| [1] |
Drai D. Why AI-driven analytics is essential for data-driven decision-making [Internet]. New York City:Forbes; 2021 Dec 27 [cited 2022 Apr 10]. Available from: sh=61a1efa373f4.
|
| [2] |
IDC forecasts companies to spend almost $ 342 billion on AI solutions in 2021 [Internet]. Needham: Businesswire; 2021 Aug 4 [cited 2024 Mar 26]. Available from:
|
| [3] |
A. Holzinger, G. Langs, H. Denk, K. Zatloukal, H. Müller. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov, 9 (4) (2019), p. e1312.
|
| [4] |
2024 AI business predictions [Internet]. London: PwC; [cited 2022 Apr 10]. Available from:
|
| [5] |
P. Linardatos, V. Papastefanopoulos, S. Kotsiantis. Explainable AI: a review of machine learning interpretability methods. Entropy, 23 (1) (2021), p. 18.
|
| [6] |
S. Reddy, S. Allan, S. Coghlan, P. Cooper. A governance model for the application of AI in health care. J Am Med Inform Assoc, 27 (3) (2020), pp. 491-497.
|
| [7] |
M. Miró-Nicolau, G. Moyà-Alcover, A. Jaume-i-Capó. Evaluating explainable artificial intelligence for X-ray image analysis. Appl Sci, 12 (9) (2022), p. 4459.
|
| [8] |
A. Sivaram, V. Venkatasubramanian. XAI-MEG: combining symbolic AI and machine learning to generate first-principles models and causal explanations. AlChE J, 68 (6) (2022), p. e17687.
|
| [9] |
A. Bhakte, V. Pakkiriswamy, R. Srinivasan. An explainable artificial intelligence based approach for interpretation of fault classification results from deep neural networks. Chem Eng Sci, 250 (2022), p. 117373.
|
| [10] |
A.M. Schweidtmann, E. Esche, A. Fischer, M. Kloft, J.U. Repke, S. Sager, et al. Machine learning in chemical engineering: a perspective. Chemie Ingenieur Technik, 93 (12) (2021), pp. 2029-2039.
|
| [11] |
A. Chakraborty, A. Sivaram, V. Venkatasubramanian. AI-DARWIN: a first principles-based model discovery engine using machine learning. Comput Chem Eng, 154 (2021), p. 107470.
|
| [12] |
D. Shin. The effects of explainability and causability on perception, trust, and acceptance: implications for explainable AI. Int J Hum Comput Stud, 146 (2021), p. 102551.
|
| [13] |
M. Pirdashti, S. Curteanu, M.H. Kamangar, M.H. Hassim, M.A. Khatami. Artificial neural networks: applications in chemical engineering. Rev Chem Eng, 29 (4) (2013), pp. 205-239.
|
| [14] |
S. Zendehboudi, N. Rezaei, A. Lohi. Applications of hybrid models in chemical, petroleum, and energy systems: a systematic review. Appl Energy, 228 (2018), pp. 2539-2566.
|
| [15] |
V. Venkatasubramanian. The promise of artificial intelligence in chemical engineering: is it here, finally>. AIChE J, 65 (2) (2019), pp. 466-478.
|
| [16] |
Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521 (7553) (2015), pp. 436-444.
|
| [17] |
H. Wu, J. Zhao. Deep convolutional neural network model based chemical process fault diagnosis. Comput Chem Eng, 115 (2018), pp. 185-197.
|
| [18] |
A.M. Turing. Computing machinery and intelligence. R. Epstein, G. Roberts, G. Beber (Eds.), Parsing the Turing test, Springer, Dordrecht (2009), pp. 23-65.
|
| [19] |
R. Vaidyanathan, V. Venkatasubramanian. On the nature of fault space classification structure developed by neural networks. Eng Appl Artif Intell, 5 (4) (1992), pp. 289-297.
|
| [20] |
W.R. Swartout, J.D. Moore. Explanation in second generation expert systems. J.M. David, J.P. Krivine, R. Simmons (Eds.), Second generation expert systems, Springer, Berlin (1993), pp. 543-585.
|
| [21] |
D. Minh, H.X. Wang, Y.F. Li, T.N. Nguyen. Explainable artificial intelligence: a comprehensive review. Artif Intell Rev, 55 (5) (2022), pp. 3503-3568.
|
| [22] |
D. Gunning, E. Vorm, J.Y. Wang, M. Turek. DARPA’s explainable AI (XAI) program: a retrospective. Appl AI Lett, 2 (4) (2021), p. e61.
