IJFS#619_bozza Ital. J. Food Sci., vol 29, 2017 - 434 PAPER GREY RELATION ANALYSIS OF SOLAR DRYING PROCESS PARAMETER ON COPRA G. PADMANABAN*1, P.K. PALANI2 and V.M.M. THILAK1 1 Department of Mechanical Engineering, Rathinam Technical Campus, Coimbatore, Tamilnadu, India 2 Department of Mechanical Engineering, Govt. College of Technology, Coimbatore, Tamilnadu, India *Corresponding author. agpn1977@gmail.com ABSTRACT The methodology for the optimization of the drying parameters on solar drying of copra was investigated and studied in this paper. This paper investigates the influence of the process parameters like initial mass, inclination angle and time period on the output parameters such as weight reduction rate and moisture content. Based on the analysis, optimal levels of parameters were determined and the same was validated through the confirmation test. The confirmation results reveal that, there is considerable improvement in the weight reduction rate, moisture content and grey relational grade and they improved by 37.36%, 32.28% and 32.94 % respectively. It is observed that the drying performance can be effectively improved with respect to the initial parametric setting. Keywords: Weight Reduction rate (WRR), moisture content, Taguchi, grey relational analysis & design of experiment Ital. J. Food Sci., vol 29, 2017 - 435 1. INTRODUCTION The use of solar dryers in the drying of agricultural products can significantly reduce or eliminate product wastage, food poisoning and so on, thereby, enhancing productivity of the farmers in obtaining higher revenue. Drying using solar radiation, that is, drying under direct sunlight, is one of the oldest techniques used by man to preserve agriculture based food and non-food products (CHANDRAKUMAR and JIWANLAL, 2013). This form of energy is free, renewable and abundant in any part of the world, especially for countries situated in the tropics. However, in order to maximize its advantage and optimize the efficiency of drying using solar radiation, appropriate measures in terms of technology need to be taken in order to make this technique a sustainable one. Such technology is known as solar drying and it is fast becoming a popular option to replace mechanical thermal dryers, owing to the high cost of fossil fuels which is growing in demand, but dwindling in supply. SRINIVASAN and BALUSAMY (2015) have studied the performance of forced convection solar dryer, integrated with heat storage materials for processing copra. PARDHI and BHAGORIA (2013) have carried out preliminary investigations and under controlled condition of drying experiments constructed a mixed-mode solar dryer with forced convection, using smooth and rough plate solar collector. DHARMALINGAM et al. (2015) have used L27 Orthogonal array to determine the Signal-to-noise ratio (S/N ratio) and Analysis of variance (ANOVA) was conducted. Based on the experiments, they concluded that pulse on-time and electrolyte concentration are the most significant parameters for Material Removal Rate (MRR). Gap current and electrolyte concentration which are the influencing parameters for lesser overcut were experimentally investigated. DHARMALINGAM et al. (2014a) have found that the influence of the process parameters is through the response surface methodology and grey relational analysis. DHANUSHKODI et al. (2014) reported that a directly forced convection solar drier, integrated with recirculation of air has been developed and its performance is tested for drying grapes under the meteorological conditions of Coimbatore, India. The specific moisture extraction rate was estimated to be 0.87 kg/kWh3. DHARMALINGAM et al. (2015) also stated that ANOVA was used for identifying the significant parameters affecting the responses. 2. MATERIALS AND METHODS 2.1 Description of the solar dryer The developed solar dryer (Fig. 2.1) consists of a glass sheet-covered flat plate solar collector, used to simplify construction and reduce costs. The solar collector is connected directly to the drying chamber without any additional air ducts. The top surface of the insulator in the collector is painted black to absorb solar radiation. The collector is covered with a transparent Ultra Violet (U.V)-stabilized glass sheet that is fixed to the collector frame using reinforced plastic clamps in the drying chamber, and a wire mesh is placed on top of the insulators. A glass is placed on top of the black V groove sheet, on which the product to be dried are spread. This arrangement allows drying air to flow around the whole surface of the product being dried. One side of this sheet is fixed to the drying chamber frame and the other side is fixed to a metal tube, allowing the sheet to be rolled up and down for loading and unloading the dryer. This fixing method is designed to facilitate the replacement of the sheets. In general, the transparent sheet can be used for 1-2 years and the air could last for 3-5 years. The glass is installed at the back of the collector Ital. J. Food Sci., vol 29, 2017 - 436 to