D.M. Madyira

F.M. Ekundayo

P.A. Adedeji

S.A. Akinlabi

T.O. Babarinde

Walter Sisulu University

Data in Brief 32 (2020) 106316 Contents lists available at ScienceDirect Data in Brief journal homepage: www.elsevier.com/locate/dib Data Article Dataset of experimental and adaptive neuro-fuzzy inference system (ANFIS) model prediction of R600a/MWCNT nanolubricant in a vapour compression system T.O. Babarinde a , ∗, S.A. Akinlabi b , D.M. Madyira a , F.M. Ekundayo c , P.A. Adedeji a a Department of Mechanical Engineering Science, University of Johannesburg, South Africa b Department of Mechanical Engineering, Walter Sisulu University, Eastern Cape, South Africa c Department of Industrial Engineering, Wayne State University, United States a r t i c l e i n f o Article history: Received 30 July 2020 Revised 4 September 2020 Accepted 9 September 2020 Available online 14 September 2020 Keywords: R600a Experimental data Power consumption MWCNT nanolubricant COP Cooling capacity ANFIS predicted data a b s t r a c t This research paper assessed the performance of R600a with the base lubricant and Multi-walled Carbon Nanotube (MWCNT) nanolubricant at steady state. It describes the in- struments required for measurement of the data parameter and its uncertainties, steps involved in preparing and replac- ing the MWCNT nanolubricant concentration with base lubri- cant in vapour compression refrigeration. The system’s tem- perature data was collected at the components inlets and outlets. Pressure data was also registered at the compres- sor outlet and inlet. The data was captured at 27 °C am- bient temperature at an interval of 30 min for 300 min. The experiment includes the experimental data collection, Adaptive Neuro-Fuzzy Inference System (ANFIS) training and testing dataset. The use of ANFIS model is explained in pre- dicting the efficiency of MWCNT nanolubricant in a vapour compression refrigerator system. The ANFIS model also pro- vides statistical output measures such as Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and determination coef- ficient ( R 2 ). The data is useful and important for replacing DOI of original article: 10.1016/j.egyr.2019.11.132 ∗ Corresponding author. E-mail address: taiwo.babarinde79@gmail.com (T.O. Babarinde). https://doi.org/10.1016/j.dib.2020.106316 2352-3409/© 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) 2 T.O. Babarinde, S.A. Akinlabi and D.M. Madyira et al. / Data in Brief 32 (2020) 106316 MWCNT nanolubricant with base lubricant in a vapour com- pression refrigeration system for researchers in the speciali- sation of energy-efficient materials in refrigeration. The data present can be reused for vapour compression refrigeration systems simulation and modelling. © 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) Specifications Table V          Subject Mechanical Engineering Specific subject area Nano materials for energy efficiency Type of data Text file, Figures, Tables How data were acquired The data collection source was from the mathematical equation and the experimental analysis. The measuring instruments used were Bordon pressure gauges, thermocouples (K-Types), mass flow metre, and electronic scales for weighing the mass of nanoparticles and refrigerant. A PC with MATLAB software was used in developing the performance prediction for the ANFIS model Data format Raw and Analysed Parameters for data collection The data collection parameters were the temperature of the refrigerant at the outlet of evaporator and condenser, the temperature and pressure discharge at the compressor and the refrigerant mass flow rate. Description of data collection The experiment’s temperature was collected at the inlet and outlet of the refrigerator components (Condenser, Compressor, Evaporator, Expansion valve). The pressure at the outlet and inlet of the compressor were measure. The experimental