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Sensor network design for maximizing process efficiency: An algorithm and its application
Author(s) -
Paul Prokash,
Bhattacharyya Debangsu,
Turton Richard,
Zitney Stephen E.
Publication year - 2015
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.14649
Subject(s) - mathematical optimization , nonlinear programming , convergence (economics) , process (computing) , nonlinear system , estimator , algorithm , integer (computer science) , integer programming , computer science , scale (ratio) , genetic algorithm , mathematics , physics , quantum mechanics , economics , programming language , economic growth , operating system , statistics
Sensor network design (SND) is a constrained optimization problem requiring systematic and effective solution algorithms for determining where best to locate sensors. A SND algorithm is developed for maximizing plant efficiency for an estimator‐based control system while simultaneously satisfying accuracy requirements for the desired process measurements. The SND problem formulation leads to a mixed integer nonlinear programming (MINLP) optimization that is difficult to solve for large‐scale system applications. Therefore, a sequential approach is developed to solve the MINLP problem, where the integer problem for sensor selection is solved using the genetic algorithm while the nonlinear programming problem including convergence of the “tear stream” in the estimator‐based control system is solved using the direct substitution method. The SND algorithm is then successfully applied to a large scale, highly integrated chemical process. © 2014 American Institute of Chemical Engineers AIChE J , 61: 464–476, 2015