|
| [23] |
P. Agarwal, M. Tamer, H. Budman. Explainability: relevance based dynamic deep learning algorithm for fault detection and diagnosis in chemical processes. Comput Chem Eng, 154 (2021), p. 107467.
|
| [24] |
Panigutti C, Perotti A, Pedreschi D. Doctor XAI:an ontology-based approach to black-box sequential data classification explanations. In: Proceedings of the 2020 Conference on Fairness, Transparency; Jan 27-30 Spain. 2020. p. Accountability, and 2020 ; Barcelona, New York City: Association for Computing Machinery; 629-39.
|
| [25] |
Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. 2017. arXiv:1702.08608.
|
| [26] |
T. Miller. Explanation in artificial intelligence: insights from the social sciences. Artif Intell, 267 (2019), pp. 1-38.
|
| [27] |
A. Barredo Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion, 58 (2020), pp. 82-115.
|
| [28] |
V. Venkatasubramanian. Teaching artificial intelligence to chemical engineers: experience from a 35-year-old course. Chem Eng Educ, 56 (4) (2022), pp. 231-240.
|
| [29] |
Maruyama Y. Symbolic and statistical theories of cognition:towards integrated artificial intelligence. In: CleophasL, MassinkM, editors. Softwareengineering and formal methods. Cham: Springer; 2021. p. 129-46.
|
| [30] |
W. Ji, W. Qiu, Z. Shi, S. Pan, S. Deng. Stiff-PINN: physics-informed neural network for stiff chemical kinetics. J Phys Chem A, 125 (36) (2021), pp. 8098-8106.
|
| [31] |
Y. Yang, P. Perdikaris. Adversarial uncertainty quantification in physics-informed neural networks. J Comput Phys, 394 (2019), pp. 136-152.
|
| [32] |
Y. Yuan, X. Dong, L. Ricardez-Sandoval. Insights into syngas combustion on a defective NiO surface for chemical looping combustion: oxygen migration and vacancy effects. J Phys Chem C, 124 (52) (2020), pp. 28359-28370.
|
| [33] |
J. Jung, I.K. Gamwo. Multiphase CFD-based models for chemical looping combustion process: fuel reactor modeling. Powder Technol, 183 (3) (2008), pp. 401-409.
|
| [34] |
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.R. Müller, W. Samek. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS One, 10 (7) (2015), p. e0130140.
|
| [35] |
Konig R, Johansson U, Niklasson L. G-REX:a versatile framework for evolutionary data mining. In: Bonchi F, Berendt B, Giannotti F, Gunopulos D, Turini F, Zaniolo C, et al., editors. Proceedings of 2008 IEEE International Conference on Data Mining Workshops; 2008 Dec 15- 19 ; Pisa, Italy. Piscataway: IEEE; 2008. p. 971-4.
|
| [36] |
Lundberg SM, Lee SI. A unified approach to interpreting model predictions. von Luxburg U, Guyon I, Bengio S, Wallach H, Fergus R, editors.Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017 Dec 4- 9 ; Long Beach, CA, USA. Red Hook: Curran Associates Inc.; 2017. p. 4768-77.
|
| [37] |
W.T. Hale, E. Safikou, G.M. Bollas. Inference of faults through symbolic regression of system data. Comput Chem Eng, 157 (2022), p. 107619.
|
| [38] |
P. Agarwal, M. Tamer, H. Budman. Assessing observability using supervised autoencoders with application to Tennessee Eastman process. IFAC-PapersOnLine, 53 (2) (2020), pp. 206-211.
|
| [39] |
P. Agarwal, M. Tamer, M.H. Sahraei, H. Budman. Deep learning for classification of profit-based operating regions in industrial processes. Ind Eng Chem Res, 59 (6) (2020), pp. 2378-2395.
|
| [40] |
T. Larsson, K. Hestetun, E. Hovland, S. Skogestad. Self-optimizing control of a large-scale plant: the Tennessee Eastman process. Ind Eng Chem Res, 40 (22) (2001), pp. 4889-4901.
|
| [41] |
J.J. Downs, E.F. Vogel. A plant-wide industrial process control problem. Comput Chem Eng, 17 (3) (1993), pp. 245-255.
|
| [42] |
C. Duan, F. Liu, A. Nandy, H.J. Kulik. Putting density functional theory to the test in machine-learning-accelerated materials discovery. J Phys Chem Lett, 12 (19) (2021), pp. 4628-4637.
