<p>This study aims to experimentally demonstrate a liquid desiccant systems effectiveness by using thermo-chemical fluid, such as aqueous solution of calcium chloride. This study evaluated the effect of operating temperatures on air properties (temperature, relative humidity, and moisture content) and system effectiveness by varying air flow rates. The system’s functionality was influenced by the operational temperature and air flow rate, and the dehumidification effectiveness was higher at low operating temperatures and low airflow rates. An ANN metamodel-based control strategy is also proposed for implementation in hybrid thermo-chemical networks with the help of system performance data and real-time data. The suggested ANN model’s results were validated using a variety of measuring techniques, including the RMSE, MAPE, correlation (R), and coefficient of determination (R<sup>2</sup>). The proposed ANN analysis achieved an excellent correlation between predicted and experimentally measured data.</p>

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Experimental and numerical investigation to optimise liquid desiccant system for advanced air conditioning

  • K. V. Shivaprasad,
  • Sumit Roy,
  • Alessandro Giampieri,
  • Andrew Smallbone,
  • Anthony Paul Roskilly

摘要

This study aims to experimentally demonstrate a liquid desiccant systems effectiveness by using thermo-chemical fluid, such as aqueous solution of calcium chloride. This study evaluated the effect of operating temperatures on air properties (temperature, relative humidity, and moisture content) and system effectiveness by varying air flow rates. The system’s functionality was influenced by the operational temperature and air flow rate, and the dehumidification effectiveness was higher at low operating temperatures and low airflow rates. An ANN metamodel-based control strategy is also proposed for implementation in hybrid thermo-chemical networks with the help of system performance data and real-time data. The suggested ANN model’s results were validated using a variety of measuring techniques, including the RMSE, MAPE, correlation (R), and coefficient of determination (R2). The proposed ANN analysis achieved an excellent correlation between predicted and experimentally measured data.