Evaluation and optimization of single-effect vapour absorption system for the dairy industry using d
Journal of Thermal Engineering 2022, Vol. 8, Issue 5, pp. 619-631; doi.org/10.18186/thermal.1189093
Abstract
Keywords: Single Effect Vapour Absorption System; RSM; ANOVA; COP; Optimization; Design of Experiments; Dairy Industry; Process Heat Applications
Introduction
The Indian dairy industry is one of the fastest-growing industries in the world. The demand for dairy products is rising progressively because of the increase in population and their lifestyle. The increase in dairy products demand also increased energy demand during the last two decades by Desai et al. [1]. More energy consumption resulted in more CO2 emissions. Oza et al. and Sharma et al. advise the use of renewable energy should be encouraged [2,19]. Renewable energy sources are one of the alternatives and the only solution for growing industries urge by Owusu et al. [3] and Mustafa et al. [23]. The dairy industries can use renewable energy to improve efficiency and reduce energy consumption in process heat applications Guiney et al. [4] and Ansari [24]. The integration of a vapour absorption system in process heat applications in the dairy industry can provide energy-efficient opportunities. Using such technology towards savings in electricity can replace the existing designs in process heat applications. The collective savings of energy in the chilling process and hot water generation may significantly affect the economy of the entire dairy plant. Canbolat et al. [5] conducted the parametric optimisation of absorption refrigeration systems and obtained COP and eCOP of the system as 0.6255 and 0.289, respectively. Oza et al. [2] optimized a 3 TR ammonia-water absorption system using Taguchi method and found the maximum COP at the low condenser and higher evaporator temperature. Lu et al. [6] optimised the heat-driven absorption refrigeration system and found the optimal COP as 0.86 at Tg = 60oC and Te = 5 oC. Iffa et al. [7] optimised different configurations of absorption refrigeration systems operated with many refrigerant pairs using the design of experiments. Parham et al. [8] optimised the absorption chiller cycle based on COP. The performance of the absorption chiller working with H2O + LiCl was compared with the absorption chiller functioning with LiBr + H2O. The results indicated that the performance of the absorption chiller working with LiBr + H2O was higher than that of the absorption chiller working with H2O + LiCl at optimal conditions. Manu et al. [9] optimised the performance parameters of the LiBr+H2O absorption refrigeration system using Taguchi towards maximum COP. Mashayekh et al. [10] obtained the optimum working conditions of absorption chillers. The worked-out optimum conditions can be used as a theoretical guide for further studies on the absorption chillers. Micallef [11] presented a linear model of absorption systems Omar and Micallef [12] developed a mathematical model of the absorption refrigeration system provided with an absorber. The results obtained from the mathematical model of the absorption refrigeration system can be used in designing and sizing such systems. Abbaspour et al. [13] found the optimal values of design parameters in a LiBr+H2O absorption system.
Costa et al. [14] fitted a quadratic polynomial model to COP and parameter settings towards optimum cycle efficiency. Anderson et al. [15] simulated the performance of four types of solar collectors with regard to their suitability for heating and cooling in the dairy industry. Finally, it is concluded that both flat plate and evacuated tubebased solar collector systems have better performance and make it sincere contribution to energy saving in the dairy industry. Sandey et al. [16] concluded that the solar energy could be used in the dairy industry for solar drying, for pumping dairy fluid, for room conditioning, for cold storage of milk & milk products, for lighting and electric fencing. Aphornratana and Sriveerakul [17] described an experimental investigation of a single-effect absorption using aqueous lithium–bromide as working fluid. A 2 kW cooling capacity experimental refrigerator was tested with various operating temperatures. It was found that the solution circulation ratio (SCR) has a strong effect on the system performance. The measured SCR was 2–5 times greater than the theoretical prediction. This was due to the low performance of the absorber. The use of solution heat exchanger could increase the COP by up to 60%. Meraj et al. [18] developed the thermal modelling of solar milk pasteurization system operated through N number of fully covered semi-transparent photovoltaic thermal integrated parabolic concentrator. It is also concluded that the proposed pasteurization system under optimal design and operating parameters can produce 216 kg of pasteurized milk and 5.7 kW h of electrical energy. Correspondingly, after simulation it is inferred that the proposed system is self-sustained under 6 h of operation. Meraj et al. [19] analysed proposed system was analysed under the constant mass flow rate of collectors fluid. Mathematical expressions have also been derived for generator temperature of the absorption unit as a function of both design and operating parameters. Azhar et al. [20] presented internal irreversibility at each component of a single-effect vapour absorption refrigeration system. Results show that for a single tube, UA value in the system component ranges from 2.99 W/K to 48.9 W/K depending on the operating conditions and design parameters of the system. The refrigeration system’s performance is influenced by various parameters such as temperatures of condenser, generator, absorber, and evaporator. Nonetheless, less work has been reported in the open literature on optimization of single-effect vapour absorption system for the dairy industry to get maximum performance in terms of COP and heat recovery in the condenser for process heat application using the design of experiments approach. Thus, the main objective of this study is to optimize the performance of a vapour absorption system for a dairy industry using the design of experiments approach. So, the dairy industry can utilise a vapour absorption system for less energy consumption.
