Geodesic
Neural network model for estimating the quality indicators of industrial fractionator products
Matematičeskoe modelirovanie, Tome 35 (2023) no. 12, pp. 3-17.

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For industrial processes, an operational assessment and forecast of the main difficult-to-measure quality indicators of output streams are required. Soft sensors have become widely used for their assessment and prediction in real time. The use of neural networks for their development allows us to take into account the nonlinear features of a technological object and allow us to obtain a more accurate forecast. However, the structure of neural networks is extensive and the choice of the optimal structure, as well as the question of the convergence of learning with a given structure is still beyond the reach of theoretical methods. In this regard, a new neural network learning algorithm and a new approach to determining the structure of a neural network are presented. The latter allows you to establish the structure of a neural network with optimal convergence of the resulting structure. The proposed algorithms are presented in detail on the example of a fractionator and have shown their effectiveness.
Keywords: fractionator model, genetic algorithm, neural network, hyperparameter optimization, learning convergence. 
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D. V. Shtakin; S. A. Shevlyagina; A. Yu. Torgashov. Neural network model for estimating the quality indicators of industrial fractionator products. Matematičeskoe modelirovanie, Tome 35 (2023) no. 12, pp. 3-17. http://geodesic.mathdoc.fr/item/MM_2023_35_12_a0/

[1] P. Kadlec, B. Gabrys, S. Strandt, “Data-driven soft sensors in the process industry”, Computers Chemical Engineering, 33:4 (2009), 795–814 | DOI

[2] M. Dam, D. N. Saraf, “Design of neural networks using genetic algorithm for on-line property estimation of crude fractionator products”, Computers Chemical Eng, 30:4 (2006), 722–729 | DOI

[3] D. Whitley, T. Starkweather, C. Bogart, “Genetic algorithms and neural networks: Optimizing connections and connectivity”, Parallel computing, 14:3 (1990), 347–361 | DOI | MR

[4] G. A.P. de Morais, B. H.G. Barbosa, D. D. Ferreira, L. S. Paiva, “Soft sensors design in a petrochemical process using an Evolutionary Algorithm”, Measurement, 148 (2019), 106920 | DOI

[5] S. Ding, C. Su, J. Yu., “An optimizing BP neural network algorithm based on genetic algorithm”, Artificial Intelligence Review, 36:2 (2011), 153–162 | DOI

[6] L. S. Iliadis, C. Jayne, “Emerging applications of deep learning and spiking ANN”, Neural Computing and Applications, 32:23 (2020), 17119–17124 | DOI | MR

[7] J. J. Zhu S. Borzooei, J. Sun, Z. J. Ren, “Deep learning optimization for soft sensing of hard-to-measure wastewater key variables”, ACS ES T Engineering, 2:7 (2022), 1341–1355 | DOI

[8] S. M.R. Loghmanian, H. Jamaluddin, R. Ahmad, “Structure optimization of neural network for dynamic system modeling using multi-objective genetic algorithm”, Neural Computing and Applications, 21:6 (2012), 1281–1295 | DOI

[9] Q. Li et al, “A soft-sensing method for product quality monitoring based on particle swarm optimization deep belief networks”, Transactions of the Institute of Measurement and Control, 44:15 (2022), 2900–2910 | DOI

[10] H. Wu, Y. Han, J. Jin, Z. Geng, “Novel deep learning based on data fusion integrating correlation analysis for soft sensor modeling”, Industrial Engineering Chemistry Research, 60:27 (2021), 10001–10010 | DOI

[11] N. M. Aszemi, P. D. D. Dominic, “Hyperparameter optimization in convolutional neural network using genetic algorithms”, Int. J. of Advanced Computer Sci. Applications, 10:6 (2019)

[12] M. Hartley, T. S.G. Olsson, “dtoolAI: Reproducibility for deep learning”, Patterns, 1:5 (2020), 100073 | DOI

[13] M. Shahriari, R. Ramler, L. Fischer, “How do deep-learning framework versions affect the reproducibility of neural network models”, Machine Learning and Knowledge Extraction, 4:4 (2022), 888–911 | DOI

[14] O. Yu. Snegirev, A. Yu. Torgashov, “Opredelenie optimalnykh parametrov adaptivnogo virtualnogo analizatora dlia massoobmennogo tekhnologicheskogo processa”, Sbornik trudov XIII Vserossiiskogo soveshchaniia po problemam upravleniia, Institut problem upravleniia im. V.A. Trapeznikova RAN, M., 2019, 2655–2659

[15] H. A. Al-Jamimi, G. M. BinMakhashen, K. Deb, T. A. Saleh, “Multiobjective optimization and analysis of petroleum refinery catalytic processes: A review”, Fuel, 288 (2021), 119678 | DOI

[16] L. Ingber, “Adaptive Simulated Annealing (ASA): Lessons Learned”, Invited Paper to a Special Issue of the Polish Journal Control and Cybernetics on “Simulated Annealing Applied to Combinatorial Optimization”, 1995 | MR

[17] E. Mezura-Montes, C. A. Coello Coello, “Constraint-handling in nature-inspired numerical optimization: Past, present and future”, Swarm and Evolutionary Computation. Constraint-handling in nature-inspired numerical optimization, 1:4 (2011), 173–194 | MR

[18] J. Forkman, “Estimator and Tests for Common Coefficients of Variation in Normal Distri-butions”, Communications in Statistics Theory and Methods, 38:2 (2009), 233–251 | DOI | MR | Zbl