Format And Type Fonts CCHHEEMMIICCAALL EENNGGIINNEEEERRIINNGG TTRRAANNSSAACCTTIIOONNSS VOL. 29, 2012 A publication of The Italian Association of Chemical Engineering Online at: www.aidic.it/cet Guest Editors: Petar Sabev Varbanov, Hon Loong Lam, Jiří Jaromír Klemeš Copyright © 2012, AIDIC Servizi S.r.l., ISBN 978-88-95608-20-4; ISSN 1974-9791 DOI: 10.3303/CET1229229 Please cite this article as: Contreras Valenzuela M. R., Rodriguez Martinez A. and Romero-Domínguez J., (2012), Experimental analysis of heat transformer using the six sigma methodology, Chemical Engineering Transactions, 29, 1369-1374 1369 Experimental Analysis of Heat Transformer Using the Six Sigma Methodology Martha R. Contreras-Valenzuela*a, Antonio Rodriguez-Martínezb, Rosenberg Javier Romero-Domínguezb a Facultad de Ciencias Químicas e Ingeniería. b Centro de Investigación en Ingeniería y Ciencias Aplicadas. Universidad Autónoma del Estado de Morelos Av. Universidad 1001, Col. Chamilpa, C.P. 62209. Cuernavaca Morelos México marthacv@uaem.mx Six Sigma Methodology (SSM) for developing new process or product consists of five steps: a) define, b) measure, c) analyze, d) design and e) verify (DMADV). SSM allow developing or improving processes that measure how many defects or failures any process has, in order to find ways to systematically eliminate them. This paper focuses on implementing DMADV to compare the performance of two designs of an experimental heat transformer. We have analyzed the temperature behaviour during the heat transformer operation. The results have been used as information to apply the SSM as follow: a) define; using statistical process control (SPC) to establish whether the temperature is “in control or out of control”. The objective is found out the opportunity areas. b) measure; we define initially a tolerance error of ±0.5 % over temperature as operation process defect. Then, the process behaviour was monitored in two stages: 1) from the first performance, data was recollected to be analyzed and, 2) another performance was monitored after implemented improvements (the objective is to compare the results). c) analyze; the result was examined in order to calculate the process capability (Cp) and the process capability index (Cpk). d) design; in this step we have used average control charts, as tools for evaluating the first design to respect the second. Finally, e) verify; the objective of the verify step is found out if the second design of the heat transformer is better than the first one. The results show that the second design of the heat transformer is better than the first design, because it has an enhanced process operation. 1. Introduction The Six Sigma Methodology (SSM) is a solving-problems technology that involves human resources, variables data and statistical measures to identify and eliminate few vital factors which produce defective products. Therefore, it generates customer’s satisfaction and the increasing of company profits consequently (Brue, 2003). Statistically, Six Sigma represents a process behaviour in which the distance between its mean and the nearest specification limit is at least six times standard deviation of the process. The objective is to centre the process on the target and reduce process variation (Markarian, 2004). The work in this paper focuses on implementing the SSM steps (define, measure, analyze, design and verify, DMADV) to compare the performance of two designs of an experimental heat transformer. The heat transformer is operating in the Applied Thermal Engineering Laboratory, CIICAp – UAEM. We 1370 have used Six Sigma to create knowledge about the process. In fact, this research is related to design process and to learn about the process performance. A heat transformer is an absorption heat pump system (Romero and Rodríguez, 2008). It is composed by four main equipments: absorber, generator, condenser and evaporator. Its objective is to increase the heat received at low temperature in to heat at high temperature that can be used as energy in other process (see Figure 1). Condenser Generator AbsorberEvaporator Pump Expansion valve 2 1 Pump 3 5 7 8 4 6 Figure 1: Block diagram of the heat transformer From the first and second design, data was recollected to be analyzed using statistical process control. The objective was to compare the result. The stream temperature was chose as variable to be monitored. 2. Methodology 2.1 Define The define step for this project, consisted of selecting all the parameters and necessaries considerations to compare the first design operation behaviour respect to the second one. We defined the customer expectations and specifications. 