Energy efficiency is one of the most important topics nowadays. It is strictly related to energy demand, energy policy, environmental pollution, and economic issues. Energy efficiency can be increased and operating costs reduced by using waste heat from other processes. One of the possibilities is to use sorption chillers to produce chilled water and desalinated water. Low-temperature waste heat is not easy to utilize because of the low energy potential. Using adsorption chillers in low-temperature conditions allows utilizing waste heat and producing useful products in many regions of the world. The paper presents the results of an experimental study carried out on a three-bed adsorption chiller with desalination function, using silica gel and water as a working pair. The laboratory test stand included one evaporator, one condenser, and three separate tanks for water, desalinated water, and brine, respectively. The test stands scheme and description were presented. All results were obtained during several test hours with stable temperature conditions in the range of 57–85 °C for the heating water. It is found that the Coefficient of Performance (COP) increased from 0.20 to 0.58 when the heating water temperature increased from 57 to 85 °C. A similar finding is reported for Specific Cooling Power (SCP), which increased from 27 to 160 W/kg as the heating water temperature increased from 57 to 85 °C. It can be concluded that the heating water temperature strongly impacts the performance of the adsorption chiller.

Since the adsorption chillers do not use primary energy as driving source the possibility to employ low temperature waste heat sources in cooling energy production receives nowadays much attention of the industry and science community. However, the performance of the thermally driven adsorption systems is lower than that of other heat driven heating/cooling systems. Low coefficients of performance are one of the main disadvantages of adsorption coolers. It is the result of a poor heat transfer coefficient between the bed and the immersed heating surfaces of a built-in heat exchanger system. The purpose of this work is to study the effect of thermal conductance values of sorption elements and evaporator as well as other design parameters on the performance of a re-heat two-stage adsorption chiller. One of the main energy efficiency factors in cooling production, i. e. cooling capacity for wide-range of both design and operating parameters is analyzed in the paper. Moreover, the work introduces artificial intelligence approach for the optimization study of the adsorption cooler. The ANFIS was employed in the work. The increase in both the bed and evaporator conductance provides better performance of the considered innovative adsorption chiller. The highest obtained value of cooling capacity is 21.7 kW and it can be achieved for the following design and operational parameters of the considered re-heat two-stage adsorption chiller: Msorb = 40 kg, t = 1300 s, T = 80?C, Csorb/Cmet = 50, hAsorb = 4000 W/K, hAevap = 4000 W/K.

A distinct advantage of adsorption chillers is their ability to be driven by heat of near ambient temperature. However the performance of the thermally driven adsorption systems is lower than that of other heat driven heating/cooling systems. It is the result of a poor heat transfer coefficient between the bed and the immersed heating surfaces of a built-in heat exchanger system. The aim of this work is to study the effect of thermal conductance values as well as other design parameters on the performance of a re-heat two-stage adsorption chiller. One of the main energy efficiency factors in cooling production, i.e. cooling capacity (CC) for wide-range of both design and operating parameters is analyzed in the paper. Moreover, the work introduces artificial intelligence (AI) approach for the optimization study of the adsorption cooler. The Adaptive Neuro – Fuzzy Inference System (ANFIS) was employed in the work. The developed ANFIS model can be applied for optimizations purposes and may constitute a submodel or a separate module in engineering calculations, capable to predict the CC of the re-heat two-stage adsorption chiller.

Adsorption cooling and desalination technologies have recently received more attention. Adsorption chillers, using eco-friendly refrigerants, provide promising abilities for low-grade waste heat recovery and utilization, especially renewable and waste heat of the near ambient temperature. However, due to the low coefficient of performance (COP) and cooling capacity (CC) of the chillers, they have not been widely commercialized. Although operating in combined heating and cooling (HC) systems, adsorption chillers allow more efficient conversion and management of low-grade sources of thermal energy, their operation is still not sufficiently recognized, and the improvement of their performance is still a challenging task. The paper introduces an artificial intelligence (AI) approach for the optimization study of a two-bed adsorption chiller operating in an existing combined HC system, driven by low-temperature heat from cogeneration. Artificial neural networks are employed to develop a model that allows estimating the behavior of the chiller. Two crucial energy efficiency and performance indicators of the adsorption chiller, i.e., CC and the COP, are examined during the study for different operating sceneries and a wide range of operating conditions. Thus this work provides useful guidance for the operating conditions of the adsorption chiller integrated into the HC system. For the considered range of input parameters, the highest CC and COP are equal to 12.7 and 0.65 kW, respectively. The developed model, based on the neurocomputing approach, constitutes an easy-to-use and powerful optimization tool for the adsorption chiller operating in the complex HC system.