Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter

In chemical process control, estimation of the process states, e.g. concentration or properties of the reactant or resultant, in real time is a key issue. Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estim...
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Gespeichert in:

Autor*in:	Zhou, Sun [verfasserIn] Wang, Yaozong [verfasserIn] Liu, Yunlong [verfasserIn] Ji, Guoli [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	State estimation Particle filter State observer Soft sensor Recursive estimation Batch process

Übergeordnetes Werk:	Enthalten in: Chemical engineering research and design - Amsterdam : Elsevier, 1983, 125, Seite 9-23
Übergeordnetes Werk:	volume:125 ; pages:9-23

DOI / URN:	10.1016/j.cherd.2017.06.033

Katalog-ID:	ELV000386529

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520			\|a In chemical process control, estimation of the process states, e.g. concentration or properties of the reactant or resultant, in real time is a key issue. Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estimation. Due to ignorance of such challenges, many existing methods would produce poor estimates in real applications. We design a new state estimation approach employing Bayesian filtering with consideration of batch-to-batch dynamics. To characterize the dynamics across batches with different time profiles, a synchronized two-dimensional (2-D) state-space model is constructed that contains synchronously equal- and unequal-length situations. Based on this model, a novel formulation of the particle filter is derived where the particles evolve along both the time and the batch dimensions so as to approximate the synchronized 2-D optimal estimates. Also, the convergence concern is addressed from a practitioner’s viewpoint. To incorporate appropriate data from previous batches into the current estimation, an on-line synchronization method based on the dynamic time warping technique is developed using a new alignment performance measure together with a transfer alignment strategy. The performance of the proposal is evaluated by case study on a numerical example and a three-state batch reaction process.
650		4	\|a State estimation
650		4	\|a Particle filter
650		4	\|a State observer
650		4	\|a Soft sensor
650		4	\|a Recursive estimation
650		4	\|a Batch process
700	1		\|a Wang, Yaozong \|e verfasserin \|4 aut
700	1		\|a Liu, Yunlong \|e verfasserin \|4 aut
700	1		\|a Ji, Guoli \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Chemical engineering research and design \|d Amsterdam : Elsevier, 1983 \|g 125, Seite 9-23 \|h Online-Ressource \|w (DE-627)312841965 \|w (DE-600)2008006-2 \|w (DE-576)090893190 \|x 1744-3563 \|7 nnns
773	1	8	\|g volume:125 \|g pages:9-23
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912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
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912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
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912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_206
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2098
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2122
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912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
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936	b	k	\|a 58.10 \|j Verfahrenstechnik: Allgemeines
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952			\|d 125 \|h 9-23

