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. * US Department of Health and Human Services, Process Analytical Technology, FDA, 2004 Challenge: Is PAT simply an evolution of what we have done all along?

5. What is RTQ? Is it PAT? 1.Process analysers –Fusion of models and latest sensors for better monitoring 2.Design, data acquisition and analysis tools –Multivariate models for deeper process analysis 3.Process control tools –Targeted models for optimisation –Mini models for exception detection 4.Continuous improvement and knowledge management tools –Holistic approach that integrates methodologies –Driven by the customer & regulatory authorities

6. What is RTQ? Is it PSE? Identify Needs Model Development Model Validation Model Calibration Model Deployment Process Systems Engineering (PSE) and Product Expertise Experimental data from laboratory/pilot plant Process and Product data Fonterra Te Rapa Process PlantTA Instruments ARG2 Rheometer University Dairy Viscosity Testing Rig

7. Equipment 1950s-70s Integration 1980s-90s Optimisation 1990s-2000s Traceability 2010s Tools: SimulatorsPinch TechSoft Sensing Smart Sensors SteamMPC* Multivariate SCM** PAT Tools Model DAEsObjectsLarge Scale Data Driven Types:Codes Exception Based * Model Predictive Control ** Supply Chain Management What is RTQ? Is it PSE?

8. RTQ: A refocus for our aims Equipment 1950s-70s Integration 1980s-90s Optimisation 1990s-2000s Traceability 2010s I’m happy with my equipment, but how do I save energy & operating costs? 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? Challenge: Capturing a customer-centric view in a traditional engineering environment

9. Two Views Customers Enterprise Plant Equipment Process ERP Historian RTO Optimal control Advanced control Regulatory control Plant 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? PlatformTechnologies Rapid Prototyping Building mini-models R/MATLAB/Python (Tools within) Pavilion Steady State & Dynamic Modelling VMGSim MATLAB Graphical User/Operator InterfacePavilion 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. Operator Displays Visualisation 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’s The 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  T 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 Tool With 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.