data were captured at 27 °C ambient temperature at 30 min interval for 300 min by the data logger, the replication of the experiment was carried out five times to ensure precision. For the data training, 70% of the experimental data is used while the remaining 30% is used for the testing of the ANFIS model Data source location Department of Mechanical Engineering Science, Faculty of Engineering and the Built Environment,University of Johannesburg, Johannesburg, South Africa. Data accessibility With the article Related research article T. O. Babarinde, S. A. Akinlabi, and D. M. Madyira, “Energy performance evaluation of R600a/MWCNT-nanolubricant as a drop-in replacement for R134a in household refrigerator system,” Energy Reports, vol. 6, pp. 639–647, 2020. https://doi.org/10.1016/j.egyr.2019.11.132 alue of the Data • The data offers energy efficiency in the design and size of the nano eco-vapour compressionrefrigerator systems. The inadequate of actual raw experimental data greatly differentiate be-tween the real experimental and expected predictive data value. The data value of the ex-periment provided in this experiment includes the anticipated data value on deviations andthe estimated value of the ANFIS model prediction for the design and sizing of nano vapourcompression refrigeration systems. • The data describes steps involved for analysing the measured data and interpreting them,mathematical calculations involved in the analysis of nanolubricant performance which isuseful to researchers and technicians in the area of cooling systems to substitute nanolubri-cant with the base or pure lubricant in vapour compression refrigeration systems. • The ANFIS model offers the testing data to predict the efficiency of MWCNT nanolubricant inthe vapour compression refrigeration system which is also useful in simulation and modellingby using other artificial intelligence approaches. T.O. Babarinde, S.A. Akinlabi and D.M. Madyira et al. / Data in Brief 32 (2020) 106316 3        • The data improve and optimise concentrations of MWCNT nanolubricant in vapour compres-sion refrigerator using R600a which can be useful in the application of other eco-friendlyhydrocarbon refrigerants in vapour compression refrigerator systems. 1. Data Description The test rig diagram for experimental in Fig. 1 defines the inlet and outlet of each refrigera-tor component and the point of collection of each data. Fig. 2 revealed the structural integrity ofMWCNT with the X-Ray diffraction. Fig. 3 illustrates the system’s performance prediction ANFISarchitecture with data values for input and output. Figs. 4 and 6 describe the performance com-parison between the experimental and ANFIS predicted data of the MWCNT nanolubricant in theFig. 1. Diagrammatic representation of the test rig for the experiment. Fig. 2. Structural integrity of MWCNT as revealed by XRD. 4 T.O. Babarinde, S.A. Akinlabi and D.M. Madyira et al. / Data in Brief 32 (2020) 106316 Fig. 3. ANFIS architecture. Fig. 4. Comparison of the cooling capacity (Qevap) of experimental and ANFIS model predicted data. r  o  t  o  t  (  R  p  c  t  c  efrigerator system. Table 1 shows the dataset of the evaporator temperature and pull downtimef the refrigerator to achieve a steady-state performance. Table 2 present the data collection cap-ured from different components test points of the system from the experiment which consistf R600a mass charge in gram (g), nanolubricant concentration ( N c ) in gram per litre (g/L), theemperature in degree Celsius ( °C) at the condenser and evaporator outlet, pressure in kilopascalkPa) at the compressor suction and discharge, and mass flow rate in gram per second (g/s) of600a. Table 3 illustrates the R600a mass charge data collection and the enthalpy in kilojouleer kilogram (kJ/kg) of R600a with nanolubricant concentration of the evaporator, compressor,ondenser, and mass