國立虎尾科技大學 |

Operationalisation of FT-NIRS Based Real-Time Monitoring for Optimisation of Anaerobic Digestion.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Operationalisation of FT-NIRS Based Real-Time Monitoring for Optimisation of Anaerobic Digestion./
Author:	Darke, Michael.
Description:	1 online resource (651 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Manures. -
Online resource:	click for full text (PQDT)
ISBN:	9798383026120

Operationalisation of FT-NIRS Based Real-Time Monitoring for Optimisation of Anaerobic Digestion.
Darke, Michael.

Operationalisation of FT-NIRS Based Real-Time Monitoring for Optimisation of Anaerobic Digestion. - 1 online resource (651 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--University of South Wales (United Kingdom), 2024.

Includes bibliographical references

Anaerobic Digestion (AD) technology is an industrialised solution that mimics the naturally occurring decomposition of organic materials by anaerobic metabolisms and produces useable products in the form of biogas and digestate. Previous work has identified FT-NIRS technology for applications in real-time multi-parameter monitoring of sludges. This thesis addresses the gap between 'proof of concept' and operationalisation, using case studies of sewage-derived digestates and feedstocks from Advanced Anaerobic Digestion (AAD) wastewater treatment plants (WWTPs) and a Plug Flow Digester AD plant, fed using Source Segregated Waste (SSW) and Agricultural Wastes (AgW). FT-NIRS monitoring programmes of a commercial Ultrasound (also known as "Sonication") (US) trial and a laboratory evaluation of Plug Flow (PF) versus a Continuous Stirred Tank Reactor (CSTR) extended the calibration intervals.The study utilised a decomposition modelling approach, extending the range of calibrations using a 'Whole of System' (WoS) approach (input-output). Principal Component Analysis (PCA) models and Partial Least Squares regressions (PLS-R), using matched FT-NIRS and matched physicochemical parameters, were evaluated using root mean square error of cross-validation (RMSECV) and R2. Confidence Intervals (CI), using uncertainty modelling, and prediction errors are defined using Mean Absolute Prediction Error (MAPE) and the CI. With the extended ranges under investigation using WoS principles, additional metrics are required to assess model performance. "Normal Operating Condition" (NOC) models using PCA were most effective at early recognition of out-of-range samples.The research shows that physicochemical parameters (e.g. Total and Volatile solids (TS, VS), volatile fatty acids (VFAs) and Carbon Oxygen Demand (COD) can be determined from fresh samples, with no sieving, drying, or filtering. The mean RMSECV was 0.47% for TS (0.38 - 0.55%, over a calibration range of 4.04 - 8.82%) and 0.32% for VS (0.29 - 0.36%, with a calibration interval of 2.55 - 7.06%). The preferred models utilised Support Vector Machines (SVM), Generalised Least Squares Weighting (GLSW), and Mean Centering (MC) preprocessing. Results using SSW-AgW appear higher due to a wide calibration interval. Within theviii preferred models, GLSW-MC performed best; the mean RMSECV was 0.86% for TS and 0.87% for VS over calibration intervals of 1.33 - 15.82% and 0.66 - 13.73%, respectively.In addition, a Total Volatile Fatty Acids (TVFA) model using wastewater sludges delivered a RMSEC of 211 mg.L-1 and RMSECV of 281 mg.L-1 over a calibration range of 68 - 4746 mg.L-1. At the same time, Soluble Carbon Oxygen Demand (CODS) gave a RMSEC of 1574 mg.L-1and RMSECV of 2018 mg.L-1 over a calibration range of 4821 - 35183 mg.L-1.Other techniques developed include recognising Ultrasonic intensity in wastewater treatments from PCA analysis, the potential to monitor PCA/PLS loadings as a data reduction tool to simplify analysis and recognise digester changes related to VFAs and using the relationships in the raw spectra behaviour to improve VFA build-up detection and speciation through an understanding of their interaction with other physicochemical parameters. The models developed would be helpful for the industrial monitoring of AD plants. Independent datasets show that the models could not be applied globally. However, the WoS method reduces calibration lead times by establishing the AD plant's process gradient. A simple at-line Spectra Acquisition Cell (SAC) improved spectral acquisition. Fully developed and deployed, the SAC could further reduce the impact of confounding variables, such as temperature, increase data collection and enhance interpretation consistency while avoiding the capital expenditure associated with online deployment.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383026120Subjects--Topical Terms:

