The present study focuses on the organic Rankine cycle (ORC) integrated into an evacuated tube heat pipe (ETHP), whose systems are an alternative solar energy system to low-efficiency planary collectors. In this work, a detailed thermodynamic and artificial neural network (ANN) analysis was conducted to evaluate the solar energy system. One of the key parameters of sustainable approaches focused on exergy efficiency is application of thermal engineering. In addition to this, sustainable engineering approaches nowadays are a necessity for improving the efficiency of all of the engineering research areas. For this reason, the ANN model is used to forecast different types of energy efficiency problems in thermodynamic literature. The examined system consists of two main parts such as the ETHP system and the ORC system used for thermal energy production. With this system, it is aimed to evaluate energy and exergy analysis results by the ANN method in the case of integrating the ORC system to ETHP, which is one of the planar collectors suitable for the roofs of the buildings. Within the scope of this study, the exergy efficiency was evaluated on the developed ANN algorithm. The effect rates of parameters such as pressure, temperature and ambient temperature affecting the exergy efficiency of ORC integrated ETHP were calculated. Ambient temperature was found to be the most influential parameter on exergy efficiency. The exergy efficiency of the whole system has been calculated as ~23.39%. The most suitable BPNN architecture for this case study is recurrent networks with dampened feedback (Jordan–Elman nets). The success rate of the developed BPNN model is 95.4%.

中文翻译：

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通过应用人工神经网络在可持续性方面对ORC集成系统的能量和火用分析进行建模

本研究的重点是有机朗肯循环（ORC）集成到真空管热管（ETHP）中，该系统是低效率平面集热器的替代太阳能系统。在这项工作中，进行了详细的热力学和人工神经网络（ANN）分析以评估太阳能系统。热能工程的应用是关注火用效率的可持续方法的关键参数之一。除此之外，当今可持续的工程方法对于提高所有工程研究领域的效率都是必不可少的。因此，ANN模型可用于预测热力学文献中不同类型的能效问题。被检查的系统包括两个主要部分，例如用于热能生产的ETHP系统和ORC系统。该系统的目的是在将ORC系统集成到ETHP的情况下，通过ANN方法评估能量和火用分析结果，ETHP是适合于建筑物屋顶的平面集热器之一。在本研究的范围内，用开发的ANN算法评估了火用效率。计算了压力，温度和环境温度等参数对ORC集成ETHP的（火用）效率的影响率。发现环境温度是对火用效率影响最大的参数。整个系统的火用效率约为23.39％。对于此案例研究，最合适的BPNN体系结构是具有衰减反馈的递归网络（Jordan–Elman网络）。所开发的BPNN模型的成功率为95.4％。