Optimization of Different Parameter of Cold Storage for Energy Conservation

Transcription

1 Vol., Issue.3, May-June pp--5 ISSN: 4-45 Optimization Different Parameter Cold Storage for Ener Conservation Amit M Patel, Pr. R I Patel *(ME-Student, Department Mechanical Engineering, GEC Dahod, India) **(Asst. Pror, Department Mechanical Engineering, GEC Dahod, India) ABSTRACT As the demand for refrigeration and Air conditioning has been increased during the last decade, the cold storage system can be used to the economic advantage over conventional plants. Ener conservation is required in the cold storage system so The Design periment is used to Optimization different cold storage on the bases performance experiments. In This periment, three levels, wall and Compror are kept as the control, The Insulating wall material was taken as PU, and different ener were taken as a result in the experiment, The Objective this paper is Optimize different in the cold storage. The various tools DOE are used for analyze the final results the experiment with the help Graphs. The analysis is being done with the help Minitab-5 stware. The analysis variance ANOVA is also performed as identified the statistical significance. The result the experiments are the optimum value insulating thickn, ener consumption rate with the help ANOVA, After the using Taguchi method determine the feasibility improving cooling capacity cold storage, establish the mathematical models relating the cold storage performance & control by regrion analysis and obtained set optimal cold storage for better performance. Key words: Insulation, area wall, compror and ener, Optimization, Cold storage, I. INTRODUCTION Cold Storage is a special kind room, the temperature, which is kept very low with the help machines and precision instruments. India is having a unique geographical position and a wide range soil thus producing variety fruits and vegetables like apples, grapes, oranges, potatoes, chilies, ginger, etc. Marine products are also being produced in large quantities due to large coastal areas. The present production level fruits and vegetables is more than million MT and keeping in view the growth rate population and demand, the production risible commodities is increasing every year. The cold storage facilities are the prime infrastructural component for such perishable commodities. Besides the role stabilizing market prices and evenly distributing both on demand basis and time basis, the cold storage industry renders other advantages and benefits to both the farmers and the consumers. The farmers get opportunity producing cash crops to get remunerative prices. The consumers get the supply perishable commodities with lower fluctuation prices. Commercially apples, potatoes, oranges are stored on large scale in the cold storages. Other important costly raw materials like dry fruits, chemicals, ences and proced foods like fruit juice/pulp, concentrate dairy products, frozen meat, fish and eggs are being stored in cold storages to regulate marketing channels these products. ig. Cold Storage II. LITERATURE REVIEW M.S. Soeylemez et al (7)[] has suggested A thermo economic optimization analysis is presented yielding a simple algebraic formula for estimating optimum insulation thickn for refrigeration applications. The effects design on the optimum insulation thickn are investigated for three test cities using an interactive computer code written in ortran 77. The equivalent full load hours method is used to estimate the ener requirements. Merrick Burden et al (4)[] has suggested rozen storage is an integral part effective food distribution. In general, both food quality retention and storage costs will increase as storage temperatures decrease. Mashud Ahmed et al ()[3] has suggested A general estimate shows that 8% communities across the United States receive their goods exclusively by transport trucks, which a significant number are climate-controlled because they carry perishable goods, pharmaceutical items and many other temperaturesensitive commodities. N.Yusf et al()[4] has suggested that study presents a procedure for selecting optimization variables in a Refrigerated Gas Plant(RGP) using Taguchi method with L7(3) orthogonal arrays. A dynamic RGP model developed under HYSYS environment is utilized as a test bed. This model comprises 7 variables and regulatory control loops. However only variables or factors with three level each are studied to determine their relative significance in maximizing RGP prit. Page