1000197
Manures.
Index Terms--Genre/Form:

554714
Electronic books.

Operationalisation of FT-NIRS Based Real-Time Monitoring for Optimisation of Anaerobic Digestion.
LDR:05267ntm a22003977 4500 001 1147727
005 20240916065453.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798383026120
035 $a (MiAaPQ)AAI31136498
035 $a (MiAaPQ)UnivSWales_ec86fccc-69cf-4e69-846e-3b9eba257ef9
035 $a AAI31136498
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Darke, Michael. $3 1473524
245 1 0 $a Operationalisation of FT-NIRS Based Real-Time Monitoring for Optimisation of Anaerobic Digestion.
264 0 $c 2024
300 $a 1 online resource (651 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Patterson, Tim;Reed, James;Esteves, Sandra.
502 $a Thesis (Ph.D.)--University of South Wales (United Kingdom), 2024.
504 $a Includes bibliographical references
520 $a Anaerobic Digestion (AD) technology is an industrialised solution that mimics the naturally occurring decomposition of organic materials by anaerobic metabolisms and produces useable products in the form of biogas and digestate. Previous work has identified FT-NIRS technology for applications in real-time multi-parameter monitoring of sludges. This thesis addresses the gap between 'proof of concept' and operationalisation, using case studies of sewage-derived digestates and feedstocks from Advanced Anaerobic Digestion (AAD) wastewater treatment plants (WWTPs) and a Plug Flow Digester AD plant, fed using Source Segregated Waste (SSW) and Agricultural Wastes (AgW). FT-NIRS monitoring programmes of a commercial Ultrasound (also known as "Sonication") (US) trial and a laboratory evaluation of Plug Flow (PF) versus a Continuous Stirred Tank Reactor (CSTR) extended the calibration intervals.The study utilised a decomposition modelling approach, extending the range of calibrations using a 'Whole of System' (WoS) approach (input-output). Principal Component Analysis (PCA) models and Partial Least Squares regressions (PLS-R), using matched FT-NIRS and matched physicochemical parameters, were evaluated using root mean square error of cross-validation (RMSECV) and R2. Confidence Intervals (CI), using uncertainty modelling, and prediction errors are defined using Mean Absolute Prediction Error (MAPE) and the CI. With the extended ranges under investigation using WoS principles, additional metrics are required to assess model performance. "Normal Operating Condition" (NOC) models using PCA were most effective at early recognition of out-of-range samples.The research shows that physicochemical parameters (e.g. Total and Volatile solids (TS, VS), volatile fatty acids (VFAs) and Carbon Oxygen Demand (COD) can be determined from fresh samples, with no sieving, drying, or filtering. The mean RMSECV was 0.47% for TS (0.38 - 0.55%, over a calibration range of 4.04 - 8.82%) and 0.32% for VS (0.29 - 0.36%, with a calibration interval of 2.55 - 7.06%). The preferred models utilised Support Vector Machines (SVM), Generalised Least Squares Weighting (GLSW), and Mean Centering (MC) preprocessing. Results using SSW-AgW appear higher due to a wide calibration interval. Within theviii preferred models, GLSW-MC performed best; the mean RMSECV was 0.86% for TS and 0.87% for VS over calibration intervals of 1.33 - 15.82% and 0.66 - 13.73%, respectively.In addition, a Total Volatile Fatty Acids (TVFA) model using wastewater sludges delivered a RMSEC of 211 mg.L-1 and RMSECV of 281 mg.L-1 over a calibration range of 68 - 4746 mg.L-1. At the same time, Soluble Carbon Oxygen Demand (CODS) gave a RMSEC of 1574 mg.L-1and RMSECV of 2018 mg.L-1 over a calibration range of 4821 - 35183 mg.L-1.Other techniques developed include recognising Ultrasonic intensity in wastewater treatments from PCA analysis, the potential to monitor PCA/PLS loadings as a data reduction tool to simplify analysis and recognise digester changes related to VFAs and using the relationships in the raw spectra behaviour to improve VFA build-up detection and speciation through an understanding of their interaction with other physicochemical parameters. The models developed would be helpful for the industrial monitoring of AD plants. Independent datasets show that the models could not be applied globally. However, the WoS method reduces calibration lead times by establishing the AD plant's process gradient. A simple at-line Spectra Acquisition Cell (SAC) improved spectral acquisition. Fully developed and deployed, the SAC could further reduce the impact of confounding variables, such as temperature, increase data collection and enhance interpretation consistency while avoiding the capital expenditure associated with online deployment.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Manures. $3 1000197
650 4 $a Reactors. $3 1372700
650 4 $a Raw materials. $3 1113584
650 4 $a Sludge. $3 1473525
650 4 $a Temperature effects. $3 1470437
650 4 $a Chemical elements. $3 565229
650 4 $a Water treatment. $3 1413675
650 4 $a Chemical oxygen demand. $3 1473526
650 4 $a Biogas. $3 888743
650 4 $a Fatty acids. $3 673135
650 4 $a Decomposition. $3 1372547
650 4 $a Hogs. $3 1473527
650 4 $a Particle size. $3 1372794
650 4 $a Physical properties. $3 1175985
650 4 $a Agriculture. $3 660421
650 4 $a Alternative energy. $3 1241221
650 4 $a Analytical chemistry. $3 1182118
650 4 $a Animal sciences. $3 1178863
650 4 $a Biogeochemistry. $3 642004
650 4 $a Environmental engineering. $3 557376
650 4 $a Industrial engineering. $3 679492
655 7 $a Electronic books. $2 local $3 554714
690 $a 0473
690 $a 0363
690 $a 0486
690 $a 0475
690 $a 0425
690 $a 0775
690 $a 0546
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of South Wales (United Kingdom). $3 1466117
773 0 $t Dissertations Abstracts International $g 85-12B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31136498 $z click for full text (PQDT)