BREWNET. Intelligent cloud connected sensors for economic small scale process optimisation

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Marilynn Harper
5 years ago
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optimisation Dr Nicholas Watson University

1 BREWNET Intelligent cloud connected sensors for economic small scale process optimisation Dr Nicholas Watson University of Nottingham Dr Syed Ali Raza Zaidi University of Leeds Network Plus: Industrial Systems in the Digital Age Conference 2018 Looking Beyond Industry 4.0

2 Agenda BREWNET Introduction Project motivation Consortium Project methodology Ultrasonic sensor Sensor design Signal and data processing Results from brewery Impact acceleration account follow on work Internet of Things LPWAN Sensor Node Design Overall Solution Open Issues Summary Lessons Learnt Future Roadmap

3 Motivation 1700 Breweries within the UK. Fermentation effects beer quality and downstream processes Traditional processes still dominate Opportunity for fermentation PAT New technologies must be: 1) Cost effective 2) Simple to use

4 Consortium Consortium Nik Watson Josep Escrig Escrig Nengjia Ren Syed Ali Raza Maryam Hafeez Rob Nigel Rix (KTN) Quality control

5 Methodology 1. Process sensors 2. Cloud empowered gateway 3. User centered predictive analytics

6 Sensor Prototype Probe design Probe with US and temperature sensor 2000 Litre Fermenter Initial sensor attached to laptop streaming data to the cloud Tri Clamp port for sensor housing

7 Data analysis 1 st Reflection 2 nd Reflection Amplitude 1 st reflection Delay between reflections Ultrasonic velocity Reflection amplitude STD of reflection amplitude Data analysis methods: Frequency content analysis Machine learning Temperature calibrations

8 Brewery results

9 Brewery results Challenges: 1) Acquiring sufficient labelled data for machine learning 2) Variability

Further Development EPSRC Impact Accelerating funding

the most suitable data processing method What is the

10 Further Development EPSRC Impact Accelerating funding provided to further develop sensor technology What is the most suitable data processing method What is the cheapest cost a sensor can be built for ABV (%) ABV% Batch 1 VS Batch batch 1 ABV% (Tilt density) 2.0 batch 2 ABV% (Tilt density) 1.0 batch 1 ABV% (Manual density) batch 2 ABV% (Manual density) /06/ /06/ /06/ /06/ /06/ /06/2018 Date

11 IoT for Food & Drink Manufacturing

12 IoT for Food and Beverage Manufacturing Operational Efficiency Performance analytics, Fault Diagnostics and Predictive Maintenance Process Efficiency Production process optimization. Cost and waste reduction. Plant automation. Food Safety Quality control. Evidence for regulatory compliance. Traceability. Supply Chain Optimization Transparency. Logistic optimization.

13 BREWNET Network Architecture Temperature & ph Sensor Probe Housing for Transducer Communication Unit Cloud Server Brewery Manager Smartphone Dashboard Time Time Microcontroller Unit Fermenter Ultrasonic Transducer Gateway Prediction Engine Time

14 Connectivity: Sensor to Gateway LoRA Key Features Low cost Extended Coverage Spread Spectrum Spectrum Occupancy: Unlicensed Battery Lifetime Frequency Band: 868 MHz, 433 MHz and 900 MHz LoRA Link Budget: Receiver Sensitivity of -136 dbm TX Power of 14 dbm 150 db Link Budget, i.e., 22 km LoS and 2 km NLoS Low Duty Cycle UE Complexity Reduction Claim: It can operate successfully at ranges exceeding 15 km in suburban settings and more than 2 km in dense urban environments. It can achieve over 10 year battery life and it can operate in networks comprising up to 1 million nodes. part 1 15 km wireless 10 year battery life iot

15 LoRA Measurements 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Packets Reveive Rate Multi Spreading Factor against Packets Receive Rate 100% 100% 99% 94% 90% 87% 79% 61% 45% 71% 30% 3% 46% 10% 0% Distance (m) SF 12 SF 9 110% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 10% Distance (m) Packets Reveive Rate Multi Spreading Factor against Packets Receive Rate SF 12 SF 9

Directional antenna may help Protocol Level Optimization Frequency

16 LoRA Coverage Theoretical Computation of Coverage Height factor for Gateway Tall Building/Mast Drone based channel sounding Antenna selection Directional antenna may help Protocol Level Optimization Frequency Hopping Packet Data reduction and Long SF Output power configuration Chain Architecture

17 LoRA Coverage 4km 20% PRR

18 Sensing Node Design SPI MCU & Communication Ultrasonic Signal Processing Temperature RTD SPI

19 Overall Design Power Management LoPY Amplifier ASIC RTD US PROBE Node 1 n LoRA/WiFi MQTT Client MULTITECH CONDUIT Gateway Publish MQTT Broker Subscribe MQTT Client DB Endpoint Application Server Visualization API Dashboard ML Engine MQTT Client API

Future Proof Design Hardware Abstraction Protocol Abstraction Bor, Martin and Roedig, Utz (2017) LoRa

20 Research Directions Chicken & Egg Problem Data vs. Connectivity Intelligent Networking Self-organization of LPWAN Containerization Cloud and Fog Computing Future Proof Design Hardware Abstraction Protocol Abstraction Bor, Martin and Roedig, Utz (2017) LoRa Transmission Parameter Selection. In: th International Conference on Distributed Computing in Sensor Systems (DCOSS) :. IEEE, pp ISBN

21 Dissemination & Future Plans Invited talks: Appetite for Engineering (October 2017) UK China 2 nd Food Detection Forum (November 2018) Mondelez Global Analytical Forum (December 2017) KTN 47 th Intelligent Sensing Program Sensing in Manufacturing (March 2018) The Future of Food Safety Conference (May 2018) Two publications in preparation (Sensor design and analytics & IoT) Future funding plans: Further BREWNET work (Multiuse sensors, integrating weather data) Sensors in harsh environments Low cost sensors and IoT for agri tech/precision farming

22 Summary Lessons Learnt: Combining technologies is challenging (compatibility, robustness) Machine learning maybe not the most suitable approach for products with acceptable large variability (e.g. craft beer) Need to publish standardized framework for IoT projects Need for Self organization & hardware abstraction Summary: Project showed that sensors can be used to determine the alcohol content in a fermenter and be used to predict optimal end point There are various different data processing methods, ultrasonic velocity is the most common but also the most expensive Commercial prototype sensor under development IoT is key enabler for manufacturing, however future proofing the IoT platforms is non trivial and design framework itself is evolving.

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