國立虎尾科技大學 |

State Estimation, Covariance Estimation, and Economic Optimization of Semi-Batch Bioprocesses.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	State Estimation, Covariance Estimation, and Economic Optimization of Semi-Batch Bioprocesses./
Author:	Alexander, Ronald Hunter.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	180 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Software. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30726045
ISBN:	9798381018905

State Estimation, Covariance Estimation, and Economic Optimization of Semi-Batch Bioprocesses.
Alexander, Ronald Hunter.

State Estimation, Covariance Estimation, and Economic Optimization of Semi-Batch Bioprocesses. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 180 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--West Virginia University, 2023.

This item must not be sold to any third party vendors.

One of the most critical aspects of any chemical process engineer is the ability to gather, analyze, and trust incoming process data as it is often required in control and process monitoring applications. In real processes, online data can be unreliable due to factors such as poor tuning, calibration drift, or mechanical drift. Outside of these sources of noise, it may not be economically viable to directly measure all process states of interest (e.g., component concentrations). While process models can help validate incoming process data, models are often subject to plant-model mismatches, unmodeled disturbances, or lack enough detail to track all process states (e.g., dissolved oxygen in a bioprocess). As a result, directly utilizing the process data or the process model exclusively in these applications is often not possible or simply results in suboptimal performance.To address these challenges and achieve a higher level of confidence in the process states, estimation theory is used to blend online measurements and process models together to derive a series of state estimates. By utilizing both sources, it is possible to filter out the noise and derive a state estimate close to the true process conditions. This work deviates from the traditional state estimation field that mostly addresses continuous processes and examines how techniques such as extended Kalman Filter (EKF) and moving horizon estimation (MHE) can be applied to semi-batch processes. Additionally, this work considers how plant-model mismatches can be overcome through parameter-based estimation algorithms such as Dual EKF and a novel parameter-MHE (P-MHE) algorithm. A galacto-oligosaccharide (GOS) process is selected as the motivating example as some process states are unable to be independently measured online and require state estimation to be implemented. Moreover, this process is representative of the broader bioprocess field as it is subject to high amounts of noise, less rigorous models, and is traditionally operated using batch/semi-batch reactors.In conjunction with employing estimation approaches, this work also explores how to effectively tune these algorithms. The estimation algorithms selected in this work require careful tuning of the model and measurement covariance matrices to balance the uncertainties between the process models and the incoming measurements. Traditionally, this is done via ad-hoc manual tuning from process control engineers. This work modifies and employs techniques such as direct optimization (DO) and autocovariance least-squares (ALS) to accurately estimate the covariance values. Poor approximation of the covariances often results in poor estimation of the states or drives the estimation algorithm to failure.

ISBN: 9798381018905Subjects--Topical Terms:

574116
Software.

State Estimation, Covariance Estimation, and Economic Optimization of Semi-Batch Bioprocesses.
LDR:03943nam a2200373 4500 001 1121922
005 20240624103713.5
006 m o d
007 cr#unu||||||||
008 240823s2023 ||||||||||||||||| ||eng d
020 $a 9798381018905
035 $a (MiAaPQ)AAI30726045
035 $a (MiAaPQ)WVirginia12835
035 $a AAI30726045
040 $a MiAaPQ $c MiAaPQ
100 1 $a Alexander, Ronald Hunter. $3 1437804
245 1 0 $a State Estimation, Covariance Estimation, and Economic Optimization of Semi-Batch Bioprocesses.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 180 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Lima, Fernando V.;Bhattacharyya, Debangsu.
502 $a Thesis (Ph.D.)--West Virginia University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a One of the most critical aspects of any chemical process engineer is the ability to gather, analyze, and trust incoming process data as it is often required in control and process monitoring applications. In real processes, online data can be unreliable due to factors such as poor tuning, calibration drift, or mechanical drift. Outside of these sources of noise, it may not be economically viable to directly measure all process states of interest (e.g., component concentrations). While process models can help validate incoming process data, models are often subject to plant-model mismatches, unmodeled disturbances, or lack enough detail to track all process states (e.g., dissolved oxygen in a bioprocess). As a result, directly utilizing the process data or the process model exclusively in these applications is often not possible or simply results in suboptimal performance.To address these challenges and achieve a higher level of confidence in the process states, estimation theory is used to blend online measurements and process models together to derive a series of state estimates. By utilizing both sources, it is possible to filter out the noise and derive a state estimate close to the true process conditions. This work deviates from the traditional state estimation field that mostly addresses continuous processes and examines how techniques such as extended Kalman Filter (EKF) and moving horizon estimation (MHE) can be applied to semi-batch processes. Additionally, this work considers how plant-model mismatches can be overcome through parameter-based estimation algorithms such as Dual EKF and a novel parameter-MHE (P-MHE) algorithm. A galacto-oligosaccharide (GOS) process is selected as the motivating example as some process states are unable to be independently measured online and require state estimation to be implemented. Moreover, this process is representative of the broader bioprocess field as it is subject to high amounts of noise, less rigorous models, and is traditionally operated using batch/semi-batch reactors.In conjunction with employing estimation approaches, this work also explores how to effectively tune these algorithms. The estimation algorithms selected in this work require careful tuning of the model and measurement covariance matrices to balance the uncertainties between the process models and the incoming measurements. Traditionally, this is done via ad-hoc manual tuning from process control engineers. This work modifies and employs techniques such as direct optimization (DO) and autocovariance least-squares (ALS) to accurately estimate the covariance values. Poor approximation of the covariances often results in poor estimation of the states or drives the estimation algorithm to failure.
590 $a School code: 0256.
650 4 $a Software. $2 gtt $3 574116
650 4 $a Food. $3 581281
650 4 $a Communication. $3 556422
650 4 $a Data processing. $2 sears $3 840972
650 4 $a Chromatography. $3 783938
650 4 $a Effluents. $3 1437805
650 4 $a Performance evaluation. $3 1246613
650 4 $a Control algorithms. $3 1372652
650 4 $a Batch processes. $3 1437806
650 4 $a Sensors. $3 1003702
650 4 $a Lactose. $3 1437807
650 4 $a Kalman filters. $3 1413639
650 4 $a Enzymes. $3 557408
650 4 $a Industrial engineering. $3 679492
650 4 $a Pharmaceutical sciences. $3 1180569
690 $a 0501
690 $a 0459
690 $a 0546
690 $a 0796
690 $a 0572
710 2 $a West Virginia University. $3 1187945
773 0 $t Dissertations Abstracts International $g 85-06B.
790 $a 0256
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30726045