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Analysis of effect factors on thermoelectric generator using Taguchi method
Measurement 149 (2020) 106992
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Analysis of effect factors on thermoelectric generator using Taguchi method Hakan Terziog˘lu Vocational School of Technical Sciences, Konya Technical University, Konya 42003, Turkey
a r t i c l e
i n f o
Article history: Received 24 January 2019 Received in revised form 21 August 2019 Accepted 24 August 2019 Available online 28 August 2019 Keywords: Thermoelectric generators Output power and efficiency Taguchi method
a b s t r a c t Due to technological developments in recent years, the need for domestic and industrial electric power is increasing day by day. Alternative energy resources have become more important to reduce production costs by converting waste energy into electricity. In this study, a research was carried out to increase the efficiency and on the factors which were effective in Thermoelectric Generators (TEG) used in the production of electrical energy by using thermal sources from alternative energy sources. In this study, the effects of heat transfer performance of the materials (copper, aluminum and brass) on which thermal water is carried, and the effects of water pressure and velocity on the performance of TEGs were investigated. Taguchi method was used to determine the performance effects in the most accurate way. Taguchi method used three levels with three factors: material (copper, aluminum and brass), engine speed (I, II and III) and water pressure (1-2.5-3.5 bar). In addition, in the Taguchi method, the orthogonal array was used and the optimum operation time was significantly reduced. In this study, TEG1-12706 and TEG1-12710 were performed in 2 different TEGs. 27 experiments were carried out for each TEG under different materials, speed and pressure of water with the experiment set up in this study. When the test results were analyzed by Taguchi method, it was determined that the material was the most important factor in determining the output power and efficiency in the production of electrical energy by using TEG (approx. 89%) and it was seen that the pressure and engine speed had almost no role. Ó 2019 Published by Elsevier Ltd.
1. Introduction The amount of energy consumed per person today is accepted as a measure of development. Intensive efforts are being made to obtain efficient, cheap and clean energy from alternative energy sources all over the world to meet the ever increasing need for electricity energy due to the developing technology. Taking into account the environmental and economic impacts of the global electricity generation systems around the world, the need for alternative energy sources is growing steadily. All energy sources such as coal, oil, natural gas, LPG, wood, biogas, all fossil fuels, and marine wave power are limited. Nuclear energy is a type of energy whose production and recycling require great attention and cost. In the studies conducted to obtain efficient, cheap and clean energy from the alternative energy sources of the world, geothermal energy, solar energy, wind energy, biomass energy, hydrogen energy, tidal energy formed by the sea waves come into prominence as renewable sources. Renewable sources such as solar energy, wind energy, biomass energy, hydrogen energy and tidal energy generated by the sea waves have negative effects on the E-mail address: [email protected] https://doi.org/10.1016/j.measurement.2019.106992 0263-2241/Ó 2019 Published by Elsevier Ltd.
environment compared to fossil resources, they can be used at certain times rather than every moment of the year, their technologies are not fully developed and they are expensive, so they have some limitations regarding continuous energy. Therefore, scientists are looking for electricity generation from alternative energy sources such as geothermal energy. Geothermal energy, which is an alternative energy source, does not have the potential to compete with exhaustible energy sources. However, when appropriate technologies are used, it is a prominent type of energy that is not pollutant-free, renewable, sustainable, domestic and environmentally friendly. Among the developing methods of renewable energy, thermoelectric generators (TEGs) are a promising device in that they can convert waste heat into electricity [1–3]. In recent decades, much efforts have been made to develop the practical applications of TEGs through waste heat recovery reported in many studies. Gao et al. [4] numerically evaluated a TEG system driven by the waste heat of the exhaust gas from a high temperature polymer electrolyte membrane fuel cell stack and obtained an optimal system configuration. Zheng et al. [5] presented a thermoelectric cogeneration system to simultaneously generate electricity and preheat water where the primary heat source of the thermoelectric cogeneration
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was waste heat from boiler exhaust. Xiong et al. [6] numerically established a two-stage thermoelectric energy harvesting system to generate electricity; the two-stage system, consisting of a top stage generator and a bottom stage generator with the same pairs of thermoelectric elements, was driven by the waste heat of blast furnace slag water. In consideration of the high cost of experimental study, some researchers attempted to investigate STEGs through simulation method, which was proved to be a more convenient and economical way [7]. Chen et al. [8] revealed that the numerical model solved by ANSYS is a feasible way to predict the performance of solar TEG. And the performances of thermal-concentrated solar thermoelectric generators (TEGs) at three different geometric types are investigated numerically. However, the study was only carried at constant illumination intensity and temperature. In another study, Chen et al. [3] intended to explore the performance of a TEG system and find the optimum conditions for maximizing the performance of the system using the Taguchi method. In their system, a three-dimensional finite element scheme was employed to analyze the TEG system. To maximize the performance of thermoelectric devices, including TEGs, a variety of optimization methods have been developed. They include the simplified conjugate-gradient method [9,10], teaching-learning-based optimization algorithm [11], genetic algorithm [12], variational method [13], and so forth. In addition to the aforementioned methods, the Taguchi Method can be effectively used to aid in designing experiments and find the optimum conditions by adopting Orthogonal Array, so time and effort can be reduced. Chen et al. [14] developed a three-dimensional model of the actuator using ANSYSTM finite element analysis simulation code, and the model was used to optimize the actuator for robust design. Denneval et al. [15] adopted the Taguchi method to design experiments in association with the analysis of variance methodologies for predicting the photophysical properties (absorption and emission maxima) of a family of pyrimidine chromophores. They concluded that the method was useful to study the influence in various parameters involved in the photophysical properties of a series of dyes and to quickly identify the optimized chromophore. The literature review above reveals that a number of methods can be utilized to maximize the performance of TEG systems. Some challenges will be encountered in experimental design when the number of parameters is large [16], and the investigation will be time-consuming once all the experiments are performed. Moreover, it is difficult to identify the influence of each parameter on physical phenomena and interpret the results. The Taguchi method can provide a systematic approach for simplifying the experimental design. Meanwhile, the Taguchi method has been widely employed to find the optimum conditions of a system. However, to the authors’ knowledge, the Taguchi method has not been utilized in the experiment design and in optimization of a TEG system yet [3]. As a result, this study was carried out to maximize the materialrelated efficiency of thermoelectric generators (TEGs) that produce electricity by utilizing geothermal sources. In this study, the effects of the materials on the power used in the transportation of hot and cold water, which must be produced for the TEGs to generate electrical energy, were investigated depending on o L27 (313) orthogonal experimental design. The Taguchi method is used because it is a powerful tool to understand the sensitivity of factors on physical behavior. Analysis of variance (ANOVA) was used to interpret the experimental results and decide which level of the factor should be used. The significant factors with their respective levels were identified and set to the optimum values. Eventually, the performance of the TEG system was maximized using the Taguchi optimization method.