|
| [43] |
J. He, L. Wang, C. Zhang, W. Sun, Z. Yin, H. Zhang, et al. A high throughput screening model of solidophilic flotation reagents for chalcopyrite based on quantum chemistry calculations and machine learning. Miner Eng, 177 (2022), p. 107375.
|
| [44] |
J. Kim, G.H. Gu, J. Noh, S. Kim, S. Gim, J. Choi, et al. Predicting potentially hazardous chemical reactions using an explainable neural network. Chem Sci, 12 (33) (2021), pp. 11028-11037.
|
| [45] |
T. Kikutsuji, Y. Mori, K. Okazaki, T. Mori, K. Kim, N. Matubayasi. Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using explainable artificial intelligence (XAI). J Chem Phys, 156 (15) (2022), p. 154108.
|
| [46] |
C.P. Langlotz, B. Allen, B.J. Erickson, J. Kalpathy-Cramer, K. Bigelow, T.S. Cook, et al. A roadmap for foundational research on artificial intelligence in medical imaging:from the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology, 291 (3) (2019), pp. 781-791.
|
| [47] |
J. Jiménez-Luna, F. Grisoni, G. Schneider. Drug discovery with explainable artificial intelligence. Nat Mach Intell, 2 (10) (2020), pp. 573-584.
|
| [48] |
I.E. Sallam, A. Abdelwareth, H. Attia, R.K. Aziz, M.N. Homsi, M. von Bergen, et al. Effect of gut microbiota biotransformation on dietary tannins and human health implications. Microorganisms, 9 (5) (2021), p. 965.
|
| [49] |
I.R. Ward, L. Wang, J. Lu, M. Bennamoun, G. Dwivedi, F.M. Sanfilippo. Explainable artificial intelligence for pharmacovigilance: what features are important when predicting adverse outcomes?. Comput Methods Programs Biomed, 212 (2021), p. 106415.
|
| [50] |
Z. Yang, W. Zhong, L. Zhao, C.Y.C. Chen. MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction. Chem Sci, 13 (3) (2022), pp. 816-833.
|
| [51] |
L. Das, A. Sivaram, V. Venkatasubramanian. Hidden representations in deep neural networks: part 2. Regression problems. Comput Chem Eng, 139 (2020), p. 106895.
|
| [52] |
A. Sivaram, L. Das, V. Venkatasubramanian. Hidden representations in deep neural networks: part 1. Classification problems. Comput Chem Eng, 134 (2020), p. 106669.
|
| [53] |
F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, et al. A comprehensive survey on transfer learning. Proc IEEE, 109 (1) (2021), pp. 43-76.
|
| [54] |
J. Lu, V. Behbood, P. Hao, H. Zuo, S. Xue, G. Zhang. Transfer learning using computational intelligence: a survey. Knowl Base Syst, 80 (2015), pp. 14-23.
|
| [55] |
G.E. Karniadakis, I.G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, L. Yang. Physics-informed machine learning. Nat Rev Phys, 3 (6) (2021), pp. 422-440.
|
| [56] |
Z. Mao, A.D. Jagtap, G.E. Karniadakis. Physics-informed neural networks for high-speed flows. Comput Methods Appl Mech Eng, 360 (2020), p. 112789.
|
| [57] |
J.L. Wu, H. Xiao, E. Paterson. Physics-informed machine learning approach for augmenting turbulence models: a comprehensive framework. Phys Rev Fluids, 3 (7) (2018), p. 074602.
|
| [58] |
H. Gao, L. Sun, J.X. Wang. PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain. J Comput Phys, 428 (2021), p. 110079.
|
| [59] |
K. Kashinath, M. Mustafa, A. Albert, J.L. Wu, C. Jiang, S. Esmaeilzadeh, et al. Physics-informed machine learning: case studies for weather and climate modelling. Phil Trans R Soc A, 379 (2194) (2021), p. 20200093.
|
| [60] |
S. Maheshwari, S. Shetty, R. Ratnakar, S. Sanyal. Role of computational science in materials and systems design for sustainable energy applications: an industry perspective. J Indian Inst Sci, 102 (1) (2022), pp. 11-37.
|
| [61] |
A. Kashefi, D. Rempe, L.J. Guibas. A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries. Phys Fluids, 33 (2) (2021), p. 027104.
|
| [62] |
L. Lu, P. Jin, G. Pang, Z. Zhang, G.E. Karniadakis. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat Mach Intell, 3 (3) (2021), pp. 218-229.
