A Case Study on the Possibilities of Industrial Internet in both PAT and Pharmaceutical Industry

Transcription

1 A Case Study on the Possibilities of Industrial Internet in both PAT and Pharmaceutical Industry IFPAC, Jan. 27 th, 2015 in Arlington (USA) Pekka Teppola 1, Pasi Hyttinen 1, Sini Kivi 1, Marko Kaarre 1, Mikko Juuti 1, Jukka Hast 1, Simo-Pekka Simonaho 2, Ossi Korhonen 2, Jarkko Ketolainen 2 and Heikki Ailisto 1 1 VTT Technical Research Centre of Finland 2 University of Eastern Finland

2 Industrial Internet & Internet of Things

3 Internet of Things and Click to edit Master Everything title style $ 14.4 Trillion

4 What does it mean: Views of industry leaders Cisco: Internet of everything 50 Billion things to be connected Information services will change the most Manufacturing has the biggest potential

5 What does it mean: Views of industry leaders GE: Industrial Internet Enormous gains

6 Why now? Affordable technologies are here Computing power grows Connectivity like never before Cloud services Sensing everything in real time People are ready Competences Acceptance Usage in every day life

7 VTT s Pro-IoT Program on Industrial Internet and IoT Fact Sheet Goal: Facts: Productivity New business One of the three major research programs in VTT Duration Budget: 27 M

8 Industrial Internet of Things (IoT) Network of intelligent devices. Embedded services and interoperability. Automated system level operation and optimization.

9 A Case of Study in Continuous Direct Compression Pharmaceutical Process

10 Continuous Direct Compression Pharmaceutical Process and Best in the Class Sensors UNIT PROCESS Feeders Mixer PAT NIR sensor 1 Booth 513 Tableting NIR sensor 2 NIR imaging Excellent equipment but still some challenges in PAT, instrumentation and MSPC: All calibration and MSPC models are slow to develop Need to collect a lot of data. Many sensors to be calibrated Multiple sampling points and reference analyses HPLC is time-consuming Weeks or months of work Lots of process data and multiple runs Lots of material, resources and time

11 Starting Point in PAT Instrumentation and Formulation Development DoE, R&D and formulation / process development time-consuming Feeders: -Excipients -Lubricant -APIs Mixing Concentration (NIR) Concentration (NIR) Tableting Sensors need reference data Representative calibration Calibration transfer Chemical imagers (NIR) Process and sensor data Fixed IT systems are inflexible Manual controls via process set points Low-level controls Sensors not linked to process automation All data analyses tools are offline No interoperability No real-time operation No connections to outside world

12 What happens around us in M2M?

13 We asked questions like what we really want to do? Click to LIW feeders edit provide Master accurate title style Real-time calibration and validation of NIR real-time mass-flow Feeders: Click -Excipients to edit Master text styles -Lubricant Mixing Second -APIs level Real-time MSPC with fault detection, isolation and identification Concentration (NIR) Measure (real-time calibration and validation) Concentration (NIR) Process and sensor data Analyze (concurrent diagnostic models) Improve (better process models) Tableting Chemical imagers (NIR) Interoperable interrogative sensors & active data mining Real-time formulation and process development Dynamic Concepts for Direct Compression with IoT in PAT Real-time DoE execution & process pruning Define (real-time DoE) Control (implementation) Automated identification of control and validation spaces plus fault modes

14 Real-time NIR calibration development and validation in 30 mins RF-ENET: NIR model development for API NIR predictions for all test data Real-time calibration In 15 minutes API Lactose Real-time use MCC MgSt Real-time validation HPLC still used for the control of the end-product (tablets) but not for developing NIR models!

15 Real-Time Multivariate Statistical Process Control (MSPC) Enabled with Real-Time NIR Predictions Second Real-time level NIR calibration Process fault Automated DoE to collect calibration data Automated validation of NIR models Fault visualization in relation to normal operating conditions (NOC) Fault detection and isolation with multivariate control charts

16 Fault identification and process understanding 1. Root cause: API feeder 3. Fault identification: all chemical concentrations are hit by the fault. 4. NIR predictions give indication on the effect of API feeder fault. 2. Result: total mass flow deviates

17 Yeah, it looked like real-time calibration and in 15 minutes You were doing a standard MSPC Cool but I did not see any IoT there?

18 OPPORTUNITIES

19 Industrial Internet of Things (IoT) Network of intelligent devices. Embedded services and interoperability. Automated system level operation and optimization. Should I redesign all my HW and SW for IoT and let them communicate??