2. Thermoelectric generator A thermoelectric generator consists of P and N type semiconductors electrically connected in series and thermally connected in parallel to each other between the two ceramic plates for electrical insulation and the provision of heat conduction as shown in Fig. 1 [17–19]. If heat is applied to one of the ceramic surfaces of the TEG and the cold is applied to the other, the temperature difference between the two surfaces is generated to produce a modular Direct Current (DC) as shown in Fig. 2, and thus a thermoelectric power generator is obtained. DC current is generated by the action of electrons in the heat flow along P and N type semiconductors [20]. 3. Taguchi orthogonal design In fact, taking into account the physical test conditions, there are many factors and interactions that affect the amount of power generated by the thermoelectric generator. The experimental study which is conducted to reveal the effects of these factors on the system should be minimized in terms of time and cost. In this study, the Taguchi experimental design was used to reduce the number of trials to be done, reduce the cost and achieve the optimum result in a shorter time. Considering the factors that are found to be effective on the system, the L27 (313) orthogonal array was selected for the experimental design and 27 physical tests were performed accordingly. The experimental data for coded factors are recorded in Table 1. The columns numbered 1, 2, and 5 with bold numbers in Table 1 shows coded 33 designs of the factors to be used for parameter optimization. For each factor, three levels are assigned. The dimensions of each factor captioned A, B and C associated with each level are shown in Table 2. 4. Material and method In this study; a closed hot-cold (thermal) water circulation system made of copper-aluminum and brass materials was designed and electrical energy production was carried out in the tests made with the TEC1-12706 (TEG06) and TEC1-12710 (TEG10) modules in the system. In this way, the effect of material type, the thermal water is passed through, on the power produced from TEGs was investigated. The block diagram of the experimental setup used to determine these effects is shown in Fig. 3. In Fig. 3, the materials and connection patterns expressed in the block diagram are detailed respectively.
Fig. 1. The structure of TEG.
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Fig. 2. Thermoelectric generator. Table 1 Experimental layout using an L27 orthogonal array. Trial No
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Experimental Factor 1
2
3
4
5
6
7
8
9
10
11
12
13
1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 A
1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 B
1 1 1 2 2 2 3 3 3 2 2 2 3 3 3 1 1 1 3 3 3 1 1 1 2 2 2
1 1 1 2 2 2 3 3 3 3 3 3 1 1 1 2 2 2 2 2 2 3 3 3 1 1 1
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 C
1 2 3 1 2 3 1 2 3 2 3 1 2 3 1 2 3 1 3 1 2 3 1 2 3 1 2
1 2 3 1 2 3 1 2 3 3 1 2 3 1 2 3 1 2 2 3 1 2 3 1 2 3 1
1 2 3 2 3 1 3 1 2 1 2 3 2 3 1 3 1 2 1 2 3 2 3 1 3 1 2
1 2 3 2 3 1 3 1 2 2 3 1 3 1 2 1 2 3 3 1 2 1 2 3 2 3 1
1 2 3 2 3 1 3 1 2 3 1 2 1 2 3 2 3 1 2 3 1 3 1 2 1 2 3
1 2 3 3 1 2 2 3 1 1 2 3 3 1 2 2 3 1 1 2 3 3 1 2 2 3 1
1 2 3 3 1 2 2 3 1 2 3 1 1 2 3 3 1 2 3 1 2 2 3 1 1 2 3
1 2 3 3 1 2 2 3 1 3 1 2 2 3 1 1 2 3 2 3 1 1 2 3 3 1 2
Table 2 Factors and their levels. Factor
Material Engine speed Pressure
Symbol
A B C
Unit
– rpm Pa
4.1. Thermoelectric generator (TEG) Module Two different TEGs, TEG06 and TEG10, were used in the system to generate heat energy. The parameters of the TEGs used are given in Table 3. 4.2. Design of the plates that the TEGs are placed on and the placement of TEGs In this study, thermal water was passed through a plate sized 100 100 as shown in Fig. 4. The dimensions of the plates are
Level 1
2
3
Cooper 1.09 1
Brass 1.69 2.5
Aluminum 2.18 3.5
designed in such a way that the piece will be integral and that the fluid flowing within the piece will maximally entwine (transmitted), taking into account the cutting edge standards (£11.5 mm, length 90 mm and half finger connectors). These plates are designed to measure how the heat distribution will give more accurate results on which material, and they are made of 3 different materials like copper, aluminum and brass [21]. Considering the design of the parts to be produced, they were preferred and produced in such a way that the volume of the fluid flowing through the part reaches the maximum rate and they cause the least heat loss [21].
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Fig. 3. Block diagram of experience setup (for aluminum).
