Experimental and Anova Analysis of Adsorption Cooling System
Jaspalsinh .B Dabhi1, Ajitkumar N. Shukla2, Sukanta Kumar Dash3
1Jaspalsinh .B Dabhi,Research Scholar, School of Technology, PDPU, Gandhinagar ,Gujrat, India.
2Ajitkumar N. Shukla, Assistant Professor, Department of Mechanical Engineering ,Vishwakarma Government Engineering College, Chandkheda, Ahmedabad, Gujrat, India.
3Sukanta Kumar Dash, Assistant Professor, Department of Chemical Engineering School of Technology, PDPU, Gandhinagar, Gujrat, India.
Manuscript received on 09 June 2019 | Revised Manuscript received on 14 June 2019 | Manuscript Published on 08 July 2019 | PP: 424-436 | Volume-8 Issue-8S3 June 2019 | Retrieval Number: H11040688S319/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this work experiments are carried out on single bed intermittent type of Adsorption Cooling System (ACS) comprising of silica gel-water as adsorbate-adsorbent pair. Regular Density (RD) type of silica gel with mesh size of 80-120 is used for performance analysis of ACS. Finally statistical analysis is carried out to predict Coefficient of Performance (COP), desorption Mass (Mdes), Mass of adsorbate (Mads), adsorbed phase concentration ratio at equilibrium (X*), Isosteric heat of adsorption (qsh) and cooling capacity (Qc) for three control variables namely inlet temperature of hot water, flow rate of water, and adsorption cycle time using ANOVA analysis. Taguchi’s design of experiments is conducted to build the array of experiments. Main effect plots and interaction plots are generated by using Analysis of variance (ANOVA) by varying the control variables up to 4 levels for performance analysis of adsorption cooling system. Signal to Noise (S/N) ratio analysis is carried out to predict the most influencing parameters contributing to various responses COP, Mdes, Mads, X*, qsh and Qc. Multiple linear regression equations are developed to build performance prediction model for response variable for all three control variables. Finally confirmation test are carried out to validate the performance prediction model. A detailed analysis report and output file is built using MINITAB-17. It is concluded that the most significant effects are inlet temperature of hot water, flow rate and adsorption cycle respectively. The interaction plot show that the effect of flow rate and adsorption cycle time is most significant and the effect of inlet temperature of hot water is observed on Isosteric heat of adsorption (qsh), and cooling capacity (Qc). Results indicates that the R-sq value significantly influence (at 95% confidence level) by COP followed by Mdes, Mads, X*, qsh and Qc. Based on the analysis of the S/N ratio optimum for combination of Flow rate is 25lpm, Temp of hot water Inlet is 65⁰, and Adsorption Cycle time is 30min.
Keywords: ANOVA, Main effect plots, Interaction plots, Adsorption cooling, Silica-gel.
Scope of the Article: Renewable Energy Technology