اخبار و اعلانات
آمار
| تعداد نشریات | 27 |
| تعداد شماره‌ها | 556 |
| تعداد مقالات | 5,734 |
| تعداد مشاهده مقاله | 8,018,327 |
| تعداد دریافت فایل اصل مقاله | 5,389,912 |
Soft Sensor Development for Monitoring ASTM-D86 Index: Effect of Feed Flow Rate Change | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقاله 6، دوره 4، شماره 2، تیر 2021، صفحه 211-220 اصل مقاله (628.71 K) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2020.34314.1275 | ||
| نویسندگان | ||
Bahareh Bidar   1؛ Mir Mohammad Khalilipour 2؛ Farhad Shahraki3؛ Jafar Sadeghi4
| ||
| 1Center for Process Integration and Control (CPIC), Department of Chemical Engineering, University of Sistan and Baluchestan, Zahedan, Iran | ||
| 2Center for Process Integration and Control (CPIC) Department of Chemical Engineering University of Sistan and Baluchestan | ||
| 3Center of Process Integration and Control (CPIC) Department of Chemical Engineering University of Sistan and Baluchestan Zahedan, PoBox: 98135-987 Iran | ||
| 4Center of Process Integration and Control (CPIC), Department of Chemical Engineering, University of Sistan and Baluchestan, Zahedan, PoBox: 98135-987 Iran | ||
| چکیده | ||
| The changes in the crude oil flow rate to an atmospheric distillation unit can influence the quality of the products. This paper presents a modification method for soft sensing model including an update term, which makes it compatible with industrial variations. A modified soft sensing structure is adopted using lookup table (LUT) method where steady-state soft sensing models are performed. The steady-state soft sensing models is proposed based on local instrumental variable (LIV) technique for an industrial atmospheric distillation unit (ADU) at Shiraz refinery, Iran. The LIV-based soft sensors are utilized tray temperature measurements to monitor ASTM-D86 index of side products for nominal flow rate (60,000bbl/day). Lookup tables have been developed based on the difference between the predicted values of ASTM-D86 index and corresponding simulation values to make update terms in different feed flow rates. The results present improvement in the predictions of LIV-based soft sensors as well as acceptable control performance in feed flow rate variations. The comparison of soft sensing results with/without lookup tables demonstrates that the proposed update term helps to predict product quality more precisely and is suitable for advanced monitoring scheme due to no complexity and low computational time. | ||
| کلیدواژه‌ها | ||
| Data-driven soft sensor؛ Local instrumental variable (LIV)؛ ASTM-D86 index؛ Crude oil distillation column؛ Lookup table method | ||
| مراجع | ||
|
[1] V. Kumar, A. Sharma, I. R. Chowdhury, S. Ganguly, and D. N. Saraf, "A crude distillation unit model suitable for online applications," Fuel Processing Technology, Vol. 73, No. 1, pp. 1-21, 2001. [2] ASTM D86: standard test method for distillation of petroleum products at atmospheric pressure, 2010. [3] T. Chatterjee and D. N. Saraf, "On-line estimation of product properties for crude distillation units," Journal of Process Control, vol. 14, No. 1, pp. 61–77, 2004. [4] D. Wang, J. Liu, and R. Srinivasan, "Data-driven soft sensor approach for quality prediction in a refining process," IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 6, No. 1, pp. 11-17, 2010. [5] A. C. Serfidan, F. Uzman, and M. Turkay, "Optimal estimation of physical properties of the products of an atmospheric distillation column using support vector regression," Computers and Chemical Engineering, Vol. 134, p. 106711, 2020. [6] C. M. Pappalardo and D. Guida, "System identification algorithm for computing the modal parameters of linear mechanical systems," Machines Vol. 6, No. 2, p. 12, 2018. [7] B. Roset and H. Nijmeijer, "Observer-based model predictive control," International Journal of Control, Vol. 77, No. 