Networks 12(6): 1529–1532.10.1109/72.96379218249985
]Search in Google Scholar
[
Cheng, C. and Chiu, M.-S. (2004a). A new data-based methodology for nonlinear process modeling, Chemical Engineering Science 59(13): 2801–2810.10.1016/j.ces.2004.04.020
]Search in Google Scholar
[
Cheng, C. and Chiu, M.-S. (2004b). A new data-based methodology for nonlinear process modeling, Chemical Engineering Science 59(13): 2801–2810.10.1016/j.ces.2004.04.020
]Search in Google Scholar
[
Corella, J. and Sanz, A. (2005). Modeling circulating fluidized bed biomass gasifiers. A pseudo-rigorous model for stationary state, Fuel Processing Technology 86(9): 1021–1053.
]Search in Google Scholar
[
Demir, B. and Erturk, S. (2007). Hyperspectral image classification using relevance vector machines, IEEE Geoscience and Remote Sensing Letters 4(4): 586–590.10.1109/LGRS.2007.903069
]Search in Google Scholar
[
Fiaschi, D. and Michelini, M. (2001). A two-phase one-dimensional biomass gasification kinetics model, Biomass and Bioenergy 21(2): 121–132.10.1016/S0961-9534(01)00018-6
]Search in Google Scholar
[
Garcia, E.K., Feldman, S., Gupta, M.R. and Srivastava, S. (2010). Completely lazy learning, IEEE Transactions on Knowledge and Data Engineering 22(9): 1274–1285.10.1109/TKDE.2009.159
]Search in Google Scholar
[
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 Energy 36(1): 374–381.10.1016/j.ijhydene.2010.09.088
]Search in Google Scholar
[
Han, P., Li, D.-Z. and Wang, Z. (2008). A study on the biomass gasification process model based on least squares SVM, Energy Conservation Technology 1(147): 3–7.
]Search in Google Scholar
[
Hou, Z.-S. and Wang, Z. (2013). From model-based control to data-driven control: Survey, classification and perspective, Information Sciences 235: 3–35.10.1016/j.ins.2012.07.014
]Search in Google Scholar
[
Hou, Z.-S. and Xu, J.-X. (2009). On data-driven control theory: The state of the art and perspective, Acta Automatica Sinica 35(6): 650–667.10.3724/SP.J.1004.2009.00650
]Search in Google Scholar
[
Huang, C.-E., Li, D. and Xue, Y. (2013). Active disturbance rejection control for the ALSTOM gasifier benchmark problem, Control Engineering Practice 21(4): 556–564.10.1016/j.conengprac.2012.11.014
]Search in Google Scholar
[
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 Energy 5(1): 013119.10.1063/1.4792487
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
Mondal, P., Dang, G. and Garg, M. (2011). Syngas production through gasification and cleanup for downstream applications: Recent developments, Fuel Processing Technology 92(8): 1395–1410.10.1016/j.fuproc.2011.03.021
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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 Engineering 20(15–16): 1561–1575.10.1016/S1359-4311(00)00023-5
]Search in Google Scholar
[
Papole, G., Focke, W.W. and Manyala, N. (2012). Characterization of medium-temperature Sasol–Lurgi gasifier coal tar pitch, Fuel 98: 243–248.10.1016/j.fuel.2012.04.002
]Search in Google Scholar
[
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 Intensification 44(7): 717–736.10.1016/j.cep.2004.09.001
]Search in Google Scholar
[
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 Bioenergy 49: 279–289.10.1016/j.biombioe.2012.12.012
]Search in Google Scholar
[
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 Development 20(4): 686–692.10.1021/i200015a019
]Search in Google Scholar
[
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 Power 37(6): 49–52.
]Search in Google Scholar
[
Ruggiero, M. and Manfrida, G. (1999). An equilibrium model for biomass gasification processes, Renewable Energy 16(1–4): 1106–1109.10.1016/S0960-1481(98)00429-7
]Search in Google Scholar
[
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 Bioenergy 22(6): 439–462.
]Search in Google Scholar
[
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 Design 90(10): 1568–1581.10.1016/j.cherd.2012.02.015
]Search in Google Scholar
[
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 Science 28(2): 247–268, DOI: 10.2478/amcs-2018-0018.10.2478/amcs-2018-0018
]Search in Google Scholar
[
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 Technology 140: 43–52.10.1016/j.biortech.2013.04.05223672938
]Search in Google Scholar
[
Srinivas, T., Gupta, A. and Reddy, B. (2009). Thermodynamic equilibrium model and exergy analysis of a biomass gasifier, Journal of Energy Resources Technology 131(3): 98–107.10.1115/1.3185354
]Search in Google Scholar
[
Sun, B., Liu, Y., Chen, X., Zhou, Q. and Su, M. (2011). Dynamic modeling and simulation of shell gasifier in IGCC, Fuel Processing Technology 92(8): 1418–1425.10.1016/j.fuproc.2011.02.017
]Search in Google Scholar
[
Taieb, S.B., Sorjamaa, A. and Bontempi, G. (2010). Multiple-output modeling for multi-step-ahead time series forecasting, Neurocomputing 73(10–12): 1950–1957.10.1016/j.neucom.2009.11.030
]Search in Google Scholar
[
Tipping, M.E. (2001). Sparse Bayesian learning and the relevance vector machine, Journal of Machine Learning Research 1(Jun): 211–244.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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 Imaging 24(10): 1278–1285.10.1109/TMI.2005.85543516229415
]Search in Google Scholar
[
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 Engineering 11(4): 1020–1036.10.1109/TASE.2013.2284545
]Search in Google Scholar
[
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 Electronics 61(11): 6399–6408.10.1109/TIE.2014.2301770
]Search in Google Scholar
[
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 Research 608–609: 1446–1453.10.4028/www.scientific.net/AMR.608-609.1446
]Search in Google Scholar
[
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 Volumes 45(15): 868–873.10.3182/20120710-4-SG-2026.00065
]Search in Google Scholar
[
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 Science 29(2): 351–362, DOI: 10.2478/amcs-2019-0026.10.2478/amcs-2019-0026
]Search in Google Scholar