flow rate. Table 4 shows the system output experimental data collection,he data set includes the system’s R600a mass charge (g), nanolubricant concentration (g/L),ompressor power consumption ( W c ) in (kW), evaporator chamber cooling capacity (Qevap) inT.O. Babarinde, S.A. Akinlabi and D.M. Madyira et al. / Data in Brief 32 (2020) 106316 5 Fig. 5. Comparison of the power consumption (Wc) of experimental and ANFIS model predicted data. Fig. 6. Comparison of the coefficient of performance (COP) of experimental and ANFIS model predicted data. Table 1 Experimental dataset of the pulldown time and evaporator temperatures of the refrigerator at steady state. Time (min) 0 (g/L) 50 g 0.2 (g/L) 0.4 (g/L) 0.6 (g/L) 0 (g/L) 60 g 0.2 (g/L) 0.4 (g/L) 0.6 (g/L) 0 (g/L) 70 g 0.2 (g/L) 0.4 (g/L) 0.6 (g/L) 0 27 27 27 27 27 28 27 27 27 27 27 27 30 9 7 0 −1 1 −2 −4 −3 5 5 5 1 60 2 0 −4 −5 −2 −6 −8 −5 2 1 −1 −3 90 −1 −2 −5 −6 −3 −6 −9 −7 1 0 −3 −5 120 −3 −4 −6 −6 −3 −7 −10 −8 1 0 −3 −5 150 −4 −4 −6 −7 −3 −7 −11 −9 0 −1 −3 −7 180 −4 −5 −7 −7 −3 −7 −11 −10 0 −1 −3 −7 210 −5 −5 −7 −8 −3 −7 −11 −10 0 −1 −3 −8 240 −5 −5 −7 −8 −3 −7 −11 −11 0 −1 −3 −8 270 −5 −5 −7 −8 −3 −7 −11 −11 0 −1 −3 −8 300 −5 −5 −7 −8 −3 −7 −11 −11 0 −1 −3 −8 6 T.O. Babarinde, S.A. Akinlabi and D.M. Madyira et al. / Data in Brief 32 (2020) 106316 Table 2 Experimental dataset of the collected pressure and temperature of the refrigerator at steady state. Mass (g) N c (g/L) T 1 ( °C) P 2 (kPa) T 3 ( °C) m ref (kg/s) 50 0 −5 750 54 0.0 0 052 50 0.2 −5 680 50 0.0 0 053 50 0.4 −7 590 44 0.0 0 056 50 0.6 −8 720 52 0.0 0 057 60 0 −3 680 50 0.0 0 052 60 0.2 −7 640 47 0.0 0 059 60 0.4 −11 600 44 0.0 0 062 60 0.6 −11 600 44 0.0 0 060 70 0 0 750 54 0.0 0 071 70 0.2 −1 750 54 0.0 0 075 70 0.4 −3 740 53 0.0 0 053 70 0.6 −8 660 48 0.0 0 060 Table 3 Experimental dataset of the refrigerant’s enthalpy at the steady-state in the refrigerator. Mass charge (g) N c (g/L) h 1 (kJ/kg) h 2 (kJ/kg) h 3 (kJ/kg) ḿ Ref (kg/s) 50 0 547.63 706.01 215.03 0.0 0 0522 50 0.2 547.63 687.65 225.57 0.0 0 0528 50 0.4 544.95 654.40 238.49 0.0 0 0559 50 0.6 543.61 669.41 216.51 0.0 0 0572 60 0 550.31 703.67 228.25 0.0 0 0521 60 0.2 544.95 681.57 230.71 0.0 0 0590 60 0.4 539.60 657.48 233.10 0.0 0 0618 60 0.6 539.60 645.28 233.10 0.0 0 0605 70 0 554.34 679.28 354.43 0.0 0 0705 70 0.2 552.99 664.10 352.99 0.0 0 0750 70 0.4 550.31 703.67 228.25 0.0 0 0531 70 0.6 543.61 670.29 226.90 0.0 0 0601 Table 4 Experimental dataset of the refrigerator’s performance at steady state. N c (g/L) Mass (g) Wc (kW) Qevap (kW) COP 0 50 0.0826 0.1736 2.1 0.2 50 0.0739 0.1700 2.3 0.4 50 0.0612 0.1714 2.8 0.6 50 0.0719 0.1870 2.6 0 60 0.0799 0.1679 2.1 0.2 60 0.0805 0.1853 2.3 0.4 60 0.0728 0.1893 2.6 0.6 60 0.0638 0.1853 2.9 0 70 0.0881 0.1410 1.6 0.2 70 0.0833 0.1500 1.8 0.4 70 0.0813 0.1709 2.1 0.6 70 0.0760 0.1902 2.5 (  T  a2 s  M  kW), and COP. Table 5 shows the system’s experimental and ANFIS model predicted data set.able 6 describes the statistical analysis of the predicted data set such as R-value, RMSE, MAD,nd MAPE. . Experimental Design, Materials and Methods A mineral oil and MWCNT nanoparticles were used to prepare the nanolubricant. The den-ity of mineral oil is 0.914 at 15 °C and the viscosity is 32 cSt at 40 °C and 4.4 cSt at 100 °C. TheWCNT nanoparticles used in the lubricant as an additive have a diameter of 10 nm ± 1 nmT.O. Babarinde, S.A. Akinlabi and D.M. Madyira et al. / Data in Brief 32 (2020) 106316 7 Table 5 Dataset showing the experimental and ANFIS model predicted data. Data Index Experimental W c (kW) ANFIS Predicted W c (kW) Experimental Qevap (kW) ANFIS Predicted Qevap (kW) Experimental COP ANFIS Predicted COP 1 0.0612 0.05301 0.1714 0.15426 2.8 2.604 2 0.0728 0.06951 0.1893 0.19301 2.6 2.650 3 0.0638 0.05934 0.1853 0.18701 2.9 2.726 4 0..0813 0.07014 0.1709 0.17403 2.1 1.911 Table 6 The ANFIS model dataset performance