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master's thesis
Primjena procesne analitičke tehnologije za praćenje procesa kristalizacije
Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology, 2022. urn:nbn:hr:149:813935
Klier, Monika
University of Zagreb
Faculty of Chemical Engineering and Technology

Institutional repository: Repository of Faculty of Chemical Engineering and Technology University of Zagreb

Cite this document

Klier, M. (2022). Primjena procesne analitičke tehnologije za praćenje procesa kristalizacije (Master's thesis). Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology. Retrieved from https://urn.nsk.hr/urn:nbn:hr:149:813935

Klier, Monika. "Primjena procesne analitičke tehnologije za praćenje procesa kristalizacije." Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, 2022. https://urn.nsk.hr/urn:nbn:hr:149:813935

Klier, Monika. "Primjena procesne analitičke tehnologije za praćenje procesa kristalizacije." Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, 2022. https://urn.nsk.hr/urn:nbn:hr:149:813935

Klier, M. (2022). 'Primjena procesne analitičke tehnologije za praćenje procesa kristalizacije', Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, accessed 20 May 2024, https://urn.nsk.hr/urn:nbn:hr:149:813935

Klier M. Primjena procesne analitičke tehnologije za praćenje procesa kristalizacije [Master's thesis]. Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology; 2022 [cited 2024 May 20] Available at: https://urn.nsk.hr/urn:nbn:hr:149:813935

M. Klier, "Primjena procesne analitičke tehnologije za praćenje procesa kristalizacije", Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:149:813935

Metadata
TitlePrimjena procesne analitičke tehnologije za praćenje procesa kristalizacije
Title (english)Application of process analytical technology for crystallization process monitoring
AuthorMonika Klier
MentorNenad Bolf (mentor)
Committee memberNenad Bolf (predsjednik povjerenstva)
Committee memberKrunoslav Žižek (član povjerenstva)
Committee memberIgor Dejanović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Chemical Engineering and Technology
Zagreb
Defense date and country2022-09-30, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Chemical Engineering
Abstract
Ovaj rad istražuje primjenu procesne analitičke tehnologije u svrhu praćenja procesa kristalizacije. Za predviđanje koncentracije otopine primjenjuje se Ramanova in-line spektroskopija pri čemu se na temelju trenutne informacije o koncentraciji otopine kontinuirano prati proces kristalizacije. Eksperimentalno se određivala koncentracija djelatne tvari ceritiniba u sustavu otapala 90 % aceton - 10 % voda. Cilj je bio izraditi kalibracijski model za kontinuirani proračun koncentracije ceritiniba tijekom kristalizacije. Za izradu kalibracijskog modela primijenile su se parcijalna regresija metodom najmanjih kvadrata i unaprijedna neuronska mreža. Kriteriji za ocjenu modela bili su kvadrat koeficijenta korelacije, R^2 i korijen srednje kvadratne pogreške, RMSE. Na temelju kriterija modeli za predviđanje koncentracije otopine i gustoće suspenzije razvijeni umjetnom neuronskom mrežom pokazali su se boljim od modela razvijenih parcijalnom regresijom metodom najmanjih kvadrata. Ovako razvijeni modeli mogu se primijeniti za procjenu koncentracije otopine u stvarnom vremenu.
Abstract (english)
This thesis explores the application of Process Analytical Technology (PAT) to crystallization process monitoring. Raman in-line spectroscopy was utilized for prediction of solution concentration and slurry density. Continuous crystallization process monitoring is based on real-time calculation of the solution concentration. The concentration of an Active Pharmaceutical Ingredient (API), Ceritinib was determined experimentally in a solution mixture of 90 % acetone and 10 % water. The goal was to build a calibration model for prediction of Ceritinib concentration during the crystallization process. Methods used to build the calibration models include Partial Least Square Regression (PLSR) and an Artificial Feed Forward Neural Network (FFNN). Criteria for model assessment were the square of the Correlation Coefficient, R^2 and the Root Mean Square Percentage Error, RMSPE. The model built with an Artificial Feed Forward Neural Network (FFNN) proved better performance than the model built by Partial Least Square Regression (PLSR) for solution concentration and slurry density prediction. The models developed in this way can be applied to estimate the solution concentration in real time.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:149:813935
ProjectNumber: KK.01.1.1.07.0017
Title: Napredno vođenje procesa kristalizacije
Title: Advanced Control of Crystallization Process
Acronym: CrystAPC
Leader: Nenad Bolf
Jurisdiction: Croatia
Funder: EK
Funding stream: EFRR
Study programmeTitle: Chemical Engineering - Graduate study
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka kemijskog inženjerstva (magistar/magistra inženjer/inženjerka kemijskog inženjerstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2023-03-31 10:11:02