Refrigeration Performance Evaluation Of Adsorption Heat Pumps Based On Porous Organic Frameworks By High-throughput Computational Screening

Adsorption heat pumps(AHPs)are devices that transfer heat for space heating or cooling via adsorption and desorption driven by solar energy or industrial waste heat,which is of great significance to reduce building energy consumption and alleviate the energy crisis.At present,AHPs are only used in a few countries.The main shortcoming that greatly limits the application of AHPs is the lower coefficient of performance(COP)than that of the traditional vapor compression heat pumps.In order to improve the COP of AHPs,high-throughput computational screening(HTCS)of a large number of working pairs for both basic and cascaded AHPs integrated with machine learning was carried out to predict the COP of AHPs.Moreover,the required properties of working pairs for high performance AHPs under given working conditions was also obtained based on theoretical study.By combining the thermodynamic process of AHPs and high-throughput computational screening,we developed an approach to evaluate the COP of a large quantity working pairs,which validated by experiment.The metal-organic frameworks(MOFs)are most promising adsorbent in AHPs due to the high surface area,large pore volume and tunable pore.The coefficient of performance for cooling(COP_C)of 1527 MOFs/ethanol working pairs was calculated.The structure-property relationship including the correlation between structural characteristics,adsorption performance and heat pump performance of working pairs was revealed.The results indicated that MOFs with medium pore size,large pore volume and surface area,high working capacity,small Henry’s coefficient,significant step-like adsorption curve and suitable average heat of enthalpy are favorable for COP_C of AHPs.Moreover,it was found that most working pairs have poor COP_C due to their small pore size and strong host-adsorbate interaction.Based on above results,we focus on the covalent-organic frameworks,which have large pore size and weak host-adsorbate interaction.The COP of 275 covalent-organic framework(COFs)/ethanol working pairs under heating,cooling,and ice making working conditions was also explored.It was found that under cooling working conditions,26.18%of the COFs exhibited excellent COP_C,while only 6.16%of the MOFs exhibited excellent performance,indicating that COFs are more promising materials in AHPs under cooling conditions.Furthermore,we synthesize a high-performance COF with COP of 0.91.Finally,a machine learning algorithm was successfully used to greatly reduce the average computational time for predicting the COP_C of a working pair from 46 days to 0.01 seconds.The maximum COP_C for basic Clausius-Clapeyron cycle AHPs is 1 in theory.However,the cascaded AHPs consisting of the high-temperature stage and the low-temperature stage through heat recuperation can increase the maximum COP_C to 2.It was found that the use of MOFs/ethanol at high-temperature stage and COFs/ethanol at low-temperature stage can achieve the highest COP_C of cascaded AHPs.The corresponding structure-property relationship analysis suggested that the working pairs of high performance cascaded AHPs should have the following characteristics:1)high working capacity in both low-temperature and high-temperature stages;2)both low-temperature and high-temperature stages has medium average enthalpy of adsorption;3)adsorbents with large pore size are favorable for low-temperature stage and adsorbents with small pore size are favorable for high-temperature stage;4)adsorbents with low Henry’s constant is preferential in low-temperature stage and adsorbents with high Henry’s constant is preferential in high-temperature stage,in other words,stepwise adsorption isotherm is required for the low-temperature stage and type I adsorption isotherm is more suitable for the high temperature stage.To this end,machine learning algorithms have been used to greatly accelerate the prediction of COP for cascaded AHPs.In summary,the optimal step location is correlated with working condition for high-performing working pairs.In given working condition,the working pair with the optimal step locations in the adsorption isotherm exhibits excellent performance.Therefore,the optimum step location of stepwise adsorption isotherm for high-performance AHPs under given working conditions was derived by theoretical study.It was found that the largest optimal step location range was required under cooling working condition,followed by heating and ice making working condition.Furthermore,the stepwise adsorption isotherm is required for cooling working condition and type I adsorption isotherm is suitable for ice making working condition.Moreover,the results indicated that the heat source temperature and evaporation temperature affect the lower and upper limit of the optimal step location range.Similar results were obtained by numerical simulation based on ideal stepwise adsorption isotherms.This work provides theoretical insights into development of high-performing working pairs from a vast number of MOF/ethanol and COF/ethanol working pairs for AHPs,which greatly facilitates the wide devlopement and application of AHPs.