Enhancing industrial liquid processing through intelligent pipeline mixing
Lead Participant:
UNIVERSITY OF BIRMINGHAM
Abstract
The purpose of this project will be to provide a conversion strategy from batch-style industrial stirred tank processes to scalable pipeline mixing. Pipeline mixing offers the opportunity for greater efficiency, reduced cost and higher throughput as well as the potential for improved quality control through real-time rapid measurement.
This overall goal will be approached through the application of advanced measurement techniques utilizing in-situ particle size measurement, microscopy and other rapid quality measurement methods combined with AI learning and machine control, all applied to first pilot and bench scale experiments examining non-Newtonian mixing, liquid dispersion and reacting systems, followed by rapid scale-up to industrial pipeline testing and validation.
The partners to this project will each contribute vital elements to its overall success; Dr. Federico Alberini at the University of Birmingham will work closely with UK partners Calgavin and 4t2 Sensors in characterizing static mixers in small scale pipe-loops while collecting data and characterizing the results using PLIF, ERT and 4t2’s custom sensor.
Dr. Suzanne Kresta and Dr. William Campbell at the University of Saskatchewan (Turbulent Multi-phase Mixing Laboratory) will likewise examine mixing energy and particle size distribution using their lab’s small scale pipe-loop and existing instrumentation, while coordinating scale-up testing and validation with local partner Saskatchewan Research Council at their state-of-the-art Pipe Flow Technology Centre.
Dr. Alexandra Komrakova at the University of Alberta will coordinate development of computational fluid dynamic modelling for in-line mixing while working with local partner AltaML in developing machine learning methodology that can be applied to in-line mixing. This machine learning method will then be integrated with NRC’s knowledge in AI data gathering and compiling from the in-line mixing instrumentation. This data-based learning method will then be applied to the development of an automation system that, together with the results of the other research centres will be used in developing a full scale test system at SRC’s pipeflow centre, integrating and demonstrating all aspects of the work.
The specific goals of the proposed project include
• Develop technique for instantaneous measurement, correlation and prediction of pipeline mixing energy (J) from advanced instrumentation
• Simulation of pipeline process reaction, population balance and mixing energy using machine learning and artificial intelligence based on direct numerical simulations involving population balances
• Use of in-situ microscopy, particle size analysis, particle tracking and tomography techniques combined with machine learning for in-line process optimization
• Integration of instrumentation, AI and machine learning for pipeline mixing with industrial process automation systems.
• Full-scale validation & testing of instrumentation/machine learning algorithm and automation in an industrial pipe-loop
This overall goal will be approached through the application of advanced measurement techniques utilizing in-situ particle size measurement, microscopy and other rapid quality measurement methods combined with AI learning and machine control, all applied to first pilot and bench scale experiments examining non-Newtonian mixing, liquid dispersion and reacting systems, followed by rapid scale-up to industrial pipeline testing and validation.
The partners to this project will each contribute vital elements to its overall success; Dr. Federico Alberini at the University of Birmingham will work closely with UK partners Calgavin and 4t2 Sensors in characterizing static mixers in small scale pipe-loops while collecting data and characterizing the results using PLIF, ERT and 4t2’s custom sensor.
Dr. Suzanne Kresta and Dr. William Campbell at the University of Saskatchewan (Turbulent Multi-phase Mixing Laboratory) will likewise examine mixing energy and particle size distribution using their lab’s small scale pipe-loop and existing instrumentation, while coordinating scale-up testing and validation with local partner Saskatchewan Research Council at their state-of-the-art Pipe Flow Technology Centre.
Dr. Alexandra Komrakova at the University of Alberta will coordinate development of computational fluid dynamic modelling for in-line mixing while working with local partner AltaML in developing machine learning methodology that can be applied to in-line mixing. This machine learning method will then be integrated with NRC’s knowledge in AI data gathering and compiling from the in-line mixing instrumentation. This data-based learning method will then be applied to the development of an automation system that, together with the results of the other research centres will be used in developing a full scale test system at SRC’s pipeflow centre, integrating and demonstrating all aspects of the work.
The specific goals of the proposed project include
• Develop technique for instantaneous measurement, correlation and prediction of pipeline mixing energy (J) from advanced instrumentation
• Simulation of pipeline process reaction, population balance and mixing energy using machine learning and artificial intelligence based on direct numerical simulations involving population balances
• Use of in-situ microscopy, particle size analysis, particle tracking and tomography techniques combined with machine learning for in-line process optimization
• Integration of instrumentation, AI and machine learning for pipeline mixing with industrial process automation systems.
• Full-scale validation & testing of instrumentation/machine learning algorithm and automation in an industrial pipe-loop
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
| UNIVERSITY OF BIRMINGHAM | £54,227 | £ 54,227 |
Participant |
||
| 4T2 SENSORS LTD | £95,074 | £ 66,552 |
| CAL GAVIN LIMITED | £89,796 | £ 62,857 |
People |
ORCID iD |
| Federico Alberini (Project Manager) |