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Thermal Characterisation of a Latent Heat Thermal Energy Storage System using a Dynamic Artificial Neural Network

This presentation discusses the thermal characterisation of a latent heat thermal energy storage (TES) system using a dynamic artificial neural network. The TES system offers technical advantages such as high energy density, volume reduction, reduced heat loss, and high thermal output. The presentation also explores the challenges in system-level simulation, system design/economic analysis, and phase change process. The development of a computationally efficient model using a dynamic neural network is discussed, along with the outcomes of the PECRE award and future applications of the model.

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Thermal Characterisation of a Latent Heat Thermal Energy Storage System using a Dynamic Artificial Neural Network

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  1. Heat energy from the sunThermal characterisation of a latent heat thermal energy storage system by means of a dynamic artificial neural networkPresentation for ETP PECRE Award Dr Faisal Ghani CEng MIET Research Associate IMPEE, Engineering & Physical Sciences Heriot-Watt University f.ghani@hw.ac.uk 6th ETP Annual Conference, University of Edinburgh, October 10th, 2017

  2. Solar energy utilisation • Solar radiation commonly converted to: • Electricity (photovoltaic) • Heat (solar thermal) • Thermal energy is 58% of UK domestic energy needs • 45% Space heating • 18% Hot water • Energy storage a critical component within solar energy systems • Supply/demand mismatch • High thermal output needed 6th Annual ETP Conference, University of Edinburgh

  3. Solar thermal systems overview • Solar thermal collectors • Fluid transfer system • Thermal Energy Storage 6th Annual ETP Conference, University of Edinburgh

  4. Thermal energy storage (TES) • A number of storage technology types currently used to store solar energy • Sensible (e.g. water) • Latent (phase change materials) • Latent heat storage offers a number of technical advantages • High energy density • Overall volume reduction • Reduced heat loss (VIP) • High thermal output (~30kW) • Thermal modulation 6th Annual ETP Conference, University of Edinburgh

  5. Solid/liquid phase change process LIQUID STATE (CHARGED) SOLID STATE (DISCHARGED) Solidification process 6th Annual ETP Conference, University of Edinburgh

  6. Collector performance enhancement • Melting process occurs at near constant temperature • Phenomenon can be leveraged for thermal modulation applications • Solar thermal collector performance degrades with temperature • Latent heat TES can minimise heat loss by modulating collector temperature Reduced collector yield due to elevated temperature Latent heat TES advantage 6th Annual ETP Conference, University of Edinburgh

  7. Collector performance enhancement Efficiency ‘plateau’ while PCM melting Collectors continue to charge latent heat TES Thermal efficiency of an evacuated tube collector while charging a latent heat TES

  8. Project challenges ? • System level simulation needed for: • Annual performance approximation. How much electricity do we need to purchase? Solar thermal performance gain? • System design/economic analysis. How many collectors? What size store? • Phase change process highly transient moving boundary problem requiring numerical solver TRNSYS simulation of a solar thermal system

  9. Development of a computationally efficient model of latent heat TES • Numerical techniques too demanding for long term simulation studies • PECRE award received from ETP to work with HSLU to develop dynamic neural network model • Step 1: Acquire training data (experimental) • Step 2: Train and modify architecture • Step 3: Test/evaluate network Tin Neural Network Model Tout Mass flow Generic neural network architecture 6th Annual ETP Conference, University of Edinburgh

  10. Neural network training data collection • Extensive experimental data collected using experimental rigs • Training data collected using apparatus located in Lucerne and Edinburgh

  11. Loop 2 Indoor experimental apparatus Loop 1 LabVIEW software 6th Annual ETP Conference, University of Edinburgh Data acquisition system

  12. Neural network testing results Tin Neural Network Model Tout Mass flow • Once trained testing was conducted using exp data • Excellent agreement found during charging phase • Some disparity emerges during discharge phase • Fast calculation time once network has been trained • Further data being collected 6th Annual ETP Conference, University of Edinburgh

  13. PECRE award outcomes • A strong collaboration developed between Heriot-Watt, Sunamp, and Lucerne University of Applied Sciences • Journal paper submitted and under review • Model to be used for H2020 Project • MSc student from HSLU spending 5 months at Heriot-Watt • A unique tool developed to model phase change phenomenon in the Sunamp heat battery • Recipient of the Kerr Macgregor for Solar Innovation awarded by SSEG for research carried out in this project

  14. How funding was spent 6th Annual ETP Conference, University of Edinburgh

  15. Thank you Dr Faisal Ghani CEng MIET Research Associate IMPEE, Engineering & Physical Sciences Heriot-Watt University f.ghani@hw.ac.uk 6th Annual ETP Conference, University of Edinburgh

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