Design Of Experiments Using Response Surface Methodology
Response Surface Methodology RSM (Response Surface Methodology) is a statistical tool used to model and analyse multivariate systems [15]. The connection between the dependent and independent variables is commonly unknown. RSM is often used to approximate the response in terms of predictive variables accordingly. The mathematical model of the second-order polynomial response surface can be expressed as:
Here, Y is the answer, ☐ is the error term, Xi (1, 2, 3 ... ... n) are process variables. Function F is generally a second, third, fourth or even higher-order polynomial operation. A quadratic polynomial and is written as: y = β 0 + ∑ i =1 βi Xi + ∑ i = j ∑ βi , j Xi X j + ∑ i =1 β ii Xi2 n
where, βo presents unknown polynomial coefficients. By the least-squares method, these unknown coefficients βi(i = 0, 1, 2, . . . n) are calculated. In this study, four parameters are considered for optimising the absorption refrigeration system, such as evaporator temperature, generator temperature, absorber temperature, and condenser temperature, as shown in Table 1. The levels of parameters are chosen based on the literature review. A series of experiments was conducted in the experimental plan to study the impact of parameters to obtain the relationship between variables based on selected parameters and their levels. At the operating temperatures, the mass flow rate at sate point 1,2,3 = 8.852 kg/s, at point 4,5,6 = 7.852 and point 7,8,9 and 10 = 1kg/s. The impact of generator temperature, absorber temperature, evaporator temperature on system COP and heat rejection in the condenser is optimized using RSM. Using various instruments such as standard probability plots, residual analysis, etc., the adequacy of models is checked.
characteristics, as it is a statistical approach. The ANOVA determines the order of impacting factors on the response. The impacts of each parameter on the COP and heat recovery in the condenser are examined. The significance level of the statistical analysis is 0.05, corresponding to a 95% confidence interval. The F-test has also been carried out for the reliability of outcomes. The parameter is considered statistically significant if the F-test value is greater than F-value. Mathematical Model A single-effect vapour absorption cogeneration sys-tem model has been developed, as shown in Figure 1. The system comprises a condenser, absorber, solution heat exchanger (SHE), evaporator, solution expansion valve, refrigerant heat exchanger (RHE), pump and refrigerant expansion valve. Fig. 1 shows the schematic of the vapour absorption cogeneration system. The cycle has two circuits: the refrigerant circuit (7-11) and LiBr–H2O solution circuit (1-6). Heat is transferred to the generator (Qg), which evaporates the refrigerant H2O at high pressure (Pc); the evaporated H2O is then convected to the condenser (7). The condenser dissipates heat (Qc), and then H2O changes phase from vapour to liquid (8). Then, the refrigerant H2O is conducted to refrigerant expansion valve (RTV) via a refrigerant heat exchanger to reach evaporation pressure (Pc); consequently, it leads to the evaporator (10). The cooling process is conducted in the evaporator once the refrigerant soaks up (Qe) from the environment; this causes the refrigerant to evaporate once again (11) and then lead to the absorber, where it combines with the weak solution coming from the generator. Once it mixes up, a LiBr-H2O solution with a low concentration is formed and release heat (Qa). After that, the solution is pumped to the generator (3) until it reaches condenser
Analysis of Variance (ANOVA) The ANOVA is used to determine the percentage of contribution of every parameter on the performance Table 1. Parameters and their operation levels Sr. No.
pressure (Pc) via a solution heat exchanger, increasing solution temperature. The cycle initiates after getting sufficient temperature in the generator. A part of the refrigerant evaporates and goes to the condenser (7). The rest of the solution with high concentration is fed to the heat exchanger (4) here, its temperature is decreased. Then it is passed by a throttle valve (STV), where its pressure is reduced to the evaporation pressure (Pe). The fundamental equations utilized in the study of the first law of thermodynamics are represented here:
Here, m is the mass flow rate and X is the mass fraction of LiBr in the solution. By using the equation (3) and (4), the mass balancing of such elements of the absorption system has been advanced as: By estimating is the coefficient of performance (COP), the whole performance of the absorption system has been calculated as: Qe COP = QHTG + Wp
Here, Wp is the pump work, Qe is the refrigerant effect, and QHTG is the heat rate in the generator.
