Optimizing a Double Stage Heat Transformer Performance by Levenberg–Marquardt Artificial Neural Network

Suset Vázquez-Aveledo, Rosenberg J. Romero, Lorena Díaz-González, Moisés Montiel-González, Jesús Cerezo

Waste heat recovery is a critical strategy for optimizing energy consumption and reducing greenhouse gas emissions. In this context, the circular economy highlights the importance of this practice as a key tool to enhance energy efficiency, minimize waste, and decrease environmental impact. Artificial neural networks are particularly well-suited for managing nonlinearities and complex interactions among multiple variables, making them ideal for controlling a double-stage absorption heat transformer. This study aims to simultaneously optimize both user-defined parameters. Levenberg–Marquardt and scaled conjugated gradient algorithms were compared from five to twenty-five neurons to determine the optimal operating conditions while the coefficient of performance and the gross temperature lift were simultaneously maximized. The methodology includes R2024a MATLAB© programming, real-time data acquisition, visual engineering environment software, and flow control hardware. The results show that applying the Levenberg–Marquardt algorithm resulted in an increase in the correlation coefficient (R) at 20 neurons, improving the thermodynamic performance and enabling greater energy recovery from waste heat.