Nonlinear Dynamic ModelBased Multiobjective Sensor Network Design Algorithm for a Plant with an EstimatorBased Control System
Abstract
Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimatorbased control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The largescale combinatorial optimization problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO_{2} capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.
 Authors:

 West Virginia Univ., Morgantown, WV (United States)
 West Virginia Univ., Morgantown, WV (United States); National Energy Technology Lab. (NETL), Morgantown, WV (United States)
 Publication Date:
 Research Org.:
 National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1395082
 Report Number(s):
 NETLPUB21062
Journal ID: ISSN 08885885
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Industrial and Engineering Chemistry Research
 Additional Journal Information:
 Journal Volume: 56; Journal Issue: 26; Journal ID: ISSN 08885885
 Publisher:
 American Chemical Society (ACS)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 42 ENGINEERING; Sensor Network Design; Dynamic Modeling; Control; Efficiency; Acid Gas Removal; Parallel Computing OSTI; Nonlinear dynamic modelbased sensor network design; Efficiency maximization; Lexicographic optimization; Parallel computing; Nonlinear model identification
Citation Formats
Paul, Prokash, Bhattacharyya, Debangsu, Turton, Richard, and Zitney, Stephen E. Nonlinear Dynamic ModelBased Multiobjective Sensor Network Design Algorithm for a Plant with an EstimatorBased Control System. United States: N. p., 2017.
Web. doi:10.1021/acs.iecr.6b04020.
Paul, Prokash, Bhattacharyya, Debangsu, Turton, Richard, & Zitney, Stephen E. Nonlinear Dynamic ModelBased Multiobjective Sensor Network Design Algorithm for a Plant with an EstimatorBased Control System. United States. doi:10.1021/acs.iecr.6b04020.
Paul, Prokash, Bhattacharyya, Debangsu, Turton, Richard, and Zitney, Stephen E. Tue .
"Nonlinear Dynamic ModelBased Multiobjective Sensor Network Design Algorithm for a Plant with an EstimatorBased Control System". United States. doi:10.1021/acs.iecr.6b04020. https://www.osti.gov/servlets/purl/1395082.
@article{osti_1395082,
title = {Nonlinear Dynamic ModelBased Multiobjective Sensor Network Design Algorithm for a Plant with an EstimatorBased Control System},
author = {Paul, Prokash and Bhattacharyya, Debangsu and Turton, Richard and Zitney, Stephen E.},
abstractNote = {Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimatorbased control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The largescale combinatorial optimization problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.},
doi = {10.1021/acs.iecr.6b04020},
journal = {Industrial and Engineering Chemistry Research},
number = 26,
volume = 56,
place = {United States},
year = {2017},
month = {6}
}
Web of Science