Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks
•Artificial Neural Networks (ANNs) were trained on stochastic multiscale model data.•ANNs were used in online nonlinear model predictive control scheme.•ANNs provided accurate predictions for industrially relevant observable values.•ANN computational costs were orders of magnitude lower than the ori...
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| Published in | Chemical engineering science Vol. 207; pp. 1230 - 1245 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
02.11.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0009-2509 1873-4405 1873-4405 |
| DOI | 10.1016/j.ces.2019.07.044 |
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| Abstract | •Artificial Neural Networks (ANNs) were trained on stochastic multiscale model data.•ANNs were used in online nonlinear model predictive control scheme.•ANNs provided accurate predictions for industrially relevant observable values.•ANN computational costs were orders of magnitude lower than the original model.•The accuracy of ANNs deteriorated for observable values subject to stochastic noise.
The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-driven models that would enable the shrinking horizon nonlinear model predictive control of a computationally intensive stochastic multiscale system. The system of choice was a simulation of thin film formation by chemical vapour deposition. Two ANNs were trained to estimate the system’s observables. The ANNs were subsequently employed in a shrinking horizon optimization scheme to obtain the optimal time-varying profiles of the manipulated variables that would meet the desired thin film properties at the end of the batch. The resulting profiles were validated using the stochastic multiscale system and a good agreement with the predictions of the ANNs was observed. Due to their observed computational efficiency, accuracy, and the ability to reject disturbances, the ANNs seem to be a promising approach for online optimization and control of computationally demanding multiscale process systems. |
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| AbstractList | •Artificial Neural Networks (ANNs) were trained on stochastic multiscale model data.•ANNs were used in online nonlinear model predictive control scheme.•ANNs provided accurate predictions for industrially relevant observable values.•ANN computational costs were orders of magnitude lower than the original model.•The accuracy of ANNs deteriorated for observable values subject to stochastic noise.
The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-driven models that would enable the shrinking horizon nonlinear model predictive control of a computationally intensive stochastic multiscale system. The system of choice was a simulation of thin film formation by chemical vapour deposition. Two ANNs were trained to estimate the system’s observables. The ANNs were subsequently employed in a shrinking horizon optimization scheme to obtain the optimal time-varying profiles of the manipulated variables that would meet the desired thin film properties at the end of the batch. The resulting profiles were validated using the stochastic multiscale system and a good agreement with the predictions of the ANNs was observed. Due to their observed computational efficiency, accuracy, and the ability to reject disturbances, the ANNs seem to be a promising approach for online optimization and control of computationally demanding multiscale process systems. |
| Author | Kimaev, Grigoriy Ricardez-Sandoval, Luis A. |
| Author_xml | – sequence: 1 givenname: Grigoriy surname: Kimaev fullname: Kimaev, Grigoriy – sequence: 2 givenname: Luis A. surname: Ricardez-Sandoval fullname: Ricardez-Sandoval, Luis A. email: laricard@uwaterloo.ca |
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| Cites_doi | 10.1021/ie030535b 10.1002/aic.16489 10.1109/TPWRS.2008.2008606 10.1016/j.compchemeng.2018.08.029 10.1109/TNNLS.2015.2512283 10.1016/S0009-2509(00)00034-8 10.1002/aic.10505 10.1016/j.jprocont.2015.07.002 10.1002/aic.690470615 10.1016/j.compchemeng.2018.03.011 10.1007/s12247-010-9090-2 10.1016/S1570-7946(10)28215-9 10.1002/we.2142 10.1016/j.compchemeng.2018.08.011 10.1016/j.cherd.2019.03.004 10.1007/978-0-8176-4793-3 10.1016/S0065-2377(05)30001-9 10.1016/j.ejor.2016.06.041 10.1002/aic.690351106 10.1016/j.ijhydene.2018.01.130 10.1002/aic.690351210 10.3390/math6110242 10.1016/0893-6080(91)90009-T 10.1016/j.compchemeng.2017.03.011 10.1090/qam/10666 10.1016/j.procs.2016.09.321 10.1016/j.cherd.2019.05.049 10.1016/S0895-7177(03)90013-6 10.1137/0111030 10.1016/j.renene.2016.06.065 10.1021/acs.iecr.8b05938 10.1016/j.compchemeng.2017.05.020 10.1016/j.compchemeng.2018.08.010 10.1016/j.ces.2011.08.020 10.1016/S0009-2509(02)00432-3 10.1109/72.329697 10.1016/j.compchemeng.2004.02.014 10.1016/j.envsoft.2006.03.004 10.1016/S0255-2701(02)00209-X 10.1021/ac00029a018 10.1006/jcph.2000.6473 10.1002/aic.10922 10.1016/j.jcat.2015.03.010 10.1103/PhysRevB.64.035401 10.1016/S0169-7439(97)00061-0 10.1002/aic.15702 10.1016/j.jhydrol.2009.12.013 10.1007/s11071-016-2957-0 10.1021/ie00021a019 10.1166/jcsmd.2015.1069 10.1016/j.ces.2010.06.004 10.1016/j.neunet.2009.10.009 10.1049/ip-gtd:20010286 10.1103/PhysRevB.49.4858 10.1002/aic.15215 10.1016/0893-6080(90)90044-L 10.1002/aic.690431115 10.1016/j.ces.2015.10.004 10.1002/aic.14039 10.1002/aic.690470113 10.1016/S0009-2509(03)00168-4 10.1016/j.jprocont.2012.07.011 10.1109/ICPST.2002.1047201 10.1016/j.jhydrol.2015.06.007 10.1016/j.compchemeng.2006.05.025 10.1016/j.biortech.2015.09.114 10.1016/j.ress.2012.05.002 10.1016/j.ces.2007.07.071 10.1016/j.jhydrol.2014.10.009 10.1016/j.ces.2015.02.027 10.1002/aic.11756 10.1016/j.biortech.2011.10.006 10.1016/j.cherd.2018.06.015 10.1021/acs.iecr.7b00682 10.1016/j.ces.2015.02.002 10.1016/j.jcrysgro.2009.12.041 10.1016/j.ces.2014.07.058 10.1021/acs.iecr.5b02942 10.1016/j.cherd.2019.02.016 10.1016/j.cherd.2018.10.006 10.1109/59.544636 10.1016/j.ces.2013.12.005 10.1002/1097-0142(20010415)91:8+<1615::AID-CNCR1175>3.0.CO;2-L 10.1016/j.ejor.2017.01.035 10.1007/BF02478259 10.1016/j.ces.2011.05.050 10.1016/j.ces.2014.05.027 |
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| Keywords | Multiscale stochastic system Nonlinear model predictive control Artificial neural network Machine learning Shrinking horizon optimization |
