Can Process Analytical Technology Lead to Real-time Quality Management for Dairy Food Products?

1. Can Process Analytical Technology Lead to Real-time Quality Management for Dairy Food Products? Brent Young*, Nick Depree, Taj Munir and David Wilson *b.young@auckland.ac.nz

2. The Nature of Dairy/Food Materials • Biological sources of variation • Highly perishable • Properties are time dependent • Properties are often not well characterised Presents considerable challenges • to process design, control and optimisation systems • to sustainably produce safe, consistent and economically viable products

3. Traditional Real-time Process Control Solutions State of the art: Stainless steel & PLCs • Temperature, pressure and flow instrumentation • Uni-variate monitoring • Manual control common • Minimal optimisation • Challenge: We have real-time process control for some variables. But what about real-time quality (RTQ) control!?

4. What is RTQ? Is it PAT? PAT Definition: Process Analytical Technology* involves the systems approach in the planning, design, control and optimization of processing plants. This is a model based framework that encompasses new enabling technologies for most areas of the processing industry. • Challenge: Is PAT simply an evolution of what we have done all along? * US Department of Health and Human Services, Process Analytical Technology, FDA, 2004

5. What is RTQ? Is it PAT? • Process analysers • Fusion of models and latest sensors for better monitoring • Design, data acquisition and analysis tools • Multivariate models for deeper process analysis • Process control tools • Targeted models for optimisation • Mini models for exception detection • Continuous improvement and knowledge management tools • Holistic approach that integrates methodologies • Driven by the customer & regulatory authorities

6. What is RTQ? Is it PSE? TA Instruments ARG2 Rheometer Fonterra Te Rapa Process Plant University Dairy Viscosity Testing Rig

7. What is RTQ? Is it PSE? • Tools: Simulators Pinch Tech Soft Sensing Smart Sensors • Steam MPC* Multivariate • SCM** PAT Tools • Model DAEs Objects Large Scale Data Driven • Types: Codes Exception Based * Model Predictive Control ** Supply Chain Management

8. RTQ: A refocus for our aims Primarily interested in equipment design & operation What are my mass & energy balances? Final QA found something amiss What happened, where & how much product is affected? I’m happy with my equipment, but how do I save energy & operating costs? • Challenge: Capturing a customer-centric view in a traditional engineering environment

9. Two Views • Challenge: Do we have two masters?

10. What are we doing? • Multivariate Exception Based Modelling • Data Mining and Rectification • Sensitivity and Factor Analysis (e.g. MPCA) • Fault Detection and Diagnosis • Models for control (e.g. MPC) • What are appropriate models? • What level of fidelity? • Always maintain a Customer/Client focus • Challenge: Building something useful without full-scale dynamic modelling

11. How are we doing it? Challenge: Building a flexible proto-typing environment,not getting bogged down producing commercial software. Challenge: Reflective Visualisation – getting timely information to make informed decisions now & tomorrow.

12. Visualisation Operator Displays Is this the best we can do?

13. Basic Ideas • Not a standard operator’s display • We already have those • Data is graphical • Dense, big screens • A3 paper • Focus on the future • What might happen & when • Future gets uncertain • Consistent Colour design • Low impact

14. Careful design of charts – resolution, colour, aspect ratio: Typical graphics layouts Ref: Tufte’sThe Visual Display of Quantitative Information

15. Advanced Visualisation

16. Sparklines– small, intense, word sized graphics. Placed inline with text, show flow and change of data Fonterra Baseline Capability:

17. Colour Maps & HMI Design

18. Chart improvement – Clarity, Resolution, Data Density

19. Focus on the future

20. Design, Data Acquisition & Analysis Tools • Data driven techniques • Data Mining & Rectification • Sensitivity & Factor Analysis • PCA • Fault Detection & Diagnosis • PCA & Discriminant Analysis • Traceability • Bayesian Belief Networks, Transfer Entropy • Model based techniques • Process Simulation

23. Next steps… • Focus on the future: Does it work? • Mini tools to tell you what will happen • Countdown • Two screens: two types of information • Dashboards: (useful or naff?) • Super big screens, or multiple screens? • Always visible? Build your own? • Getting away from trends & PFDs: • Moving “blobs”

24. This mini tool warns against blockage of the SFB – increasing DT between 2 probes indicates poor flow or sticky powder This right hand plot shows a zoom into small detailed region using the mini-tool

25. A mini tool looking at stability of feed solids to drier Example stable for most of cypher but sudden change at end Real version is interactive version

26. Vitamin D Dosing Mini ToolWith over dosing (right)Without (below)

27. Coffee Sediments SQC Coffee sediments scores plot (right) N.B. colours are start/middle/end of run How to display in real time for operators? -> ‘Snakes on a Plane’ planned! (e.g. below)

28. Acknowledgments I2C2 • Drs Irina Boiarkine & Ville Rimpilainen (UOA) • Arrian Prince-Pike (AUT) Fonterra • Advanced Process Control Group, Drs Tristan Hunter & Nigel Russell (Fonterra) Primary Growth Partnership Program (PGP)

29. Can Process Analytical Technology Lead to Real-time Quality Management for Dairy Food Products? Brent Young*, Nick Depree, Taj Munir and David Wilson *b.young@auckland.ac.nz

30. We never said we were statisticians.