Public Funding for Cal Gavin Limited

As we move to a decarbonised world, the focus on energy efficiency in the process industry becomes ever greater. This industry alone uses at least 11% of the UK's heating energy demand with the focus now narrowing on making every step of the process as efficient as possible to reduce energy use and carbon dioxide emissions. The process industry largely uses heat exchangers to heat and cool process fluids or to recover energy. CALGAVIN specialises in the research, design, specification and manufacturing of in-tube heat transfer enhancement devices for process exchangers which it sells worldwide. In-tube inserts are in general metal structures that are installed into the tubes and manipulate the fluid flow to improve the heat transfer between two fluids and that allow heat exchangers to be smaller, less expensive and more efficient. However, some fluids have properties that make them difficult to enhance. Such fluids can be highly viscous which gives them a thick, honey-like texture that severely inhibits the effectiveness of heat transfer. Other types of fluids are comparatively little studied, "complex" fluids that change how easily or otherwise they flow, depending on how much stress is applied to them. High viscosity and complex fluids are used in a huge variety of products including petrochemicals, plastics, polymers, paints/coatings, food products and fast-moving consumer goods. Currently insert geometries, established for use in mixing fluids inside tubes, are also used for enhancing heat exchanger performance but not having been optimised for this use, they carry a high penalty in terms of large pumping power needed relative to the limited heat transfer enhancement gained. CALGAVIN will use the now available, state-of-the-art technologies for measurement, in conjunction with the University of Birmingham, to further develop insert technology made specially for high viscous and complex flows. Each process has varying characteristics, requiring unique designs and for which clients require process guarantees. The resulting joint research, development and testing programme will lead to CALGAVIN expanding its range of unique products and technical solutions to satisfy its growing list of processors worldwide.

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

The broad aim of this project is to enhance the heat transfer coefficient of heat exchangers typically used in the power generation and process industries by applying specialist surface-coating technologies. One common industrial process involving heat transfer is vapour condensation. Drop-wise condensation, where vapour is condensed as droplets on the surface of a cooling heat-exchanger, is a phenomenon proven to be more efficient than film-wise condensation. Prior research suggests that drop-wise condensation has the potential to increase efficiency by an order of magnitude. The objective of this project is to evaluate and quantify the industrial application of surface coatings that have the potential to induce drop-wise condensation in heat-exchangers, initially targeting those incorporated within power generation plants. The challenges, which relate primarily to the level of attainment and the sustainability of drop-wise condensation, form the research focus of this project. CalGavin’s research will generate evidence-based data from the operation of an instrumented bespoke test-rig constructed to offer steam to the external surfaces of condensing tubes in a way that emulates the real-life conditions of a ‘shell and tube’ condenser in a typical power generation application. The analysis of data gathered from tests on coatings, exchanger enhancement levels and performance longevity will inform decision taking on the suitability of specific coatings. The ultimate goal is to obtain evidence that demonstrates clear quantifiable benefits to power-generation clients. Given the research is successful, this innovative technology offers users the potential for significant cost savings in equipment purchase, and reductions in both the use of water and emissions of carbon dioxide. Ultimately, it is anticipated that this technology could be incorporated in other condensation applications to provide similar performance improvements and associated benefits.