Table 3 Features of TEG06 and TEG10. Performance Specifications
TEC1-12706
Hot Side Temperature (°C) Qmax (Watt) DTmax (°C) Imax (Amper) Vmax (Volt) Module Resistance (Ohm)
25 °C 50 66 6.4 14.4 1.98
TEC1-12710 50 °C 57 75 6.4 16.4 2.3
25 °C 85 66 10.5 15.2 1.08
50 °C 96 75 10.5 17.4 1.24
TEG10 and TEG06 are placed on the plates in Fig. 4 as in Fig. 5. In addition, as shown in Fig. 5, a temperature sensor (thermocouple) is placed on the surface of each plate to measure the temperature of each plate. The electrical connection of the TEG’s on each plate with the other plates given in Fig. 5 is schematically shown in Fig. 6 [22]. As shown in Fig. 6, an experimental block consists of 2 plates and 8 TEGs. In the system, this structure is used on copper, aluminum and brass blocks and are shown in Fig. 7.
thermal water entering the plates and the thermal water coming out of the plates are continuously measured by the sensors [22]. In the designed system, two heat sources were utilized by connecting two heat exchangers in series for hot water. This allows the hot water in the system to reach the desired value faster. Because the mains water supplied to the system is affected by the heat exchange of the plates in which the hot and cold water are conveyed due to the thinness of the thermoelectric modules, the heat was kept at 20 °C using the air exchanger to cool the cold water. The water heating and cooling systems used in the test apparatus are shown in Fig. 9. Two separate expansion tanks are used for the cold and hot closed water systems in the system, as shown in Fig. 10. In this way, refilling the water decreasing in the small water leakage that may occur in the system, the pressure was kept at the same value for a long time. In the experiments, the temperature values on the plates and the voltage and current values produced by the thermoelectric modules are measured using measuring instruments. To see that the system works under load, 121 red wires of 10 mm are connected parallel to each other at the output of the TEGs to provide electrical loading of the system.
4.3. The experimental setup 4.4. Running of experiments The experimental setup used to pass the thermal water from the prepared blocks in Fig. 6 is given in Fig. 8. In the experimental setup shown in Fig. 8, two independent closed systems for the circulation of the thermal water are designed. The closed system and the flow direction are shown with red arrows for the hot water and blue arrows for cold water. Hot and cold water navigated through the system by means of two separate WILO brand RSL 15/5–3 ku models of 84 W combi boiler. In these closed systems, the pressure of the system is set at 1, 2.5 and 3.5 bar levels by pushing water from outside to hot and cold sides. Experiments were carried out at different pressure values. In the designed system, the temperatures and pressures of the
Experiments were carried out at 3 different pressures at 3 different engines speeds and the power values produced by the TEGs were observed. In this study, 27 experiments were conducted in accordance with the orthogonal array L27 (313), with each material type and TEG type at 1, 2.5 and 3.5 bar when the circulating dampers are at 1.09, 1.69 and 2.18 ms1. In the experiments conducted to determine the effect of the type of material conveying thermal water on the power obtained from the TEGs, (V) and current (A) values obtained from geothermal energy were measured from the initiation of the system up to reaching the hot water value of 100 °C. The completion time
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Fig. 4. Plates with thermal water (a) Design (b) Picture.
Fig. 5. Image of TEG modules placed on copper-aluminum-brass material.
Fig. 6. The connection of TECs in the plate and with each other.
5
6
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Fig. 7. Image of TEG06 and TEG10 blocks formed by Copper-Aluminum and Brass materials.
Fig. 8. Image of the connection of the designed system (for Aluminum plate).
of each experiment is between 30 and 70 min. In experiments, the limit was determined as 100 °C assuming that about 88% of geothermal energy in Turkey is at low and medium temperatures (30–100 °C). 4.5. Evaluation of data Table 4 shows the experimental parameter design order and the corresponding responses for L27 (313) given in Table 1 in accordance with the study run. The material (A) is shown in the first parameter column, the motor speed is in the second parameter column (B), and the pressure (C) is in the last parameter column. As the reaction values, the power and their S/N ratios take place on the right side of the table for TEG06 and TEG10 separately.
The Taguchi method prefers the signal / noise ratio (S / N) to measure variations in experimental design. It is used to measure the quality characteristic deviates from the desired value. In the S/N ratio, signal refers to the desired real value, whereas noise refers to the undesired factors in measured values [23]. To obtain the optimum process parameters, the larger S/N ratio denotes better performance. However, the signal and noise do not represent the realistic quantities in a thermoelectric generator system. As a result, the optimal combination of operating conditions can be obtained from the profiles of the S/N ratio [3]. There are three basic categories to seek the best results of experiments; they are the ‘‘nominal-the-better”, the ‘‘larger-the-better”, and the ‘‘smaller-th e-better”. The aim of the present study is to maximize output power of a TEG system and the larger-the-better criterion is thus
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Fig. 9. Images of (a) heating (b) Cooling systems in Designed system.
Fig. 10. Image of the expansion tanks in the system.
