Control Strategy Based on Artificial Intelligence for a Double-Stage Absorption Heat Transformer

Thermal energy recovery systems have different candidates to mitigate CO2 emissions as recommended by the UN in its list of SDGs. One these promising is thermal absorption transformers, which generally use lithium-water bromide working fluid. A Double Stage Heat Transformer (DSHT) a machine that allows at higher temperature than it supplied through effect steam concentrated solution lithium bromide. There are very precise thermodynamic models allow us calculate all possible operating conditions DSHT. To perform control systems, Artificial Intelligence (AI) proposed with two computational techniques—Fuzzy Logic (FL) and Neural Network (ANN)—to real-time set variables maximize product’s Gross Temperature Lift (GTL) Coefficient Performance (COP) The values for Determination (R2), Mean Square Error Root (MRSE), Bias (MBE) types techniques were analyzed compared purpose identifying them may be more accurate (temperatures, pressures, concentration flows) highest COP an interval value entered user. result analysis evaluated concluded strategy DSHT will based on calculation refrigerant flow second evaporator 30 neurons, 300 weights 40 bias, Fuzzy technique. goodness-of-fit was having R2 0.98 provided data. Future AI controllers must power 3.9−04 kg/KJ.