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| References | Lang, Waibel, Hinton (b0245) 1990; 3 Venkatasubramanian (b0440) 2019; 65 Thomas, Kardos, Joseph (b0425) 1994 Bird, Stewart, Lightfoot (b0045) 2002 Borggaard, Thodberg (b0050) 1992; 64 Nandi, Ghosh, Tambe, Kulkarni (b0305) 2001; 47 Hasenauer, Jagiella, Hross, Theis (b0155) 2015; 3 Dong, Zhang, Huang (b0120) 2001 Luo, Heun, Kennedy, Wollschläger, Henzler (b0285) 1994; 49 Lian, Zeng, Yao, Tang, Chen (b0280) 2016; 27 Jalali, Nieuwenhuyse, Picheny (b0180) 2017; 261 Kwon, Nayhouse, Christofides, Orkoulas (b0230) 2014; 107 Rasoulian, Ricardez-Sandoval (b0350) 2016; 140 Christofides, P.D., Armaou, A., Lou, Y., Varshney, A. (2009). Control and optimization of multiscale process systems. In: Levine W.S., (Ed.), first ed., Birkhäuser Boston, New York, NY. Krishnapura, Jutan (b0210) 2000; 55 Zhang, Ding, Christofides (b0490) 2019; 147 Sutskever, Hinton (b0410) 2010; 23 Oliveira (b0320) 2004; 28 Lawrynczuk (b0255) 2016; 86 Dayhoff, DeLeo (b0105) 2001; 91 Bach-Andersen, Rømer-Odgaard, Winther (b0035) 2018; 21 Seagaran (b0370) 2007 Du Tao, Wang Xiuli, Wang Xifan. (n.d.). A combined model of wavelet and neural network for short term load forecasting. In: Proceedings. International Conference on Power System Technology (Vol. 4, pp. 2331–2335). IEEE. Kimaev, Ricardez-Sandoval (b0200) 2018; 140 Soni, Parker (b0400) 2004; 43 Khanmohammadi, Tutun, Kucuk (b0190) 2016; 95 Rasoulian, Ricardez-Sandoval (b0345) 2015; 136 Shaikh, Al-Dahhan (b0375) 2003; 42 Yuan, Jiao, Quddus, Kwon, Mashuga (b0485) 2019; 58 Adomaitis (b0005) 2003; 38 Ozkaya, Demir, Bilgili (b0325) 2007; 22 Kumar, Venkateswarlu (b0215) 2012; 103 Chaffart, Rasoulian, Ricardez-Sandoval (b0060) 2016; 62 Bashir, El-Hawary (b0040) 2009; 24 Rasoulian, Ricardez-Sandoval (b0340) 2015; 34 Salciccioli, Stamatakis, Caratzoulas, Vlachos (b0355) 2011; 66 Ding, Zhang, Kim, Tran, Wu, Christofides (b0115) 2019; 145 Sharma, Das, Samanta (b0380) 2006; 52 Garg, Mhaskar (b0140) 2017; 56 Yıldız, Uzun, Ceylan, Topcu (b0480) 2016; 200 Joseph, Hanratty (b0185) 1993; 32 Venkatasubramanian, Vaidyanathan (b0450) 1992 Venkatasubramanian, Chan (b0445) 1989; 35 Levenberg (b0265) 1944; 2 Svozil, Kvasnicka, Pospichal (b0415) 1997; 39 Kleijnen (b0205) 2017; 256 Kwon, Nayhouse, Orkoulas, Christofides (b0225) 2014; 119 Crose, Zhang, Tran, Christofides (b0100) 2018; 113 Gandhi, Joshi, Jayaraman, Kulkarni (b0135) 2007; 62 Venkatasubramanian (b0435) 2009; 55 Lam, Vlachos (b0240) 2001; 64 Solvason (b0390) 2011 Hornik (b0160) 1991; 4 Akkisetty, Lee, Reklaitis, Venkatasubramanian (b0020) 2010; 5 Huang, Yang (b0165) 2001; 148 Lapedes, A., Farber, R. (1987). Nonlinear signal processing using neural networks: Prediction and system modelling. In: IEEE international conference on neural networks, San Diego, CA, USA, 21 Jun 1987. Retrieved from Adomaitis (b0015) 2010; 312 Fausett (b0125) 1993 Raimondeau, Vlachos (b0330) 2000; 160 Chow, Leung (b0075) 1996; 11 Ding, Ping (b0110) 2012; 22 Watanabe, Matsuura, Abe, Kubota, Himmelblau (b0465) 1989; 35 Kwon, Nayhouse, Christofides (b0235) 2015; 54 Sarkar, Modak (b0360) 2003; 58 Wu, Tran, Ren, Barnes, Chen, Christofides (b0475) 2019; 145 Hagan, Menhaj (b0150) 1994; 5 Marquardt (b0290) 1963; 11 Li, Liu, Li, Fang, Liu, Mei, Wang (b0275) 2018; 43 Lee, Mohr, Kwon, Wu (b0260) 2018; 118 Chitsazan, Nadiri, Tsai (b0070) 2015; 528 Meidanshahi, Corbett, Adams, Mhaskar (b0300) 2017; 103 Sundaram, Ghosh, Caruthers, Venkatasubramanian (b0405) 2001; 47 Antanasijevic, Pocajt, Peric-Grujic, Ristic (b0030) 2014; 519 Narasingam, Kwon (b0310) 2018; 119 Tiwari, Chatterjee (b0430) 2010; 382 Oladyshkin, Nowak (b0315) 2012; 106 Crose, Sang-Il Kwon, Nayhouse, Ni, Christofides (b0090) 2015; 136 Vlachos (b0455) 1997; 43 Crose, Kwon, Tran, Christofides (b0095) 2017; 100 Solvason, Chemmangattuvalappil, Eden (b0395) 2010; 28 Alexandridis, Sarimveis (b0025) 2005; 51 Garg, Corbett, Mhaskar, Hu, Flores-Cerrillo (b0145) 2017; 106 Siddhamshetty, Wu, Kwon (b0385) 2018; 136 Christofides, Armaou (b0080) 2006; 30 Cheimarios, Kokkoris, Boudouvis (b0065) 2010; 65 Rasoulian, Ricardez-Sandoval (b0335) 2014; 116 Kwon, Nayhouse, Christofides, Orkoulas (b0220) 2013; 59 Vlachos (b0460) 2005; 30 Li, Croiset, Ricardez-Sandoval (b0270) 2015; 326 Adomaitis (b0010) 2010 Kimaev, Ricardez-Sandoval (b0195) 2017; 63 Frenkel, Smit (b0130) 2001 Huang, Zhan, Chen, Lü (b0170) 2003; 58 Huang, Zhang, Orkoulas, Christofides (b0175) 2011; 66 McCulloch, Pitts (b0295) 1943; 5 Chaffart, Ricardez-Sandoval (b0055) 2018; 119 Schroeder. (2007). An Introduction to Thermal Physics. Pearson Education. Wong, Chee, Li, Wang (b0470) 2018; 6 Crose (10.1016/j.ces.2019.07.044_b0095) 2017; 100 Zhang (10.1016/j.ces.2019.07.044_b0490) 2019; 147 Lian (10.1016/j.ces.2019.07.044_b0280) 2016; 27 Huang (10.1016/j.ces.2019.07.044_b0170) 2003; 58 Chitsazan (10.1016/j.ces.2019.07.044_b0070) 2015; 528 Hagan (10.1016/j.ces.2019.07.044_b0150) 1994; 5 Raimondeau (10.1016/j.ces.2019.07.044_b0330) 2000; 160 Seagaran (10.1016/j.ces.2019.07.044_b0370) 2007 Shaikh (10.1016/j.ces.2019.07.044_b0375) 2003; 42 Li (10.1016/j.ces.2019.07.044_b0270) 2015; 326 10.1016/j.ces.2019.07.044_b0365 Solvason (10.1016/j.ces.2019.07.044_b0390) 2011 Rasoulian (10.1016/j.ces.2019.07.044_b0340) 2015; 34 Vlachos (10.1016/j.ces.2019.07.044_b0460) 2005; 30 Yıldız (10.1016/j.ces.2019.07.044_b0480) 2016; 200 Joseph (10.1016/j.ces.2019.07.044_b0185) 1993; 32 Kumar (10.1016/j.ces.2019.07.044_b0215) 2012; 103 Yuan (10.1016/j.ces.2019.07.044_b0485) 2019; 58 10.1016/j.ces.2019.07.044_b0085 Huang (10.1016/j.ces.2019.07.044_b0165) 2001; 148 Garg (10.1016/j.ces.2019.07.044_b0145) 2017; 106 Tiwari (10.1016/j.ces.2019.07.044_b0430) 2010; 382 Chaffart (10.1016/j.ces.2019.07.044_b0055) 2018; 119 Kimaev (10.1016/j.ces.2019.07.044_b0200) 2018; 140 Khanmohammadi (10.1016/j.ces.2019.07.044_b0190) 2016; 95 Kwon (10.1016/j.ces.2019.07.044_b0225) 2014; 119 Oliveira (10.1016/j.ces.2019.07.044_b0320) 2004; 28 Thomas (10.1016/j.ces.2019.07.044_b0425) 1994 Borggaard (10.1016/j.ces.2019.07.044_b0050) 1992; 64 Siddhamshetty (10.1016/j.ces.2019.07.044_b0385) 2018; 136 Marquardt (10.1016/j.ces.2019.07.044_b0290) 1963; 11 Alexandridis (10.1016/j.ces.2019.07.044_b0025) 2005; 51 Jalali (10.1016/j.ces.2019.07.044_b0180) 2017; 261 Venkatasubramanian (10.1016/j.ces.2019.07.044_b0440) 2019; 65 Li (10.1016/j.ces.2019.07.044_b0275) 2018; 43 Garg (10.1016/j.ces.2019.07.044_b0140) 2017; 56 Gandhi (10.1016/j.ces.2019.07.044_b0135) 2007; 62 Venkatasubramanian (10.1016/j.ces.2019.07.044_b0450) 1992 Chaffart (10.1016/j.ces.2019.07.044_b0060) 2016; 62 Crose (10.1016/j.ces.2019.07.044_b0090) 2015; 136 Vlachos (10.1016/j.ces.2019.07.044_b0455) 1997; 43 Chow (10.1016/j.ces.2019.07.044_b0075) 1996; 11 McCulloch (10.1016/j.ces.2019.07.044_b0295) 1943; 5 Adomaitis (10.1016/j.ces.2019.07.044_b0010) 2010 Venkatasubramanian (10.1016/j.ces.2019.07.044_b0435) 2009; 55 Kleijnen (10.1016/j.ces.2019.07.044_b0205) 2017; 256 Nandi (10.1016/j.ces.2019.07.044_b0305) 2001; 47 Hasenauer (10.1016/j.ces.2019.07.044_b0155) 2015; 3 Venkatasubramanian (10.1016/j.ces.2019.07.044_b0445) 1989; 35 Fausett (10.1016/j.ces.2019.07.044_b0125) 1993 Sutskever (10.1016/j.ces.2019.07.044_b0410) 2010; 23 Solvason (10.1016/j.ces.2019.07.044_b0395) 2010; 28 Ding (10.1016/j.ces.2019.07.044_b0110) 2012; 22 Soni (10.1016/j.ces.2019.07.044_b0400) 2004; 43 Rasoulian (10.1016/j.ces.2019.07.044_b0335) 2014; 116 Wu (10.1016/j.ces.2019.07.044_b0475) 2019; 145 Crose (10.1016/j.ces.2019.07.044_b0100) 2018; 113 Ding (10.1016/j.ces.2019.07.044_b0115) 2019; 145 Lee (10.1016/j.ces.2019.07.044_b0260) 2018; 118 Kwon (10.1016/j.ces.2019.07.044_b0235) 2015; 54 Dong (10.1016/j.ces.2019.07.044_b0120) 2001 Kwon (10.1016/j.ces.2019.07.044_b0230) 2014; 107 Wong (10.1016/j.ces.2019.07.044_b0470) 2018; 6 Watanabe (10.1016/j.ces.2019.07.044_b0465) 1989; 35 Frenkel (10.1016/j.ces.2019.07.044_b0130) 2001 Luo (10.1016/j.ces.2019.07.044_b0285) 1994; 49 Bach-Andersen (10.1016/j.ces.2019.07.044_b0035) 2018; 21 Hornik (10.1016/j.ces.2019.07.044_b0160) 1991; 4 Svozil (10.1016/j.ces.2019.07.044_b0415) 1997; 39 Salciccioli (10.1016/j.ces.2019.07.044_b0355) 2011; 66 Sundaram (10.1016/j.ces.2019.07.044_b0405) 2001; 47 Bird (10.1016/j.ces.2019.07.044_b0045) 2002 Ozkaya (10.1016/j.ces.2019.07.044_b0325) 2007; 22 Lang (10.1016/j.ces.2019.07.044_b0245) 1990; 3 Huang (10.1016/j.ces.2019.07.044_b0175) 2011; 66 Christofides (10.1016/j.ces.2019.07.044_b0080) 2006; 30 Krishnapura (10.1016/j.ces.2019.07.044_b0210) 2000; 55 Kimaev (10.1016/j.ces.2019.07.044_b0195) 2017; 63 Oladyshkin (10.1016/j.ces.2019.07.044_b0315) 2012; 106 Sharma (10.1016/j.ces.2019.07.044_b0380) 2006; 52 10.1016/j.ces.2019.07.044_b0420 Cheimarios (10.1016/j.ces.2019.07.044_b0065) 2010; 65 Levenberg (10.1016/j.ces.2019.07.044_b0265) 1944; 2 Meidanshahi (10.1016/j.ces.2019.07.044_b0300) 2017; 103 Rasoulian (10.1016/j.ces.2019.07.044_b0350) 2016; 140 Adomaitis (10.1016/j.ces.2019.07.044_b0015) 2010; 312 Sarkar (10.1016/j.ces.2019.07.044_b0360) 2003; 58 Narasingam (10.1016/j.ces.2019.07.044_b0310) 2018; 119 Lam (10.1016/j.ces.2019.07.044_b0240) 2001; 64 Adomaitis (10.1016/j.ces.2019.07.044_b0005) 2003; 38 Akkisetty (10.1016/j.ces.2019.07.044_b0020) 2010; 5 Kwon (10.1016/j.ces.2019.07.044_b0220) 2013; 59 Lawrynczuk (10.1016/j.ces.2019.07.044_b0255) 2016; 86 Dayhoff (10.1016/j.ces.2019.07.044_b0105) 2001; 91 Antanasijevic (10.1016/j.ces.2019.07.044_b0030) 2014; 519 Bashir (10.1016/j.ces.2019.07.044_b0040) 2009; 24 10.1016/j.ces.2019.07.044_b0250 Rasoulian (10.1016/j.ces.2019.07.044_b0345) 2015; 136 |
| References_xml | – volume: 106 start-page: 179 year: 2012 end-page: 190 ident: b0315 article-title: Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion publication-title: Reliab. Eng. Syst. Saf. – volume: 42 start-page: 599 year: 2003 end-page: 610 ident: b0375 article-title: Development of an artificial neural network correlation for prediction of overall gas holdup in bubble column reactors publication-title: Chem. Eng. Process. Process Intensif. – volume: 32 start-page: 1951 year: 1993 end-page: 1961 ident: b0185 article-title: Predictive control of quality in a batch manufacturing process using artificial neural network models publication-title: Ind. Eng. Chem. Res. – volume: 3 start-page: 101 year: 2015 end-page: 121 ident: b0155 article-title: Data-driven modelling of biological multi-scale processes publication-title: J. Coupled Syst. Multiscale Dyn. – volume: 65 start-page: 466 year: 2019 end-page: 478 ident: b0440 article-title: The promise of artificial intelligence in chemical engineering: is it here, finally? publication-title: AIChE J. – volume: 148 start-page: 222 year: 2001 ident: b0165 article-title: Evolving wavelet-based networks for short-term load forecasting publication-title: IEE Proc. – Generat. Transmiss. Distribut. – volume: 51 start-page: 2495 year: 2005 end-page: 2506 ident: b0025 article-title: Nonlinear adaptive model predictive control based on self-correcting neural network models publication-title: AIChE J. – volume: 95 start-page: 237 year: 2016 end-page: 244 ident: b0190 article-title: A new multilevel input layer artificial neural network for predicting flight delays at JFK airport publication-title: Procedia – Procedia Comput. Sci. – reference: Du Tao, Wang Xiuli, Wang Xifan. (n.d.). A combined model of wavelet and neural network for short term load forecasting. In: Proceedings. International Conference on Power System Technology (Vol. 4, pp. 2331–2335). IEEE. – volume: 56 start-page: 7491 year: 2017 end-page: 7502 ident: b0140 article-title: Subspace identification-based modeling and control of batch particulate processes publication-title: Ind. Eng. Chem. Res. – volume: 91 start-page: 1615 year: 2001 end-page: 1635 ident: b0105 article-title: Artificial neural networks: opening the black box publication-title: Cancer – volume: 28 start-page: 1285 year: 2010 end-page: 1290 ident: b0395 article-title: Multi-Scale chemical product design using the reverse problem formulation publication-title: Comput. Aid. Chem. Eng. – volume: 39 start-page: 43 year: 1997 end-page: 62 ident: b0415 article-title: Introduction to multi-layer feed-forward neural networks publication-title: Chemomet. Intell. Laborat. Syst. – volume: 43 start-page: 3031 year: 1997 end-page: 3041 ident: b0455 article-title: Multiscale Integration Hybrid Algorithms for Homogeneous-Heterogeneous Reactors publication-title: AIChE J. – volume: 65 start-page: 5018 year: 2010 end-page: 5028 ident: b0065 article-title: Multiscale modeling in chemical vapor deposition processes: coupling reactor scale with feature scale computations publication-title: Chem. Eng. Sci. – volume: 107 start-page: 47 year: 2014 end-page: 57 ident: b0230 article-title: Modeling and control of crystal shape in continuous protein crystallization publication-title: Chem. Eng. Sci. – volume: 28 start-page: 755 year: 2004 end-page: 766 ident: b0320 article-title: Combining first principles modelling and artificial neural networks: a general framework publication-title: Comput. Chem. Eng. – volume: 22 start-page: 1773 year: 2012 end-page: 1784 ident: b0110 article-title: Dynamic output feedback model predictive control for nonlinear systems represented by Hammerstein – Wiener model publication-title: J. Process Control – volume: 103 start-page: 39 year: 2017 end-page: 57 ident: b0300 article-title: Subspace model identification and model predictive control based cost analysis of a semicontinuous distillation process publication-title: Comput. Chem. Eng. – volume: 59 start-page: 2317 year: 2013 end-page: 2327 ident: b0220 article-title: Modeling and control of protein crystal shape and size in batch crystallization publication-title: AIChE J. – start-page: 547 year: 1992 end-page: 552 ident: b0450 article-title: Diagnosing noisy process data using neural networks publication-title: IFAC Symposia Series – volume: 27 start-page: 2683 year: 2016 end-page: 2695 ident: b0280 article-title: Landslide displacement prediction with uncertainty based on neural networks with random hidden weights publication-title: IEEE Trans. Neural Networks Learn. Syst. – year: 2010 ident: b0010 article-title: Multiscale modeling and optimization of an atomic layer deposition process for nanomanufacturing applications publication-title: IFAC Proceedings Volumes (Vol. 43, pp. 859–864) – volume: 49 start-page: 4858 year: 1994 end-page: 4865 ident: b0285 article-title: Surface roughness and conductivity of thin Ag films publication-title: Phys. Rev. B – volume: 47 start-page: 126 year: 2001 end-page: 141 ident: b0305 article-title: Artificial neural-network-assisted stochastic process optimization strategies publication-title: AIChE J. – volume: 200 start-page: 42 year: 2016 end-page: 47 ident: b0480 article-title: Application of artificial neural networks to co-combustion of hazelnut husk–lignite coal blends publication-title: Bioresour. Technol. – year: 2002 ident: b0045 article-title: Transport Phenomena – volume: 54 start-page: 11903 year: 2015 end-page: 11914 ident: b0235 article-title: Multiscale, multidomain modeling and parallel computation: application to crystal shape evolution in crystallization publication-title: Ind. Eng. Chem. Res. – volume: 86 start-page: 1193 year: 2016 end-page: 1214 