Integrating Sensor Based Process Monitoring and Advanced Process Control

Transcription

1 Integrating Sensor Based Process Monitoring and Advanced Process Control Jeroen Jansen Assistant Professor/Head of Department Analytical Chemistry Institute for Molecules and Materials

2 Process Monitoring = Chemometrics: the Chemical Data Science Chemometrics is the science of extracting information from chemical systems by data-driven means to solve problems Advanced Analytical Technology Sensitive On-line/intact Easy-to-use. Chemical Data Science We know what we measure! We know why we measure it! Challenging problems Data Science Statistics Mathematics Algorithms. X = TP T + E Industry Environment Healthcare. 2

3 Laboratory of Analytical RU 45 years of Chemometrics Prof. Lutgarde Buydens (Dean of Faculty) Dr. Jeroen Jansen (Assistant Professor, acting Head of Department) 5 current PhD students 6 visiting/joint PhD students 4 Postdocs * 2 guest professors (E. van den Heuvel, Beata Walczak) 2 industrial Guest Researchers Last 5 years: 11 bachelor students 21 master students 3 HBO students 3

4 What chemometrics can do Calibration: decompose abstract spectroscopic data into contributions of constituents Institute for Molecules and Materials

5 Colour Concentration Concentration Spectroscopy: the physics to obtain chemical data Colour oduction-near-infrared-nir-spectroscopy

6 Colour Concentration Concentration Spectroscopy: the physics to obtain chemical/experimental information Colour DOI: /DCC

7 Fat Sucrose Flour Water Colour Concentration Concentration Spectroscopy: the physics to obtain chemical/experimental information from analytical data

8 What chemometrics can do Enhancing calibration to remove spectroscopic artefacts to enhance chemical information Institute for Molecules and Materials

9 Calibration in Analytical Spectroscopy Physical response of molecular mixtures to external stimuli, both informative and non-informative Data preprocessing can improve calibration by focus on chemical information Reasonable preprocessing can also deteriorate information Model complexity (# LVs) Complexity of the model (no. of LVs) Grid search O: Strategy Model Performance RMSEP (RMSEP) Strategy Raw data Lowest RMSEP Raw data Highest RMSEP Best RMSEP Worst RMSEP 9 25 Individual optimization

10 Data preprocessing may focus on chemical information Model complexity (# LVs) Many valid preprocessing steps are possible Finding the best strategy costs days by brute force NIR needs to provide on-line results 25 Complexity of the model (no. of LVs) Grid search O: Strategy Model Performance RMSEP (RMSEP) Strategy Raw data Lowest RMSEP Raw data Highest RMSEP Best RMSEP Worst RMSEP 25 Individual optimization plexity 15 1

11 Efficient Calibration in Analytical Spectroscopy Design of Experiments provides informative strategies From calculated effects, the most efficient strategy can be optimized The resulting DoE strategy borders on the most efficient calculated in minutes instead of days 11

12 Preprocessing for calibration Online pp-enhanced Calibration improves compliance prediction by 23% Integration with process measurements improves with further 28% Based on historical data alone Integration into adaptive processing reduces raw material cost by 4% minimum (All from RU case studies from the last five years) 12

13 What chemometrics will do Integrating Sensor Based Process Monitoring and Advanced Process Control (INSPEC) Institute for Molecules and Materials

14 Integrating Sensor Based Process Monitoring and Advanced Process Control 1. Developing chemometrics for on-line integration of PAT with process measurements 2. Integrating rigorous first-principle knowledge with empirical process measurements for informative dynamic models 3. Enhancing Advanced Process Control by combining PAT, process measurements and dynamic models 14

15 1. Developing chemometrics for on-line integration of PAT with process measurements Sensor Drifts Spikes, sensor failures Process Drifts Batch-to-batch variation Affects the model, but also the preprocessing 15

16 2. Integrating rigorous first-principle knowledge with empirical process measurements for informative dynamic models There are things we know we know There are things we know we do not know There are things we do not know we do not know 16

17 3. Enhancing Advanced Process Control by combining PAT, process measurements and dynamic models Monitoring Process Control operations Controlling the process based on monitored observations Reducing - Rework - Product variability - Energy consumption Available in approachable OPC-based Validation environment 17

18 Integrating Sensor Based Process Monitoring and Advanced Process Control Getting the best out of - Process Analytical Technology, - Process Measurements and - Rigorous dynamic models Five challenging case studies from pilot plants to running industrial processes Post Doc Wanted!!! (3y) 18