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Comparison of machine learning approaches for spectroscopy applications
Mälardalens universitet, Akademin för innovation, design och teknik, Innovation och produktrealisering.ORCID-id: 0000-0002-2978-6217
Mälardalens universitet, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
2022 (engelsk)Inngår i: Proceedings of the 63rd International Conference of Scandinavian Simulation Society / [ed] Lars O. Nord; Tiina Komulainen; Corinna Netzer; Gaurav Mirlekar; Berthe Dongmo-Engeland; Lars Eriksson, 2022, s. 80-85Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In energy production the characterization of the fuel is a key aspect for modelling and optimizing the operation of a power plant. Near-infrared spectroscopy is a wellestablished method for characterization of different fuels and is widely used both in laboratory environments and in power plants for real-time results. It can provide a fast and accurate estimate of key parameters of the fuel, which for the case of biomass can include moisture content, heating value, and ash content. These instruments provide a chemical fingerprint of the samples and require a calibration model to relate that to the parameters of interest.

A near-infrared spectrometer can provide point data whereas a hyperspectral imaging camera allows the simultaneous acquisition of spatial and spectral information from an object. As a result, an installation above a conveyor belt can provide a distribution of the spectral data on a plane. This results in a large amount of data that is difficult to handle with traditional statistical analysis. Furthermore, storage of the data becomes a key issue, therefore a model to predict the parameters of interest should be able to be updated continuously in an automated way. This makes hyperspectral imaging data a prime candidate for the application of machine learning techniques. This paper discusses the modelling approach for hyperspectral imaging, focusing on data analysis and assessment of machine learning approaches for the development of calibration models.

sted, utgiver, år, opplag, sider
2022. s. 80-85
Serie
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 192
Emneord [en]
machine learning, calibration models, hyperspectral imaging, near-infrared spectroscopy
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-61125DOI: 10.3384/ecp192012ISBN: 978-91-7929-545-5 (tryckt)OAI: oai:DiVA.org:mdh-61125DiVA, id: diva2:1716756
Konferanse
Proceedings of the 63rd International Conference of Scandinavian Simulation Society, SIMS 2022, Trondheim, Norway, September 20-21, 2022
Tilgjengelig fra: 2022-12-06 Laget: 2022-12-06 Sist oppdatert: 2023-11-29bibliografisk kontrollert
Inngår i avhandling
1. Machine Learning Techniques for Enhanced Heat Transfer Modelling
Åpne denne publikasjonen i ny fane eller vindu >>Machine Learning Techniques for Enhanced Heat Transfer Modelling
2024 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

With the continuous growth of global energy demand, processes from power generation to electronics cooling become vitally important. The role of heat transfer in these processes is crucial, facilitating effective monitoring, control, and optimisation. Therefore, advancements and understanding of heat transfer directly correlate to system performance, lifespan, safety, and cost-effectiveness, and they serve as key components in addressing the world's increasing energy needs.

The field of heat transfer faces the challenge of needing intensive studies while retaining fast computational speeds to allow for system optimisation. While advancements in computational power are significant, current numerical models lack in handling complex physical problems such as ill-posed. The domain of heat transfer is rapidly evolving, driven by a wealth of data from experimental measurements and numerical simulations. This data influx presents an opportunity for machine learning techniques, which can be used to harness meaningful insights about the underlying physics.

Therefore, the current thesis aims to the explore machine learning methods concerning heat transfer problems. More precisely, the study looks into advanced algorithms such as deep, convolutional, and physics-informed neural networks to tackle two types of heat transfer: subcooled boiling and convective heat transfer. The thesis further addresses the effective use of data through transfer learning and optimal sensor placement when available data is sparse, to learn the system behaviour. This technique reduces the need for extensive datasets and allows models to be trained more efficiently. An additional aspect of this thesis revolves around developing robust machine learning models. Therefore, significant efforts have been directed towards accounting for the uncertainty present in the model, which can further illuminate the model’s behaviour. This thesis shows the machine learning model's ability for accurate prediction. It offers insights into various parameters and handles uncertainties and ill-posed problems. The study emphasises machine learning's role in optimising heat transfer processes. The findings highlight the potential of synergistic application between traditional methodologies and machine learning models. These synergies can significantly enhance the design of systems, leading to greater efficiency.

sted, utgiver, år, opplag, sider
Västerås: Mälardalens universitet, 2024
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 399
HSV kategori
Forskningsprogram
energi- och miljöteknik
Identifikatorer
urn:nbn:se:mdh:diva-64898 (URN)978-91-7485-625-5 (ISBN)
Disputas
2024-02-13, Delta, Mälardalens universitet, Västerås, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2023-12-01 Laget: 2023-11-29 Sist oppdatert: 2024-01-23bibliografisk kontrollert

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