Enhancing the performance of an electrical tomography based multi-phase flow meter using machine learning algorithms

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The monitoring of product formulation is of significance in the performance and management of many manufacturingindustries such as fast-moving consumable goods (FMCG) production.For several decades, the direct sampling methods via a quality control (QC) laboratory have been the principle method ofmeasuring product quality. However, these sampling-based methods are often time-consuming and unrepresentative totrue product conditions. Other controls are time-based (e.g. clean for 15mins; mix for 3hrs) when an in-processmeasurement can make significant savings.ITS has developed an industrial tomography meter that utilizes electrical process tomography technology and is able tooffer an accurate in-line concentration / quality measurement. The industrial meter has already been applied intomanufacturing applications with good performance. It is believed that the same technology can be more widely applied toFMCG industries such as in-line product recognition to reduce waste and increase productivity in continuous productionlines. The technology can be further extended to real-time measurement of clean in place (CIP).The principle of ITS's tomography meter is mainly based on multiphase electrical measurements, as the above-mentionedapplications all involve multiple ingredients, which have distinct electrical properties. For monitoring multi-phase processesusing an ITS tomography meter, an external instrument is required to track the conductivity variations of the primary liquid,so the system output can be independent to the background fluctuations, which could be influenced by either ionic concentration or temperature of the aqueous medium. Ideally, a conductivity probe can be installed in a sampling tank,where the probe measurement can potentially reflect the in-situ status of the liquid in the sensor. However practically, it ischallenging to find a suitable location that can reliably indicate the conductivity property of the primary material. In addition,the probe cannot be installed in series with the process pipelines due to the flow disturbances and the risk of productcontamination.To overcome the issue, ITS has developed a machine learning algorithm that can extract the liquid conductivity changebased on the existing raw sensor measurements, this new algorithm allows the system to accurately monitor the in-lineliquid conductivity, aiming to eliminate the system dependency on the conductivity probe. Furthermore, for single-phaseprocesses, the machines learning algorithm can also be used to identify the completeness of product formulation or batchtransition process, which realises a true in-situ fluid quality control relative to the product formulation.This innovation has been tested at a lab scale and shown to be robust across a range of process conditions (TRL4). Thenext stage will be investigating the robustness of the machine learning algorithm when it is applied in scale-up processenvironments (TRL5). The flow rigs facility from Saskatchewan Research Council (SRC) and the University of Birmingham(UoB) provides an ideal platform for characterising the algorithm performance using their benchmark technologies, such ashigh-resolution gamma-ray tomography and Positron-emission tomography (PET). The machine learning knowledge andexpertise from the National Research Council (NRC) would also add significant value to the project in terms of algorithmoptimisation.