摘要：3-bed adsorption cooling systems have the advantage of maintaining a uniform chilled water temperature. Because the number of parameters in a 3-bed system is very large, using artificial neural networks (ANN) is often suggested as an alternative to conducting experiments in many recent studies. We systematically determined the optimal time allocation for a 3-bed, 2-evaporation adsorption cooling system, using ANN with five variables, i.e., adsorption/desorption time ratio (fad), high/low evaporator time ratio (fp), cycle time (τ), and the time lag between each adsorption bed (δ2, δ3). Each case was methodically modeled using a process that our research team had previously developed and verified. When the coefficient of performance (COP) and specific cooling power (SCP) estimated by ANN were compared with the actual results, errors were within ±4%. Finally, the best strategy for each performance indicator, i.e., COP, SCP, and standard deviation of chilled-out temperature, was proposed for the 5 operating parameters of fad, fp, τ, δ2, and δ3. The cycle time was found to have 42.9% relative importance for the COP, and 33.1% relative importance for the SCP, but the most influential factor to the SCP was the high/low pressure evaporator time ratio with a relative importance of 33.3%.
