Development of artificial neural network models for indirect evaporative coolers in multi-climate regions based on field measurement

Abstract

Data-driven artificial neural networks (ANN) based on data drivers with their powerful self-learning capability and high adaptability are gaining increasing attention for the application in modeling indirect evaporative coolers (IEC). However, most ANN models of IEC in existing studies are either limited to a specific climate region or conventional IEC configurations. In this paper, a multi-region back-propagation (BP) neural network model for predicting the performance of advanced dew-point IECs is developed. Operational data for ANN model development were collected through field measurements in IEC projects in three typical climate zone cities in China (Dunhuang, Yulin, and Fuzhou), covering arid, moderately wet, and humid regions. A comparative study of three single-region neural network models was conducted in terms of both convergence characteristics and statistical performance metrics. Each model contains two input variables (inlet air temperature, inlet air humidity) and one output variable (wet-bulb efficiency). The results show that the model fits best in Yulin, followed by Dunhuang and Fuzhou, with total correlation coefficients of 0.9918, 0.9477, and 0.8946, respectively. The predicted values of wet-bulb efficiency are in good agreement with the actual operating data, and the deviation of almost all predicted values is within ±10%. The application values of IEC ANN models are as follows. First, the IEC-ANN model can predict IEC performance adaptively based on dynamic operational data. Therefore, it can provide the best operating strategy and design parameters for different situations. In addition, it can provide a fast response to guide the system operation when auxiliary control is required. Most importantly, this new approach requires only a limited number of tests rather than exhaustive experimental studies or dealing with complex mathematical models, and future manufacturers can use neural network technology to evaluate the performance of dew-point indirect evaporative coolers, thus saving engineering budget and time.