Decentralized Non-Derivative Algorithm for Real-Time Optimising Control of Large-Scale Industrial Processes

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Designing controller systems for large-scale processes in a centralized form are faced with computational burden due to the large number of decision variables involved, however these difficulties can be overcome by decentralization or decomposition of the optimal control problems into smaller sub-systems in a hierarchical manner. Hence, this paper presents a decentralized adaptive real-time optimizing (RTO) control scheme for determining and maintaining the optimum steady-state operating conditions for inter-connected large scale industrial processes. The proposed strategy extends the applicability of the centralized non-derivative algorithm and by structuring it hierarchically in a decentralized fashion as in the Integrated System Optimisation and Parameter Estimation (ISOPE) technique. The proposed algorithm has been formulated in two different ways of utilising the available measurement from the plants: the first method employs input-output feedback and the second method uses only output feedback. The simulation examples are provided to illustrate and compares the methods in which the results show that the algorithm with input-output structure performs better with less number of set-point changes and faster convergence. Since the algorithm is designed in the decentralize form, the computation difficulties may adequately be dealt with.

     

     


  • Keywords


    Coordination; decentralize; large-scale processes; optimal control; real-time optimisation.

  • References


      [1] Blanco TR, Sarabia D, Navia, D, Prada CD (2015), Modifier- adaptive methodology for RTO applied to distillation columns. IFAC-PapersOnline, 48-8, 223-228.

      [2] Shahidi SW, Paulen R, Engell S (2015), Two-layer hierar chical predictive control via negotiation of active constraints. IFAC-PapersOnline, 48-23, 404-409.

      [3] Wang W & Ohmori H (2016), Decentralize disturbance attenuation control for large-scale power system. IFAC-PapersOnline, 49-4, 043-048.

      [4] Gao W, Wen S, Engel S (2016), A reliable modifier- adaptation strategy for real-time optimization, Computers and Chemical Engineering. 91, 318-328, 1895.

      [5] Liu ZY & Roberts P (1989a), Non-derivative algorithm for optimization and steady-state systems, J. Systems Sci., 20(8), 1483-1497.

      [6] Roberts P (1979), An algorithm for steady-state system optimization and parameter estimation. Int. J. Syst. Sci., 10(7), 719-734.

      [7] Brdys M & Roberts P (1986), Optimal structure for steady-state adaptive optimizing control of large-scale industrial processes. Int. J. System Sci. 17(10), 1449-1474.

      [8] Chen S (1986), Integrated system optimization and parameter estimation methods for on-line control of industrial processes. PhD. Thesis, The City University, London.

      [9] Brdys MA, Abdullah N, Roberts PD (1990), Hierarchical adaptive technique for optimizing control of large-scale steady state system, iterative strategies, and their convergence. IMA Journal of Mathematical Control & Information, 7, 199 – 233.

      [10] Liu ZY, Roberts PD (1989b), Sequence model approximation approach for optimization and control of steady-state systems. Int. J. Control, 49(6), 1895-1913.

      [11] Brdys MA, Abdullah N, Roberts PD (1990), Augmented model based double iterative loop techniques for hierarchical control of complex industrial processes. Int. J. Control, 52, 3, 549-570.

      [12] Abdullah N (1980), Augmented integrated system optimization and parameter estimation techniques for on-line hierarchical control of large scale industrial process. PhD. thesis, Control Engineering Centre, The City University, London, England.

      [13] Findeisen W, Bailey FN, Brdys M, Malinnowski K, Tatjewski P, Wozniak A (1980), Control and coordination in hierarchical systems, Wiley, London.

 

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Article ID: 24898
 
DOI: 10.14419/ijet.v8i1.2.24898




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