20 Answer: Definitively NO!!! Let us just virtualize our physical process! Feeders: Click -Excipients to edit Master text styles -Lubricant Mixing Second -APIs level MSPC MODELS REAL-TIME PROCESS R&D REAL-TIME PROCESS DATA FLOWS MONITORING AND TROUBLESHOOTING MODELS TROUBLE-SHOOTING MODELS INTERROGATIVE MODELS Concentration (NIR) Concentration (NIR) PROCESS DATABASE AND SENSOR DATA REAL-TIME MODELING SYNCHRONIZING PROCESS AND SENSOR DATA Tableting CLONING DATA FLOWS in REAL-TIME through VIRTUALIZATION LAYER SENSOR FUSION MODELING TROUBLE-SHOOTING MODELS INTERROGATIVE MODELS Chemical imagers (NIR) REAL-TIME SENSOR DATA FLOWS SENSOR MODELS AND DIAGNOSTICS PREDICTION MODELS SERVICE DIAGNOSTICS PHYSICAL PROCESS VIRTUAL PROCESS TROUBLE-SHOOTING MODELS REAL-TIME R&D

21 Opportunities for Sensors: Dynamically Execute and Validate Models Concentration PHYSICAL PROCESS REMAINS INTACT!!! Feeders: -Excipients Click -Lubricant to edit Master Mixing text styles -APIs (1) Troubleshooting with spectral libraries REAL-TIME PROCESS DATA FLOWS SENSOR FUSION MODELING INTERROGATIVE MODELS TROUBLE-SHOOTING MODELS ACTIVE DATA MINING (NIR) Concentration (NIR) PROCESS DATABASE AND SENSOR DATA SYNCHRONIZING PROCESS AND SENSOR DATA Tableting CLONING DATA FLOWS in REAL-TIME through VIRTUALIZATION LAYER PREDICTION MODELS (PRODUCTION, RELEASE CANDIDATES, R&D, EXPLORATORY) MgSt LACT MCC API MgSt LACT MCC API Chemical imagers (NIR) REAL-TIME SENSOR DATA FLOWS (2) (3) MgSt LACT MCC API Detection of foreign chemicals PRODUCTION ç VALIDATE CANDIDATES ç R&D SENSOR MODELS AND DIAGNOSTICS SERVICE DIAGNOSTICS TROUBLE-SHOOTING MODELS PHYSICAL PROCESS VIRTUAL PROCESS REAL-TIME R&D

22 Real-Time Sensor R&D and Calibrations Production version Release Click to edit Master text styles candidate Real-time R&D HTML5 COMPARISON VIEW Run and validate all calibration models in real-time and in parallel. Run additional models for data mining, temporary troubleshooting, diagnostics etc. DATA REAL-TIME VALIDATION: ALL IN PARALLEL MODEL Real-time R&D Compute chemometric models for a given NIR sensor in real-time and validate simultaneously against production and release candidate versions. Similarly, transfer models to down-stream NIR sensors.

23 Opportunities for Manufacturing: Dynamically Execute and Validate Models Concentration PHYSICAL PROCESS REMAINS INTACT!!! Control (implementation) Feeders: -Excipients Click -Lubricant to edit Master Mixing text styles -APIs (1) Define (real-time DoE) Measure (real-time calibration and validation) REAL-TIME PROCESS DATA FLOWS TRUE CONTINUOUS QUALITY, PRODUCT & PROCESS IMPROVEMENT Analyze (concurrent diagnostic models) Improve (better process models) (NIR) Concentration (NIR) PROCESS DATABASE AND SENSOR DATA (2) SYNCHRONIZING PROCESS AND SENSOR DATA Tableting CLONING DATA FLOWS in REAL-TIME through VIRTUALIZATION LAYER MSPC MODELS (PRODUCTION, RELEASE CANDIDATES, R&D, EXPLORATORY) PRODUCTION ç Chemical imagers (NIR) REAL-TIME SENSOR DATA FLOWS (3) VALIDATE CANDIDATES ç R&D START OPTIMIZATION WITH PROCESS MODELS SIMULATE & STUDY PHYSICAL PROCESS VIRTUAL PROCESS HERE WE EXPERIMENT AND LEARN IN REAL- TIME!!!

24 Real-Time Formulation and Process Development Real-time Formulation and Process Development Automate DoEs to collect data for computing correlations and distributions, identifying effects, defining NOCs, exploring process faults and design, control, and validation spaces

25 Our IoT Platform Real-time and parallel software R&D and service development platform Python LabView Matlab HTML5 Data Flow from Physical Sensors, Systems and Services Virtualisation, multiplexing and communication layer Flexible and Fully Scalable IoT PARALLEL OPERATION, VALIDATION AND R&D FULL SCALABILITY AND FLEXIBILITY LOCAL VS GLOBAL OPERATION FROM SENSOR TO CLOUD REDUNDANCY FOR CRITICAL COMPONENTS AUTOMATED DEPLOYMENT AGILE AND DEVOPS PRACTICES Graphical User Interfaces (GUIs) and communication layers End-user systems Sensor supplier systems 3rd party service systems Fully scalable from sensors to factory intranet, industrial internet, and cloud systems

26 Scalability and Parallelism In sensors analyzers Cloud driver (input layer) Cloud driver (input layer) Cloud driver (input layer) System 3 Applet or System 2 servlet Applet or System 1 servlet Applet or servlet Applet or servlet Applet or servlet Applet or servlet Cloud driver (output layer) Cloud driver (output layer) Cloud driver (output layer)...big & fast data factories of future (IoT)... and cloud systems. Applets or servlets from customers, vendors, 3 rd party service providers, VTT or other prosumers that are used in sandboxes simultaneously and independently.