17, pp. 1452-1462, 2006. [8] P. Kadlec, B. Gabrys, and S. Strandt, "Data-driven soft sensors in the process industry," Computers and Chemical Engineering, Vol. 33, No. 4, pp. 795–814, 2009. [9] H. Singh, A. K. Pani, and H. K. Mohanta, "Quality monitoring in petroleum refinery with regression neural network: Improving prediction accuracy with appropriate design of training set," Measurement, Vol. 134, pp. 698-709, 2019. [10] H. A. Behrooz, "Robust set-point optimization of inferential control system of crude oil distillation units," ISA Transactions, Vol. In Press, Corrected Proof, 2019. [11] F. Griesing-Scheiwe, Y. A. W. Shardt, G. Pérez-Zuñiga, and X. Yang, "Soft sensor design for variable time delay and variable sampling time," Journal of Process Control, Vol. 92, pp. 310-318, 2020. [12] L. Fortuna, S. Graziani, A. Rizzo, and M. G. Xibilia, Soft sensors for monitoring and control of industrial processes. New York: Springer, 2007. [13] H. J. Galicia, Q. P. He, and J. Wang, "A reduced order soft sensor approach and its application to a continuous digester," Journal of Process Control, Vol. 21, No. 4, pp. 489–500, 2011. [14] J. Liu, "Developing a soft sensor based on sparse partial least squares with variable selection," Journal of Process Control, Vol. 24, No. 7, pp. 1046-1056, 2014. [15] K. Zheng and K. Funatsu, "Partial constrained least squares (PCLS) and application in soft sensor," Chemometrics and Intelligent Laboratory Systems, Vol. 177, pp. 64-73, 2018. [16] Z. Ge, "Mixture Bayesian Regularization of PCR Model and Soft Sensing Application," IEEE Transactions on Industrial Electronics, Vol. 62, No. 7, pp. 4336-4343, 2015. [17] Z. Ge, C. Yang, and Z. Song, "Improved kernel PCA-based monitoring approach for nonlinear processes," Chemical Engineering Science, Vol. 64, No. 9, pp. 2245–2255, 2009. [18] X. Shi and W. Xiong, "LWS based PCA subspace ensemble model for soft sensor development," IFAC-PapersOnLine, Vol. 51, No. 18, pp. 649-654, 2018. [19] L. Fortuna, P. Giannone, S. Graziani, and M. G. Xibilia, "Virtual instruments based on stacked neural networks to improve product quality monitoring in a refinery," IEEE Transactions on Instrumentation and Measurement, Vol. 56, No. 1, pp. 95-101, 2007. [20] J. C. B. Gonzaga, L. A. C. Meleiro, C. Kiang, and R. M. Filho, "ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process," Computers & Chemical Engineering, Vol. 33, No. 1, pp. 43-49, 2009. [21] K. Sun, S.-h. Huang, S.-S. Jang, and D. S.-H. Wong, "Development of soft sensor with neural network and nonlinear variable selection for crude distillation unit process," Computer Aided Chemical Engineering, Vol. 38, pp. 337-342, 2016. [22] N. Bakhtadze, E. M. Maximov, and R. T. Valiakhmetov, "Fuzzy soft sensors for chemical and oil refining processes," IFAC Proceedings Volumes, Vol. 41, No. 2, pp. 46-50, 2008. [23] E. AbdulJalee and K. Aparna, "Neuro-fuzzy Soft Sensor Estimator for Benzene Toluene Distillation Column," Procedia Technology, Vol. 25, pp. 92-99, 2016. [24] T. Zhao, P. Li, and J. Cao, "Soft sensor modeling of chemical process based on self-organizing recurrent interval type-2 fuzzy neural network," ISA Transactions, Vol. 84, pp. 237-246, 2019. [25] P. V. C. Souza, "Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature," Applied Soft Computing, Vol. 92, p. 106275, 2020. [26] P. Jain, I. Rahman, and B. D. Kulkarni, "Development of a soft sensor for a batch distillation column using support vector regression techniques," Chemical Engineering Research and Design, Vol. 85, No. 2, pp. 283–287, 2007. [27] Y. Liu and J. Chen, "Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes," Journal of Process Control, Vol. 23, No. 6, pp. 793–804, 2013. [28] S. Herceg, Ž. U. Andrijić, and N. Bolf, "Development of soft sensors for isomerization process based on support vector machine regression and