Representation of a Thermosiphon System Via Neural Networks Considering Installation Parameters

The research of alternative forms of energy production became more important in a context where the natural resources are scarce. In this sense, thermosiphon systems have been developed as an alternative way of energy economy for the water heating process using a renewable energy source: the sun. A thermosiphon system is greatly influenced by several parameters: the ambient temperature (tamb), the input water temperature (tin), the solar irradiance (G), the flow rate (m), the inclination of the solar collector (I), the height of the water storage tank (H) and mainly by the manufacturing process. Nowadays, there are interests in the development of analytical models that consider parameters of installation such as: height of the water storage tank and inclination of the solar collector. These analytical models can be complex and non-linear. In the last decades, ANN (i.e. artificial neural networks) have been used to represent many kinds of industrial processes, dealing with the complexity and non-linearity of them. Moreover, ANN are capable to deal with manufacturing aspects unconsidered by the analytical models but that are important to determine the efficiency of the real thermosiphon system. In this work, ANN have been proposed as a new alternative to represent thermosiphon system considering the different parameters related to the efficiency. A trained ANN can eliminate the necessity of new laboratory experiments for real and new conditions of installation