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HPTLC-Fingerprinting and application of machine learning to characterize pyrolysis oils from different origin of biomass

  • The research employed HPTLC Pro System and other HPTLC instruments from CAMAG® to conduct various laboratory tests, aiming to compile a database for subsequent analyses. Utilizing MATLAB, distinct codes were developed to reveal patterns within analyzed biomasses and pyrolysis oils (sewage sludge, fermentation residue, paper sludge, and wood). Through meticulous visual and numerical analysis,The research employed HPTLC Pro System and other HPTLC instruments from CAMAG® to conduct various laboratory tests, aiming to compile a database for subsequent analyses. Utilizing MATLAB, distinct codes were developed to reveal patterns within analyzed biomasses and pyrolysis oils (sewage sludge, fermentation residue, paper sludge, and wood). Through meticulous visual and numerical analysis, shared characteristics among different biomasses and their respective pyrolysis oils were revealed, showcasing close similarities within each category. Notably, minimal disparity was observed in fermentation residue and wood biomasses with a similarity coefficient of 0.22. Similarly, for pyrolysis oils, the minimal disparity was found in fermentation residues 1 and 3, with a disparity coefficient of 1.41. Despite higher disparity coefficients in certain results, specific biomasses and pyrolysis oils, such as fermentation residue and sewage sludge, exhibited close similarities, with disparity coefficients of 0.18 and 0.55, respectively. The database, derived from triplicate experimentation, now serves as a valuable resource for rapid analysis of newly acquired raw materials. Additionally, the utility of HPTLC PRO as an investigation tool, enabling simultaneous analysis of up to five samples, was emphasized, although areas for improvement in derivatization methods were identified.show moreshow less

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Metadaten
Document Type:Master's Thesis
Zitierlink: https://opus.hs-offenburg.de/8678
Bibliografische Angaben
Title (English):HPTLC-Fingerprinting and application of machine learning to characterize pyrolysis oils from different origin of biomass
Author:Felipe José Ochoa González
Advisor:Melanie Broszat, Corinna Henninger
Year of Publication:2024
Granting Institution:Hochschule Offenburg
Page Number:76
Language:English
Inhaltliche Informationen
Institutes:Fakultät Maschinenbau und Verfahrenstechnik (M+V)
Institutes:Abschlussarbeiten / Master-Studiengänge / MPE
DDC classes:600 Technik, Medizin, angewandte Wissenschaften
500 Naturwissenschaften und Mathematik / 540 Chemie
GND Keyword:Analytische Chemie; Biomasse; HPTLC; Maschinelles Lernen
Tag:Analytical Chemistry; Machine learning
Formale Angaben
Open Access: Closed 
Licence (German):License LogoUrheberrechtlich geschützt