Integrating deep learning models and multiparametric programming
Tài liệu tham khảo
Afram, 2017, Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: a state of the art review and case study of a residential HVAC system, Energy Build., 141, 96, 10.1016/j.enbuild.2017.02.012
Ahmadi-Moshkenani, 2018, Combinatorial approach toward multiparametric quadratic programming based on characterizing adjacent critical regions, IEEE Trans. Autom. Control, 63, 3221, 10.1109/TAC.2018.2791479
Asher, 2015, A review of surrogate models and their application to groundwater modeling, Water Resour. Res., 51, 5957, 10.1002/2015WR016967
Avraamidou, 2019, A multi-parametric optimization approach for bilevel mixed-integer linear and quadratic programming problems, Comput. Chem. Eng., 125, 98, 10.1016/j.compchemeng.2019.01.021
Bemporad, 2002, The explicit linear quadratic regulator for constrained systems, Automatica, 38, 3, 10.1016/S0005-1098(01)00174-1
Beykal, 2018, Optimal design of energy systems using constrained grey-box multi-objective optimization, Comput. Chem. Eng., 116, 488, 10.1016/j.compchemeng.2018.02.017
Burnak, 2019, Integrated process design, scheduling, and control using multiparametric programming, Comput. Chem. Eng., 125, 164, 10.1016/j.compchemeng.2019.03.004
Burnak, 2018, Simultaneous process scheduling and control: amultiparametric programming-based approach, Ind. Eng. Chem. Res., 57, 3963, 10.1021/acs.iecr.7b04457
Charitopoulos, 2018, Multi-parametric mixed integer linear programming under global uncertainty, Comput. Chem. Eng., 116, 279, 10.1016/j.compchemeng.2018.04.015
Chen, 2018, Approximating explicit model predictive control using constrained neural networks, 1520
Chiang, 2017, Big data analytics in chemical engineering, Annu. Rev. Chem. Biomol. Eng., 8, 63, 10.1146/annurev-chembioeng-060816-101555
Eaton, 1992, Model-predictive control of chemical processes, Chem. Eng. Sci., 47, 705, 10.1016/0009-2509(92)80263-C
Eckle, 2019, A comparison of deep networks with relu activation function and linear spline-type methods, Neural Netw., 110, 232, 10.1016/j.neunet.2018.11.005
Fischetti, 2018, Deep neural networks and mixed integer linear optimization, Constraints, 23, 296, 10.1007/s10601-018-9285-6
Grimstad, B., Andersson, H., 2019a. Relu networks as surrogate models in mixed-integer linear programs. arXiv:1907.03140.
Grimstad, 2019, Relu networks as surrogate models in mixed-integer linear programs, Comput. Chem. Eng., 106580, 10.1016/j.compchemeng.2019.106580
Gupta, 2011, A novel approach to multiparametric quadratic programming, Automatica, 47, 2112, 10.1016/j.automatica.2011.06.019
Herceg, 2013, Multi-Parametric Toolbox 3.0, 502
Himmelblau, 2000, Applications of artificial neural networks in chemical engineering, Korean J. Chem. Eng., 17, 373, 10.1007/BF02706848
Himmelblau, 2008, Accounts of experiences in the application of artificial neural networks in chemical engineering, Ind. Eng. Chem. Res., 47, 5782, 10.1021/ie800076s
Hough, 2017, Application of machine learning to pyrolysis reaction networks: reducing model solution time to enable process optimization, Comput. Chem. Eng., 104, 56, 10.1016/j.compchemeng.2017.04.012
Jones, 2006, Multiparametric linear complementarity problems, 5687
Karasuyama, 2012, Multi-parametric solution-path algorithm for instance-weighted support vector machines, Mach. Learn., 88, 297, 10.1007/s10994-012-5288-5
Katz, 2020
Katz, 2018, The impact of model approximation in multiparametric model predictive control, Chem. Eng. Res. Des., 139, 211, 10.1016/j.cherd.2018.09.034
