Journal article
Distributed state estimation in large-scale processes decomposed into observable subsystems using community detection
Computers & chemical engineering, v 156, p107544
01 Jan 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Adequate frequent information on state variables of a process is sometimes needed for effective control and monitoring of the process. However, it is not often available in practice, which can be addressed using a state estimator. This work deals with distributed state estimation in large-scale processes. The decomposition of a process into observable subsystems is formulated as an optimization problem, which is solved using an efficient whale optimization algorithm. Four nonlinear state estimation methods (ex-tended Kalman, unscented Kalman, spherical unscented Kalman, and cubature Kalman filtering) are then implemented and compared using distributed and centralized architectures on a process consisting of two reactors and a separator, and the Tennessee Eastman process. A parallelization strategy that improves the computational efficiency of the distributed architecture is proposed. Simulation results show that the parallel implementation of the distributed filtering methods is computationally more efficient than their centralized counterparts while yielding similarly accurate state estimates. (c) 2021 Elsevier Ltd. All rights reserved.
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Details
- Title
- Distributed state estimation in large-scale processes decomposed into observable subsystems using community detection
- Creators
- Leila Samandari Masooleh - Drexel UniversityJeffrey E. Arbogast - Air LiquideWarren D. Seider - University of PennsylvaniaUlku Oktem - Near-Miss Management, LLC, 1800 JFK Blvd., Suite 300, Philadelphia, PA 19103, USAMasoud Soroush - Drexel University
- Publication Details
- Computers & chemical engineering, v 156, p107544
- Publisher
- Elsevier
- Number of pages
- 14
- Grant note
- CBET-1704915; CBET1704833 / U.S. National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000720474900001
- Scopus ID
- 2-s2.0-85117709413
- Other Identifier
- 991019168857304721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Chemical