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Developing a Soft Sensor for MTBE Process Based on a Small Sample

  • automation in industry
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Abstract

The paper discusses the development of a soft sensor (SS) for an MTBE plant in the cases when the training sample either is small or does not comprise the whole of quality range because of process plant non-stationarity as well as the difficulty and high cost of retrieving more information. In order to build a soft sensor that provides higher accuracy in estimating the quality of the output product, an algorithm for extending the initial training sample using a rigorous model of the distillation column of the methyl-tert-butyl ether production under condition of exactly unknown values of the Murphree trays efficiency has been proposed. The resulting SS enables 40% improvement of product quality prediction.

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References

  1. Dozortsev, V. M., Itskovich, E. L. & Kneller, D. V. Advanced Process Control: 10 Years in Russia. Automat. Promyshl. no. 1, 12–19 (2013).

    Google Scholar 

  2. Bakhtadze, N. N. Virtual Analyzers: Identification Approach. Autom. Remote Control 65(no. 11), 1691–1709 (2004).

    Article  MathSciNet  Google Scholar 

  3. Kadlec, P., Gabrys, B. & Strandt, S. Data-Driven Soft Sensors in the Process Industry. Computers Chem. Eng. 33(no. 4), 795–814 (2009).

    Article  Google Scholar 

  4. Andrijić, Z. ¸U., Cvetnić, M. & Bolf, N. Soft Sensor Models for a Fractionation Reformate Plant Using Small and Bootstrapped Data Sets. Brazilian J. Chem. Eng. 35(no. 2), 745–756 (2018).

    Article  Google Scholar 

  5. Fortuna, L., Graziani, S. & Xibilia, M. G. Comparison of Soft-Sensor Design Methods for Industrial Plants Using Small Data Sets. IEEE Trans. Instr. Measurem 58(no. 8), 2444–2451 (2009).

    Article  Google Scholar 

  6. Napolia, G. & Xibiliab, M. G. Soft Sensor Design for a Topping Process in the Case of Small Datasets. Computers Chem. Eng. 35, 2447–2456 (2011).

    Article  Google Scholar 

  7. Chen, Z.-S., Zhu, B., He, Y.-L. & Yu, L.-A. A PSO Based Virtual Sample Generation Method for Small Sample Sets: Applications to Regression Datasets. Eng. Appl. Artific. Intell. 59, 236–243 (2017).

    Article  Google Scholar 

  8. Shang, C., Yang, F., Huang, D. & Lyu, W. Data-Driven Soft Sensor Development Based on Deep Learning Technique. J. Process Control no. 24, 223–233 (2014).

    Article  Google Scholar 

  9. Abrams, D. S. & Prausnitz, J. M. Statistical Thermodynamics of Liquid Mixtures: A New Expression for the Excess Gibbs Energy of Partly or Completely Miscible Systems. AIChE J. 21(no. 1), 116–128 (1975).

    Article  Google Scholar 

  10. Korn, G. & Korn, T. Mathematical Handbook for Scientists and Engineers: Definitions, Theorems, and Formulas for Reference and Review. (McGraw-Hill, New York, 1968).

    MATH  Google Scholar 

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Authors and Affiliations

  1. Institute of Automation and Control Processes, Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    S. A. Samotylova & A. Yu. Torgashov

Authors
  1. S. A. Samotylova
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  2. A. Yu. Torgashov
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Samotylova, S., Torgashov, A. Developing a Soft Sensor for MTBE Process Based on a Small Sample. Autom Remote Control 81, 2132–2142 (2020). https://doi.org/10.1134/S0005117920110120

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  • DOI: https://doi.org/10.1134/S0005117920110120

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