|
| [63] |
Y. LeCun, Y. Bengio. Arbib ( Ed.),Convolutional networks for images, speech, and time series. M.A. The handbook of brain theory and neural networks, MIT Press, Cambridge (1995), pp. 255-258.
|
| [64] |
J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, et al. Graph neural networks: a review of methods and applications. AI Open, 1 (2020), pp. 57-81.
|
| [65] |
M. Raissi, P. Perdikaris, G.E. Karniadakis. Numerical Gaussian processes for time-dependent and nonlinear partial differential equations. SIAM J Sci Comput, 40 (1) (2018), pp. A172-A198.
|
| [66] |
Erichson NB, Muehlebach M, Mahoney MW. Physics-informed autoencoders for Lyapunov-stable fluid flow prediction. 2019. arXiv:1905.10866.
|
| [67] |
N. Geneva, N. Zabaras. Transformers for modeling physical systems. Neural Netw, 146 (2022), pp. 272-289.
|
| [68] |
G. Pang, L. Lu, G.E. Karniadakis. fPINNs: fractional physics-informed neural networks. SIAM J Sci Comput, 41 (4) (2019), pp. A2603-A2626.
|
| [69] |
L. Yang, D. Zhang, G.E. Karniadakis. Physics-informed generative adversarial networks for stochastic differential equations. SIAM J Sci Comput, 42 (1) (2020), pp. A292-A317.
|
| [70] |
L. Lu, X. Meng, Z. Mao, G.E. Karniadakis. DeepXDE: a deep learning library for solving differential equations. SIAM Rev, 63 (1) (2021), pp. 208-228.
|
| [71] |
Y. Wang, X. Han, D. Guo, L. Lu, Y. Chen, M. Ouyang. Physics-informed recurrent neural networks with fractional-order constraints for the state estimation of lithium-ion batteries. Batteries, 8 (10) (2022), p. 148.
|
| [72] |
L. Yuan, Y.Q. Ni, X.Y. Deng, S. Hao. A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations. J Comput Phys, 462 (2022), p. 111260.
|
| [73] |
M. Raissi, P. Perdikaris, G.E. Karniadakis. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys, 378 (2019), pp. 686-707.
|
| [74] |
Raissi M, Perdikaris P, Karniadakis GE. Physics informed deep learning (part I): data-driven solutions of nonlinear partial differential equations. 2017. arXiv:1711.10561.
|
| [75] |
Raissi M, Perdikaris P, Karniadakis GE. Physics informed deep learning (part II): data-driven discovery of nonlinear partial differential equations. 2017. arXiv:1711.10566.
|
| [76] |
S. Cai, Z. Mao, Z. Wang, M. Yin, G.E. Karniadakis. Physics-informed neural networks (PINNs) for fluid mechanics: a review. Acta Mech Sin, 37 (2021), pp. 1727-1738.
|
| [77] |
S. Cai, H. Li, F. Zheng, F. Kong, M. Dao, G.E. Karniadakis, et al. Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease. Proc Natl Acad Sci USA, 118 (13) (2021), Article e2100697118.
|
| [78] |
A.M. Quintino, D.L.L.N. da Rocha, R. Fonseca Jr, O.M.H. Rodriguez. Flow pattern transition in pipes using data-driven and physics-informed machine learning. J Fluids Eng, 143 (3) (2021), p. 031401.
|
| [79] |
M. De Florio, E. Schiassi, R. Furfaro. Physics-informed neural networks and functional interpolation for stiff chemical kinetics. Chaos, 32 (6) (2022), p. 063107.
|
| [80] |
S.I. Ngo, Y.I. Lim. Solution and parameter identification of a fixed-bed reactor model for catalytic CO2 methanation using physics-informed neural networks. Catalysts, 11 (11) (2021), p. 1304.
|
| [81] |
M.S. Alhajeri, F. Abdullah, Z. Wu, P.D. Christofides. Physics-informed machine learning modeling for predictive control using noisy data. Chem Eng Res Des, 186 (2022), pp. 34-49.
|
| [82] |
X. Zeng, C.D. Xue, K.J. Chen, Y.J. Li, K.R. Qin. Deep-learning-assisted extraction of height-averaged velocity from scalar signal transport in a shallow microfluidic channel. Microfluid Nanofluidics, 26 (5) (2022), p. 36.
|
| [83] |
M.A.F. Afzal, A. Sonpal, M. Haghighatlari, A.J. Schultz, J. Hachmann. A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules. Chem Sci, 10 (36) (2019), pp. 8374-8383.