Results And Discussion
Optimization Using RSM In the first step, to determine the impact of process parameters on the COP and heat recovery in the condenser, the RSM method has been applied. The COP and heat recovery in the condenser is estimated from the experimental work. The COP of the system and Qc are found for each operating parameter value as suggested by the design expert software V 12 and shown in Table 2. 28 runs have different parameters. From the above table, it is inferred that experiments have been performed as per the design of experiments along with a different combination of parameters value. The multiple regression analysis has provided the following equations, based on the design of experiments analysis: COP=+0.6955+0.0759 × Tg − 0.3808 × Tc − 0.4338 × Ta − 0.4275 × Te − 0.1323 × Tg × Tc − 0.3654 × Tg × Ta +0.9587 × Tg Te − 0.0266 × Tc Ta − 0.0051 × Tc Te +0.1291 × Ta Te
× Ta − 25.02 × Te − 9.19 × Tg × Tc − 97.38 × Tg × Ta + 32.06 × Tg × Te +10.29 × Tc Ta +48.94
× Tc Te − 94.32 × Ta Te ANOVA for RSM model The analysis of variance of both the models of COP and Qc have been shown in Tables 3 and 4, respectively. Probability tests and F tests were performed to verify the suitability of the model. To study the significance of each coefficient, p-values are used, which also reveal the strength of interaction of each variable. A lower value of p shows the greater meaning of the corresponding coefficient. The F-test results are automatically verified by the software and estimate the probability of all terms of the regression equation. It Table 2. CCD design matrix for COP and Qc obtained through DOE Tg
is significant if the probability is greater than the proposed model, it is less than 0.005. As shown in Table 3 and 4, the F value of 3.07 for COP and F value of 10.93 for heat rejection in the condenser Q c can be seen. The p values of the models of COP and Q c can also seen from Tables 3 and 4 as less than 0.0001. The condenser temperature Tc has a big impact on the COP in this case. Some of the significant terms of the model are Tg, Tc, Te, Tg×Ta, Tc×Te and Ta×Te, in addition to the ANOVA results. The term Te² has the greatest effect on Q c, in the
case of heat dissipation from the condenser. Fig. 2 and 3 are the normal residual plots for COP and Q c, which reveal the studentized residuals with a percentage of general probability, respectively. Effects of the Operating Variables The effects of various operating parameters on the COP and heat dissipated by the condenser have been shown graphically in Figs. 4 to 15. The graphs have been generated using Design-Expert software.
Figure 3. Normal plot of residuals for Qc. Optimum conditions predicted by RSM The optimum parameters towards maximum COP and Qc are forecasted by implementing RSM technique using design expert software, as shown in Table 6. The optimum values of parameters for maximum COP and Qc are Tg = 95.1oC, Tc = 45.3oC, Ta = 28.4oC and Te =15.0oC. The predicted COP and Qc at optimum parameters are 0.853 and 2488.79 kW, respectively.
Figure 10. Effect of Tg and Tc on Qc. Figure 7. Effect of Ta and Tc on COP.
Figure 11. Effect of Tg and Ta on Qc. Figure 8. Effect of Te and Tc on COP.
Figure 13. Effect of Ta and Tc on Qc. Figure 15. Effect of Te and Ta on Qc. Table 5. Optimization solution predicted by DOE
Figure 14. Effect of Te and Tc on Qc. The above table shows that for 10 experimental runs, optimized COP and Qc are given. Experimental validation of the predicted model by RSM An experimental set-up with a cooling capacity of 1.5 kW has been developed for the feasibility assessment of its applicability in the dairy industry. The test facility has been shown schematically in fig. 16. All the four seamless vessels used as evaporator, condenser, generator and absorber have been made of copper. In the generator, baffle plates have been provided at the upper end to get the pool boiling and avoid liquid solution droplets going out with water vapours. The evaporator has been designed like a spray column to ensure maximum heat transfer in the present case. The absorber used in the experimental facility act as a falling film column. The solution has been made to spray over a cooling coil in the absorber to form a liquid film over the coil to get maximum heat transfer in the present case.