2.2 Measure To collect data and monitor the status of the temperature for each stream in two stages, one for the current design of the heat transformer and two when the design changes have been implemented. 2.3 Analyze The data was examined in order to calculate the process capability (Cp). Using the sigma level proposed by Richard Levin and David S. Rubin (Levin et al. 2004) showed in Table 1. We established the Sigma level for each stream. In the case of the process capability index (Cpk) we made a different analysis. First we calculated the percentage of data out of specifications. Then we compared the results against the percentage showed in Table 1. The objective was to establish if the heat transformer is capability or incapability to reach the client specifications. Table 1: Sigma level. Minimum values for the process capability index for different ability level and maximum percentage of product out of specifications. Ability level Minimum values for the process capability Cp Maximum percentage of data out of specifications (used to evaluate Cpk) ± 3σ 1.00 0.26 % ± 4σ 1.33 0.0064 % ± 5σ 1.66 0.00006 % ± 6σ 2.00 0.00001 % (less than) 1371 Calculation formulae: 6 LSLUSL Cp   (1)   3   USL Cpk o   3 LSL Cpk   (2) Where: USL is the Upper Specification Limit, LSL is the Lower Specification Limit, µ is the mean of the process, σ is the standard data deviation. 2.4 Design In this step we have used average control charts, as tools for evaluating the first design to respect the second one. 2.5 Verify The objective of the verify step is found out if the second design of the heat transformer is better than the first one. 3. Results and discussion 3.1 Define In order to implement the methodology, we have made the following considerations: a) The temperature was defined as critical variable of the process. b) We chose only 8 streams (see Figure 1) to be monitored, from 21 that made up the complete system. c) The monitoring of the data was carried out when the system reached the equilibrium state. d) The operation parameters defined by the designer are showed in table 2, considering a tolerance of ±0.5 °C. Table 2: Operation parameters defined by the designer as requirement of the client. Stream identification First design Temperature °C Second design Temperature °C 1 Steam output from evaporator 84.91 83.91 2 Heating water input to evaporator 93.21 92.79 3 Solution output from generator 83.05 81.59 4 Heating water input to generator 93.46 93.07 5 Solution input to absorber 89.75 90.12 6 Solution output from absorber 96.22 95.67 7 Steam input to condenser 83.05 81.59 8 Condensed vapour output from condenser 28.12 31.23 3.2 Measure For every test, a total of 600 data was recollected for all streams using sensors on-line, with three hours of duration each one. We carried out three tests for the first design and four tests for the second design. The results obtained after data processing are showed in Table 3. 1372 Table 3: Calculation results for each stream. First design Results Stream 1 Stream 2 Stream 3 Stream 4 Stream 5 Stream 6 Stream 7 Stream 8 72.13 92.92 81.04 93.19 83.57 83.98 42.77 28.57 1.97 0.11 0.68 0.12 0.76 0.98 0.45 0.08 Σ 0.53 0.03 0.18 0.03 0.20 0.26 0.12 0.02 USL-LSL 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 6σ 3.16 0.18 1.09 0.19 1.22 1.57 0.72 0.13 Cp (6σ) 0.32 5.66 0.92 5.19 0.82 0.64 1.40 7.78 Cp (12σ) 0.16 2.83 0.46 2.59 0.41 0.32 0.70 3.89 Cpk (6σ) 1.00 0.00 1.00 0.00 1.00 1.00 1.00 0.01 Second design Results Stream 1 Stream 2 Stream 3 Stream 4 Stream 5 Stream 6 Stream 7 Stream 8 85.86 92.32 82.69 92.66 88.16 93.24 50.15 31.31 0.68 0.20 0.21 0.25 0.88 1.25 0.12 0.09 Σ 0.18 0.05 0.06 0.07 0.24 0.33 0.03 0.02 USL-LSL 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 6σ 1.09 0.32 0.34 0.40 1.41 2.01 0.19 0.14 Cp (6σ) 0.92 3.11 2.96 2.49 0.71 0.50 5.19 6.92 Cp (12σ) 0.46 1.56 1.48 1.25 0.35 0.25 2.59 3.46 Cpk (6σ) 1.00 0.29 1.00 0.09 1.00 1.00 1.00 0.00 3.3 Analysis The objective of this step was to establish whether the process is capable or not to reach the client requirements. Thus, we calculate the percentage of data out of specifications using the areas under the normal curve. As we can observe, for the first design, the calculation results of Cpk for each stream indicate us that the streams 1, 3, 5, 6 and 7 were incapable to achieve the target of design. In the case of the second design the streams 1, 2, 3, 5, 6, 7 did not meet the client requirements too (see Table 4). Table 4: Analysis results First design Results Stream 1 Stream 2 Stream 3 Stream 4 Stream 5 Stream 6 Stream 7 Stream 8 Cp (6σ) 0.32 5.66 0.92 5.19 0.82 0.64 1.40 7.78 Sigma level sigma 2 sigma 6 sigma 2 sigma 6 sigma 2 sigma 2 sigma 4 sigma 6 Cpk (6σ) 1.00 0.00 1.00 0.00 1.00 1.00 1.00 0.01 Second design Results Stream 1 Stream 2 Stream 3 Stream 4 Stream 5 Stream 6 Stream 7 Stream 8 Cp (6σ) 0.92 3.11 2.96 2.49 0.71 0.50 5.19 6.92 Sigma level 2 sigma 6 sigma 6 sigma 6 sigma 2 sigma 2 sigma 6 sigma 6 sigma Cpk (6σ) 1.00 0.29 1.00 0.09 1.00 1.00 1.00 0.00 As we can observe, the sigma level for the second design is better than the firs design, because the level was improve in streams 3 and 7. 