This work presents a numerical approach to compute optimal operating conditions that maximize the absorption flux into a heat exchanger designed for absorption refrigeration systems. Experimental data were obtained from a test circuit that operates in bubble absorption mode with an inner vapor distributor into a Plate Heat Exchanger-type (PHE-type) and interacts with ammonia vapor, NH3-H2O refrigerant, and cooling water. An artificial neural network (ANN) was trained to correlate the thermal properties of the solution and absorption flux in function of easily measurable parameters (concentrations, mass flows, and pressures of saturated and diluted solutions, flow and temperature of the ammonium vapor, environment temperature, and solution temperature). According to results, ANN is adequate to correlate the operational parameters and the transport phenomena inside the heat exchanger with a precision > 99%. ANN also quantitatively identified the ammonium vapor flow (43.1%), dilute solution flow (18.1%), and dilute solution concentration (13.1%) as the variables most importantly in influencing absorption flux optimization. Subsequently, a multivariable inverse artificial neural network was applied to improve the mass transfer into the PHE-type.It was identified that simultaneous optimization of the ammonia and dilute concentration flow rates improves the absorption flow performance by up to 96.3% under a worst-case scenario (ammonia flow rate<1.4 kg/min) and even 7.04% when even when operating near the amino vapor flow limit (ammonia flow rate>2.0 kg/min). Finally, it was confirmed that incorporating the diluted solution concentration into the optimization contributes to improving the performance of the absorption process 1%. Results obtained are relevant in the search to produce more competitive absorption cooling systems, demonstrating the feasibility of improving the performance of heat exchangers without structural modifications. The proposed methodology represents an interesting option to be implemented to improve performance in solar cooling systems.

这项工作提出了一种数值方法来计算最佳操作条件，以最大限度地提高吸收式制冷系统设计的热交换器的吸收通量。实验数据来自一个测试回路，该回路以气泡吸收模式运行，内部蒸汽分配器进入板式换热器型（PHE 型）并与氨蒸汽、NH3-H2O 制冷剂和冷却水相互作用。训练人工神经网络 (ANN) 以根据易于测量的参数（饱和和稀释溶液的浓度、质量流量和压力、铵蒸气的流量和温度、环境温度）关联溶液的热特性和吸收通量和溶液温度）。根据结果​​，ANN 足以以 > 99% 的精度关联操作参数和热交换器内的传输现象。ANN 还定量确定了铵蒸气流量 (43.1%)、稀溶液流量 (18.1%) 和稀溶液浓度 (13.1%) 是影响吸收通量优化的最重要变量。随后，应用多变量逆人工神经网络来改善向 PHE 型的传质。研究表明，同时优化氨和稀浓度流速可在最坏情况下将吸收流动性能提高 96.3%即使在接近氨基蒸气流量限制（氨气流速>2.0 kg/min）的情况下运行时（氨气流速<1.4 kg/min），甚至可以达到7.04%。最后，已证实，将稀释溶液浓度纳入优化有助于将吸收过程的性能提高 1%。获得的结果与寻求生产更具竞争力的吸收式冷却系统相关，证明了在不修改结构的情况下提高热交换器性能的可行性。所提出的方法代表了一个有趣的选择，可以用来提高太阳能冷却系统的性能。