adopted. The S/N ratio in terms of output power or efficiency is written as:
S=N ¼ 10log10
1 y2
ð1Þ
where y is the measured output value (P) from the TEG system. The optimal level of the process parameters can be found by considering the highest S/N ratio value. When the power obtained from TEG06 by 27 tests is examined, it is determined that the smallest value is 1.038 W for test combination 2 and that the maximum power value is 2.347 W for test combination 13. When the power value obtained from TEG10 is considered, it is determined that the smallest value is 0.912 W for test combination 2 and that the maximum power value is 3.638 W for test combination 21. This pronounced difference between the maximum and the minimum power reveals that the combination of the 3 factors with appropriate levels plays an important role on the performance of the TEG system. 4.6. Analysis of S/N ratios In light of the predicted values of power, a response table was developed shown in Table 4. First, the obtained S/N values were
grouped by the factor level for each design parameter column in the array. For example, the mean S/N ratio of Factor A at Level 1 in terms of output power is equal to (2.09 + 0.327 + 1.114 + 2.32 + 2.21 + 0.704 + 3.164 + 4.132 + 3.952)/9 = 2.224. Consequently, the grouped values were summed and divided by the number of responses, two. The absolute differences between the highest and the lowest average were calculated. These procedures were repeated for each of the other columns and the response table was developed as shown in Table 5. The response graphs were plotted as shown in Fig. 11 reflecting these results. The effect of each factor is the difference between the maximum mean S/N ratio and the minimum one. A higher effect of a factor corresponds to a higher impact of the factor on the output power. Fig. 11(a–b) depicts that Factor A (material) has the greatest effect on the output power in that its effects are 6.827 dB and 10.419 dB, respectively. This behavior is consistent with the observation of Hsiao et al [24]. Other factors contribute almost equally with scatter ranging from 7 to 3. The optimal parameter and their levels for TEG06, as seen from Fig. 11(a), are A1B3C1. From the S/N ratio analysis in Fig. 11(b), the optimal parameters for TEG10 were determined as A2B3C1. This result is determined according to ‘’S/ N’’ response values. The highest S/N values in Fig. 11 are considered and the levels with these values are expressed writing next to the parameters in numbers. The number 2 in the term A2B3C1 is the most ideal level of A factor, the number 3 is the most ideal level of B factor and the number 1 is the most ideal level of C factor. A, B and C factors and the terms mentioned by the numbers are given in Table 2.
4.7. Analysis of variance The purpose of the analysis of variance (ANOVA) is to investigate which design parameters significantly affect the quality characteristic. This is accomplished by separating the total variability of the S/N ratios, which is measured by the sum of the squared deviations from the total mean S/N ratio, into contributions by each of the design parameters and the error. Tables 6 and 7 show ANOVA tables for TEG06 and TEG10, respectively. The contribution of each factor on total variation is indicated in percentage (PC%) in the rightmost column of the charts. In the ANOVA analysis, it is decided based on the P (significance) value that a parameter or interaction is effective on the response or not. Taking the 95% confidence interval into
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8 Table 4 Experimental factor and measured response. Trial No
Factor
Response
S/N Ratio
A
B (rpm)
C (Pa)
TEG06 P (W)
TEG10 P (W)
TEG06 (dB)
TEG10 (dB)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
1.272 1.038 1.137 1.306 1.290 1.084 1.439 1.609 1.576 2.255 2.118 2.308 2.347 2.111 2.044 2.256 2.181 2.148 1.339 1.294 1.255 1.377 1.255 1.216 1.379 1.255 1.358
1.106 0.912 1.023 1.254 1.274 1.273 1.392 1.485 1.600 2.503 1.936 1.674 2.476 2.807 2.180 2.507 2.386 2.665 3.244 3.056 3.638 3.174 3.379 3.403 3.066 3.522 3.434
2.090 0.327 1.114 2.320 2.210 0.704 3.164 4.132 3.952 7.063 6.517 7.266 7.411 6.491 6.211 7.068 6.772 6.641 2.533 2.236 1.969 2.781 1.969 1.698 2.791 1.969 2.658
0.874 0.800 0.197 1.969 2.102 2.097 2.870 3.435 4.082 7.969 5.736 4.473 7.876 8.965 6.767 7.984 7.554 8.514 10.221 9.702 11.217 10.032 10.576 10.636 9.731 10.935 10.717
Table 5 Response table of the TEG06 and TEG10. Level
1 2 3 Delta Rank
TEG06
TEG10
A
B
C
A
B
C
2.224 6.827 2.290 4.603 1
3.457 3.533 4.350 0.893 2
4.136 3.625 3.579 0.556 3
1.869 7.315 10.419 8.549 1
5.510 6.780 7.314 1.804 2
6.614 6.467 6.522 0.147 3
Bold values indicate the levels of the significant factors for which the best result is obtained and the optimal design are calculated.
consideration, it is concluded that the parameter is effective on the response when P < 0.05 (5% significance level). Accordingly, it is seen that the most effective parameters for the TEG06 power are the material at a rate of 85.22%, the material and the motor speed interaction at a rate of 4.77% and the motor speed at a rate of 3.09%, depending on the analysis values given in Table 6. Based on the values given for the TEG10 power in Table 7, it was determined that the most effective parameter was material (89.9%). Following that, the other effective parameters are the motor speed (4.12%), the material and motor speed interaction (2.37%) and the motor speed and pressure interaction (1.46%). For each given ANOVA table it is possible to determine from the ratios that other parameters besides the effective parameters mentioned are also somewhat effective. However, since these effects are rather low compared to the rest, they are neglected and interpreted as if they were not effective.
5. Conclusion
Fig. 11. Profiles of mean S/N ratio for TEG06 and TEG10.