ident: b0255 article-title: Nonlinear predictive control of dynamic systems represented by Wiener-Hammerstein models publication-title: Nonlinear Dyn. – volume: 47 start-page: 1387 year: 2001 end-page: 1406 ident: b0405 article-title: Design of fuel additives using neural networks and evolutionary algorithms publication-title: AIChE J. – volume: 55 start-page: 2 year: 2009 end-page: 8 ident: b0435 article-title: DROWNING IN DATA: Informatics and modeling challenges in a data-rich networked world publication-title: AIChE J. – volume: 160 start-page: 564 year: 2000 end-page: 576 ident: b0330 article-title: Low-dimensional approximations of multiscale epitaxial growth models for microstructure control of materials publication-title: J. Comput. Phys. – volume: 6 year: 2018 ident: b0470 article-title: Recurrent Neural Network-Based Model Predictive Control for Continuous Pharmaceutical Manufacturing publication-title: Mathematics – volume: 66 start-page: 5955 year: 2011 end-page: 5967 ident: b0175 article-title: Dynamics and control of aggregate thin film surface morphology for improved light trapping: implementation on a large-lattice kinetic Monte Carlo model publication-title: Chem. Eng. Sci. – volume: 5 start-page: 115 year: 1943 end-page: 133 ident: b0295 article-title: A logical calculus of the ideas immanent in nervous activity publication-title: Bull. Math. Biophys. – volume: 140 start-page: 33 year: 2018 end-page: 43 ident: b0200 article-title: Multilevel Monte Carlo for noise estimation in stochastic multiscale systems publication-title: Chem. Eng. Res. Des. – reference: Schroeder. (2007). An Introduction to Thermal Physics. Pearson Education. – volume: 35 start-page: 1803 year: 1989 end-page: 1812 ident: b0465 article-title: Incipient fault diagnosis of chemical processes via artificial neural networks publication-title: AIChE J. – volume: 326 start-page: 15 year: 2015 end-page: 25 ident: b0270 article-title: Carbon nanotube growth: First-principles-based kinetic Monte Carlo model publication-title: J. Catal. – volume: 261 start-page: 279 year: 2017 end-page: 301 ident: b0180 article-title: Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise publication-title: Eur. J. Oper. Res. – volume: 58 start-page: 3531 year: 2019 end-page: 3537 ident: b0485 article-title: Developing quantitative structure-property relationship models to predict the upper flammability limit using machine learning publication-title: Ind. Eng. Chem. Res. – volume: 5 start-page: 989 year: 1994 end-page: 993 ident: b0150 article-title: Training feedforward networks with the Marquardt algorithm publication-title: IEEE Trans. Neural Netw. – volume: 119 start-page: 101 year: 2018 end-page: 111 ident: b0310 article-title: Data-driven identification of interpretable reduced-order models using sparse regression publication-title: Comput. Chem. Eng. – volume: 140 start-page: 90 year: 2016 end-page: 103 ident: b0350 article-title: Stochastic nonlinear model predictive control applied to a thin film deposition process under uncertainty publication-title: Chem. Eng. Sci. – volume: 116 start-page: 590 year: 2014 end-page: 600 ident: b0335 article-title: Uncertainty analysis and robust optimization of multiscale process systems with application to epitaxial thin film growth publication-title: Chem. Eng. Sci. – volume: 11 start-page: 431 year: 1963 end-page: 441 ident: b0290 article-title: An algorithm for least-squares estimation of nonlinear parameters publication-title: J. Soc. Ind. Appl. Math. – reference: Lapedes, A., Farber, R. (1987). Nonlinear signal processing using neural networks: Prediction and system modelling. In: IEEE international conference on neural networks, San Diego, CA, USA, 21 Jun 1987. Retrieved from – volume: 43 start-page: 3381 year: 2004 end-page: 3393 ident: b0400 article-title: Closed-loop control of fed-batch bioreactors: a shrinking-horizon approach publication-title: Ind. Eng. Chem. Res. – reference: Christofides, P.D., Armaou, A., Lou, Y., Varshney, A. (2009). Control and optimization of multiscale process systems. In: Levine W.S., (Ed.), first ed., Birkhäuser Boston, New York, NY. – volume: 62 start-page: 7078 year: 2007 end-page: 7089 ident: b0135 article-title: Development of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas–liquid systems publication-title: Chem. Eng. Sci. – volume: 119 start-page: 30 year: 2014 end-page: 39 ident: b0225 article-title: Crystal shape and size control using a plug flow crystallization configuration publication-title: Chem. Eng. Sci. – volume: 118 start-page: 283 year: 2018 end-page: 295 ident: b0260 article-title: Kinetic Monte Carlo modeling of multivalent binding of CTB proteins with GM1 receptors publication-title: Comput. Chem. Eng. – volume: 100 start-page: 129 year: 2017 end-page: 140 ident: b0095 article-title: Multiscale modeling and run-to-run control of PECVD of thin film solar cells publication-title: Renew. Energy – volume: 52 start-page: 3018 year: 2006 end-page: 3028 ident: b0380 article-title: ANN–based prediction of two-phase gas– liquid flow patterns in a circular conduit publication-title: AIChE J. – volume: 2 start-page: 164 year: 1944 end-page: 168 ident: b0265 article-title: A method for the solution of certain non-linear problems in least squares publication-title: Q. Appl. Math. – volume: 519 start-page: 1895 year: 2014 end-page: 1907 ident: b0030 article-title: Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo Simulation uncertainty analysis publication-title: J. Hydrol. – volume: 382 start-page: 20 year: 2010 end-page: 33 ident: b0430 article-title: Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs) publication-title: J. Hydrol. – volume: 103 start-page: 300 year: 2012 end-page: 308 ident: b0215 article-title: Estimating biofilm reaction kinetics using hybrid mechanistic-neural network rate function model publication-title: Bioresour. Technol. – year: 2001 ident: b0120 article-title: Adaptive neural network short term load forecasting with wavelet decompositions publication-title: 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502) (Vol. vol.2, p. 6) – volume: 55 start-page: 3803 year: 2000 end-page: 3812 ident: b0210 article-title: A neural adaptive controller publication-title: Chem. Eng. Sci. – volume: 136 start-page: 50 year: 2015 end-page: 61 ident: b0090 article-title: Multiscale modeling and operation of PECVD of thin film solar cells publication-title: Chem. Eng. Sci. – volume: 22 start-page: 815 year: 2007 end-page: 822 ident: b0325 article-title: Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors publication-title: Environ. Modell. Softw. – volume: 256 start-page: 1 year: 2017 end-page: 16 ident: b0205 article-title: Regression and Kriging metamodels with their experimental designs in simulation: a review publication-title: Eur. J. Oper. Res. – volume: 312 start-page: 1449 year: 2010 end-page: 1452 ident: b0015 article-title: Development of a multiscale model for an atomic layer deposition process publication-title: J. Cryst. Growth – volume: 113 start-page: 184 year: 2018 end-page: 195 ident: b0100 article-title: Multiscale three-dimensional CFD modeling for PECVD of amorphous silicon thin films publication-title: Comput. Chem. Eng. – volume: 21 start-page: 29 year: 2018 end-page: 41 ident: b0035 article-title: Deep learning for automated drivetrain fault detection publication-title: Wind Energy – volume: 58 start-page: 81 year: 2003 end-page: 87 ident: b0170 article-title: Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm publication-title: Chem. Eng. Sci. – volume: 136 start-page: 675 year: 2018 end-page: 686 ident: b0385 article-title: Optimization of simultaneously propagating multiple fractures in hydraulic fracturing to achieve uniform growth using data-based model reduction publication-title: Chem. Eng. Res. Des. – volume: 30 start-page: 1 year: 2005 end-page: 61 ident: b0460 article-title: A review of multiscale analysis: examples from systems biology, materials engineering, and other fluid-surface interacting systems publication-title: Adv. Chem. Eng. – volume: 64 start-page: 545 year: 1992 end-page: 551 ident: b0050 article-title: Optimal minimal neural interpretation of spectra publication-title: Anal. Chem. – volume: 11 start-page: 1736 year: 1996 end-page: 1742 ident: b0075 article-title: Neural network based short-term load forecasting using weather compensation publication-title: IEEE Trans. Power Syst. – start-page: 505 year: 1994 end-page: 509 ident: b0425 article-title: Shrinking Horizon model predictive control applied to autoclave curing of composite laminate materials publication-title: American Control Conference – volume: 147 start-page: 529 year: 2019 end-page: 544 ident: b0490 article-title: Multiscale computational fluid dynamics modeling of thermal atomic layer deposition with application to chamber design publication-title: Chem. Eng. Res. Des. – volume: 5 start-page: 161 year: 2010 end-page: 168 ident: b0020 article-title: Population balance model-based hybrid neural network for a pharmaceutical milling process publication-title: J. Pharm. Innovat. – volume: 58 start-page: 3131 year: 2003 end-page: 3142 ident: b0360 article-title: ANNSA: a hybrid artificial neural network/simulated annealing algorithm for optimal control problems publication-title: Chem. Eng. Sci. – volume: 119 start-page: 465 year: 2018 end-page: 479 ident: b0055 article-title: Optimization and control of a thin film growth process: a hybrid first principles/artificial neural network based multiscale modelling approach publication-title: Comput. Chem. Eng. – year: 2001 ident: b0130 article-title: Understanding Molecular Simulation: From Algorithms to Applications – volume: 64 year: 2001 ident: b0240 article-title: Multiscale model for epitaxial growth of films: growth mode transition publication-title: Phys. Rev. B – volume: 35 start-page: 1993 year: 1989 end-page: 2002 ident: b0445 article-title: A neural network methodology for process fault diagnosis publication-title: AIChE J. – volume: 62 year: 2016 ident: b0060 article-title: Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems publication-title: AIChE J. – year: 1993 ident: b0125 article-title: Fundamentals of Neural Networks: Architectures, Algorithms, and Applications – volume: 145 start-page: 159 year: 2019 end-page: 172 ident: b0115 article-title: Microscopic modeling and optimal operation of thermal atomic layer deposition publication-title: Chem. Eng. Res. Des. – volume: 43 start-page: 5512 year: 2018 end-page: 5521 ident: b0275 article-title: Application of general regression neural network to model a novel integrated fluidized bed gasifier publication-title: Int. J. Hydrogen Energy – volume: 136 start-page: 38 year: 2015 end-page: 49 ident: b0345 article-title: A robust nonlinear model predictive controller for a multiscale thin film deposition process publication-title: Chem. Eng. Sci. – volume: 106 start-page: 183 year: 2017 end-page: 190 ident: b0145 article-title: Subspace-based model identification of a hydrogen plant startup dynamics publication-title: Comput. Chem. Eng. – year: 2007 ident: b0370 article-title: Programming Collective Intelligence: Building Smart Web 2.0 Applications – volume: 38 start-page: 159 year: 2003 end-page: 175 ident: b0005 article-title: A reduced-basis discretization method for chemical vapor deposition reactor simulation publication-title: Math. Comput. Modell. – volume: 24 start-page: 20 year: 2009 end-page: 27 ident: b0040 article-title: Applying wavelets to short-term load forecasting using PSO-based neural networks publication-title: IEEE Trans. Power Syst. – volume: 63 start-page: 3361 year: 2017 end-page: 3373 ident: b0195 article-title: A comparison of efficient uncertainty quantification techniques for stochastic multiscale systems publication-title: AIChE J. – volume: 66 start-page: 4319 year: 2011 end-page: 4355 ident: b0355 article-title: A review of multiscale modeling of metal-catalyzed reactions: mechanism development for complexity and emergent behavior publication-title: Chem. Eng. Sci. – volume: 3 start-page: 23 year: 1990 end-page: 43 ident: b0245 article-title: A time-delay neural network architecture for isolated word recognition publication-title: Neural Netw. – volume: 34 start-page: 70 year: 2015 end-page: 81 ident: b0340 article-title: Robust multivariable estimation and control in an epitaxial thin film growth process under uncertainty publication-title: J. Process Control – volume: 23 start-page: 239 year: 2010 end-page: 243 ident: b0410 article-title: Temporal-kernel recurrent neural networks publication-title: Neural Netw. – volume: 145 start-page: 173 year: 2019 end-page: 183 ident: b0475 article-title: Model predictive control of phthalic anhydride synthesis in a fixed-bed catalytic reactor via machine learning modeling publication-title: Chem. Eng. Res. Des. – year: 2011 ident: b0390 article-title: Integrated Multiscale Chemical Product Design using Property Clustering and Decomposition Techniques in a Reverse Problem Formulation – volume: 528 start-page: 52 year: 2015 end-page: 62 ident: b0070 article-title: Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging publication-title: J. Hydrol. – volume: 4 start-page: 251 year: 1991 end-page: 257 ident: b0160 article-title: Approximation capabilities of multilayer feedforward networks publication-title: Neural Netw. – volume: 30 start-page: 1670 year: 2006 end-page: 1686 ident: b0080 article-title: Control and optimization of multiscale process systems publication-title: Comput. Chem. Eng. – volume: 43 start-page: 3381 issue: 13 year: 2004 ident: 10.1016/j.ces.2019.07.044_b0400 article-title: Closed-loop control of fed-batch bioreactors: a shrinking-horizon approach publication-title: Ind. Eng. Chem. Res. doi: 10.1021/ie030535b – volume: 65 start-page: 466 issue: 2 year: 2019 ident: 10.1016/j.ces.2019.07.044_b0440 article-title: The promise of artificial intelligence in chemical engineering: is it here, finally? publication-title: AIChE J. doi: 10.1002/aic.16489 – volume: 24 start-page: 20 issue: 1 year: 2009 ident: 10.1016/j.ces.2019.07.044_b0040 article-title: Applying wavelets to short-term load forecasting using PSO-based neural networks publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2008.2008606 – volume: 119 start-page: 465 year: 2018 ident: 10.1016/j.ces.2019.07.044_b0055 article-title: Optimization and control of a thin