Kim, 2019, Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques, Optim. Lett., 1
Kopanos, 2014, Reactive scheduling by a multiparametric programming rolling horizon framework: a case of a network of combined heat and power units, Ind. Eng. Chem. Res., 53, 4366, 10.1021/ie402393s
Lee, 2018, Explicit model predictive control for linear time-variant systems with application to double-lane-change maneuver, PLoS One, 13, 10.1371/journal.pone.0208071
Ljung, 1999
Montufar, 2014, On the number of linear regions of deep neural networks, 2924
Nagata, 2003, Optimization of a fermentation medium using neural networks and genetic algorithms, Biotechnol. Lett., 25, 1837, 10.1023/A:1026225526558
Ning, 2019, Optimization under uncertainty in the era of big data and deep learning: when machine learning meets mathematical programming, Comput. Chem. Eng., 125, 434, 10.1016/j.compchemeng.2019.03.034
Oberdieck, 2016, On multi-parametric programming and its applications in process systems engineering, Chem. Eng. Res. Des., 116, 61, 10.1016/j.cherd.2016.09.034
Oberdieck, 2016, POP – parametric optimization toolbox, Ind. Eng. Chem. Res., 55, 8979, 10.1021/acs.iecr.6b01913
Oberdieck, 2017, Explicit model predictive control: a connected-graph approach, Automatica, 76, 103, 10.1016/j.automatica.2016.10.005
Oberdieck, 2015, Explicit hybrid model-predictive control: the exact solution, Automatica, 58, 152, 10.1016/j.automatica.2015.05.021
Pfrommer, 2018, Optimisation of manufacturing process parameters using deep neural networks as surrogate models, Procedia CIRP, 72, 426, 10.1016/j.procir.2018.03.046
Pinkus, 1999, Approximation theory of the MLP model in neural networks, Acta Numer., 8, 143, 10.1017/S0962492900002919
Poole, 2010
van Ravenzwaaij, 2018, A simple introduction to Markov chain monte–carlo sampling, Psychonom. Bull. Rev., 25, 143, 10.3758/s13423-016-1015-8
Rister, 2017, Piecewise convexity of artificial neural networks, Neural Netw., 94, 34, 10.1016/j.neunet.2017.06.009
Schweidtmann, 2019, Deterministic global process optimization: accurate (single-species) properties via artificial neural networks, Comput. Chem. Eng., 121, 67, 10.1016/j.compchemeng.2018.10.007
Shang, 2019, Data analytics and machine learning for smart process manufacturing: recent advances and perspectives in the big data era, Engineering, 10.1016/j.eng.2019.01.019
Shokry, 2018, Data-driven soft-sensors for online monitoring of batch processes with different initial conditions, Comput. Chem. Eng., 118, 159, 10.1016/j.compchemeng.2018.07.014
Steuer, 2006, Portfolio optimization: new capabilities and future methods, Z. Betriebswirtschaft, 76, 199, 10.1007/s11573-006-0006-z
Tran, 2018, Bayesian model averaging for estimating the spatial temperature distribution in a steam methane reforming furnace, Chem. Eng. Res. Des., 131, 465, 10.1016/j.cherd.2017.09.027
Tso, 2019, Multi-scale energy systems engineering for optimal natural gas utilization, Catal. Today
Wittmann-Hohlbein, 2013, On the global solution of multi-parametric mixed integer linear programming problems, J. Glob. Optim., 57, 51, 10.1007/s10898-012-9895-2
Wu, 2019, Real-time adaptive machine-learning-based predictive control of nonlinear processes, Ind. Eng. Chem. Res
Wu, 2019, Machine learning-based predictive control of nonlinear processes. Part I: theory, AIChE J., 65, e16734, 10.1002/aic.16734
Wu, 2019, Machine-learning-based predictive control of nonlinear processes. Part II: computational implementation, AIChE J., 65, e16734, 10.1002/aic.16734