In this study, the effect of the speed and the pressure of water and the type of material in which the thermal water is transmitted on the energy obtained from the TEG was observed. As a result of the experiments carried out, it was seen that the material in which the thermal water was transmitted has an effect on the power
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Table 6 Results of the ANOVA for TEG06. Symbol
Parameter
Degrees of freedom
Sum of squares
Mean square
F
P
PC (%)
A B C AxB AxC BxC Residual Error Total
Material Engine Speed Pressure Material*Engine Speed Material*Pressure Engine Speed*Pressure
2 2 2 4 4 4 8 26
125.330 4.409 1.718 6.804 0.176 1.975 2.219 142.631
62.665 2.205 0.859 1.701 0.044 0.494 0.277
225.910 7.950 3.100 6.130 0.160 1.780
0.000 0.013 0.101 0.015 0.953 0.226
87.87 3.09 1.20 4.77 0.12 1.38 1.56
Table 7 Results of the ANOVA for TEG10. Symbol
Parameter
Degrees of freedom
Sum of squares
Mean square
F
P
PC (%)
A B C AxB AxC BxC Residual Error Total
Material Engine Speed Pressure Material*Engine Speed Material*Pressure Engine Speed*Pressure
2 2 2 4 4 4 8 26
337.133 15.454 0.099 8.902 4.285 5.484 3.672 375.028
168.566 7.727 0.050 2.225 1.071 1.371 0.459
367.23 16.83 0.11 4.85 2.33 2.99
0.000 0.001 0.899 0.028 0.143 0.088
89.90 4.12 0.03 2.37 1.14 1.46 0.98
produced by TEG. As a result of the tests, the best result in TEG06 was 3.43 W when the speed of the motor was at 3 rd level and the pressure was 3.5 bar using Aluminum material; and the best result in TEG10 was 2.43 W when the speed of the motor was at 3 rd level and the pressure was 2.5 bar using copper material. This shows that according to the TEG to be used, the material in which the thermal water is conveyed also varies. In summary, we employed numerical techniques along with Taguchi optimization method using L27 (313) orthogonal array to optimize segmented TEGs and analysis of variance (ANOVA). Three operating parameters (i.e., material, engine speed and pressure) along with 3 levels were considered to optimize power output of TEG system. Finally, Taguchi results were validated against the results obtained using traditional optimization technique and a TEG configuration for simultaneous optimization of power was obtained. According to the results, the following summaries can be made: A L27 (313) orthogonal array is built to figure out the sensitivity of the performance to the variations of the 3 factors. The analysis the signal-to-noise ratio suggests that the influences of the 3 factors on the power of TEG06 and TEG10 are ranked as: material > engine speed > pressure, and their values for optimum operation are 6.827 dB, 4.350 dB, 4.136 dB, 10.419 dB, 7.314 dB and 6.614 dB, respectively. This reflects that the material is the most important factor in determining the performance of the TEG06 and TEG10 systems. The effects of the variables on power of TEG systems were determined by the ANOVA. The most significant variable on power for TEG06 and TEG10 systems were found to be the material with 87.87% and 89.9%, respectively. As it is shown in this study, the Taguchi method provides a systematic and efficient methodology for the design optimization of the system parameters with far less effect than would be required for most optimization techniques. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References [1] Z. Niu et al., Investigation and design optimization of exhaust-based thermoelectric generator system for internal combustion engine, Energy Convers. Manage. 85 (2014) 85–101, https://doi.org/10.1016/j. enconman.2014.05.061. [2] S. Yu et al., Start-up modes of thermoelectric generator based on vehicle exhaust waste heat recovery, Appl. Energy 138 (2015) 276–290, https://doi. org/10.1016/j.apenergy.2014.10.062. [3] W.-H. Chen, S.-R. Huang, Y.-L. Lin, Performance analysis and optimum operation of a thermoelectric generator by Taguchi method, Appl. Energy 158 (2015) 44–54, https://doi.org/10.1016/j.apenergy.2015.08.025. [4] X. Gao et al., Numerical model of a thermoelectric generator with compact plate-fin heat exchanger for high temperature PEM fuel cell exhaust heat recovery, Int. J. Hydrogen Energy 37 (10) (2012) 8490–8498, https://doi.org/ 10.1016/j.ijhydene.2012.03.009. [5] X. Zheng et al., Experimental study of a domestic thermoelectric cogeneration system, Appl. Therm. Eng. 62 (1) (2014) 69–79, https://doi.org/10.1016/j. applthermaleng.2013.09.008. [6] B. Xiong et al., Modeling and performance analysis of a two-stage thermoelectric energy harvesting system from blast furnace slag water waste heat, Energy 77 (2014) 562–569, https://doi.org/10.1016/j. energy.2014.09.037. [7] L. Tayebi, Z. Zamanipour, D. Vashaee, Design optimization of micro-fabricated thermoelectric devices for solar power generation, Renewable Energy 69 (2014) 166–173, https://doi.org/10.1016/j.renene.2014.02.055. [8] W.-H. Chen et al., Modeling and simulation for the design of thermalconcentrated solar thermoelectric generator, Energy 64 (2014) 287–297, https://doi.org/10.1016/j.energy.2013.10.073. [9] Y.-X. Huang et al., Geometry optimization of thermoelectric coolers using simplified conjugate-gradient method, Energy 59 (2013) 689–697, https://doi. org/10.1016/j.energy.2013.06.069. [10] J.-H. Meng, X.-X. Zhang, X.-D. Wang, Multi-objective and multi-parameter optimization of a thermoelectric generator module, Energy 71 (2014) 367– 376, https://doi.org/10.1016/j.energy.2014.04.082. [11] R.V. Rao, V. Patel, Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm, Eng. Appl. Artif. Intell. 26 (1) (2013) 430–445, https://doi.org/10.1016/j. engappai.2012.02.016. [12] C. Favarel et al., Numerical optimization of the occupancy rate of thermoelectric generators to produce the highest electrical power, Energy 68 (2014) 104–116, https://doi.org/10.1016/j.energy.2014.02.030. [13] R.J. Stevens, S.J. Weinstein, K.S. Koppula, Theoretical limits of thermoelectric power generation from exhaust gases, Appl. Energy 133 (2014) 80–88, https:// doi.org/10.1016/j.apenergy.2014.07.075. [14] R.-S. Chen, C. Kung, G.-B. Lee, Analysis of the optimal dimension on the electrothermal microactuator, J. Micromech. Microengineering 12 (3) (2002) 291. [15] C. Denneval et al., Prediction of photophysical properties of pyrimidine chromophores using Taguchi method, Dyes Pigm. 110 (2014) 49–55, https:// doi.org/10.1016/j.dyepig.2014.05.030. [16] N. Yusoff, M. Ramasamy, S. Yusup, Taguchi’s parametric design approach for the selection of optimization variables in a refrigerated gas plant, Chem. Eng. Res. Des. 89 (6) (2011) 665–675, https://doi.org/10.1016/j.cherd.2010.09.021.