evaluation. Variables R2 RMSE MAD MAPE Cooling capacity (Qevap) 0.692 0.02152 0.01635 0.0956 Power consumption (Wc) 0.302 0.00710 0.04910 0.0724 COP 0.883 0.28101 0.21190 0.0892                      ± 04,5 nm ± 0,5 nm ± 3–6 μm, the density of 0.068 g/cm 3 as specified by the manufacturer(Sigma Aldrich). Every sample of MWCNT nanolubricant (0.2, 0.4, and 0.6 g/L) was prepared us-ing a litre of mineral oil. The MWCNT nanoparticles and lubricant were stirred together with thehelp of magnetic stirrer for 45 min. After that, the ultrasonic homogeniser was then used to ho-mogenised MWCNT nanolubricant together under a temperature range of 15–20 °C for 180 min[1 , 2] . The used R600a refrigerant has GWP of 3 and ODP of zero. The refrigerator that served asthe test rig for the experiment was a 70-l capacity household vapour compression system with100 W power ratings hermetic compressor, expansion valve, 9.8 m length air-cooled condenserand 1.5 m length capillary tube. Each concentration of nanolubricants was measured for 50, 60, and 70 g. The refrigerant(600a) mass charge with charging scale was inserted into the system compressor. Evacuationand flushing of the system were also performed to ensure greater data accuracy for each exper-iment. The temperature measurements were taken with thermocouples at each inlet and outletof components for the refrigerator. The two pressure gauges were attached to a compressor tocalculate the compressor’s suction and discharge pressure. The experimental test was conducted and repeated at an ambient temperature of 27 °C for300 min at an interval of 30 min for 5 times. Thermocouples and pressure gauges instrumentuncertainties were respectively + 3 °C and 1 %. Fig. 1 depicts an experimental set-up schematic-diagram. Using Refprop version 9.0 [3] , the experimental data output readings were used todetermine system performance. Consideration was given to performances such as power consumption (Wc), cooling capacity(Qevap), and COP. The efficiency of R600a in MWCNT nanolubricant, such as power consumption (Wc), coolingcapacity (Qevap), and COP, was analysed according to Madyral et al. [4] using Eqs. (1 )–(3) ′ m The Q evap was computed with Eq. (1 ) Q e v ap = ′ m Re f ( h 1 − h 4 ) e v ap (1) Where h 1 , h 4 and ḿref represent enthalpy of refrigerator evaporator at outlet, inlet and massflowrate, respectively. The condenser’s enthalpy outlet ( h 3 ) at steady state is equal to enthalpyof the evaporator’s outlet ( h 4 ). Hence, h 3 = h 4 The calculation of W c result data follows Eq. (2 ) W c = ′ m Re f ( h 2 − h 1 ) c (2) Where the compressor outlet enthalpy is h 2 8 T.O. Babarinde, S.A. Akinlabi and D.M. Madyira et al. / Data in Brief 32 (2020) 106316   F  i  t  f  e  b  t  i  tW a l   o  t  s  j  a  t  t  t  l The COP data is calculated utilizing Eq. (3 ) COP = Q e v W c (3)ANFIS modelling technique is a five-layer feed forward network that combines ANN anduzzy logic technology to build a hybrid model capable of practical fit and efficient performancen a system where imprecision and uncertainty can exist. Within ANFIS, all nodes are adap-ive for both the first and fourth layers while other layers have non-adaptive nodes. The modelollows the Fuzzy Inference System Takagi Sugeno (TSFIS). The input data for these model arevaporator temperature ( T 2 ), condensing temperature ( T 3 ), refrigerant mass charge and nanolu-ricant concentration ( N c ). As stated earlier, 70% of the data was used for training while 30% ofhe data was used in the model testing. The ANFIS MATLAB model program can be found onlinen the supplimentary file. The rules of the ANFIS model are established by an if-then principlehat connects the precedent with the consequence as follows: Condition 1: If X 1 is A 1 AND X 2 is B 1 then f 1 = r 1 X 1 + s 1 X 2 + t 1 . Condition 2: If X 1 is A 2 AND X 2 is B 2 then f 2 = r 2 X 1 + s 2 X 2 + t 2 . here X 1 and X 2 are model inputs, the parameters of A 1 A 2 , 1 and B 2 are fuzzy and r, s, t. The model for the ANFIS structure is shown in Fig. 4 . The parameterisation for each layer iss follows: Layer 1: This layer consists of adaptive nodes with fuzzy membership function which calcu-ates the output function by O 1 j = μA j ( I 1 ) , j = 1 , 2 (4)O 1 j = μB j( I 2 ) , j = 1 , 2 (5)Layer 2: The nodes in this layer are non-adaptive and, according to Eq. (9) , the firing powerf each law is measured from a multiplicative operator. O 1 j = μA j ( I 1 ) . μB j ( I 2 ) , j = 1 , 2 (6)Layer 3: Nodes in this layer are also set with the normalisation of the firing strength w i inhe j th node executed using the ratio of its firing strength and the combination of all firingtrengths according to all rules. O 3 j w̄ j = w j w 1 + w 2 , j = 1 , 2 (7)Layer 4: Nodes in this layer are adaptive, and defuzzification is performed. The effect of the th rule on the output of the layer is expressed here according to Eq. (12) and node parametersre defined by r j ,s,t j . O 4 j = w̄ l ( r j I 1 + s j I 2 + t j ) = w̄ l z j (8)Layer 5: This layer consists of non-adoptive nodes that combine all incoming signals fromhe previous layer by a summing function O 5 j = ∑ j w i z j = ∑ j w j z j ∑ j w j (9)The network has been tested according to Adedeji et al. [5] . ANFIS predicted values and sta-istical performance metrics such as determination R 2 , RMSE, MAD and MAPE were comparedo the experimentally determined model outputs. Those measurements were determined as fol-ows: R 2 = 1 −∑ N k =1 [ y k − ̂ y k ] ∑ N k =1 [ y k − y k ] (10)T.O. Babarinde, S.A. Akinlabi and D.M. Madyira et al. / Data in Brief 32 (2020) 106316 9        Root Mean Square Error (RMSE) RMSE = √ ∑ N k =1 [ y k − ̂ y k ] 2 N (11) Mean Absolute Deviation (MAD) MAD = 1 N ∑ N k =1 | y k − ȳ | (12) Mean Absolute Percentage Error (MAPE) MAP E = 1 N ∑ N k =1 ∣∣∣∣y k − ̂ y k y k ∣∣∣∣ × 100% (13) Where the observed value is y k , ˆ yk is predicted value, and ȳ is the observed mean Declaration of Competing Interest None. Acknowledgments The authors acknowledge the University of Johannesburg, South Africa for funding this re-search. Supplementary Materials Supplementary material associated with this article can be found, in the online version, atdoi: 10.1016/j.dib.2020.106316 . References [1] T. O. Babarinde, S. A. Akinlabi, and D. M. Madyira, “Investigation of vapour compression refrigeration system usingR600a with graphene Nano lubricant achived for every mass charge of R600a with graphene natural refrigerantshave been adopted as substitute for HFC refrigerants in VCRS and R600a is one of the,” vol. 063104, p. 63104, 2012. [2] F. M. Ekundayo and T. O. Babarinde, “Performance investigation of TiO2 nano fluid based lubricant on exergy of VCRSworking with a mixture Of R290/R600,” vol. 8, no. 10, pp. 2672–2678, 2019. [3] E. W. Lemmon, M. L. Huber, and M. O. Mclinden, “NIST reference fluid thermodynamic and transport properties —REFPROP.”[4] D. M. Madyira , et al. , Investigation of household refrigerator system with varied capillary tube length investigationof household refrigerator system with varied capillary tube length, J. Phys.: Conf. Ser. 1378 (2019) 042056 . [5] P. A. Adedeji , S. Akinlabi , N. Madushele , O. O. Olatunji , Wind turbine power output very short-term forecast : acomparative study of data clustering techniques in a PSO-ANFIS model, J. Clean. Prod. 254 (2020) 1–16 . 

Dataset of experimental and adaptive neuro-fuzzy inference system (ANFIS) model prediction of R600a/MWCNT nanolubricant in a vapour compression system

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Dataset of experimental and adaptive neuro-fuzzy inference system (ANFIS) model prediction of R600a/MWCNT nanolubricant in a vapour compression system

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