2 Vol., Issue.3, May-June pp--5 ISSN: 4-45 III. EXPERIMENTAL SET UP The experimental set up at Super Refrigeration, Near Shyamal Cross Road,Ahmedabad on prepared model cold storage, The compror, condenser unit, evaporation unit, expansion valve were used and special experimental cold storage box was attached with refrigeration system, the device, Watt meter and thermo couple are to attached with this cooling unit here the design experiments is based on Taguchi Methodolo A 3-actor and 3-level,L Orthogonal array design is used to conduct the experiments. The levels are.5,.75 and. meter. wall are.,.5 and. m and Compror.,.5 nd.7, the inside Temperature is kept constant at ⁰ C through out the experimentation. Here total L = experiments will conducted. perimental Constant Inside temperature:- C Thermal conductivity :-.5 W/m k Material:- PU Parameter and Range Selection To select the and its levels for experimentations, several exploratory experiments were conducted to determine important control factors. Out several available controllable input on the cold storage, following were selected with maximum feasible range. Sy mb ol Control actor Uni t 3 A Mt B wall Mt..5. C..5.7 Table Parameter and Range selection IV. INDENTATIONS AND EQUATIONS *Calculation Heat Transfer through wall, Ceiling & loor Q = UA To Ti Where, A = Wall, m U = Over all Heat Transfer Co-efficient KJ/s m K Ti = Internal Temperature ⁰C To = ternal Temperature ⁰C U = H + X K + X K + X3 K3 + H H= Outside heat transfer Co-efficient H= Inside heat transfer Co-efficient Ki= Thermal conductivity materials Xi= Materials *Calculation Total Refrigeration required Total refrigeration required Total eat removed = 3.5 Ton refrigeration = 3.5 KJ/s The total amount electrical ener consumption a typical refrigeration system may be determined by the equivalent full loads hours ener estimation as follows[] E = Q t COP E= Annual total ener consumption refrigeration system (kw/h) t =Equivalant full load hours operation refrigeration system (hrs) COP=Co-efficient performance refrigeration plant V igures and Tables Selection Orthogonal Array Knowing the number and the number levels, the proper orthogonal array can be selected. The number proc in our experimental runs is three. i.e. wall with 3 levels, wall with 3 levels and Compror with 3 levels. The numbers levels in all control factors are equal. Therefore 3- level orthogonal is required in our experimental plan. The L orthogonal array as shown in table for All factor 3- levels design. Therefore total x 3 = 7 experimental runs are performed for the observations Observation Tables p.. Control Parameter Thick n wall Ener Watt Heat Transf er rate W/m K Table: Observation table In this study most important output performances in Cold Storage such as Ener consumption(e), Heat Transfer Rate(Q) are considered for optimizing Cold storage parameter. The Ener (E) value (in Watt) was obtained by using watt meter, The Heat Transfer Rate was measured by using Thermo couple The Heat Transfer Rate (HTR) is calculated as, Q=UA T () Page

3 Mean SN ratios Mean SN ratios International Journal Modern Engineering Research (IJMER) Vol., Issue.3, May-June pp--5 ISSN: 4-45 Where U= Thermal Coefficient wall, A= wall, T= Temperature Difference Main Effects Plot for SN ratios on Heat Transfer Rate About Analysis stware MINITAB Minitab is a statistics package used for analysis experimental data. It was developed at the Pennsylvania State University by researchers Barbara. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiner in 7. The goal robust experimentation is to find an optimal combination control factor settings that achieve robustn against (insensitivity to) noise factors. MINITAB calculates response tables and generates main effects and interaction plots for Signal-to-noise ratios (S/N ratios) vs. the control factors Signal-to-noise: Smaller is better..7. wall.5. or the individual response maximization or minimization, chart & gives optimum value each control factor. Chart interprets that A3,B,C3 gives minimum result Ener, Analysis Variance for Ener Source D Seq SS Adj Adj P SS MS wall Compr or Error Total 8 84 Table: Analysis Variance for Ener R-Sq = 7.5% R-Sq(adj) =.8% Analysis Variance for Heat Transfer Rate It provides standard orthogonal array for Taguchi methodolo for experiment design. It also performs regrion analysis to establish relation between two or more variables. It also helps in generating various types tables and graphs. Analysis Data for Ener Consumption Rate Main Effects Plot for SN ratios on Ener.75.. wall.5. Sour ce D Seq SS Adj SS Thic.. kne.. wall Com.8.8 pres Error.3. 3 Total Adj MS P Table: Analysis Variance for Heat Transfer Rate R-Sq = 7.53% R-Sq(adj) =.% Signal-to-noise: Smaller is better.7 3 Page

