The manufacture of a vast range of consumer products involve the flow of materials through pipelines, often in large volumes and at high speeds. Monitoring the state of the materials (rheology) during manufacture is critical to ensure product quality and consistency, but is currently difficult to perform in situ. In most cases, samples are taken every few hours and tested in a remote laboratory. Thus, a failed test requires disposal of material produced prior to the detected failure, and often leads to long delays in re-starting the manufacturing line, resulting in significant waste and environmental impact. **Rheality Ltd** have developed a system that can measure the state of materials whilst still in the pipeline, providing real-time information that should lead to reduced wastage and more efficient, and hence environmentally friendly, manufacturing. It is relatively simple and inexpensive to install but requires our proprietary software that uses machine learning and Big Data to interpret the signals and determine the rheological state, flow dynamics. The technology can also detect developing leaks or blockages before they become critical, which is critical for water supply systems. With low-cost components, simple installation and high value software, we expect our system to become the standard monitoring tool across a wide range of industries. To give an example of rheological issues relating to everyday life are for example paint or ketchup. When using paint we expect certain flow properties. When dunking the brush into the paint we expect it to stick to it and not to drip when lifting. When painting the wall we expect the paint to stick easily and evenly to the wall. When having ketchup we do not look for a product that is runny or will sit forever in the bottle. All these products may look simple, however, are sophisticated in the way how they are produced. And to ensure that these formulations behave the way we expect them to is subject to the science of **rheology - the study of flow and deformation of matter.**

Rheality Ltd was incorporated on 14th January 2020 and is a spin-out from the University of Birmingham. The company's innovation is based on the technology arising from the work of Dr Federico Alberini, in the School of Chemical Engineering at the University of Birmingham. Rheality aims to demonstrate, in a relevant environment, a cost-effective, real-time, in-line rheology/liquid fingerprint measurement device to monitor state of materials flowing through pipelines at large volumes even at high speeds improving quality & process control. The technology and its process has been patented. Rheality has secured an exclusive option from the University of Birmingham to exclusive licence of the Intellectual Property, upon receiving IUK funds. Last March the company was awarded £300,000 by IUK as part of the ICURe programme. We are confident that Rheality's technology meets an urgent market need. Fluid state knowledge represents a major challenge and significant cost to a variety of industries, including but not limited to FMCG, Chemicals, Oil&Gas and Pharmaceutical. The measurement of intermediate and/or final product state is fundamental to process & quality control as the performance of a finished product is directly linked to its product state during processing. Moreover a tight control of the process will enable a drastic reduction in waste and a fast integration, in existing plants, of new formulations. This is, currently, a key challenge for industry which needs to control the consistency of their product in-line after the introduction of greener chemicals in the existing formulations. This is not possible without a technology that enables the direct monitoring of the process like Rheality does. Rheology provides direct measurements of product state and it's the ideal gold standard to ensure the desired product performance/quality. The technology is enabled through a novel passive acoustic approach which reliably and robustly predicts rheological properties of fluids. Our novel approach utilises an external piezo-sensor to measure passive signals within the fluid to create an acoustic fingerprint. Machine-learning algorithms then extrapolate the rheology based on a correlation between the observed frequency spectra and those stored in a database for fluids of known rheology. The key innovation of this technology is through the provision of a minimally invasive sensor system on a proprietary pipe segment to measure the real-time rheological properties of all types of fluids in a format compatible with large-scale manufacturing. We use passive acoustic emission sensors that until now have only been used for structural surveillance, but not to monitor fluid flows. We have developed unique software to interpret acoustic spectra with rapid de-noising and feature selection elements using machine learning algorithms that can be programmed into a basic data processing unit. To our knowledge there is nothing similar to our technology in development and we are driving innovation in this area. The initial target customers are FMCG manufacturers (batch/continuous processing), however with this specific project we want to expand into the Petrochemical sector, in particular in paints and catalyst slurry manufacture.