10
H. Terziog˘lu / Measurement 149 (2020) 106992
[17] D. Zhao, G. Tan, A review of thermoelectric cooling: materials, modeling and applications, Appl. Therm. Eng. 66 (1–2) (2014) 15–24, https://doi.org/ 10.1016/j.applthermaleng.2014.01.074. [18] C.-H. Cheng, S.-Y. Huang, T.-C. Cheng, A three-dimensional theoretical model for predicting transient thermal behavior of thermoelectric coolers, Int. J. Heat Mass Transf. 53 (9–10) (2010) 2001–2011, https://doi.org/10.1016/j. ijheatmasstransfer.2009.12.056. [19] M. Kotsur, Optimal control of distributed parameter systems with application to transient thermoelectric cooling, Adv. Electr. Computer Eng. 15 (2) (2015) 117–122. [20] C.-T. Hsu et al., An effective Seebeck coefficient obtained by experimental results of a thermoelectric generator module, Appl. Energy 88 (12) (2011) 5173–5179, https://doi.org/10.1016/j.apenergy.2011.07.033.
[21] G. Yalçın, M. Selek, H. Terziog˘lu, Termoelektrık jeneratör ile maksimum enerji elde edilmesi için levha tasarimi, Organizing Committee Members (2016) 909–916. [22] A.C. Ag˘açayak et al., The impact of different electrical connection types in thermoelectric generator modules on power, Int. J. Eng. Res. Sci. 3 (12) (2017) 46–55. [23] T. Canel, A.U. Kaya, B. Celik, Parameter optimization of nanosecond laser for microdrilling on PVC by Taguchi method, Opt. Laser Technol. 44 (8) (2012) 2347–2353, https://doi.org/10.1016/j.optlastec.2012.04.023. [24] Y. Hsiao, Y. Tarng, W. Huang, Optimization of plasma arc welding parameters by using the Taguchi method with the grey relational analysis, Mater. Manuf. Processes 23 (1) (2007) 51–58, https://doi.org/10.1080/10426910701524527.
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Measurement journal homepage: www.elsevier.com/locate/measurement
Analysis of effect factors on thermoelectric generator using Taguchi method Hakan Terziog˘lu Vocational School of Technical Sciences, Konya Technical University, Konya 42003, Turkey
a r t i c l e
i n f o
Article history: Received 24 January 2019 Received in revised form 21 August 2019 Accepted 24 August 2019 Available online 28 August 2019 Keywords: Thermoelectric generators Output power and efficiency Taguchi method
a b s t r a c t Due to technological developments in recent years, the need for domestic and industrial electric power is increasing day by day. Alternative energy resources have become more important to reduce production costs by converting waste energy into electricity. In this study, a research was carried out to increase the efficiency and on the factors which were effective in Thermoelectric Generators (TEG) used in the production of electrical energy by using thermal sources from alternative energy sources. In this study, the effects of heat transfer performance of the materials (copper, aluminum and brass) on which thermal water is carried, and the effects of water pressure and velocity on the performance of TEGs were investigated. Taguchi method was used to determine the performance effects in the most accurate way. Taguchi method used three levels with three factors: material (copper, aluminum and brass), engine speed (I, II and III) and water pressure (1-2.5-3.5 bar). In addition, in the Taguchi method, the orthogonal array was used and the optimum operation time was significantly reduced. In this study, TEG1-12706 and TEG1-12710 were performed in 2 different TEGs. 27 experiments were carried out for each TEG under different materials, speed and pressure of water with the experiment set up in this study. When the test results were analyzed by Taguchi method, it was determined that the material was the most important factor in determining the output power and efficiency in the production of electrical energy by using TEG (approx. 89%) and it was seen that the pressure and engine speed had almost no role. Ó 2019 Published by Elsevier Ltd.
1. Introduction The amount of energy consumed per person today is accepted as a measure of development. Intensive efforts are being made to obtain efficient, cheap and clean energy from alternative energy sources all over the world to meet the ever increasing need for electricity energy due to the developing technology. Taking into account the environmental and economic impacts of the global electricity generation systems around the world, the need for alternative energy sources is growing steadily. All energy sources such as coal, oil, natural gas, LPG, wood, biogas, all fossil fuels, and marine wave power are limited. Nuclear energy is a type of energy whose production and recycling require great attention and cost. In the studies conducted to obtain efficient, cheap and clean energy from the alternative energy sources of the world, geothermal energy, solar energy, wind energy, biomass energy, hydrogen energy, tidal energy formed by the sea waves come into prominence as renewable sources. Renewable sources such as solar energy, wind energy, biomass energy, hydrogen energy and tidal energy generated by the sea waves have negative effects on the E-mail address: [email protected] https://doi.org/10.1016/j.measurement.2019.106992 0263-2241/Ó 2019 Published by Elsevier Ltd.