dynamic polynomial models," Chemical Engineering Research and Design, Vol. 149, pp. 95-103, 2019. [29] Q. Li, Q. Du, W. Ba, and C. Shao, "Multiple-input multiple-output soft sensors based on KPCA and MKLS-SVM for quality prediction in atmospheric distillation column," International Journal of Innovative Computing, Information and Control, Vol. 8, No. 12, pp. 8215-8230, 2012. [30] Q. Jiang and X. Yan, "Weighted kernel principal component analysis based on probability density estimation and moving window and its application in nonlinear chemical process monitoring," Chemometrics and Intelligent Laboratory Systems, Vol. 127, pp. 121–131, 2013. [31] X. Zhang, Q. Zhu, Z.-Y. Jiang, Y. He, and Y. Xu, "A novel ensemble model using PLSR integrated with multiple activation functions based ELM: Applications to soft sensor development," Chemometrics and Intelligent Laboratory Systems, Vol. 183, pp. 147-157, 2018. [32] H. Liu, C. Yang, M. Huang, and C. Yoo, "Soft sensor modeling of industrial process data using kernel latent variables-based relevance vector machine," Applied Soft Computing, Vol. 90, p. 106149, 2020. [33] P. C. Young, "The data-based mechanistic approach to the modelling, forecasting and control of environmental systems," Annual Reviews in Control, Vol. 30, No. 2, pp. 169–182, 2006. [34] H. Gharehbaghi and J. Sadeghi, "A novel approach for prediction of industrial catalyst deactivation using soft sensor modeling," Catalysts, Vol. 6, No. 7, pp. 93-109, 2016. [35] B. Bidar, J. Sadeghi, F. Shahraki, and M. M. Khalilipour, "Data-driven soft sensor approach for online quality prediction using state dependent parameter models," Chemometrics and Intelligent Laboratory Systems, Vol. 162, pp. 130–141, 2017. [36] P. C. Young, Recursive estimation and time-series analysis, Second ed. New York: Springer, 2011. [37] B. Bidar, M. M. Khalilipour, F. Shahraki, and J. Sadeghi, "A data-driven soft-sensor for monitoring ASTM-D86 of CDU side products using local instrumental variable (LIV) technique," Journal of the Taiwan Institute of Chemical Engineers, Vol. 84, pp. 49-59, 2018. [38] M. M. Khalilipour, J. Sadeghi, F. Shahraki, and K. Razzaghi, "Nonsquare multivariable non-minimal statespace-proportional integral plus (NMSS-PIP) controlfor atmospheric crude oil distillation column," Chemical Engineering Research and Design, Vol. 113, pp. 140-150, 2016. [39] M. M. Khalilipour, F. Shahraki, J. Sadeghi, and k. Razzaghi, "Control Structure Design and Dynamic Modeling for Atmospheric Distillation Column: Effect of Increase Capacity," International Journal of Industrial Electronics, Control and Optimization, Vol. 1, No. 2, pp. 103-113, 2018. [40] J. Sadeghi, "Modelling and control of non-linear systems using State-Dependent Parameter (SDP) models and Proportional-Integral-Plus (PIP) control method," Doctor of Philosophy, Centre for Research on Environmental System and Statistics, Lancaster University, United Kingdom, 2006. [41] B. Bidar, F. Shahraki, J. Sadeghi, and M. M. Khalilipour, "Soft Sensor Modeling Based on Multi-State-Dependent Parameter Models and Application for Quality Monitoring in Industrial Sulfur Recovery Process," IEEE Sensors Journal, Vol. 18, No. 11, 2018. [42] A. B. Hocking, M. A. Aiello, J. C. Knight, S. Shiraishi, M. Yamaura, and N. Arechiga, "Proving Properties of Simulink Models that Include Discrete Valued Functions," SAE World Congress and Exhibition, 2016. [43] J. J. Macias, P. Angelov, and X. Zhou, "A method for predicting quality of the crude oil distillation," in International Symposium on Evolving Fuzzy Systems, Ambleside, UK, IEEE, pp. 214–220, 2006. [44] W. Zhang, X. Chen, K. Cui, T. Xie, and N. Yuan, "DOA Estimation for Coprime Linear Array Based on MI-ESPRIT and Lookup Table," Sensors, Vol. 18, No. 9, pp. 1-11, 2018. | ||
|
آمار تعداد مشاهده مقاله: 229 تعداد دریافت فایل اصل مقاله: 174 |
||