|
| [84] |
Z. He, F. Ni, W. Wang, J. Zhang. A physics-informed deep learning method for solving direct and inverse heat conduction problems of materials. Mater Today Commun, 28 (2021), p. 102719.
|
| [85] |
Y.T. Shih, Y. Shi, L. Huang. Predicting glass properties by using physics- and chemistry-informed machine learning models. J Non-Cryst Solids, 584 (2022), p. 121511.
|
| [86] |
W. Song, W. Du, C. Fan, M. Yang, F. Qian. Adaptive weighted hybrid modeling of hydrocracking process and its operational optimization. Ind Eng Chem Res, 60 (9) (2021), pp. 3617-3632.
|
| [87] |
L. Rajulapati, S. Chinta, B. Shyamala, R. Rengaswamy. Integration of machine learning and first principles models. AlChE J, 68 (6) (2022), p. e17715.
|
| [88] |
J.A. Arrieta-Escobar, F.P. Bernardo, A. Orjuela, M. Camargo, L. Morel. Incorporation of heuristic knowledge in the optimal design of formulated products: application to a cosmetic emulsion. Comput Chem Eng, 122 (2019), pp. 265-274.
|
| [89] |
J. Schubert, R. Simutis, M. Dors, I. Havlík, A. Lübbert. Hybrid modelling of yeast production processes—combination of a priori knowledge on different levels of sophistication. Chem Eng Technol, 17 (1) (1994), pp. 10-20.
|
| [90] |
B. Kosko. Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice-Hall, Inc., Upper Saddle River (1992).
|
| [91] |
Y. Yuan, X. Dong, L. Ricardez-Sandoval. A multi-scale model for syngas combustion on NiO oxygen carrier for chemical looping combustion: the role of nearest neighbors. Fuel Process Technol, 229 (2022), p. 107172.
|
| [92] |
Y. Yuan, X. Dong, L. Ricardez-Sandoval. A multi-scale simulation of syngas combustion reactions by Ni-based oxygen carriers for chemical looping combustion. Appl Surf Sci, 531 (2020), p. 147277.
|
| [93] |
J. Sansana, M.N. Joswiak, I. Castillo, Z. Wang, R. Rendall, L.H. Chiang, et al. Recent trends on hybrid modeling for Industry 4.0. Comput Chem Eng, 151 (2021), p. 107365.
|
| [94] |
J. Petch, S. Di, W. Nelson. Opening the black box: the promise and limitations of explainable machine learning in cardiology. Can J Cardiol, 38 (2) (2022), pp. 204-213.
|
| [95] |
S. Pouyanfar, S. Sadiq, Y. Yan, H. Tian, Y. Tao, M.P. Reyes, et al. A survey on deep learning: algorithms, techniques, and applications. ACM Comput Surv, 51 (5) (2019), p. 92.
|
| [96] |
M. Von Stosch, R. Oliveira, J. Peres, S. Feyo de Azevedo. Hybrid semi-parametric modeling in process systems engineering: past, present and future. Comput Chem Eng, 60 (2014), pp. 86-101.
|
| [97] |
M. Von Stosch, S. Davy, K. Francois, V. Galvanauskas, J.M. Hamelink, A. Luebbert, et al. Hybrid modeling for quality by design and PAT—benefits and challenges of applications in biopharmaceutical industry. Biotechnol J, 9 (6) (2014), pp. 719-726.
|
| [98] |
Z.W. Ulissi, A.J. Medford, T. Bligaard, J.K. Nørskov. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat Commun, 8 (1) (2017), p. 14621.
|
| [99] |
A.A. Peterson. Acceleration of saddle-point searches with machine learning. J Chem Phys, 145 (7) (2016), p. 074106.
|
| [100] |
Rochac JFR, Zhang N, Thompson L, Oladunni T. A data augmentation-assisted deep learning model for high dimensional and highly imbalanced hyperspectral imaging data. In:Proceedings of 2019 9th International Conference on Information Science and Technology; 2019 Aug 2-5; Hulunbuir, China. Piscataway: IEEE; 2019. p. 362-7.
|
| [101] |
H. Ponce, P.V. de Campos Souza, A.J. Guimarães, G. Gonzalez-Mora. Stochastic parallel extreme artificial hydrocarbon networks: an implementation for fast and robust supervised machine learning in high-dimensional data. Eng Appl Artif Intell, 89 (2020), p. 103427.
|
| [102] |
N. Sharma, Y. Liu. A hybrid science-guided machine learning approach for modeling chemical processes: a review. AlChE J, 68 (5) (2022), p. e17609.
