Modeling of soft sensor for chemical process

被引：0
|
作者：
Cao, Pengfei [1 ]
Luo, Xionglin [1 ]
机构：
[1] Research Institute of Automation, China University of Petroleum
来源：
Huagong Xuebao/CIESC Journal | 2013年 / 64卷 / 03期
关键词：
Data-driven modeling; Identification; Modeling; Nonlinear dynamic modeling; Nonlinear modeling; Soft sensor;
D O I：
10.3969/j.issn.0438-1157.2013.03.003
中图分类号：
学科分类号：
摘要：
In the commercial chemical process, many primary product variables cannot be measured online, and soft sensor is an important means to solve this problem. Soft sensing modeling is the core issue of soft sensor. The relationship between soft sensing modeling and identification and nonlinear modeling is presented. The dynamic relationship between quality variables and variables that are easy to measure exists between the increments, and identification depends on incremental data, while soft sensing modeling depends on the measured data to get the relationship. Nonlinear modeling establishes the static relationship between these variables, ignoring the dynamic characteristics, which soft sensing modeling should take into account. With deeper understanding of the chemical process properties, the types and structures of soft sensing model have undergone a great change in the last decades, and soft sensing modeling method evolves from mechanism modeling to data-driven modeling, from linear modeling to nonlinear modeling, and from static modeling to dynamic modeling. The development of the soft sensing modeling method is reviewed. The advantages and disadvantages of the proposed methods are analyzed, and the applications of these methods are shown. In the end, the hot issues and the directions of development of soft sensing modeling method are presented. © All Rights Reserved.
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页码：788 / 800
页数：12
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共 110 条
  • [61] Yan W., Shao H., Wang X., Soft sensing modeling based on support vector machine and Bayesian model selection, Computers & Chemical Engineering, 28, 8, pp. 1489-1498, (2004)
  • [62] Fernando D.S., Adriana N.A., Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression, Computers & Chemical Engineering, 32, 12, pp. 3264-3273, (2008)
  • [63] Alexandra G., Jus K., Tor A.J., Explicit stochastic predictive control of combustion plants based on Gaussian process models, Automatica, 44, 6, pp. 1621-1631, (2008)
  • [64] Kocijan J., Likar B., Gas-liquid separator modeling and simulation with Gaussian-process models, Simulation Modelling Practice and Theory, 16, 8, pp. 910-922, (2008)
  • [65] Rainer P., Multiple-step-ahead prediction in control systems with Gaussian process models and TS-fuzzy models, Engineering Applications of Artificial Intelligence, 20, 8, pp. 1023-1035, (2007)
  • [66] Bojan L., Jus K., Predictive control of a gas-liquid separation plant based on a Gaussian process model, Computers & Chemical Engineering, 31, 3, pp. 142-152, (2007)
  • [67] Li X., Su H., Chu J., Multiple model soft sensor based on affinity propagation, Gaussian process and Bayesian committee machine, CIESC Journal, 17, 1, pp. 95-99, (2009)
  • [68] Fu Y., Su H., Zhang Y., Et al., Adaptive soft-sensor modeling algorithm based on FCMISVM and its application in PX adsorption separation process, Journal of Chemical Industry and Engineering(China), 59, 5, pp. 746-751, (2008)
  • [69] Babak R., A cluster validity index for fuzzy clustering, Fuzzy Sets and Systems, 161, 6, pp. 3014-3025, (2010)
  • [70] Qi H.Y., Zhou X.G., Liu L.H., Et al., A hybrid neural network-first principles model for fixed-bed reactor, Chemical Engineering Science, 54, 14, pp. 2512-2526, (1999)
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