film growth process: a hybrid first principles/artificial neural network based multiscale modelling approach publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2018.08.029 – volume: 27 start-page: 2683 issue: 12 year: 2016 ident: 10.1016/j.ces.2019.07.044_b0280 article-title: Landslide displacement prediction with uncertainty based on neural networks with random hidden weights publication-title: IEEE Trans. Neural Networks Learn. Syst. doi: 10.1109/TNNLS.2015.2512283 – volume: 55 start-page: 3803 issue: 18 year: 2000 ident: 10.1016/j.ces.2019.07.044_b0210 article-title: A neural adaptive controller publication-title: Chem. Eng. Sci. doi: 10.1016/S0009-2509(00)00034-8 – volume: 51 start-page: 2495 issue: 9 year: 2005 ident: 10.1016/j.ces.2019.07.044_b0025 article-title: Nonlinear adaptive model predictive control based on self-correcting neural network models publication-title: AIChE J. doi: 10.1002/aic.10505 – volume: 34 start-page: 70 year: 2015 ident: 10.1016/j.ces.2019.07.044_b0340 article-title: Robust multivariable estimation and control in an epitaxial thin film growth process under uncertainty publication-title: J. Process Control doi: 10.1016/j.jprocont.2015.07.002 – volume: 47 start-page: 1387 issue: 6 year: 2001 ident: 10.1016/j.ces.2019.07.044_b0405 article-title: Design of fuel additives using neural networks and evolutionary algorithms publication-title: AIChE J. doi: 10.1002/aic.690470615 – volume: 113 start-page: 184 year: 2018 ident: 10.1016/j.ces.2019.07.044_b0100 article-title: Multiscale three-dimensional CFD modeling for PECVD of amorphous silicon thin films publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2018.03.011 – volume: 5 start-page: 161 issue: 4 year: 2010 ident: 10.1016/j.ces.2019.07.044_b0020 article-title: Population balance model-based hybrid neural network for a pharmaceutical milling process publication-title: J. Pharm. Innovat. doi: 10.1007/s12247-010-9090-2 – volume: 28 start-page: 1285 year: 2010 ident: 10.1016/j.ces.2019.07.044_b0395 article-title: Multi-Scale chemical product design using the reverse problem formulation publication-title: Comput. Aid. Chem. Eng. doi: 10.1016/S1570-7946(10)28215-9 – volume: 21 start-page: 29 issue: 1 year: 2018 ident: 10.1016/j.ces.2019.07.044_b0035 article-title: Deep learning for automated drivetrain fault detection publication-title: Wind Energy doi: 10.1002/we.2142 – volume: 118 start-page: 283 year: 2018 ident: 10.1016/j.ces.2019.07.044_b0260 article-title: Kinetic Monte Carlo modeling of multivalent binding of CTB proteins with GM1 receptors publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2018.08.011 – volume: 145 start-page: 159 year: 2019 ident: 10.1016/j.ces.2019.07.044_b0115 article-title: Microscopic modeling and optimal operation of thermal atomic layer deposition publication-title: Chem. Eng. Res. Des. doi: 10.1016/j.cherd.2019.03.004 – ident: 10.1016/j.ces.2019.07.044_b0085 doi: 10.1007/978-0-8176-4793-3 – volume: 30 start-page: 1 year: 2005 ident: 10.1016/j.ces.2019.07.044_b0460 article-title: A review of multiscale analysis: examples from systems biology, materials engineering, and other fluid-surface interacting systems publication-title: Adv. Chem. Eng. doi: 10.1016/S0065-2377(05)30001-9 – volume: 256 start-page: 1 issue: 1 year: 2017 ident: 10.1016/j.ces.2019.07.044_b0205 article-title: Regression and Kriging metamodels with their experimental designs in simulation: a review publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2016.06.041 – volume: 35 start-page: 1803 issue: 11 year: 1989 ident: 10.1016/j.ces.2019.07.044_b0465 article-title: Incipient fault diagnosis of chemical processes via artificial neural networks publication-title: AIChE J. doi: 10.1002/aic.690351106 – volume: 43 start-page: 5512 issue: 11 year: 2018 ident: 10.1016/j.ces.2019.07.044_b0275 article-title: Application of general regression neural network to model a novel integrated fluidized bed gasifier publication-title: Int. J. Hydrogen Energy doi: 10.1016/j.ijhydene.2018.01.130 – volume: 35 start-page: 1993 issue: 12 year: 1989 ident: 10.1016/j.ces.2019.07.044_b0445 article-title: A neural network methodology for process fault diagnosis publication-title: AIChE J. doi: 10.1002/aic.690351210 – volume: 6 issue: 11 year: 2018 ident: 10.1016/j.ces.2019.07.044_b0470 article-title: Recurrent Neural Network-Based Model Predictive Control for Continuous Pharmaceutical Manufacturing publication-title: Mathematics doi: 10.3390/math6110242 – volume: 4 start-page: 251 issue: 2 year: 1991 ident: 10.1016/j.ces.2019.07.044_b0160 article-title: Approximation capabilities of multilayer feedforward networks publication-title: Neural Netw. doi: 10.1016/0893-6080(91)90009-T – volume: 103 start-page: 39 year: 2017 ident: 10.1016/j.ces.2019.07.044_b0300 article-title: Subspace model identification and model predictive control based cost analysis of a semicontinuous distillation process publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2017.03.011 – volume: 2 start-page: 164 issue: 2 year: 1944 ident: 10.1016/j.ces.2019.07.044_b0265 article-title: A method for the solution of certain non-linear problems in least squares publication-title: Q. Appl. Math. doi: 10.1090/qam/10666 – volume: 95 start-page: 237 year: 2016 ident: 10.1016/j.ces.2019.07.044_b0190 article-title: A new multilevel input layer artificial neural network for predicting flight delays at JFK airport publication-title: Procedia – Procedia Comput. Sci. doi: 10.1016/j.procs.2016.09.321 – volume: 147 start-page: 529 year: 2019 ident: 10.1016/j.ces.2019.07.044_b0490 article-title: Multiscale computational fluid dynamics modeling of thermal atomic layer deposition with application to chamber design publication-title: Chem. Eng. Res. Des. doi: 10.1016/j.cherd.2019.05.049 – volume: 38 start-page: 159 issue: 1–2 year: 2003 ident: 10.1016/j.ces.2019.07.044_b0005 article-title: A reduced-basis discretization method for chemical vapor deposition reactor simulation publication-title: Math. Comput. Modell. doi: 10.1016/S0895-7177(03)90013-6 – volume: 11 start-page: 431 issue: 2 year: 1963 ident: 10.1016/j.ces.2019.07.044_b0290 article-title: An algorithm for least-squares estimation of nonlinear parameters publication-title: J. Soc. Ind. Appl. Math. doi: 10.1137/0111030 – volume: 100 start-page: 129 year: 2017 ident: 10.1016/j.ces.2019.07.044_b0095 article-title: Multiscale modeling and run-to-run control of PECVD of thin film solar cells publication-title: Renew. Energy doi: 10.1016/j.renene.2016.06.065 – volume: 58 start-page: 3531 issue: 8 year: 2019 ident: 10.1016/j.ces.2019.07.044_b0485 article-title: Developing quantitative structure-property relationship models to predict the upper flammability limit using machine learning publication-title: Ind. Eng. Chem. Res. doi: 10.1021/acs.iecr.8b05938 – volume: 106 start-page: 183 year: 2017 ident: 10.1016/j.ces.2019.07.044_b0145 article-title: Subspace-based model identification of a hydrogen plant startup dynamics publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2017.05.020 – year: 2001 ident: 10.1016/j.ces.2019.07.044_b0130 – volume: 119 start-page: 101 year: 2018 ident: 10.1016/j.ces.2019.07.044_b0310 article-title: Data-driven identification of interpretable reduced-order models using sparse regression publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2018.08.010 – volume: 66 start-page: 5955 issue: 23 year: 2011 ident: 10.1016/j.ces.2019.07.044_b0175 article-title: Dynamics and control of aggregate thin film surface morphology for improved light trapping: implementation on a large-lattice kinetic Monte Carlo model publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2011.08.020 – volume: 58 start-page: 81 issue: 1 year: 2003 ident: 10.1016/j.ces.2019.07.044_b0170 article-title: Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm publication-title: Chem. Eng. Sci. doi: 10.1016/S0009-2509(02)00432-3 – ident: 10.1016/j.ces.2019.07.044_b0250 – volume: 5 start-page: 989 issue: 6 year: 1994 ident: 10.1016/j.ces.2019.07.044_b0150 article-title: Training feedforward networks with the Marquardt algorithm publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.329697 – volume: 28 start-page: 755 issue: 5 year: 2004 ident: 10.1016/j.ces.2019.07.044_b0320 article-title: Combining first principles modelling and artificial neural networks: a general framework publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2004.02.014 – volume: 22 start-page: 815 issue: 6 year: 2007 ident: 10.1016/j.ces.2019.07.044_b0325 article-title: Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors publication-title: Environ. Modell. Softw. doi: 10.1016/j.envsoft.2006.03.004 – volume: 42 start-page: 599 issue: 8–9 year: 2003 ident: 10.1016/j.ces.2019.07.044_b0375 article-title: Development of an artificial neural network correlation for prediction of overall gas holdup in bubble column reactors publication-title: Chem. Eng. Process. Process Intensif. doi: 10.1016/S0255-2701(02)00209-X – volume: 64 start-page: 545 issue: 5 year: 1992 ident: 10.1016/j.ces.2019.07.044_b0050 article-title: Optimal minimal neural interpretation of spectra publication-title: Anal. Chem. doi: 10.1021/ac00029a018 – volume: 160 start-page: 564 issue: 2 year: 2000 ident: 10.1016/j.ces.2019.07.044_b0330 article-title: Low-dimensional approximations of multiscale epitaxial growth models for microstructure control of materials publication-title: J. Comput. Phys. doi: 10.1006/jcph.2000.6473 – start-page: 505 year: 1994 ident: 10.1016/j.ces.2019.07.044_b0425 article-title: Shrinking Horizon model predictive control applied to autoclave curing of composite laminate materials – volume: 52 start-page: 3018 issue: 9 year: 2006 ident: 10.1016/j.ces.2019.07.044_b0380 article-title: ANN–based prediction of two-phase gas– liquid flow patterns in a circular conduit publication-title: AIChE J. doi: 10.1002/aic.10922 – volume: 326 start-page: 15 year: 2015 ident: 10.1016/j.ces.2019.07.044_b0270 article-title: Carbon nanotube growth: First-principles-based kinetic Monte Carlo model publication-title: J. Catal. doi: 10.1016/j.jcat.2015.03.010 – volume: 64 issue: 3 year: 2001 ident: 10.1016/j.ces.2019.07.044_b0240 article-title: Multiscale model for epitaxial growth of films: growth mode transition publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.64.035401 – volume: 39 start-page: 43 issue: 1 year: 1997 ident: 10.1016/j.ces.2019.07.044_b0415 article-title: Introduction to multi-layer feed-forward neural networks publication-title: Chemomet. Intell. Laborat. Syst. doi: 10.1016/S0169-7439(97)00061-0 – year: 1993 ident: 10.1016/j.ces.2019.07.044_b0125 – volume: 63 start-page: 3361 issue: 8 year: 2017 ident: 10.1016/j.ces.2019.07.044_b0195 article-title: A comparison of efficient uncertainty quantification techniques for stochastic multiscale systems publication-title: AIChE J. doi: 10.1002/aic.15702 – volume: 382 start-page: 20 issue: 1–4 year: 2010 ident: 10.1016/j.ces.2019.07.044_b0430 article-title: Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs) publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2009.12.013 – start-page: 547 year: 1992 ident: 10.1016/j.ces.2019.07.044_b0450 article-title: Diagnosing noisy process data using neural networks – volume: 86 start-page: 1193 year: 2016 ident: 10.1016/j.ces.2019.07.044_b0255 article-title: Nonlinear predictive control of dynamic systems represented by Wiener-Hammerstein models publication-title: Nonlinear Dyn. doi: 10.1007/s11071-016-2957-0 – ident: 10.1016/j.ces.2019.07.044_b0365 – volume: 32 start-page: 1951 issue: 9 year: 1993 ident: 10.1016/j.ces.2019.07.044_b0185 article-title: Predictive control of quality in a batch manufacturing process using artificial neural network models publication-title: Ind. Eng. Chem. Res. doi: 10.1021/ie00021a019 – volume: 3 start-page: 101 issue: 2 year: 2015 ident: 10.1016/j.ces.2019.07.044_b0155 article-title: Data-driven modelling of biological multi-scale processes publication-title: J. Coupled Syst. Multiscale Dyn. doi: 10.1166/jcsmd.2015.1069 – volume: 65 start-page: 5018 issue: 17 year: 2010 ident: 10.1016/j.ces.2019.07.044_b0065 article-title: Multiscale modeling in chemical vapor deposition processes: coupling reactor scale with feature scale computations publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2010.06.004 – volume: 23 start-page: 239 issue: 2 year: 2010 ident: 10.1016/j.ces.2019.07.044_b0410 article-title: Temporal-kernel recurrent neural networks publication-title: Neural Netw. doi: 10.1016/j.neunet.2009.10.009 – volume: 148 start-page: 222 issue: 3 year: 2001 ident: 10.1016/j.ces.2019.07.044_b0165 article-title: Evolving wavelet-based networks for short-term load forecasting publication-title: IEE Proc. – Generat. Transmiss. Distribut. doi: 10.1049/ip-gtd:20010286 – volume: 49 start-page: 4858 issue: 7 year: 1994 ident: 10.1016/j.ces.2019.07.044_b0285 article-title: Surface roughness and conductivity of thin Ag films publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.49.4858 – volume: 62 issue: 7 year: 2016 ident: 10.1016/j.ces.2019.07.044_b0060 article-title: Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems publication-title: AIChE J. doi: 10.1002/aic.15215 – year: 2001 ident: 10.1016/j.ces.2019.07.044_b0120 article-title: Adaptive neural network short term load forecasting with wavelet decompositions – volume: 3 start-page: 23 issue: 1 year: 1990 ident: 10.1016/j.ces.2019.07.044_b0245 article-title: A time-delay neural network architecture for isolated word recognition publication-title: Neural Netw. doi: 10.1016/0893-6080(90)90044-L – volume: 43 start-page: 3031 issue: 11 year: 1997 ident: 10.1016/j.ces.2019.07.044_b0455 article-title: Multiscale Integration Hybrid Algorithms for Homogeneous-Heterogeneous Reactors publication-title: AIChE J. doi: 10.1002/aic.690431115 – volume: 140 start-page: 90 year: 2016 ident: 10.1016/j.ces.2019.07.044_b0350 article-title: Stochastic nonlinear model predictive control applied to a thin film deposition process under uncertainty publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2015.10.004 – volume: 59 start-page: 2317 issue: 7 year: 2013 ident: 10.1016/j.ces.2019.07.044_b0220 article-title: Modeling and control of protein crystal shape and size in batch crystallization publication-title: AIChE J. doi: 10.1002/aic.14039 – volume: 47 start-page: 126 issue: 1 year: 2001 ident: 10.1016/j.ces.2019.07.044_b0305 