79.0. 27.4 13.7 0.714 2334.82 1
Table 6. The details of the measuring instruments Sr. Measuring No Instruments
0.5. psi
The solution heated up with the help of ETC in the generator, and the cold solution from the absorber has been made to in counter current directions via the annular duct and the inner tube, respectively. A bypass flow control valve has been employed at the inlet of the heat exchanger to measure the heat
Figure 16. (I) Experimental test rig, (II) Evacuated tube collector used to get the required generator temperature and (III) Internal views of the generator, condenser, evaporator and absorber. Table 7. The detailed specifications of the ETC collector Material of Glass
0.2. kg/cm2
exchanger effectiveness. Voltage and current transducers have been used for measuring supply power. The cooling water flow rates across the condenser and the absorber have been controlled with the help of solenoid control valves. All of the vessels used as the main components of the vapour absorption system were provided with sightglasses to observe inside liquid levels. Infra-red switches have been employed on the sight glasses to measure the
fluid levels. In the steady-state condition, the liquid volume has been measured using sight-glass over a finite time interval. The performance of the practical mechanism has been analysed using experimental observations taken above a linear-state processing time of 60 minutes. The experimental outcomes have been recorded by varying the absorber temperatures, generator, condenser and evaporator.
Further, the details of the measuring instruments have been given in Table 6. To verify the accuracy of the predicted model, experiments have been performed over very close values of the optimized parameters as shown in Table 7. The experiments have been conducted on the test facility developed at NISE Gurugram, India, as shown in Fig. 16. The best experimental observations of COP and Qc as 0.926 and 2518.01 kW, respectively, have been in close approximation with the optimized values of COP and Qc. The plots of predicted vs actual, residuals vs predicted, and residuals vs run are shown in Fig. 18-23.
Figure 17. Photographic view of the Evacuated Tube Collector used in the experiment.
Table 8. Experimental observations close to the predicted optimized parameters Sr. No
Figure 23. Residuals vs run plot for Qc. = 28.4 oC and Te =15.0 oC, respectively and was in good agreement with the optimized COP. Further, the maximum Qc was obtained as 2518.01 kW, and the evaporator load is 2349 kW at the same optimal conditions. The experimentally validated optimum conditions have been suggested to be very useful in designing a better vapour absorption system.
Acknowledgement
The authors oblige the recognition of the experimental test facility given through the National Institute of Solar Energy (NISE), Gurugram, India.
Conclusion
In this study, the RSM technique optimises process parameters for a single-effect vapour absorption system so that the dairy industry can utilise a single effect vapour absorption system operating at best-operating conditions. The maximum COP of 0.926 was obtained experimentally at temperatures at the generator, condenser, absorber and evaporator as Tg = 95.1 oC, Tc = 45.3 oC, Ta
Data Availability Statement
The authors confirm that the data that supports the findings of this study are available within the article. Raw data that support the finding of this study are available from the corresponding author, upon reasonable request.
Conflict Of Interest
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethics
There are no ethical issues with the publication of this manuscript.
Nomenclature
Symbols and abbreviations ANOVA Analysis of variance COP Coefficient of performance DOE Design of experiment EES Engineering equation solver LiBr Lithium bromide . m Mass flow rate (kg/s) . mr Mass flow rate f refrigerant (kg/s) P Pressure (kPa) Qe Refrigerating effect (kW) QG Heat input of generator T Temperature (°C) Ta Absorber temperature (°C) Tb Boundary temperature (K) Tc Condenser temperature (°C) Te Evaporator temperature (°C) Tg enerator temperature (°C) X Concentration of Lithium bromide in solution (%) Subscripts a, Abs Absorber c Condenser D Destruction e Evaporator g Generator Ex. Expansion i Represents, corresponding state points o Outlet condition p Pump r Refrigerant RTV Expansion valve S Strong SHE Solution heat exchanger STV Solution throttle valve W Weak
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SOLANKI, A.; PAL, Y. Evaluation and optimization of single-effect vapour absorption system for the dairy industry using d. Journal of Thermal Engineering 2022, Vol. 8, pp. 619-631. https://doi.org/10.18186/thermal.1189093