Jun Li This email address is being protected from spambots. You need JavaScript enabled to view it.1 and Zhengmei Zhao1
1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, P.R. China
Received: September 20, 2020 Accepted: January 11, 2021 Publication Date: June 1, 2021
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
The critical issue in soft sensing modeling of the chemical processes is how to model multidimensional data with noise and strong nonlinearity. To handle the uncertainty of modeling and minimize the impact of uncertainty, a class of method using interval type-2 Takagi–Sugeno–Kang (TSK) fuzzy logic systems (FLS) combining the principal component analysis (PCA) algorithm are proposed. First, the linear principal components from the input variables of model can be effectively extracted by the PCA algorithm. The number of fuzzy rules is regarded as the clustering center, and the fuzzy c-means (FCM) clustering algorithm is then applied. The result of clustering centers is used as the centers of the fuzzy rule antecedent. Second, soft sensing models are established based on three types of interval type-2 TSK FLS, namely, A1-C1, A2-C0 and A2-C1, and the backpropagation(BP) algorithm is used to update the parameters of the antecedent and consequent membership function. To verify the effectiveness of the proposed method, different types of interval type-2 TSK FLS were applied to the soft sensing modeling prediction of three key product yields for industrial fluidized catalytic cracking unit. Experimental results confirm that, compared with other types of FLS methods and support vector machine, the employed Type-2 TSK FLS method with different types can achieve better prediction accuracy, among them, the A2-C1 TSK FLS method has higher modeling accuracy and faster convergence.
Keywords: soft sensors; modeling; interval type-2 TSK fuzzy logic system; principal component analysis; chemical process
REFERENCES
[1] Francisco A.A. Souza, Rui Araújo, and Jérôme Mendes. Review of soft sensor methods for regression applications, 2016.
[2] Petr Kadlec, Ratko Grbi´c, and Bogdan Gabrys. Review of adaptation mechanisms for data-driven soft sensors. Computers and Chemical Engineering, 35(1):1–24, 2011.
[3] Yi Liu, Naiping Hu, Haiqing Wang, and Ping Li. Soft chemical analyzer development using adaptive leastsquares support vector regression with selective pruning and variable moving window size. Industrial and Engineering Chemistry Research, 48(12):5731–5741, jun 2009.
[4] Sreˇcko Herceg, Željka Ujevi´c Andriji´c, and Nenad Bolf. Development of soft sensors for isomerization process based on support vector machine regression and dynamic polynomial models. Chemical Engineering Research and Design, 149:95–103, 2019.
[5] Chitra Murugan and Pappa Natarajan. Estimation of fungal biomass using multiphase artificial neural network based dynamic soft sensor. Journal of Microbiological Methods, 159:5–11, 2019.
[6] Usman T Khan and Caterina Valeo. Dissolved oxygen prediction using a possibility theory based fuzzy neural network. Hydrology and Earth System Sciences, 20(6):2267–2293, 2016.
[7] Chin Ying Teh, Yi Wen Kerk, Kai Meng Tay, and Chee Peng Lim. On Modeling of Data-Driven Monotone Zero-Order TSK Fuzzy Inference Systems Using a System Identification Framework. IEEE Transactions on Fuzzy Systems, 26(6):3860–3874, 2018.
[8] Junpeng Li, Changchun Hua, Yana Yang, and Xinping Guan. Bayesian Block Structure Sparse Based T-S Fuzzy Modeling for Dynamic Prediction of Hot Metal Silicon Content in the Blast Furnace. IEEE Transactions on Industrial Electronics, 65(6):4933–4942, 2018.
[9] Mohammad Foad Samadi and Mehrdad Saif. StateSpace Modeling and Observer Design of Li-Ion Batteries Using Takagi-Sugeno Fuzzy System. IEEE Transactions on Control Systems Technology, 25(1):301–308, 2017.
[10] J.M. Mendel. Uncertain rule-based fuzzy systems and new directions. Springer, Switzerland, Cham, 2017.
[11] Jerry M. Mendel. General type-2 fuzzy logic systems made simple: A tutorial. IEEE Transactions on Fuzzy Systems, 22(5):1162–1182, 2014.
[12] Ana Belen Cara, Christian Wagner, Hani Hagras, Hector Pomares, and Ignacio Rojas. Multiobjective optimization and comparison of nonsingleton type-1 and singleton interval type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems, 21(3):459–476, 2013.
[13] Yang Chen, Dazhi Wang, and Shaocheng Tong. Forecasting studies by designing Mamdani interval type-2 fuzzy logic systems: With the combination of BP algorithms and KM algorithms. Neurocomputing, 174:1133– 1146, 2016.
[14] Abbas Khosravi and Saeid Nahavandi. Load forecasting using interval type-2 fuzzy logic systems: Optimal type reduction. IEEE Transactions on Industrial Informatics, 10(2):1055–1063, 2014.
[15] L. Li, W. H. Lin, and H. Liu. Type-2 fuzzy logic approach for short-term traffic forecasting. IEE Proceedings -Intelligent Transport Systems, 153(1):33–40, 2006.
[16] Pascual Noradino Montes Dorantes and Gerardo Maximiliano Mendez. Type-2 fuzzy logic systems for temperature evaluation in ladle furnace. IEEE Latin America Transactions, 14(8):3914–3920, 2016.
[17] Yunrui Bi, Xiaobo Lu, Zhe Sun, Dipti Srinivasan, and Zhixin Sun. Optimal Type-2 Fuzzy System for Arterial Traffic Signal Control. IEEE Transactions on Intelligent Transportation Systems, 19(9):3009–3027, 2018.
[18] Soleiman Hosseinpour, Mortaza Aghbashlo, and Meisam Tabatabaei. Biomass higher heating value (HHV) modeling on the basis of proximate analysis using iterative network-based fuzzy partial least squares coupled with principle component analysis (PCA-INFPLS). Fuel, 222:1–10, 2018.
[19] Witold Pedrycz and Hesam Izakian. Cluster-Centric Fuzzy Modeling. IEEE Transactions on Fuzzy Systems, 22(6):1585–1597, 2014.
[20] Qilian Liang and Jerry M. Mendel. Modeling MPEG VBR Video Traffic Using Type-2 Fuzzy Logic Systems. In Pedrycz W. (eds) Granular Computing. Physica, Heidelberg, pages 367–383. 2001.
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