Abstract
This work presents a study of the Q/A factor in optimised designs of solar thermal networks of the Solar Heat for Industrial Processes (SHIP) type, which utilise Parabolic Trough Collector (PTC) technology for both winter and summer seasons. A MINLP optimisation problem with 9 decision variables is solved using the heuristic technique of Particle Swarm Optimization (PSO) coupled with a proposed and validated transient thermohydraulic-economic model. The inlet temperature of the Heat Transfer Fluid (HTF) was varied across low, medium, and high ranges, along with the target temperature and thermal load required by the process. For predicting the Q/A factor, a multivariable polynomial regression using Artificial Neural Networks (ANN) was employed. It was found that the HTF inlet temperature and the temperature required by the industrial process are the variables that most significantly impact the Q/A factor. The average optimised Q/A factor values are lower than the commonly used factor of 0.7 kW·m?², resulting in 0.51 ± 0.03; and 0.65 ± 0.03 kW·m?² with deviations of 26.54 % and 7.30 % for winter and summer.
