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<p>Absorption heat transformers (AHTs) are a promising technology for industrial waste heat recovery, enhancing energy efficiency and sustainability. However, achieving quick start-up and stable operation remains challenging due to complex thermal interdependencies and limited understanding of transient dynamics. This study proposes a methodology to reduce AHT stabilization time by identifying critical thermal variables in transient-state conditions. The approach combines correlation and significance analyses to determine variables most influential on output temperatures across main components, such as heat source/cooling water temperatures, pressures, mass flow rates, and solution concentrations. The methodology was tested using experimental data from a 2-kW AHT prototype, supported by an artificial neural network (AHT-ANN) model to simulate stabilization time reductions. Results identified three critical transient variables: desorber heating water input temperature, condenser cooling water input temperature, and system low pressure. Strategic management of these parameters reduced start-up stabilization time by up to 36.1%. Additionally, the model’s coefficient of performance aligned closely with experimental values (discrepancies &lt; 0.0625), validating its accuracy and consistency with baseline energy metrics. This methodology offers a practical framework to minimize start-up delays in AHT systems while maintaining adaptability across configurations and operating conditions. By advancing insights into transient behavior, the study paves the way for improved operational efficiency and broader industrial adoption of AHTs in waste heat recovery.</p>

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Quick stabilization in absorption heat transformer start-up by identifying critical thermal variables

  • 期刊论文

  • 发表日期：

    2025年4月8日

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  • Luis Adrián López-Pérez,
  • Tabai Torres-Díaz,
  • Jorge Carbajal-Carbajal,
  • Sandro Guadalupe Pérez,
  • Armando Huicochea

摘要

Absorption heat transformers (AHTs) are a promising technology for industrial waste heat recovery, enhancing energy efficiency and sustainability. However, achieving quick start-up and stable operation remains challenging due to complex thermal interdependencies and limited understanding of transient dynamics. This study proposes a methodology to reduce AHT stabilization time by identifying critical thermal variables in transient-state conditions. The approach combines correlation and significance analyses to determine variables most influential on output temperatures across main components, such as heat source/cooling water temperatures, pressures, mass flow rates, and solution concentrations. The methodology was tested using experimental data from a 2-kW AHT prototype, supported by an artificial neural network (AHT-ANN) model to simulate stabilization time reductions. Results identified three critical transient variables: desorber heating water input temperature, condenser cooling water input temperature, and system low pressure. Strategic management of these parameters reduced start-up stabilization time by up to 36.1%. Additionally, the model’s coefficient of performance aligned closely with experimental values (discrepancies < 0.0625), validating its accuracy and consistency with baseline energy metrics. This methodology offers a practical framework to minimize start-up delays in AHT systems while maintaining adaptability across configurations and operating conditions. By advancing insights into transient behavior, the study paves the way for improved operational efficiency and broader industrial adoption of AHTs in waste heat recovery.