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Title: Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based 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 estimator-based 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 large-scale 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:
 [1]; ORCiD logo [1];  [1];  [2]
  1. West Virginia Univ., Morgantown, WV (United States)
  2. West Virginia Univ., Morgantown, WV (United States); National Energy Technology Lab. (NETL), Morgantown, WV (United States)
Publication Date:
Research Org.:
National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1395082
Report Number(s):
NETL-PUB-21062
Journal ID: ISSN 0888-5885
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Industrial and Engineering Chemistry Research
Additional Journal Information:
Journal Volume: 56; Journal Issue: 26; Journal ID: ISSN 0888-5885
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 model-based 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 Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based 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 Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System. United States. doi:10.1021/acs.iecr.6b04020.
Paul, Prokash, Bhattacharyya, Debangsu, Turton, Richard, and Zitney, Stephen E. Tue . "Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System". United States. doi:10.1021/acs.iecr.6b04020.
@article{osti_1395082,
title = {Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based 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 estimator-based 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 large-scale 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 = {Tue Jun 06 00:00:00 EDT 2017},
month = {Tue Jun 06 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
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