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Due to their capability of dealing with nonlinear problems, Artificial Neural Networks (ANN) are widely used with several purposes. Once trained, they are capable to solve unprecedented situations, keeping tolerable errors in their outputs. However, humans can not assimilate the knowledge kept by those nets, since such knowledge is implicitly represented by their connections weights. So, in order to facilitate the extraction of rules that describe the knowledge of ANN, Formal Concept Analysis (FCA) and the NextClosure algorithm have been used. Such method is presented in this work, combining ANN, FCA and the NextClosure algorithm to compute the minimal implication base (Stem Base). As an example, solar energy systems are the domain application considered here, due to their importance as substitutes of traditional energy systems.

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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 Cool water tank source: the sun. A thermosiphon system is greatly influenced by several parameters: the ambient temperature (Tamb), the input OLtPLt water temperature (Tin), the solar irradiance (G), the flow rate warm (in), the inclination of the solar collector (I), the height of the Storage tank water water storage tank (H) and mainly by the manufacturing process. Inp Nowadays, there are interests in the development of analytical water Solar collector 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 F _ with the complexity and non-linearity of them. Moreover, ANN Fig. 1. Schematic diagram of thermosiphon system.are capable to deal with manufacturing aspects unconsidered by the analytical models but that are important to determine the The performance of a thermosiphon system has been efficiency of the real thermosiphon system. In this work, ANN investigated, both experimentally and analytically, by have been proposed as a new alternative to represent numerous researches [1-8]. The efficiency of this kind of thermosiphon system considering the different parameters systems can be calculated through the equation: related to the efficiency. A trained ANN can eliminate the necessity of new laboratory experiments for real and new mc (T -T T conditions of installation. p out in1