Economic oriented stochastic optimization in process control using Taguchi’s method

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• 作者：András Király (1)
  László Dobos (1)
  János Abonyi (1)
  
• 关键词：Monte ; Carlo simulation ; Model predictive control ; Economic assessment ; Stochastic optimization
• 刊名：Optimization and Engineering
• 出版年：2013
• 出版时间：November 2013
• 年：2013
• 卷：14
• 期：4
• 页码：547-563
• 全文大小：
• 作者单位：András Király (1)
  László Dobos (1)
  János Abonyi (1)
  
  1. Department of Process Engineering, University of Pannonia, Egyetem street 10, Veszprém, 8200, Hungary
  
• ISSN：1573-2924

文摘

The optimal operating region of complex production systems is situated close to process constraints related to quality or safety requirements. Higher profit can be realized only by assuring a relatively low frequency of violation of these constraints. We defined a Taguchi-type loss function to aggregate these constraints, target values, and desired ranges of product quality. We evaluate this loss function by Monte-Carlo simulation to handle the stochastic nature of the process and apply the gradient-free Mesh Adaptive Direct Search algorithm to optimize the resulted robust cost function. This optimization scheme is applied to determine the optimal set-point values of control loops with respect to pre-determined risk levels, uncertainties and costs of violation of process constraints. The concept is illustrated by a well-known benchmark problem related to the control of a linear dynamical system and the model predictive control of a more complex nonlinear polymerization process. The application examples illustrate that the loss function of Taguchi is an ideal tool to represent performance requirements of control loops and the proposed Monte-Carlo simulation based optimization scheme is effective to find the optimal operating regions of controlled processes.