environment compared to fossil resources, they can be used at certain times rather than every moment of the year, their technologies are not fully developed and they are expensive, so they have some limitations regarding continuous energy. Therefore, scientists are looking for electricity generation from alternative energy sources such as geothermal energy. Geothermal energy, which is an alternative energy source, does not have the potential to compete with exhaustible energy sources. However, when appropriate technologies are used, it is a prominent type of energy that is not pollutant-free, renewable, sustainable, domestic and environmentally friendly. Among the developing methods of renewable energy, thermoelectric generators (TEGs) are a promising device in that they can convert waste heat into electricity [1–3]. In recent decades, much efforts have been made to develop the practical applications of TEGs through waste heat recovery reported in many studies. Gao et al. [4] numerically evaluated a TEG system driven by the waste heat of the exhaust gas from a high temperature polymer electrolyte membrane fuel cell stack and obtained an optimal system configuration. Zheng et al. [5] presented a thermoelectric cogeneration system to simultaneously generate electricity and preheat water where the primary heat source of the thermoelectric cogeneration
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was waste heat from boiler exhaust. Xiong et al. [6] numerically established a two-stage thermoelectric energy harvesting system to generate electricity; the two-stage system, consisting of a top stage generator and a bottom stage generator with the same pairs of thermoelectric elements, was driven by the waste heat of blast furnace slag water. In consideration of the high cost of experimental study, some researchers attempted to investigate STEGs through simulation method, which was proved to be a more convenient and economical way [7]. Chen et al. [8] revealed that the numerical model solved by ANSYS is a feasible way to predict the performance of solar TEG. And the performances of thermal-concentrated solar thermoelectric generators (TEGs) at three different geometric types are investigated numerically. However, the study was only carried at constant illumination intensity and temperature. In another study, Chen et al. [3] intended to explore the performance of a TEG system and find the optimum conditions for maximizing the performance of the system using the Taguchi method. In their system, a three-dimensional finite element scheme was employed to analyze the TEG system. To maximize the performance of thermoelectric devices, including TEGs, a variety of optimization methods have been developed. They include the simplified conjugate-gradient method [9,10], teaching-learning-based optimization algorithm [11], genetic algorithm [12], variational method [13], and so forth. In addition to the aforementioned methods, the Taguchi Method can be effectively used to aid in designing experiments and find the optimum conditions by adopting Orthogonal Array, so time and effort can be reduced. Chen et al. [14] developed a three-dimensional model of the actuator using ANSYSTM finite element analysis simulation code, and the model was used to optimize the actuator for robust design. Denneval et al. [15] adopted the Taguchi method to design experiments in association with the analysis of variance methodologies for predicting the photophysical properties (absorption and emission maxima) of a family of pyrimidine chromophores. They concluded that the method was useful to study the influence in various parameters involved in the photophysical properties of a series of dyes and to quickly identify the optimized chromophore. The literature review above reveals that a number of methods can be utilized to maximize the performance of TEG systems. Some challenges will be encountered in experimental design when the number of parameters is large [16], and the investigation will be time-consuming once all the experiments are performed. Moreover, it is difficult to identify the influence of each parameter on physical phenomena and interpret the results. The Taguchi method can provide a systematic approach for simplifying the experimental design. Meanwhile, the Taguchi method has been widely employed to find the optimum conditions of a system. However, to the authors’ knowledge, the Taguchi method has not been utilized in the experiment design and in optimization of a TEG system yet [3]. As a result, this study was carried out to maximize the materialrelated efficiency of thermoelectric generators (TEGs) that produce electricity by utilizing geothermal sources. In this study, the effects of the materials on the power used in the transportation of hot and cold water, which must be produced for the TEGs to generate electrical energy, were investigated depending on o L27 (313) orthogonal experimental design. The Taguchi method is used because it is a powerful tool to understand the sensitivity of factors on physical behavior. Analysis of variance (ANOVA) was used to interpret the experimental results and decide which level of the factor should be used. The significant factors with their respective levels were identified and set to the optimum values. Eventually, the performance of the TEG system was maximized using the Taguchi optimization method.
2. Thermoelectric generator A thermoelectric generator consists of P and N type semiconductors electrically connected in series and thermally connected in parallel to each other between the two ceramic plates for electrical insulation and the provision of heat conduction as shown in Fig. 1 [17–19]. If heat is applied to one of the ceramic surfaces of the TEG and the cold is applied to the other, the temperature difference between the two surfaces is generated to produce a modular Direct Current (DC) as shown in Fig. 2, and thus a thermoelectric power generator is obtained. DC current is generated by the action of electrons in the heat flow along P and N type semiconductors [20]. 3. Taguchi orthogonal design In fact, taking into account the physical test conditions, there are many factors and interactions that affect the amount of power generated by the thermoelectric generator. The experimental study which is conducted to reveal the effects of these factors on the system should be minimized in terms of time and cost. In this study, the Taguchi experimental design was used to reduce the number of trials to be done, reduce the cost and achieve the optimum result in a shorter time. Considering the factors that are found to be effective on the system, the L27 (313) orthogonal array was selected for the experimental design and 27 physical tests were performed accordingly. The experimental data for coded factors are recorded in Table 1. The columns numbered 1, 2, and 5 with bold numbers in Table 1 shows coded 33 designs of the factors to be used for parameter optimization. For each factor, three levels are assigned. The dimensions of each factor captioned A, B and C associated with each level are shown in Table 2. 4. Material and method In this study; a closed hot-cold (thermal) water circulation system made of copper-aluminum and brass materials was designed and electrical energy production was carried out in the tests made with the TEC1-12706 (TEG06) and TEC1-12710 (TEG10) modules in the system. In this way, the effect of material type, the thermal water is passed through, on the power produced from TEGs was investigated. The block diagram of the experimental setup used to determine these effects is shown in Fig. 3. In Fig. 3, the materials and connection patterns expressed in the block diagram are detailed respectively.
Fig. 1. The structure of TEG.
