Journal Published Online: 03 October 2018
Volume 2, Issue 2

Stochastic Programming Approach versus Estimator-Based Approach for Sensor Network Design for Maximizing Efficiency

CODEN: SSMSCY

Abstract

The measurement technology with sensors plays a key role in achieving efficient operation of the process plants, and optimal sensor placement is very important in this endeavor. The focus of the current work is on the development of sensor placement algorithms to obtain the numbers, locations, and types of sensors for a large-scale process with the estimator-based control system. Two sensor placement algorithms are developed and investigated. In one algorithm, dynamics in the process efficiency loss that are due to the estimator-based control system that receives measurements from a candidate sensor network are explicitly accounted for. For a large-scale process with a large number of candidate sensor locations, this approach leads to a computationally expensive mixed integer nonlinear programming problem. In another algorithm, the estimation error is accounted for in terms of probability distributions, and therefore, a stochastic programming approach is used to solve the sensor placement problem. A novel algorithm called BONUS is used to solve the problem. The developed sensor placement algorithms are implemented in an acid gas removal unit as part of an integrated gasification combined cycle power plant with precombustion carbon dioxide capture. In this article, we compare and contrast these two sensor placement algorithms by evaluating the efficiency loss of the optimal sensor network synthesized by each of these algorithms along with their computational performance.

Author Information

Sen, Pallabi
Center for Uncertain Systems: Tools for Optimization & Management (CUSTOM), Vishwamitra Research Institute, 2714 Crystal Way, Crystal Lake, USA
Diwekar, Urmila
Center for Uncertain Systems: Tools for Optimization & Management (CUSTOM), Vishwamitra Research Institute, Crystal Lake, IL, USA
Bhattacharyya, Debangsu
Department of Chemical and Biomedical Engineering, 1374 Evansdale Drive, West Virginia University, Morgantown, WV, USA
Pages: 17
Price: Free
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Details
Stock #: SSMS20180021
ISSN: 2520-6478
DOI: 10.1520/SSMS20180021