suck ambient air into the collector. The blowers are intentionally installed below to constantly reduce its temperature, thus, maintaining its efficiency. Both the collector and the drying chamber are installed on mild steel block substructures. All parts of the dryer, including the back insulator and metal frames were designed using a modular concept, which facilitates the transport and installation of the dryer. This solar dryer uses solar energy both in the thermal form of the drying process and in the electrical form for driving the blower, by means of the solar collector and solar module, respectively. Therefore, the dryer could be used in rural areas where there is no supply of electricity. The dryer is a passive system in the sense that it has no moving parts. The sun rays entering through the collector glazing energizes it. The absorption of the rays is enhanced by the inside surface of the collector painted black and the absorbed energy heats the air inside the collector. The greenhouse effect achieved within the collector drives the air current through the drying chamber. If the vents are open, the hot air rises and escapes through the upper vent in the drying chamber, while cooler air at ambient temperature enters through the lower vent in the collector. The Fig. 2.2 shows the copra drying process in the solar dryer. Figure 2.1. Arrangement of solar dryer. Figure 2.2. Copra in solar dryer. Ital. J. Food Sci., vol 29, 2017 - 437 2.2. Optimization Methodology The optimization of process parameters is the key step in the Taguchi method (DHARMALINGAM et al. 2014b). Twenty seven experimental runs (L27) based on the Orthogonal Array (OA) of Taguchi methods have been carried out (DHARMALINGAM et al. 2014c). The multi-response optimization (Grey Relational Analysis) of the process parameters has been performed for drying, using weight reduction rate and moisture content. The drying time, weight reduction rate and moisture content are noted twice for every trial. 3. RESULTS AND CONCLUSIONS 3.1. Major results and inferences The assignment of factors with their levels identified inthis investigation are given in Table 3.1. Table 3.1. Drying process parameters and their corresponding levels. Symbol Factors Level 1 Level 2 Level 3 A Initial mass Mi (g) 1000 750 500 B Inclination Angle(°) 30 45 60 C Time period (Hr) 11AM -12 Noon 1-2 PM 3-4 PM Based on the Taguchi’s L27 OA, drying experiments were conducted on solar dryer for copra. The experimental results were gathered for each trial. The S/N ratios were calculated for all the responses since the objective of this work was to maximize the weight reduction rate and the minimization of moisture content. Therefore, for Weight Reduction Rate (WRR), the larger-is-better type was considered and for moisture content, smaller-is- better type was considered for the analysis. The S/N ratio was computed for each of the twenty-seven trial conditions for the weight reduction rate and moisture content and is shown in Table 3.2 below. The weight reduction rate (%) in the Table 3.2 was calculated by measuring the weight of Copra after drying between different time intervals. The optimal setting levels for each factor resulting from the S/N ratios are shown in Table 3.3. Ital. J. Food Sci., vol 29, 2017 - 438 Table 3.2. Experimental results for L27 OA of copra. Trial No. Initial Mass (Mi) g Inclination Angle (º) Time period Weight reduction rate (%) Moisture content S/N Ratio for WRR S/N ratio for Moisture Content Grey Relational Weight reduction rate (%) Grey Relational Moisture content Grey Grade 1 1000 30 11-12 23.25 69.38 27.328 -36.825 0.5052 0.3358 0.4205 2 1000 30 1-2 36.37 42.18 31.215 -32.502 0.8871 0.4159 0.6515 3 1000 30 3-4 15.12 21.08 23.591 -26.477 0.3940 0.6233 0.5086 4 1000 45 11-12 27.12 33.09 28.666 -30.394 0.5878 0.4707 0.5293 5 1000 45 1-2 34.32 45.60 30.711 -33.179 0.8144 0.4010 0.6077 6 1000 45 3-4 14.46 22.36 23.203 -26.989 0.3831 0.5980 0.4905 7 1000 60 11-12 26.32 35.12 28.406 -30.911 0.6160 0.4560 0.5360 8 1000 60 1-2 33.35 44.06 30.462 -32.881 0.7827 0.4074 0.5951 9 1000 60 3-4 12.21 24.06 21.734 -27.626 0.3468 0.5692 0.4580 10 750 30 11-12 25.32 37.06 28.069 -31.378 0.5479 0.4435 0.4957 11 750 30 1-2 37.35 51.56 31.446 -34.246 0.9249 0.3794 0.6522 12 750 30 3-4 13.35 14.86 22.510 -23.440 0.3333 0.8325 0.5829 13 750 45 11-12 24.35 35.06 27.730 -30.896 0.5704 0.4564 0.5134 14 750 45 1-2 37.31 50.46 31.437 -34.059 0.9233 0.3830 0.6532 15 750 45 3-4 13.35 18.48 22.510 -25.334 0.3650 0.6884 0.5267 16 750 60 11-12 28.52 32.18 29.103 -30.152 0.6716 0.4780 0.5748 17 750 60 1-2 35.51 70.72 31.007 -36.991 0.8556 0.3333 0.5945 18 750 60 3-4 14.14 12.48 23.009 -21.924 0.3457 1.0000 0.6729 19 500 30 11-12 29.35 35.42 29.352 -30.985 0.6414 0.4540 0.5477 20 500 30 1-2 38.36 46.98 31.678 -33.438 0.9663 0.3955 0.6809 21 500 30 3-4 15.12 15.90 23.591 -24.028 0.3940 0.7817 0.5879 22 500 45 11-12 28.20 28.08 29.005 -28.968 0.6631 0.5168 0.5900 23 500 45 1-2 38.35 47.42 31.675 -33.519 0.9659 0.3938 0.6798 24 500 45 3-4 16.98 