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publishDate	2017
allfields	10.1016/j.cherd.2017.06.033 doi (DE-627)ELV000386529 (ELSEVIER)S0263-8762(17)30362-3 DE-627 ger DE-627 rda eng 540 660 DE-600 58.10 bkl Zhou, Sun verfasserin (orcid)0000-0002-4264-4600 aut Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In chemical process control, estimation of the process states, e.g. concentration or properties of the reactant or resultant, in real time is a key issue. Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estimation. Due to ignorance of such challenges, many existing methods would produce poor estimates in real applications. We design a new state estimation approach employing Bayesian filtering with consideration of batch-to-batch dynamics. To characterize the dynamics across batches with different time profiles, a synchronized two-dimensional (2-D) state-space model is constructed that contains synchronously equal- and unequal-length situations. Based on this model, a novel formulation of the particle filter is derived where the particles evolve along both the time and the batch dimensions so as to approximate the synchronized 2-D optimal estimates. Also, the convergence concern is addressed from a practitioner’s viewpoint. To incorporate appropriate data from previous batches into the current estimation, an on-line synchronization method based on the dynamic time warping technique is developed using a new alignment performance measure together with a transfer alignment strategy. The performance of the proposal is evaluated by case study on a numerical example and a three-state batch reaction process. State estimation Particle filter State observer Soft sensor Recursive estimation Batch process Wang, Yaozong verfasserin aut Liu, Yunlong verfasserin aut Ji, Guoli verfasserin aut Enthalten in Chemical engineering research and design Amsterdam : Elsevier, 1983 125, Seite 9-23 Online-Ressource (DE-627)312841965 (DE-600)2008006-2 (DE-576)090893190 1744-3563 nnns volume:125 pages:9-23 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_206 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.10 Verfahrenstechnik: Allgemeines AR 125 9-23
spelling	10.1016/j.cherd.2017.06.033 doi (DE-627)ELV000386529 (ELSEVIER)S0263-8762(17)30362-3 DE-627 ger DE-627 rda eng 540 660 DE-600 58.10 bkl Zhou, Sun verfasserin (orcid)0000-0002-4264-4600 aut Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In chemical process control, estimation of the process states, e.g. concentration or properties of the reactant or resultant, in real time is a key issue. Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estimation. Due to ignorance of such challenges, many existing methods would produce poor estimates in real applications. We design a new state estimation approach employing Bayesian filtering with consideration of batch-to-batch dynamics. To characterize the dynamics across batches with different time profiles, a synchronized two-dimensional (2-D) state-space model is constructed that contains synchronously equal- and unequal-length situations. Based on this model, a novel formulation of the particle filter is derived where the particles evolve along both the time and the batch dimensions so as to approximate the synchronized 2-D optimal estimates. Also, the convergence concern is addressed from a practitioner’s viewpoint. To incorporate appropriate data from previous batches into the current estimation, an on-line synchronization method based on the dynamic time warping technique is developed using a new alignment performance measure together with a transfer alignment strategy. The performance of the proposal is evaluated by case study on a numerical example and a three-state batch reaction process. State estimation Particle filter State observer Soft sensor Recursive estimation Batch process Wang, Yaozong verfasserin aut Liu, Yunlong verfasserin aut Ji, Guoli verfasserin aut Enthalten in Chemical engineering research and design Amsterdam : Elsevier, 1983 125, Seite 9-23 Online-Ressource (DE-627)312841965 (DE-600)2008006-2 (DE-576)090893190 1744-3563 nnns volume:125 pages:9-23 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_206 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.10 Verfahrenstechnik: Allgemeines AR 125 9-23
allfields_unstemmed	10.1016/j.cherd.2017.06.033 doi (DE-627)ELV000386529 (ELSEVIER)S0263-8762(17)30362-3 DE-627 ger DE-627 rda eng 540 660 DE-600 58.10 bkl Zhou, Sun verfasserin (orcid)0000-0002-4264-4600 aut Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In chemical process control, estimation of the process states, e.g. concentration or properties of the reactant or resultant, in real time is a key issue. Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estimation. Due to ignorance of such challenges, many existing methods would produce poor estimates in real applications. We design a new state estimation approach employing Bayesian filtering with consideration of batch-to-batch dynamics. To characterize the dynamics across batches with different time profiles, a synchronized two-dimensional (2-D) state-space model is constructed that contains synchronously equal- and unequal-length situations. Based on this model, a novel formulation of the particle filter is derived where the particles evolve along both the time and the batch dimensions so as to approximate the synchronized 2-D optimal estimates. Also, the convergence concern is addressed from a practitioner’s viewpoint. To incorporate appropriate data from previous batches into the current estimation, an on-line synchronization method based on the dynamic time warping technique is developed using a new alignment performance measure together with a transfer alignment strategy. The performance of the proposal is evaluated by case study on a numerical example and a three-state batch reaction process. State estimation Particle filter State observer Soft sensor Recursive estimation Batch process Wang, Yaozong verfasserin aut Liu, Yunlong verfasserin aut Ji, Guoli verfasserin aut Enthalten in Chemical engineering research and design Amsterdam : Elsevier, 1983 125, Seite 9-23 Online-Ressource (DE-627)312841965 (DE-600)2008006-2 (DE-576)090893190 1744-3563 nnns volume:125 pages:9-23 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_206 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.10 Verfahrenstechnik: Allgemeines AR 125 9-23
