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Zhang, Jingqiong; Zhang, Wenbiao; He, Yuting; Yan, Yong (2016)
Publisher: IOP Publishing Ltd
Languages: English
Types: Article
Subjects: T
The amount of coke deposition on catalyst pellets is one of the most important indexes of catalytic property and service life. As a result, it is essential to measure this and analyze the active state of the catalysts during a continuous production process. This paper proposes a new method to predict the amount of coke deposition on catalyst pellets based on image analysis and soft computing. An image acquisition system consisting of a flatbed scanner and an opaque cover is used to obtain catalyst images. After imaging processing and feature extraction, twelve effective features are selected and two best feature sets are determined by the prediction tests. A neural network optimized by a particle swarm optimization algorithm is used to establish the prediction model of the coke amount based on various datasets. The root mean square error of the prediction values are all below 0.021 and the coefficient of determination R 2, for the model, are all above 78.71%. Therefore, a feasible, effective and precise method is demonstrated, which may be applied to realize the real-time measurement of coke deposition based on on-line sampling and fast image analysis.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Gross E, Liu J H C, Toste F D and Somorjai G A 2012 Control of selectivity in heterogeneous catalysis by tuning nanoparticle properties and reactor residence time Nature chemistry 4(11) 947-952
    • [2] Ren X H, Bertmer M, Stapf S, Demco D E, Blümich B, Kern C and Jess A 2002 Deactivation and regeneration of a naphtha reforming catalyst Appl. Catal. A: General 228(1) 39-52
    • [3] Aguayo A T, Sánchez del Campo A E, Gayubo A G, Tarrío A and Bilbao J 1999 Deactivation by coke of a catalyst based on a SAPO 34 in the transformation of methanol into olefins Journal of Chemical Technology and Biotechnology 74(4) 315-321
    • [4] Wood J and Gladden L F 2002 Effect of coke deposition upon pore structure and self-diffusion in deactivated industrial hydroprocessing catalysts Appl. Catal. A: General 249(2) 241-253
    • [5] Chen D, Rebo H P, Moljord K and Holmen A 1997 The role of coke deposition in the conversion of methanol to olefins over SAPO-34 Studies in Surface Science and Catalysis 111 159-166
    • [6] Dejaifve P, Auroux A, Gravelle P C and Védrine J C 1982 Methanol conversion on acidic ZSM-5, offretite, and mordenite zeolites: A comparative study of the formation and stability of coke deposits Journal of Catalysis 70(1) 123-136
    • [7] Bibby D M, Milestone N B, Patterson J E and Aldridge L P 1986 Coke formation in zeolite ZSM-5 Journal of Catalysis 97(2) 493-502
    • [8] Sun Z, Gu X and Yu J 2001 Coking models of reactor in continuous catalytic reforming Journal of East China University of Science and Technology 27(5) 568-571
    • [9] Bai Z W 2000 Determination of the components of mixture by thermogravimetric analysis Journal of Instrumental Analysis 19(4) 83-85
    • [10] Li B H and Gonzalez R D 1998 The measurement of small amounts of coke by a sensitive TGA/FTIR technique Catalysis letters 54(1-2) 5-8
    • [11] Wang J A, Chen L F, Li C L and Novaro O 2001 Characterization of structure and combustion behavior of the coke Formed on a hydroisomerization catalyst Studies in Surface Science and Catalysis 139 53-60
    • [12] Bayraktar O and Kugler E L 2003 Coke content of spent commercial fluid catalytic cracking (FCC) catalysts Journal of thermal analysis and calorimetry 71(3) 867-874
    • [13] Tang Y Q, Cao Y J, Wang J D, Yang Y R, Jiang B B and Liao Z W 2011 A model for determination of amount of coke deposit on catalyst based on vibration signal analysis Chinese Journal of Mechanical Engineering 1 78-84
    • [14] Tang Y Q,Wang J D, Liao Z W, Yang Y R and Xie Z K 2010 Measurement of the amount of coke deposit on catalyst based on acoustic emission frequency shift Acta Petrolei Sinica (Petroleum Processing Section) 6 917-921
    • [15] Zhang J Q, Zhang W B and Yan Y 2015 Coke deposition detection through the analysis of catalyst images Proc. IEEE Int. Conf. on Imaging Systems and Techniques (Macau, China, 16-18 September 2015) pp 16-20
    • [16] Otsu N 1979 A threshold selection method from gray-level histograms IEEE Trans. on Systems, Man and Cybernetics 9(1) 62-66
    • [17] Stricker M and Orengo M 1995 Similarity of color images Proc. SPIE Int. Conf. on Electronic Imaging: Science and Technology (San Jose, CA, 5 February 1995) pp 381-392
    • [18] Zhang Z L, Yang J G,Wang Y L, Dou D Y and Xia W C 2014 Ash content prediction of coarse coal by image analysis and GA-SVM Powder Technology 268 429-435
    • [19] Mitani Y and Hamamoto Y 2010 A consideration of pan-sharpen images by HSI transformation approach Proc. 2010 Annual Conf. of the Society of Instrument and Control Engineers (Taipei, Taiwan, 18-21 August 2010) pp 1283-1284
    • [20] Mei Y and Androutsos D 2008 Color texture retrieval using wavelet decomposition in the independent components color space Proc. IEEE Int. Conf. on Electrical and Computer Engineering (Niagara Falls, Canada, 4-7 May 2008) pp 1379-1382
    • [21] Haralick R M, Shanmugam K and Dinstein I H 1973 Textural features for image classification IEEE Trans.on Systems, Man and Cybernetics 6 610-621
    • [22] Honeycutt C E and Plotnick R 2008 Image analysis techniques and gray-level co-occurrence matrices (GLCM) for calculating bioturbation indices and characterizing biogenic sedimentary structures Computers & Geosciences 34(11) 1461-1472
    • [23] Gonzalez R C andWoods R E 2007 Digital Image Processing 3rd (USA: Pearson Education)
    • [24] Anderson J A 1995 An Introduction to Neural Networks (MA: MIT Press)
    • [25] Chen F F, Jiang X F and Jiang Z Y 2011 The research of recognition on oceanic internal waves based on gray gradient co-occurrence matrix and BP neural network Proc. IEEE Int. Symposium on Photonics and Optoelectronics (Wuhan, China, 16-18 May 2011) pp 1-4
    • [26] Shi Y and Eberhart R C 1999 Empirical study of particle swarm optimization Proc. IEEE Int. Congress on Evolutionary Computation (Washington D.C., USA, 6-9 July 1999) pp 3
    • [27] Hsu C W and Lin C J 2002 A comparison of methods for multiclass support vector machines IEEE Trans. on Neural Networks 13(2) 415-425
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