Primjena procesne analitičke tehnologije za praćenje procesa kristalizacije

master's thesis

• Title (english): Application of process analytical technology for crystallization process monitoring
• Author: Monika Klier
• Mentor: Nenad Bolf (mentor)
• Committee member: Nenad Bolf (predsjednik povjerenstva)
• Committee member: Krunoslav Žižek (član povjerenstva)
• Committee member: Igor Dejanović (član povjerenstva)
• Granter: University of Zagreb; Faculty of Chemical Engineering and Technology; Zagreb
• Defense date and country: 2022-09-30, 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: kristalizacija; djelatna tvar; procesna analitička tehnologija; umjetna neuronska mreža; parcijalna regresija metodom najmanjih kvadrata; kontinuirano praćenje procesa
• Keywords (english): crystallization; active pharmaceutical ingredient; process analytical technology; artificial neural network; Partial Least Square Regression; continuous process monitoring
• Language: croatian
• URN:NBN: urn:nbn:hr:149:813935
• Project: Number: 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 programme: Title: 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 resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2023-03-31 10:11:02