|
| [103] |
H. Fang, J. Zhou, Z. Wang, Z. Qiu, Y. Sun, Y. Lin, et al. Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations. Front Chem Sci Eng, 16 (2) (2022), pp. 274-287.
|
| [104] |
A.R. Khoei, S. Keshavarz, S.O.R. Biabanaki. Optimal design of powder compaction processes via genetic algorithm technique. Finite Elem Anal Des, 46 (10) (2010), pp. 843-861.
|
| [105] |
R. González-García, R. Rico-Martínez, I.G. Kevrekidis. Identification of distributed parameter systems: a neural net based approach. Comput Chem Eng, 22 (Suppl 1) (1998), pp. S965-S968.
|
| [106] |
Z. Yang, B. Lu, W. Wang. Coupling artificial neural network with EMMS drag for simulation of dense fluidized beds. Chem Eng Sci, 246 (2021), p. 117003.
|
| [107] |
I. Pan, L.R. Mason, O.K. Matar. Data-centric engineering: integrating simulation, machine learning and statistics. Challenges and opportunities. Chem Eng Sci, 249 (2022), p. 117271.
|
| [108] |
D.C. Psichogios, L.H. Ungar. A hybrid neural network—first principles approach to process modeling. AlChE J, 38 (10) (1992), pp. 1499-1511.
|
| [109] |
R.F. Nielsen, N. Nazemzadeh, L.W. Sillesen, M.P. Andersson, K.V. Gernaey, S.S. Mansouri. Hybrid machine learning assisted modelling framework for particle processes. Comput Chem Eng, 140 (2020), p. 106916.
|
| [110] |
B. Schenker, M. Agarwal. Online-optimized feed switching in semi-batch reactors using semi-empirical dynamic models. Control Eng Pract, 8 (12) (2000), pp. 1393-1403.
|
| [111] |
K. Guan, Y. Gao, Q. Zeng, X. Luan, Y. Zhang, L. Cheng, et al. Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane. Chin J Chem Eng, 28 (6) (2020), pp. 1733-1743.
|
| [112] |
X. Lu, J. Deng, Y. Xiao, X. Zhai, C. Wang, X. Yi. Recent progress and perspective on thermal-kinetic, heat and mass transportation of coal spontaneous combustion hazard. Fuel, 308 (2022), p. 121234.
|
| [113] |
Z. Niu, V.J. Pinfield, B. Wu, H. Wang, K. Jiao, D.Y.C. Leung, et al. Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design. Energy Environ Sci, 14 (5) (2021), pp. 2549-2576.
|
| [114] |
A. Nikolopoulos, C. Samlis, M. Zeneli, N. Nikolopoulos, S. Karellas, P. Grammelis. Introducing an artificial neural network energy minimization multi-scale drag scheme for fluidized particles. Chem Eng Sci, 229 (2021), p. 116013.
|
| [115] |
D. Chaffart, L.A. Ricardez-Sandoval. Optimization and control of a thin film growth process: a hybrid first principles/artificial neural network based multiscale modelling approach. Comput Chem Eng, 119 (2018), pp. 465-479.
|
| [116] |
D.D. Vvedensky, A. Zangwill, C.N. Luse, M.R. Wilby. Stochastic equations of motion for epitaxial growth. Phys Rev E, 48 (2) (1993), pp. 852-862.
|
| [117] |
Y. Yuan, J. Zhu. Intelligent intercommunicating multiscale engineering: the engineering of the future. Engineering, 30 (2023), pp. 13-19.
|
| [118] |
H. You, Y. Yuan, J. Li, L.R. Sandoval. A multi-scale model for CO2 capture: a nickel-based oxygen carrier in chemical-looping combustion. IFAC-PapersOnLine, 51 (18) (2018), pp. 97-102.
|
| [119] |
P.V. de Campos Souza. Fuzzy neural networks and neuro-fuzzy networks: a review the main techniques and applications used in the literature. Appl Soft Comput, 92 (2020), p. 106275.
|
| [120] |
A. Sherstinsky. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D, 404 (2020), p. 132306.
|
| [121] |
L. Wen, X. Li, L. Gao, Y. Zhang. A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans Ind Electron, 65 (7) (2018), pp. 5990-5998.
|
| [122] |
A.T.D. Perera, P. Kamalaruban. Applications of reinforcement learning in energy systems. Renew Sustain Energy Rev, 137 (2021), p. 110618.
|
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