2021, Volume 7, Issue 9
Engineering >> 2021, Volume 7, Issue 9 doi: 10.1016/j.eng.2021.04.022
Intelligent Ironmaking Optimization Service on a Cloud Computing Platform by Digital Twin
The State Key Laboratory of Industrial Control Technology & College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
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Abstract
The shortage of computation methods and storage devices has largely limited the development of multiobjective optimization in industrial processes. To improve the operational levels of the process industries, we propose a multi-objective optimization framework based on cloud services and a cloud distribution system. Real-time data from manufacturing procedures are first temporarily stored in a local database, and then transferred to the relational database in the cloud. Next, a distribution system with elastic compute power is set up for the optimization framework. Finally, a multi-objective optimization model based on deep learning and an evolutionary algorithm is proposed to optimize several conflicting goals of the blast furnace ironmaking process. With the application of this optimization service in a cloud factory, iron production was found to increase by 83.91 t∙d-1, the coke ratio decreased 13.50 kg∙t-1, and the silicon content decreased by an average of 0.047%.
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References
[ 1 ] Pan Y, Yang C, An R, Sun Y. Robust principal component pursuit for fault detection in a blast furnace process. Ind Eng Chem Res 2018;57(1):283–91. link1
[ 2 ] Zhou P, Song H, Wang H, Chai T. Datadriven nonlinear subspace modeling for prediction and control of molten iron quality indices in blast furnace ironmaking. IEEE Trans Control Syst Technol 2017;25(5):1761–74. link1
[ 3 ] Ge Z, Song Z, Ding SX, Huang B. Data mining and analytics in the process industry: the role of machine learning. IEEE Access 2017;5:20590–616. link1
[ 4 ] Chen S, Gao C. Linear priors mined and integrated for transparency of blast furnace black-box svm model. IEEE Trans Industr Inform 2020;16(6):3862–70. link1
[ 5 ] Zhou P, Zhang S, Dai P. Recursive learning-based bilinear subspace identification for online modeling and predictive control of a complicated industrial process. IEEE Access 2020;8:62531–41. link1
[ 6 ] Li J, Hua C, Yang Y, Guan X. Fuzzy classifier design for development tendency of hot metal silicon content in blast furnace. IEEE Trans Industr Inform 2018;14 (3):1115–23. link1
[ 7 ] Huang J, Chen Z, Jiang Z, Gui W. 3D topography measurement and completion method of blast furnace burden surface using high-temperature industrial endoscope. IEEE Sens J 2020;20(12):6478–91. link1
[ 8 ] Li Y, Zhang S, Zhang J, Yin Y, Xiao W, Zhang Z. Data-driven multiobjective optimization for burden surface in blast furnace with feedback compensation. IEEE Trans Industr Inform 2020;16(4):2233–44. link1
[ 9 ] Chen J, Hu K, Wang Qi, Sun Y, Shi Z, He S. Narrowband Internet of Things: implementations and applications. IEEE Internet Things J 2017;4(6):2309–14. link1
[10] Linthicum DS. Connecting fog and cloud computing. IEEE Cloud Comput 2017;4(2):18–20. link1
[11] Iqbal MA, Aleem M, Ibrahim M, Anwar S, Islam MA. Amazon cloud computing platform EC2 and VANET simulations. Int J Ad Hoc Ubiquitous Comput 2019;30(3):127–36. link1
[12] Zhang G, Ravishankar MN. Exploring vendor capabilities in the cloud environment: a case study of Alibaba Cloud Computing. Inf Manage 2019;56 (3):343–55. link1
[13] He W, Qian F, Lam J, Chen G, Han QL, Kurths J. Quasi-synchronization of heterogeneous dynamic networks via distributed impulsive control: error estimation, optimization and design. Automatica 2015;62:249–62. link1
[14] Zhu X, Zhu Y, Zheng W. Spectral rotation for deep one-step clustering. Pattern Recognit 2020;105:107175. link1
[15] Zhao C, Sun Y. Step-wise sequential phase partition (SSPP) algorithm based statistical modeling and online process monitoring. Chemom Intell Lab Syst 2013;125:109–20. link1
[16] Liu JW, Ren ZP, Lu RK, Luo XL. GMM discriminant analysis with noisy label for each class. Neural Comput Appl 2021;33(4):1171–91. link1
[17] Sabetsarvestani Z, Renna F, Kiraly F, Rodrigues M. Source separation with side information based on Gaussian mixture models with application in art investigation. IEEE Trans Signal Process 2020;68:558–72. link1
[18] Li Z, Yang C, Liu W, Zhou H, Li Y. Research on hot metal Si-content prediction based on LSTM-RNNl. CIESC J 2018;69:992–7. link1
[19] Che Z, Purushotham S, Cho K, Sontag D, Liu Y. Recurrent neural networks for multivariate time series with missing values. Sci Rep 2018;8(1):6085. link1
[20] Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. Comput Sci 2014. link1
[21] Zhou H, Zhang H, Yang C. Hybrid-model-based intelligent optimization of ironmaking process. IEEE Trans Ind Electron 2020;67(3):2469–79. link1
[22] Whitley D. A genetic algorithm tutorial. Stat Comput 1994;4(2):65–85. link1
[23] Bozkurt E, Perez MAS, Hovius R, Browning NJ, Rothlisberger U. A genetic algorithm based design and experimental characterization of a highly thermostable metalloprotein. J Am Chem Soc 2018;140(13):4517–21. link1
[24] Yang C, Zhou H, Li Z. A multi-objective optimization model based on long short-term memory and non-dominated sorting genetic algorithm II. In: Proceedings of 2017 Chinese Automation Congress (CAC); 2017 Oct 20–22; Jinan, China. New York: IEEE; 2017. p. 1635–40. link1
[25] Koumousis VK, Katsaras CP. A sawtooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans Evol Comput 2006;10(1):19–28. link1
[26] Zhou H, Li Y, Yang C, Sun Y. Mixed-framework-based energy optimization of chemi-mechanical pulping. IEEE Trans Industr Inform 2020;16(9):5895–904. link1
[27] Srinivas M, Patnaik LM. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 1994;24(4):656–67. link1
[28] Łukasik S, Kowalski PA, Charytanowicz M, Kulczycki P. Clustering using flower pollination algorithm and Calinski–Harabasz index. In: Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC); 2016 Jul 24–29; Vancouver, BC, Canada. New York: IEEE; 2016. p. 2724–8. link1
[29] Adler D. Genetic algorithms and simulated annealing: a marriage proposal. In: Proceedings of IEEE International Conference on Neural Networks; San Francisco, CA, USA. New York: IEEE; 1993. p. 1104–9. link1
[30] Yi JH, Deb S, Dong J, Alavi AH, Wang GG. an improved NSGA-III algorithm with adaptive mutation operator for big data optimization problems. Future Gener Comput Syst 2018;88:571–85. link1
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