3.4 Design With the data recollected in the measurement step we built control charts for each stream in order to compare the operation behaviour for the two heat transformer designs. The Figure 2 shows the comparison of four streams. As we can observe, the control charts shows a better operation behaviour 1373 a) b) LCL 92.89 92.92 UCL 92.94 91.86 91.96 92.06 92.16 92.26 92.36 92.46 92.56 92.66 92.76 92.86 92.96 93.06 93.16 93.26 93.36 93.46 93.56 0 5 10 15 20 25 30 35 Te m p e ra tu re ° C Data subgroups Heating water input to evaporator LCL 92.28 92.32 UCL 92.36 91.60 91.80 92.00 92.20 92.40 92.60 92.80 93.00 93.20 0 5 10 15 20 25 30 35 Te m p e ra tu re ° C Data subgroups Heating water input to evaporator LCL 71.77 72.13 ULC 72.49 56.50 59.50 62.50 65.50 68.50 71.50 74.50 77.50 80.50 83.50 0 5 10 15 20 25 30 35 Te m p e ra tu re ° C Data subgroups Steam output from evaporator LCL 85.74 85.86 UCL 85.99 84.20 84.70 85.20 85.70 86.20 86.70 87.20 0 5 10 15 20 25 30 35 Te m p e ra tu re ° C Data subgroups Steam output from evaporator LCL 83.43 83.57 UCL 83.71 76.75 77.75 78.75 79.75 80.75 81.75 82.75 83.75 84.75 85.75 86.75 87.75 88.75 89.75 0 5 10 15 20 25 30 35 Te m p e ra tu re ° C Data subgroups Solution input to absorber UCL 87.99 88.16 UCL 88.32 84.33 84.83 85.33 85.83 86.33 86.83 87.33 87.83 88.33 88.83 0 5 10 15 20 25 30 35 Solution Input to absorber Te m p e ra tu re ° C Data subgroup LCL 28.56 28.57 UCL 28.59 28.00 28.20 28.40 28.60 28.80 29.00 29.20 29.40 0 5 10 15 20 25 30 35 Te m p e ra tu re ° C Data subgroups Condensed vapor outlet from condenser LCL 31.29 31.31 UCL 31.33 30.56 30.76 30.96 31.16 31.36 31.56 31.76 31.96 0 5 10 15 20 25 30 35 Te m p e ra tu re ° C Data subgroups Condensed vapor outlet from condenser Figure 1: Heat transformer operation. a) Control charts first design. b) Control charts second design 1374 for the second design as follow: (a) The data variability in the second design is smaller than the data variability in the first design. For example the data dispersion in the chart called steam output from evaporator is 27°C for the first design whereas in the second design is only 3°C. (b) There are more points inside of control limits in the second design. For example in the chart of heating water input to the evaporator, for the first design only one point is inside of control limits while for the second design there are five point inside of limits. (c) Nevertheless, the control charts shows the temperature out of control in the second design, it behaviour is better than the first design, for all the streams. Subsequently, we assume that the second design is better than the first design, because it has an enhanced process operation. 4. Conclusions In this work we have implemented the six sigma methodology to compare the performance of two designs of an experimental heat transformer. Using the temperature behaviour during the heat transformer operation we measured the process and the results have been used to calculate the process capability (Cp) and the process capability index (Cpk). We have used average control charts, as tools for evaluating the first design to respect the second one. Finally, the objective is found out if the second design of the heat transformer is better than the first one. Finally, the application of Six Sigma in chemical process to obtain knowledge is a good way to understand the process behaviour and to found possible alternatives of design. References Brue G., 2003. Six Sigma para Directivos. McGraw-Hill/Interamericana de España, S.A.U. Madrid. pp 11. Levin R., Rubin D., Balderas M., Del Valle J., Gómez R. 2004. Statistics for Management. Seven edition. Published by Pearson Prentice Hall. Markirian J., 2004. Six Sigma: quality processing through statistical analysis. Plastics Aditives & Computing. 28-31. Romero-Domínguez, R.J. and Rodríguez-Martínez, A., 2008, Optimal water purification using low grade waste heat in an absorption heat transformer, Desalination, 220, 506–513.