article-title: Artificial neural-network-assisted stochastic process optimization strategies publication-title: AIChE J. doi: 10.1002/aic.690470113 – volume: 58 start-page: 3131 issue: 14 year: 2003 ident: 10.1016/j.ces.2019.07.044_b0360 article-title: ANNSA: a hybrid artificial neural network/simulated annealing algorithm for optimal control problems publication-title: Chem. Eng. Sci. doi: 10.1016/S0009-2509(03)00168-4 – volume: 22 start-page: 1773 issue: 9 year: 2012 ident: 10.1016/j.ces.2019.07.044_b0110 article-title: Dynamic output feedback model predictive control for nonlinear systems represented by Hammerstein – Wiener model publication-title: J. Process Control doi: 10.1016/j.jprocont.2012.07.011 – ident: 10.1016/j.ces.2019.07.044_b0420 doi: 10.1109/ICPST.2002.1047201 – volume: 528 start-page: 52 year: 2015 ident: 10.1016/j.ces.2019.07.044_b0070 article-title: Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.06.007 – volume: 30 start-page: 1670 issue: 10–12 year: 2006 ident: 10.1016/j.ces.2019.07.044_b0080 article-title: Control and optimization of multiscale process systems publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2006.05.025 – volume: 200 start-page: 42 year: 2016 ident: 10.1016/j.ces.2019.07.044_b0480 article-title: Application of artificial neural networks to co-combustion of hazelnut husk–lignite coal blends publication-title: Bioresour. Technol. doi: 10.1016/j.biortech.2015.09.114 – volume: 106 start-page: 179 year: 2012 ident: 10.1016/j.ces.2019.07.044_b0315 article-title: Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2012.05.002 – volume: 62 start-page: 7078 issue: 24 year: 2007 ident: 10.1016/j.ces.2019.07.044_b0135 article-title: Development of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas–liquid systems publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2007.07.071 – volume: 519 start-page: 1895 issue: 1 year: 2014 ident: 10.1016/j.ces.2019.07.044_b0030 article-title: Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo Simulation uncertainty analysis publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2014.10.009 – volume: 136 start-page: 50 year: 2015 ident: 10.1016/j.ces.2019.07.044_b0090 article-title: Multiscale modeling and operation of PECVD of thin film solar cells publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2015.02.027 – year: 2002 ident: 10.1016/j.ces.2019.07.044_b0045 – year: 2011 ident: 10.1016/j.ces.2019.07.044_b0390 – volume: 55 start-page: 2 issue: 1 year: 2009 ident: 10.1016/j.ces.2019.07.044_b0435 article-title: DROWNING IN DATA: Informatics and modeling challenges in a data-rich networked world publication-title: AIChE J. doi: 10.1002/aic.11756 – volume: 103 start-page: 300 issue: 1 year: 2012 ident: 10.1016/j.ces.2019.07.044_b0215 article-title: Estimating biofilm reaction kinetics using hybrid mechanistic-neural network rate function model publication-title: Bioresour. Technol. doi: 10.1016/j.biortech.2011.10.006 – volume: 136 start-page: 675 year: 2018 ident: 10.1016/j.ces.2019.07.044_b0385 article-title: Optimization of simultaneously propagating multiple fractures in hydraulic fracturing to achieve uniform growth using data-based model reduction publication-title: Chem. Eng. Res. Des. doi: 10.1016/j.cherd.2018.06.015 – volume: 56 start-page: 7491 issue: 26 year: 2017 ident: 10.1016/j.ces.2019.07.044_b0140 article-title: Subspace identification-based modeling and control of batch particulate processes publication-title: Ind. Eng. Chem. Res. doi: 10.1021/acs.iecr.7b00682 – volume: 136 start-page: 38 year: 2015 ident: 10.1016/j.ces.2019.07.044_b0345 article-title: A robust nonlinear model predictive controller for a multiscale thin film deposition process publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2015.02.002 – year: 2007 ident: 10.1016/j.ces.2019.07.044_b0370 – volume: 312 start-page: 1449 issue: 8 year: 2010 ident: 10.1016/j.ces.2019.07.044_b0015 article-title: Development of a multiscale model for an atomic layer deposition process publication-title: J. Cryst. Growth doi: 10.1016/j.jcrysgro.2009.12.041 – year: 2010 ident: 10.1016/j.ces.2019.07.044_b0010 article-title: Multiscale modeling and optimization of an atomic layer deposition process for nanomanufacturing applications – volume: 119 start-page: 30 year: 2014 ident: 10.1016/j.ces.2019.07.044_b0225 article-title: Crystal shape and size control using a plug flow crystallization configuration publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2014.07.058 – volume: 54 start-page: 11903 issue: 47 year: 2015 ident: 10.1016/j.ces.2019.07.044_b0235 article-title: Multiscale, multidomain modeling and parallel computation: application to crystal shape evolution in crystallization publication-title: Ind. Eng. Chem. Res. doi: 10.1021/acs.iecr.5b02942 – volume: 145 start-page: 173 year: 2019 ident: 10.1016/j.ces.2019.07.044_b0475 article-title: Model predictive control of phthalic anhydride synthesis in a fixed-bed catalytic reactor via machine learning modeling publication-title: Chem. Eng. Res. Des. doi: 10.1016/j.cherd.2019.02.016 – volume: 140 start-page: 33 year: 2018 ident: 10.1016/j.ces.2019.07.044_b0200 article-title: Multilevel Monte Carlo for noise estimation in stochastic multiscale systems publication-title: Chem. Eng. Res. Des. doi: 10.1016/j.cherd.2018.10.006 – volume: 11 start-page: 1736 issue: 4 year: 1996 ident: 10.1016/j.ces.2019.07.044_b0075 article-title: Neural network based short-term load forecasting using weather compensation publication-title: IEEE Trans. Power Syst. doi: 10.1109/59.544636 – volume: 107 start-page: 47 year: 2014 ident: 10.1016/j.ces.2019.07.044_b0230 article-title: Modeling and control of crystal shape in continuous protein crystallization publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2013.12.005 – volume: 91 start-page: 1615 issue: 8 SUPPL. year: 2001 ident: 10.1016/j.ces.2019.07.044_b0105 article-title: Artificial neural networks: opening the black box publication-title: Cancer doi: 10.1002/1097-0142(20010415)91:8+<1615::AID-CNCR1175>3.0.CO;2-L – volume: 261 start-page: 279 issue: 1 year: 2017 ident: 10.1016/j.ces.2019.07.044_b0180 article-title: Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2017.01.035 – volume: 5 start-page: 115 issue: 4 year: 1943 ident: 10.1016/j.ces.2019.07.044_b0295 article-title: A logical calculus of the ideas immanent in nervous activity publication-title: Bull. Math. Biophys. doi: 10.1007/BF02478259 – volume: 66 start-page: 4319 issue: 19 year: 2011 ident: 10.1016/j.ces.2019.07.044_b0355 article-title: A review of multiscale modeling of metal-catalyzed reactions: mechanism development for complexity and emergent behavior publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2011.05.050 – volume: 116 start-page: 590 year: 2014 ident: 10.1016/j.ces.2019.07.044_b0335 article-title: Uncertainty analysis and robust optimization of multiscale process systems with application to epitaxial thin film growth publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2014.05.027 |
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| Title | Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks |
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