allfieldsGer	10.1016/j.cherd.2017.06.033 doi (DE-627)ELV000386529 (ELSEVIER)S0263-8762(17)30362-3 DE-627 ger DE-627 rda eng 540 660 DE-600 58.10 bkl Zhou, Sun verfasserin (orcid)0000-0002-4264-4600 aut Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In chemical process control, estimation of the process states, e.g. concentration or properties of the reactant or resultant, in real time is a key issue. Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estimation. Due to ignorance of such challenges, many existing methods would produce poor estimates in real applications. We design a new state estimation approach employing Bayesian filtering with consideration of batch-to-batch dynamics. To characterize the dynamics across batches with different time profiles, a synchronized two-dimensional (2-D) state-space model is constructed that contains synchronously equal- and unequal-length situations. Based on this model, a novel formulation of the particle filter is derived where the particles evolve along both the time and the batch dimensions so as to approximate the synchronized 2-D optimal estimates. Also, the convergence concern is addressed from a practitioner’s viewpoint. To incorporate appropriate data from previous batches into the current estimation, an on-line synchronization method based on the dynamic time warping technique is developed using a new alignment performance measure together with a transfer alignment strategy. The performance of the proposal is evaluated by case study on a numerical example and a three-state batch reaction process. State estimation Particle filter State observer Soft sensor Recursive estimation Batch process Wang, Yaozong verfasserin aut Liu, Yunlong verfasserin aut Ji, Guoli verfasserin aut Enthalten in Chemical engineering research and design Amsterdam : Elsevier, 1983 125, Seite 9-23 Online-Ressource (DE-627)312841965 (DE-600)2008006-2 (DE-576)090893190 1744-3563 nnns volume:125 pages:9-23 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_206 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.10 Verfahrenstechnik: Allgemeines AR 125 9-23
allfieldsSound	10.1016/j.cherd.2017.06.033 doi (DE-627)ELV000386529 (ELSEVIER)S0263-8762(17)30362-3 DE-627 ger DE-627 rda eng 540 660 DE-600 58.10 bkl Zhou, Sun verfasserin (orcid)0000-0002-4264-4600 aut Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In chemical process control, estimation of the process states, e.g. concentration or properties of the reactant or resultant, in real time is a key issue. Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estimation. Due to ignorance of such challenges, many existing methods would produce poor estimates in real applications. We design a new state estimation approach employing Bayesian filtering with consideration of batch-to-batch dynamics. To characterize the dynamics across batches with different time profiles, a synchronized two-dimensional (2-D) state-space model is constructed that contains synchronously equal- and unequal-length situations. Based on this model, a novel formulation of the particle filter is derived where the particles evolve along both the time and the batch dimensions so as to approximate the synchronized 2-D optimal estimates. Also, the convergence concern is addressed from a practitioner’s viewpoint. To incorporate appropriate data from previous batches into the current estimation, an on-line synchronization method based on the dynamic time warping technique is developed using a new alignment performance measure together with a transfer alignment strategy. The performance of the proposal is evaluated by case study on a numerical example and a three-state batch reaction process. State estimation Particle filter State observer Soft sensor Recursive estimation Batch process Wang, Yaozong verfasserin aut Liu, Yunlong verfasserin aut Ji, Guoli verfasserin aut Enthalten in Chemical engineering research and design Amsterdam : Elsevier, 1983 125, Seite 9-23 Online-Ressource (DE-627)312841965 (DE-600)2008006-2 (DE-576)090893190 1744-3563 nnns volume:125 pages:9-23 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_206 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.10 Verfahrenstechnik: Allgemeines AR 125 9-23
language	English
source	Enthalten in Chemical engineering research and design 125, Seite 9-23 volume:125 pages:9-23
sourceStr	Enthalten in Chemical engineering research and design 125, Seite 9-23 volume:125 pages:9-23
format_phy_str_mv	Article
bklname	Verfahrenstechnik: Allgemeines
institution	findex.gbv.de
topic_facet	State estimation Particle filter State observer Soft sensor Recursive estimation Batch process
dewey-raw	540
isfreeaccess_bool	false
container_title	Chemical engineering research and design
authorswithroles_txt_mv	Zhou, Sun @@aut@@ Wang, Yaozong @@aut@@ Liu, Yunlong @@aut@@ Ji, Guoli @@aut@@
publishDateDaySort_date	2017-01-01T00:00:00Z
hierarchy_top_id	312841965
dewey-sort	3540
id	ELV000386529
language_de	englisch
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author	Zhou, Sun
spellingShingle	Zhou, Sun ddc 540 bkl 58.10 misc State estimation misc Particle filter misc State observer misc Soft sensor misc Recursive estimation misc Batch process Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter
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topic_title	540 660 DE-600 58.10 bkl Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter State estimation Particle filter State observer Soft sensor Recursive estimation Batch process
topic	ddc 540 bkl 58.10 misc State estimation misc Particle filter misc State observer misc Soft sensor misc Recursive estimation misc Batch process
topic_unstemmed	ddc 540 bkl 58.10 misc State estimation misc Particle filter misc State observer misc Soft sensor misc Recursive estimation misc Batch process
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title	Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter
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title_full	Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter
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title_sort	synchronized bayesian state estimation in batch processes using a two-dimensional particle filter
