کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
172641 | 458554 | 2013 | 7 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Branch and bound method for regression-based controlled variable selection Branch and bound method for regression-based controlled variable selection](/preview/png/172641.png)
• A new branch and bound algorithm for regression based globally optimal controlled variable selection is proposed.
• Local shortcoming of existing self-optimizing control approach is overcome.
• New pruning algorithm makes the branch and bound much more efficient.
• Efficiency and effectiveness of the algorithm are demonstrated though numerical examples.
Self-optimizing control is a promising method for selection of controlled variables (CVs) from available measurements. Recently, Ye, Cao, Li, and Song (2012) have proposed a globally optimal method for selection of self-optimizing CVs by converting the CV selection problem into a regression problem. In this approach, the necessary conditions of optimality (NCO) are approximated by linear combinations of available measurements over the entire operation region. In practice, it is desired that a subset of available measurements be combined as CVs to obtain a good trade-off between the economic performance and the complexity of control system. The subset selection problem, however, is combinatorial in nature, which makes the application of the globally optimal CV selection method to large-scale processes difficult. In this work, an efficient branch and bound (BAB) algorithm is developed to handle the computational complexity associated with the selection of globally optimal CVs. The proposed BAB algorithm identifies the best measurement subset such that the regression error in approximating NCO is minimized and is also applicable to the general regression problem. Numerical tests using randomly generated matrices and a binary distillation column case study demonstrate the computational efficiency of the proposed BAB algorithm.
Journal: Computers & Chemical Engineering - Volume 54, 11 July 2013, Pages 1–7