Optimal design and size of a desiccant cooling system with onsite energy generation and thermal storage using a multilayer perceptron neural network and a genetic algorithm
RIS ID
131829
Abstract
A design optimization strategy for rotary desiccant cooling (RDC) systems integrated with a photovoltaic thermal collector-solar air heater (PVT-SAH) and a phase change material based thermal energy storage (TES) (named RDC-PVT-SAH-TES) is presented in this paper. The optimization method was developed using a multilayer perceptron neural network (MPNN) and a genetic algorithm to maximize the specific net electricity generation (SNEG) of RDC-PVT-SAH-TES systems while maintaining the required cooling demand with the assistance of an electric heater. A dimension reduction method was used to determine the main design parameters of the RDC-PVT-SAH-TES system. An RDC-PVT-SAH-TES system was simulated using TRNSYS and the simulation data were utilized for training and validation of the MPNN model and for dimension reduction analysis. A comparison of the design solution identified by this optimization method with a baseline design showed that the SNEG and the solar thermal contribution of this RDC-PVT-SAH-TES system can be increased from 3.77 kWh/m2 to 10.32 kWh/m2 and from 91.5% to 99.4%, respectively. The optimization method developed could be potentially adapted to facilitate optimal design and size of other engineering systems with onsite energy generation and thermal storage.
Publication Details
Ren, H., Ma, Z., Lin, W., Wang, S. & Li, W. (2019). Optimal design and size of a desiccant cooling system with onsite energy generation and thermal storage using a multilayer perceptron neural network and a genetic algorithm. Energy Conversion and Management, 180 598-608.