4 Mean SN ratios International Journal Modern Engineering Research (IJMER) Vol., Issue.3, May-June pp--5 ISSN: 4-45 p Wall Compror SNR_Ener SNR_Heat transfer rate Table: S/N Ratio for Response S/N ratio for response as shown in table can be calculated using above equations. However we have obtained it using MINITAB 5 stware. rmalization S/N Ratio for Response Step : In the grey relational analysis, a data preprocing is first performed in order to normalize the raw data for analysis. rmalization is a transformation performed on a single data input to distribute the data evenly and scale it into an acceptable range for further analysis. In this study, a linear normalization the S/N ratio is performed in the range between zero and unity. S/N ratio for response as shown in table are normalized for further analysis using following equations. p e ss Are a rmaliz ed rmaliz ed Wa SNR SNR ll Ener Heat transfer rate Table 4 rmalization S/N Ratio for Response Are a Coefficient GRC GRC Ener HTR Table Calculating GRC and GRG for Response The higher grey relational grade reveals that the corresponding experimental result is closer to the ideally normalized value. periment 7 has the best multiple performance characteristic among experiments, because it has the highest grey relational grade shown in Table 5.The higher the value the grey relational grade, the closer the corresponding factor combination is, to the optimal. A higher grey relational grade implies better product quality; therefore, on the basis the grey relational grade, the factor effect can be estimated and the optimal level for each controllable factor can also be determined. Main Effect actor on Grey Relation Grade ollowing graph shows the grey relational grade graph. Basically, the larger the grey relational grade, the better is the multiple performance characteristic Main Effects Plot for SN ratios on GRG.75.. wall.5. Calculation GRC and GRG for Response - -8 p Control actor Grey Relation GRG..5 Signal-to-noise: Larger is better.7 4 Page

5 Vol., Issue.3, May-June pp--5 ISSN: 4-45 or the combined responses maximization or minimization, chart gives optimum value each control factor. Chart interprets that A3, B and C3 gives optimum result. The mean the grey relational grade for each level the other Cold storage can be computed in a similar manner. The mean the grey relational grade for each level the Cold storage is summarized and shown in the following Table. Symbol Control actor A B wall C Compror Table Main Effects actors on Grey Relational Grade Mathematical Model The mathematical model for predicting the response in Cold storage can be derived using methods like Regrion analysis. Regrion Equation for Ener The Regrion Equation is Ener(E) = wall - Predictor Coefficient SE T P Coef Constant wall Compror R-Sq =.5% Table Regrion coefficient for Ener In the regrion analysis, the P value factors, and wall are l than.5, therefore these factors are significant. The co-efficient determination (R) indicates the goodn fit for model. The value R is.5% which indicates that model is fit for prediction. Regrion Equation for Heat Transfer Rate The regrion equation is HTR(Q) = wall Predictor Coefficient SE T P Coef Constant wall Compror Table.8 Regrion coefficient for Heat Transfer Rate R-Sq =.% The results analysis indicate that the compror is not much significant in Heat Transfer Rate. Here the value R is.%, which is quiet high; therefore model is suitable for result prediction. Proc Parameter Orthogonal Array Grey Relation Design A3 B C3 A3 B C3 ECR HTR VII. CONCLUTION In the Present Research work there was developed cold storage model at super Refrigeration and also developed Refrigeration system, both are attached with each other for experiment. In this work our main objective is to Optimum insulation thickn, wall, compror capacity cold storage with the help Taguchi method, There was Three factor, wall and compror consider and take Three level each.5,.75,. and..5. and., o.5,.7 in Taguchi Method. we are using L Orthogonal Array Design. In the research work there is using ANOVA for Develop different Graphs S/N Ratio,here concluded that orthogonal array and Grey relation design both are gave same result as best optimum value. Mt, area wall. Mt and compror.7 and also study Regrion analysis to develop Mathematical model for calculating direct optimum value all VII. REERENCES [] M.S.Soylemez, M.Unsal(7) Optimum Insulation for Refrigerant applications, Ener Conservation and Management 4() 3-. [] Merrick Burden, Gilbert Sylvia, Edward Kolbe(4) Optimal storage temperature Design for rozen ood Seafood inventories: Application to Pacific Whiting surimi, IIET, 4 Japan Proceeding [3] Mashud Ahmed, Oliver Meade, Mario A. Medina() Reducing heat transfer across the insulated walls refrigerated truck trailers by the application phase change materials, Ener Conservation and Management 5 () [4] N.Yusf and M.Ramasamy(), Selection RGP Optimization variables using Taguchi Method, Journal Applied Science (4) , ISSN [5] ASHRAE Hand Book undamentals, New York, 8 [] Duffie J A Beckman WA. Solar engineering thermal proces. New York: Wiley,8 [7] Heat and Mass Transfer By Dr. D S Kumar, S.K.Kataria and Sons, New Delhi 5 Page