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This research proposed an artificial neural network model with dynamic training in order to predict both fluids outlet temperatures on a monoethanolamine heat exchangers system, doing the training, validation and testing with 31 680 data points gathered through the passive experimentation method. The 4-3-2 multilayer perceptron achieved correlations above 98.76 %, and it was corroborated that dynamic training strategy provided more accurate results than single training. Mean absolute errors of 0.419 and 0.372 K were obtained when applying the first approach (for rich amine and lean amine outlet temperatures as output variables, respectively), as compared to 2.214 and 1.181 K when implementing the second one. Since calculated deviations have no significant implication on the technological process under analysis, proposed model is considered appropriate for simulation of the studied heat exchangers performance. [ABSTRACT FROM AUTHOR]

Nitrate based salts have application as a heat transfer fluid and thermal energy storage media in solar field installations and are normally used from 200°C up to maximum temperatures of ~550°C. Molten K2CO3-Na2CO3-Li2CO3 could potentially be used as heat transfer fluid and thermal energy storage media to replace nitrate salts due to its wider temperature operating window (400-900 °C), which improves the heat transfer efficiency. There will be improved operability and the process will be more economical viable if the lower temperature at which carbonate salts can operate could be decreased. This paper explores the melting point and high temperature stability of K2CO3-Na2CO3- Li2CO3 based salt mixtures, the effect of atmosphere and the effect of additives to the melt using experimental investigation and thermodynamic modeling. [ABSTRACT FROM AUTHOR]