27 Define (real-time DoE) VTT s Mobic Technology: IoT Connectivity Platform for Process Industry Click to edit Master IoT opportunities: title style Measure (real-time calibration and validation) Analyze (concurrent diagnostic models) Click to edit Master text development. styles Control (implementation) For process industry Improve (better process models) User case (process industry perspective): With real-time Mobic technology, process industry can speed up their PAT and process development significantly and add intelligence to process diagnostics. Everything can be used and updated smoothly and in real-time! User case (sensor supplier perspective): With real-time Mobic and SpectraCloud technologies, sensor companies can develop, operate, maintain sensors in real-time and add new interrogative features to sensors. Pre-industrial production scale implementation of VTT s IoT platform in pharma process. New IoT enablers: Fifth Results: level Virtualization of existing physical process, sensors, laboratory instruments and automation system real-time & parallel sensor, PAT, drug formulation and process New features to process sensors by adding parallel interrogative functionality that are actively troubleshooting processes. Real-time sensor calibration and validation for shortening manufacturing times. IoT platform implemented in existing PROMIS Center pilot manufacturing line quick and easy IoT real-time NIR calibration and validation speed-up from weeks to less than 30 mins New real-time interrogative MEMS-sensors for process diagnostics. Real-time pharmaceutical drug formulation and process R&D; automated data collection Benefits: Sensor and process performance, flexibility, availability, robustness, connectivity, easy of maintenance and interoperability. Real-time MSPC models and their validation, comparisons, support, diagnostics and feedback over 150% speed-up in R&D and deployment. Remote usage and operation. Parallel MSPC models (production, R&D, trouble-shooting, disruptive R&D ) True continuous process and quality development Contact persons: Marko Kaarre (Marko.Kaarre@vtt.fi, ), Pekka Teppola (Pekka.Teppola@vtt.fi, ) and Pasi Hyttinen (Pasi.Hyttinen@vtt.fi, )

28 VTT s SpectraCloud Technology: Real-Time Sensor Life-Cycle Management and Sensor R&D For sensor suppliers Use case example: With SpectraCloud technology, sensor companies can speed up their sensor R&D and simultaneously validate different sensor software versions in real-time and independently. IoT challenges: Most of the sensor problems relate to software: SW testing and validation takes long time. High supplier and end-user risks involved in SW upgrades: versions, calibrations, etc. Sensor network lifecycle management has severe problems: Lack of support & availability/usability issues. Poor services. Future Factories have interrogative sensors that actively troubleshoot processes. Results so far: Real-time simultaneous development, testing and validation of sensor software. Sandboxes: multiple sensor calibrations and smooth transitions between them. First sensor demos with commercial sensors from Spectral Engines Ltd. Benefits so far: Sensor performance, availability, robustness, connectivity, easy of maintenance and interoperability. Real-time sensor R&D and validation, comparisons, support, diagnostics and feed-back => over 500% speed-up in calibration + shifting from manual to fully automated. Parallel SW versions (prod, R&D, trouble-shooting, disruptive R&D ) => new services. Embedded services with IoT and sensor life cycle mgmt => new business opportunities. Contact persons: Pekka Teppola (Pekka.Teppola@vtt.fi, ) Pasi Hyttinen (Pasi.Hyttinen@vtt.fi, )

29 Do not take my word Let us look at how GE thinks

30 IMAGINE YOUR PROCESS AND ALL PAT SENSORS Click VIRTUALIZED to edit IN Master COMPANY S title style OWN CLOUD IMAGINE THAT THIS CLOUD IS SAFELY ISOLATED Click to edit Master FROM text THE styles PHYSICAL PROCESS AND THAT YOU CAN ORGANIZE AND ANALYZE DATA FREELY IMAGINE THE Fifth ABILITY level TO DYNAMICALLY RUN AND VALIDATE ANY NUMBER OF CHEMOMETRIC MODELS IN REAL-TIME WHEN YOU ARE DONE JUST USE THE VIRTUAL DATA FLOWS WITH YOUR DEPLOYED MODELS AND LET THEM WORK FOR YOU!!

31 Acknowledgements Teams in PROMIS Centre Research Partners and Customers Funding Organizations (TEKES, EU) VISIT us in our annual customer seminar Sense 15 at March at Espoo, Finland Demos: - piloting services on continuous manufacturing - IoT for sensors and processes, - new sensors and devices (NIR transmission, multipoint-nir, process sampling devices)

32 THANK YOU FOR YOUR ATTENTION