title_auth	Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter
abstract	In chemical process control, estimation of the process states, e.g. concentration or properties of the reactant or resultant, in real time is a key issue. Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estimation. Due to ignorance of such challenges, many existing methods would produce poor estimates in real applications. We design a new state estimation approach employing Bayesian filtering with consideration of batch-to-batch dynamics. To characterize the dynamics across batches with different time profiles, a synchronized two-dimensional (2-D) state-space model is constructed that contains synchronously equal- and unequal-length situations. Based on this model, a novel formulation of the particle filter is derived where the particles evolve along both the time and the batch dimensions so as to approximate the synchronized 2-D optimal estimates. Also, the convergence concern is addressed from a practitioner’s viewpoint. To incorporate appropriate data from previous batches into the current estimation, an on-line synchronization method based on the dynamic time warping technique is developed using a new alignment performance measure together with a transfer alignment strategy. The performance of the proposal is evaluated by case study on a numerical example and a three-state batch reaction process.
abstractGer	In chemical process control, estimation of the process states, e.g. concentration or properties of the reactant or resultant, in real time is a key issue. Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estimation. Due to ignorance of such challenges, many existing methods would produce poor estimates in real applications. We design a new state estimation approach employing Bayesian filtering with consideration of batch-to-batch dynamics. To characterize the dynamics across batches with different time profiles, a synchronized two-dimensional (2-D) state-space model is constructed that contains synchronously equal- and unequal-length situations. Based on this model, a novel formulation of the particle filter is derived where the particles evolve along both the time and the batch dimensions so as to approximate the synchronized 2-D optimal estimates. Also, the convergence concern is addressed from a practitioner’s viewpoint. To incorporate appropriate data from previous batches into the current estimation, an on-line synchronization method based on the dynamic time warping technique is developed using a new alignment performance measure together with a transfer alignment strategy. The performance of the proposal is evaluated by case study on a numerical example and a three-state batch reaction process.
abstract_unstemmed	In chemical process control, estimation of the process states, e.g. concentration or properties of the reactant or resultant, in real time is a key issue. Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estimation. Due to ignorance of such challenges, many existing methods would produce poor estimates in real applications. We design a new state estimation approach employing Bayesian filtering with consideration of batch-to-batch dynamics. To characterize the dynamics across batches with different time profiles, a synchronized two-dimensional (2-D) state-space model is constructed that contains synchronously equal- and unequal-length situations. Based on this model, a novel formulation of the particle filter is derived where the particles evolve along both the time and the batch dimensions so as to approximate the synchronized 2-D optimal estimates. Also, the convergence concern is addressed from a practitioner’s viewpoint. To incorporate appropriate data from previous batches into the current estimation, an on-line synchronization method based on the dynamic time warping technique is developed using a new alignment performance measure together with a transfer alignment strategy. The performance of the proposal is evaluated by case study on a numerical example and a three-state batch reaction process.
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title_short	Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter
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author2	Wang, Yaozong Liu, Yunlong Ji, Guoli
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up_date	2024-07-06T17:47:31.033Z
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Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estimation. Due to ignorance of such challenges, many existing methods would produce poor estimates in real applications. We design a new state estimation approach employing Bayesian filtering with consideration of batch-to-batch dynamics. To characterize the dynamics across batches with different time profiles, a synchronized two-dimensional (2-D) state-space model is constructed that contains synchronously equal- and unequal-length situations. Based on this model, a novel formulation of the particle filter is derived where the particles evolve along both the time and the batch dimensions so as to approximate the synchronized 2-D optimal estimates. Also, the convergence concern is addressed from a practitioner’s viewpoint. To incorporate appropriate data from previous batches into the current estimation, an on-line synchronization method based on the dynamic time warping technique is developed using a new alignment performance measure together with a transfer alignment strategy. The performance of the proposal is evaluated by case study on a numerical example and a three-state batch reaction process.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">State estimation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Particle filter</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">State observer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Soft sensor</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Recursive estimation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Batch process</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yaozong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yunlong</subfield><subfield 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Synchronized Bayesian state estimation in batch processes using a two-dimensional particle filter

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