Heat effects due to mixing (dilution) the mixed acid solutions—Application of neural networks to approximate and generalize experimental data

• We model heat effects due to mixing (dilution) of mixed acid solutions.
• We use neural networks (NN) to approximate and generalize the experimental data.
• NN supply smart and accurate method to estimate the considered heat effects.
• This makes possible modeling processes using mixed acids – e.g. nitration.
• No knowledge on NN is required – practical calculation examples are included.

The feed-forward neural networks have been used to approximate the specific molar enthalpy and the specific molar heat capacity of the mixed acid solutions. The nets have been trained with experimental data taken from the literature, so the values of the specific molar enthalpy and the specific molar heat capacity at the reference temperature T = 0 °C could be successively estimated for any composition of the mixed acid. Two principal methods have been considered and tested. In the first method two independent neural nets have been employed: the net NN-H, which approximates separately the specific molar enthalpy and the net NN-C, to approximate the specific molar heat capacity, respectively. In the second method only one net is employed (the net NN-HC), which simultaneously approximates both the specific molar enthalpy and the specific molar heat capacity. Then following both mentioned methods, the trained neural nets have been used to model the heat effects due to dilution of mixed acid solutions, carried out at various conditions – i.e. at any temperature and composition. Using these nets, both, the integral and the differential enthalpy balance can be carried out, so the smart and accurate method to model the mixed acid dilution has been elaborated. The proposed methods and their prediction accuracy have been successfully verified with our own experimental data carried out in the RC1 reaction calorimeter.