Optimal design criterion for Heat Transformer operating with Water Carrol

Morales, C V Valdez; Romero, R.J.

doi:10.15224/978-1-63248-102-3-63

Cited by 1 publication

(7 citation statements)

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“…The names and units of measurement of inputs and outputs are shown in Table 1. According to the study carried out, in an SSHT cycle [41] the maximum values of the COP SSHT implies the lowest value of GTL SSHT , as can be seen in the upper left part of Figure 3. In addition, the highest values of GTL SSHT are obtained in the minimum values of COP SSHT for each T AB between 70 and 75 • C. Maximum values of COP SSHT or maximum values of GTL SSHT are not the best scenarios to consider as a control strategy.…”

Section: Artificial Intelligencementioning

confidence: 85%

“…where WPi is the work done by pump number i (1 to 4), which-according to the literature-is less than 5% and, in most conditions, its effect on the COP can be neglected [41]. In a Double-Stage Heat Transformer (DSHT), the heat transferred to the stage 1 Absorber is used to power the stage 2 Evaporator, while the heat generated from stage 2 is also fed by the same heat source that feeds the stage 1 Generator and stage 1 Evaporator.…”

Section: Double-stage Heat Transformer (Dsht)mentioning

confidence: 99%

“…The need arises to develop a strategy for automatic control of a DSHT operating with water-lithium bromide with the purpose of calculating the refrigerant flow for the maximum value of COP and GTL, which guarantees the recovery of the thermal energy of the system with the greatest efficiency for a selected operation condition. For this, the implementation of intelligent control is carried out, with the evaluation of two computational techniques associated with Artificial Intelligence, in a certain way that is autonomous, nonlinear, and learning [40], the nonlinearity of which corresponds well to thermodynamic models [41].…”

Section: Introductionmentioning

confidence: 99%

“…where WP i is the work done by pump number i (1 to 4), which-according to the literatureis less than 5% and, in most conditions, its effect on the COP can be neglected [41].…”

mentioning

confidence: 99%

“…The thermodynamic operating data of an SSHT with lithium bromide were calculated and are shown in According to the study carried out, in an SSHT cycle [41] the maximum values of the COPSSHT implies the lowest value of GTLSSHT, as can be seen in the upper left part of Figure 3. In addition, the highest values of GTLSSHT are obtained in the minimum values of COPSSHT for each TAB between 70 and 75 °C.…”

mentioning

confidence: 99%

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

Vázquez-Aveledo,

Romero,

Montiel-González

et al. 2023

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

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Section: Artificial Intelligencementioning

confidence: 85%

Section: Double-stage Heat Transformer (Dsht)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…where WP i is the work done by pump number i (1 to 4), which-according to the literatureis less than 5% and, in most conditions, its effect on the COP can be neglected [41].…”

mentioning

confidence: 99%

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confidence: 99%

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