ANFIS-based accurate modeling of silica gel adsorption cooling cycle

Using an eco-friendly system to provide cooling power for buildings is a great challenge. The adsorption cooling (AC) cycle is a promising thermally driven technology to provide green cooling energy for buildings. It uses environmentally friendly working fluids and could be powered by low-grade thermal energy. Despite these advantages, it suffers from low performance compared to electrical-driven cooling systems. Achieving high performance of the AC cycle relies basically on the state of operation that needs to be optimized to get the compromise between the specific cooling power (SCP) and the coefficient of performance (COP). So, the key objective of the paper is to model the outputs (SCP and COP) of the cooling cycle in terms of its operating conditions using artificial intelligence. The evaporator temperature, chilled water cycle time, chilled water mass flow rate are the main operating parameters (inputs) of the system that are optimized. An accurate Adaptive Network-based Fuzzy Inference System (ANFIS) model of COP and SCP is created based on experimental datasets. ANFIS can accurately grasp the trend of the data and hence produce an accurate model even if the data describes a highly nonlinear system. To verify the performance superiority of the fuzzy model, the attained findings are compared with ANOVA. The ANOVA uses the linear regression method to obtain the mathematical model of the system. For the COP model and during the training phase, the coefficient of determination is increased from 0.7477 by using ANOVA to 0.9997 by using ANFIS (33.7% increase). Also, during the testing phase, the coefficient of determination is increased from 0.6522 to 0.9725 (32.9% increase). Regarding the SCP model, the coefficient of determination is increased by 27.66% and 46.6% for training and testing, respectively. The RMSE values are found to be 0.0026 and 0.0437, respectively, for training and testing for modeling the ANFIS model of COP. These values prove the superiority of fuzzy modeling compared with ANOVA. Also, the RMSE values are decreased by 97.37% and 80.91% using ANFIS compared with ANOVA, respectively, for COP and SCP models. Therefore, the obtained results demonstrated the excellence of the ANFIS model of implementing the AC cycle in comparison with ANOVA.

Automated summary

This study aims to optimize the operating conditions of an adsorption cooling cycle using artificial intelligence. By creating an accurate ANFIS model, it is demonstrated that fuzzy modeling can more accurately predict the performance of the cooling cycle compared to ANOVA modeling.