21.90 24.599 -26.809 0.4258 0.6066 0.5162 25 500 60 11-12 26.12 38.48 28.339 -31.705 0.6112 0.4351 0.5232 26 500 60 1-2 39.14 47.00 31.852 -33.442 1.0000 0.3954 0.6977 27 500 60 3-4 13.35 23.20 22.510 -27.310 0.3650 0.5831 0.4741 Footnote: The values showed in bold are optimized values. Ital. J. Food Sci., vol 29, 2017 - 439 Fig. 3.1 shows the residual plots for grey grade. Table 3.4 shows the ANOVA table, which indicates the significance of process parameters on the Weight Reduction Rate and Moisture Content. In the Table 3.4, the F-test is a matter of including the correct variances in the ratio. The F-statistic is this ratio of variation between sample means to the variation within the samples. Table 3.3. Response table for the grey relational grade. Table 3.4. ANOVA for grey relation grade. Factors Design of Factor Sum of squares Mean square=sumof squares /2 F value F 0.05 %of contribution=sum of squares/total sum of squares Initial Mass 2 0.002141 0.00107 13.55 0 1.56 significant Inclination Angle 2 0.015905 0.007952 100.69 0 11.62 significant Time Period 2 0.117244 0.056822 742.26 0 85.66 significant Error 20 0.00158 0.000079 1.15 Total 26 0.13687 100% S = 0.00888699; R-Sq = 98.85%; R-Sq (adj) = 98.50%. Footnote: The values showed in bold are optimized values. Figure 3.1. Residual plots for grey grade. Process parameters Level 1 Level 2 Level 3 Initial mass Mi (g) 0.5330 0.5851 0.5886 Inclination Angle(0c) 0.5698 0.5674 0.5696 Time period 0.5256 0.6458 0.5353 *Optimum Levels Mean grey grade = 0.5466 Ital. J. Food Sci., vol 29, 2017 - 440 3.2. Analysis for weight reduction rate and Moisture Content of copra The optimal values for the maximum weight reduction rate has an initial mass of 500 g, inclination angle is 300 and time period is between 1-2 AN (after noon). The interaction has been plotted to pictorially depict the interaction process parameters on weight reduction rate and moisture content. In the full interaction plot, two panels per pair of process parameters have been shown in Figs. 3.2 and 3.3. Figure 3.2. Interaction plot for WRR. Figure 3.3. Interaction plot for MC. Ital. J. Food Sci., vol 29, 2017 - 441 3.3 Confirmation test After identifying the most influential parameters, the final phase is to verify the predicted results (WRR and Moisture content) by conducting the confirmation test. The A3B1C2 through the GRA is an optimal parameter combination of the drying process. Therefore, the combination A3B1C2 was seen as a confirmatory test. The predicted Grey relational grade can be calculated using the optimum parameters as Where, αo = average grey relational grade of the optimal level, αm = overallmean grey relational grade. α predicted= 0.57045 +(0.58534 - 0.57045)+(0.645329-0.57045)+(0.58534 - 0.57045) = 0.6722 Table 3.4 shows the confirmatory test for weight reduction and moisture content of copra. Based on the confirmatory test, the weight reduction and moisture content were improved by 37.36 % and 32.28 % respectively, with respect to the initial parametric setting. The optimized parameter combination suggested for the higher WRR and Lower Moisture content has an intial mass of 500 g, the inclination angle of 30o and time period of 1-2 hours. The percentage contribution of factors on grey relational grade within a period of time is 86% and inclination angle is 12%. The error and initial mass percentage of contribution of factors are 1% respectively. Table 3.4. Confirmatory test table. The following data were observed from the present investigationwhich focuses on optimization and analysis of the solar drying process parameters of copra using Grey Relational Analysis and ANOVA. a. Based on the confirmatory test, improvement in Weight Reduction Rateand Moisture content is 37.36 % and 32.28 % respectively. b. The grey relational grade is improved by 32.94 %. c. The parameter combination suggested for the higher WRR and lesser Moisture Content have an Initial mass of 500 g, inclination angle of 300 and time period 1-2 AN. d. The results of ANOVA, the time period and Inclinational angle are the significant drying parameters which affect the weight reduction rate and moisture content. The modified design of the direct air dryer modelled during this research will maximize the efficiency of drying using solar radiation. This modified design can be used to replace Initial levels of drying parameters Optimal combination levels of drying parameters Prediction Experiment Improvement Level A1B1C1 A3B1C2 A3B1C2 WRR (gr) 23.25 38.36 38.36 37.36% Moisture content 69.38 46.98 46.98 32.28% Grey grade 0.5123 0.6722 0.6811 32.94% Ital. J. Food Sci., vol 29, 2017 - 442 mechanicaland thermal dryers which are dependent on high cost fuel. This modified design is brought into existence by keeping the copra as the food component. The researches on carrying out direct drying of other food components using this modified design can be carried out as future research work. REFERENCES Chandrakumar B.P and Jiwanlal L.B. 2013. 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