Ullah Sibghat, Ali Muzaffar, Sheikh Muhammad Fahad, Chaudhary Ghulam Qadar, Kerbache Laoucine
Mechanical Engineering Department, University of Engineering and Technology, Taxila, Pakistan.
Department of Mechanical Engineering, University of Management and Technology, Sialkot Campus, Lahore, Pakistan.
Heliyon. 2024 Apr 17;10(9):e29777. doi: 10.1016/j.heliyon.2024.e29777. eCollection 2024 May 15.
In this Paper solar desiccant air conditioning system integrated with cross flow Maisotsenko cycle (M-cycle) indirect evaporative cooler is used to investigate the performance of whole system in different range of parameters. Solar evacuated tube electric heater is used to supply the regeneration temperature to the desiccant wheel, whereas, Desiccant Wheel (DW) and M-cycle is used to handle latent load and sensible load separately. Major contribution of this research is to predict system level performance parameters of a Solar Assisted Desiccant Air Conditioning (Sol-DAC) system using Radial Basis Function Neural Network (RBF-NN) under real transient experimental inlet conditions. Nine parameters are mainly considered as input parameters to train the RBF-NN model, which are, supply Air temperature at the process side of desiccant wheel, supply air humidity ratio at process side of the desiccant wheel, outlet temperature from the desiccant wheel at process side, outlet humidity ratio from the desiccant wheel at process side, regeneration temperature at regeneration side of the DW, outlet temperature from the heat recovery wheel at process side, outlet humidity ratio out from the Heat Recovery Wheel (HRW) at process side, temperature before heat recovery wheel regeneration side of the system, humidity ratio before heat recovery wheel regeneration side of the system. Four parameters are considered as the output of the RBF-NN model, namely: output temperature, output humidity, Cooling Capacity (CC), and Coefficient of Performance (COP). The results of the RBF-NN model shows that the best Mean Squared Error (MSE) and Regression coefficient (R) for outlet temperature prediction are 0.00998279 and 0.99832 when regeneration temperature is 70 °C and inlet humidity at 18 g/kg. Best MSE and R for predication of outlet humidity are 0.0102932 and 0.99485 when the regeneration temperature is 70 °C and inlet humidity at 16 g/kg. Best MSE and R for predication of COP are 0.0106691 and 0.9981 when the regeneration temperature is 70 °C and inlet humidity 12 g/kg. Best MSE and R for predication of CC are 0.0144943 and 0.99711 when the regeneration temperature is 70 °C and inlet humidity 14 g/kg. Experimental and predicted performance parameters were in close agreement and showed minimal deviation. Investigations of predicted results revealed that trained RBF-NN model was capable of predicting the trend of output result under the varying input condition.
在本文中，采用集成了横流迈索森科循环（M循环）间接蒸发冷却器的太阳能除湿空调系统，来研究整个系统在不同参数范围内的性能。使用太阳能真空管电加热器为除湿转轮提供再生温度，而除湿转轮（DW）和M循环分别用于处理潜热负荷和显热负荷。本研究的主要贡献在于，在实际瞬态实验入口条件下，使用径向基函数神经网络（RBF-NN）预测太阳能辅助除湿空调（Sol-DAC）系统的系统级性能参数。九个参数主要被视为训练RBF-NN模型的输入参数，它们是：除湿转轮过程侧的送风温度、除湿转轮过程侧的送风湿度比、除湿转轮过程侧的出口温度、除湿转轮过程侧的出口湿度比、除湿转轮再生侧的再生温度、过程侧热回收转轮的出口温度、过程侧热回收转轮（HRW）的出口湿度比、系统热回收转轮再生侧之前的温度、系统热回收转轮再生侧之前的湿度比。四个参数被视为RBF-NN模型的输出，即：输出温度、输出湿度、制冷量（CC）和性能系数（COP）。RBF-NN模型的结果表明，当再生温度为70°C且入口湿度为18g/kg时，出口温度预测的最佳均方误差（MSE）和回归系数（R）分别为0.00998279和0.99832。当再生温度为70°C且入口湿度为16g/kg时，出口湿度预测的最佳MSE和R分别为0.0102932和0.99485。当再生温度为70°C且入口湿度为12g/kg时，COP预测的最佳MSE和R分别为0.0106691和0.9981。当再生温度为70°C且入口湿度为14g/kg时，CC预测的最佳MSE和R分别为0.0144943和0.99711。实验和预测的性能参数非常接近，偏差极小。对预测结果的研究表明，经过训练的RBF-NN模型能够预测在变化的输入条件下输出结果的趋势。
