Multilayered approach to model predictive industrial process control

Бесплатный доступ

The method of the industrial processes efficiency increasing based on the multilayered approach to the control task solution is proposed in the article. According to this approach the existing technology regulations of estimation process conducting in terms of the true accuracy of operating parameters holding is used as the regulatory constraint defining their acceptable region. Within the field specified the process of the controlled operating parameters values optimization according to the current data about operation is implemented. The optimization process is based on multidimensional simplex method usage and orthogonal planning of problem solving in combination with the method of effective domain elliptic approximation. The example of using the proposed method for the blast-furnace process efficiency increasing is given.

Еще

Blast-furnace process, multilayered approach, elliptic approximation method, modelpredictive control

Короткий адрес: https://sciup.org/147155088

IDR: 147155088   |   DOI: 10.14529/ctcr160112

Список литературы Multilayered approach to model predictive industrial process control

  • Kazarinov L.S., Barbasova T.A. Case Study of the Conservation Power Plant Concept to Energy Conservation in a Metallurgical Works. Procedia Engineering, 2015, vol. 129, pp. 578-586. DOI: DOI: 10.1016/j.proeng.2015.12.060
  • Kazarinov L.S., Barbasova Т.А., Kolesnikova О.V, Shnayder D.A. Complex Hydraulic Network Dispatching Control Based on Signal-Oriented Macromodel. 1st Conference on Modelling, Identification and Control of Nonlinear Systems (MICNON-2015), Saint Petersburg, Russia, June, 24-26, 2015, vol. 48, iss. 11, pp. 92-96. DOI: DOI: 10.1016/j.ifacol.2015.09.165
  • Kazarinov L.S., Barbasova T.A, Kolesnikova O.V., Zakharova A.A. Method of Multilevel Rationing and Optimal Forecasting of Volumes of Electric Energy Consumption by an Industrial Enterprise. Automatic Control and Computer Sciences, 2014, vol. 48, no. 6, pp. 324-333. DOI: DOI: 10.3103/S0146411614060054
  • Lee E.B., Markus. L. Foundations of Оptimal Control Theory. New York: Wiley, 1967. 576 p.
  • Tsypkin Ya.Z. Adaptatsiaya i obuchenie v avtomaticheskikh sistemakh (Adaptation and Training in Automatic Systems). Moscow, Science Publ., 1968. 400 p.
  • Richalet J.B., Rault A., Testud J.L., Papon J. Model Predictive Heuristic Control: Applications to Industrial Processes. Automatica, 1978, 14, pp. 413-428.
  • Clarke D.W. et al. Generalized Predictive Control. Part I & II. Automatica, 1987, vol. 23, no. 2, pp. 137-160.
  • Clarke D.W. et al. Properties of Generalized Predictive Control. Automatica, 1989, vol. 25, no. 6, pp. 859-875.
  • De Keyser R.M.C., Van de Velde Ph.G.A., Dumortier F.A.G. A Comparative Study of Self-adaptive Long-range Predictive Control Methods. Automatica, 1988, vol. 24, no. 2, pp. 149-163.
  • Garcia C.E., Prett D.M., Morari M. Model Predictive Control: Theory and Practice a Survey. Automatica, 1989, 25(3), pp. 335-348.
  • Ricker N.L. Model Predictive Control: State of the Art. In Y. Arkun, W.H. Ray (Eds.), Chemical Process Control CPC IV, Fourth International Conference on Chemical Process Control. Amsterdam: Elsevier, 1991, pp. 271-296.
  • Morari M., Lee J.H. Model Predictive Control: The Good, the Bad, theUgly. Chemical Process Control CPC IV, Fourth International Conference on Chemical Process Control. Amsterdam, Elsevier, 1991, pp. 419-444.
  • Muske K. R., Rawlings J.B. Model Predictive Control with Linear Models. A.I.CH.E. Journal, 1993, 39(2), pp. 262-287.
  • Mayn D.Q. Nonlinear Model Predictive Control: An Assessment. Fifth International Conference on Chemical Process Control AICHE and CACHE, 1997, pp. 217-231.
  • Lee, J. H., & Cooley, B. Recent Advances in Model Predictive Control and Other Related Areas. Fifth International Conference on Chemical Process Control AICHE and CACHE, 1997, pp. 201-216.
  • José Manuel Mesa Fernández, Valeriano Álvarez Cabal, Vicente Rodríguez Montequin, Joaquín Villanueva Balsera. Online Estimation of Electric Arc Furnace Tap Temperature by Using Fuzzy Neural Networks. Engineering Applications of Artificial Intelligence, 2008, 21, pp. 1001-1012.
Еще
Статья научная