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3
Fig. 2. Thermoelectric generator. Table 1 Experimental layout using an L27 orthogonal array. Trial No
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Experimental Factor 1
2
3
4
5
6
7
8
9
10
11
12
13
1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 A
1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 B
1 1 1 2 2 2 3 3 3 2 2 2 3 3 3 1 1 1 3 3 3 1 1 1 2 2 2
1 1 1 2 2 2 3 3 3 3 3 3 1 1 1 2 2 2 2 2 2 3 3 3 1 1 1
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 C
1 2 3 1 2 3 1 2 3 2 3 1 2 3 1 2 3 1 3 1 2 3 1 2 3 1 2
1 2 3 1 2 3 1 2 3 3 1 2 3 1 2 3 1 2 2 3 1 2 3 1 2 3 1
1 2 3 2 3 1 3 1 2 1 2 3 2 3 1 3 1 2 1 2 3 2 3 1 3 1 2
1 2 3 2 3 1 3 1 2 2 3 1 3 1 2 1 2 3 3 1 2 1 2 3 2 3 1
1 2 3 2 3 1 3 1 2 3 1 2 1 2 3 2 3 1 2 3 1 3 1 2 1 2 3
1 2 3 3 1 2 2 3 1 1 2 3 3 1 2 2 3 1 1 2 3 3 1 2 2 3 1
1 2 3 3 1 2 2 3 1 2 3 1 1 2 3 3 1 2 3 1 2 2 3 1 1 2 3
1 2 3 3 1 2 2 3 1 3 1 2 2 3 1 1 2 3 2 3 1 1 2 3 3 1 2
Table 2 Factors and their levels. Factor
Material Engine speed Pressure
Symbol
A B C
Unit
– rpm Pa
4.1. Thermoelectric generator (TEG) Module Two different TEGs, TEG06 and TEG10, were used in the system to generate heat energy. The parameters of the TEGs used are given in Table 3. 4.2. Design of the plates that the TEGs are placed on and the placement of TEGs In this study, thermal water was passed through a plate sized 100 100 as shown in Fig. 4. The dimensions of the plates are
Level 1
2
3
Cooper 1.09 1
Brass 1.69 2.5
Aluminum 2.18 3.5
designed in such a way that the piece will be integral and that the fluid flowing within the piece will maximally entwine (transmitted), taking into account the cutting edge standards (£11.5 mm, length 90 mm and half finger connectors). These plates are designed to measure how the heat distribution will give more accurate results on which material, and they are made of 3 different materials like copper, aluminum and brass [21]. Considering the design of the parts to be produced, they were preferred and produced in such a way that the volume of the fluid flowing through the part reaches the maximum rate and they cause the least heat loss [21].
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Fig. 3. Block diagram of experience setup (for aluminum).
Table 3 Features of TEG06 and TEG10. Performance Specifications
TEC1-12706
Hot Side Temperature (°C) Qmax (Watt) DTmax (°C) Imax (Amper) Vmax (Volt) Module Resistance (Ohm)
25 °C 50 66 6.4 14.4 1.98
TEC1-12710 50 °C 57 75 6.4 16.4 2.3
25 °C 85 66 10.5 15.2 1.08
50 °C 96 75 10.5 17.4 1.24
TEG10 and TEG06 are placed on the plates in Fig. 4 as in Fig. 5. In addition, as shown in Fig. 5, a temperature sensor (thermocouple) is placed on the surface of each plate to measure the temperature of each plate. The electrical connection of the TEG’s on each plate with the other plates given in Fig. 5 is schematically shown in Fig. 6 [22]. As shown in Fig. 6, an experimental block consists of 2 plates and 8 TEGs. In the system, this structure is used on copper, aluminum and brass blocks and are shown in Fig. 7.
thermal water entering the plates and the thermal water coming out of the plates are continuously measured by the sensors [22]. In the designed system, two heat sources were utilized by connecting two heat exchangers in series for hot water. This allows the hot water in the system to reach the desired value faster. Because the mains water supplied to the system is affected by the heat exchange of the plates in which the hot and cold water are conveyed due to the thinness of the thermoelectric modules, the heat was kept at 20 °C using the air exchanger to cool the cold water. The water heating and cooling systems used in the test apparatus are shown in Fig. 9. Two separate expansion tanks are used for the cold and hot closed water systems in the system, as shown in Fig. 10. In this way, refilling the water decreasing in the small water leakage that may occur in the system, the pressure was kept at the same value for a long time. In the experiments, the temperature values on the plates and the voltage and current values produced by the thermoelectric modules are measured using measuring instruments. To see that the system works under load, 121 red wires of 10 mm are connected parallel to each other at the output of the TEGs to provide electrical loading of the system.
4.3. The experimental setup 4.4. Running of experiments The experimental setup used to pass the thermal water from the prepared blocks in Fig. 6 is given in Fig. 8. In the experimental setup shown in Fig. 8, two independent closed systems for the circulation of the thermal water are designed. The closed system and the flow direction are shown with red arrows for the hot water and blue arrows for cold water. Hot and cold water navigated through the system by means of two separate WILO brand RSL 15/5–3 ku models of 84 W combi boiler. In these closed systems, the pressure of the system is set at 1, 2.5 and 3.5 bar levels by pushing water from outside to hot and cold sides. Experiments were carried out at different pressure values. In the designed system, the temperatures and pressures of the
Experiments were carried out at 3 different pressures at 3 different engines speeds and the power values produced by the TEGs were observed. In this study, 27 experiments were conducted in accordance with the orthogonal array L27 (313), with each material type and TEG type at 1, 2.5 and 3.5 bar when the circulating dampers are at 1.09, 1.69 and 2.18 ms1. In the experiments conducted to determine the effect of the type of material conveying thermal water on the power obtained from the TEGs, (V) and current (A) values obtained from geothermal energy were measured from the initiation of the system up to reaching the hot water value of 100 °C. The completion time
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Fig. 4. Plates with thermal water (a) Design (b) Picture.
Fig. 5. Image of TEG modules placed on copper-aluminum-brass material.
Fig. 6. The connection of TECs in the plate and with each other.
5
6
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Fig. 7. Image of TEG06 and TEG10 blocks formed by Copper-Aluminum and Brass materials.
Fig. 8. Image of the connection of the designed system (for Aluminum plate).
of each experiment is between 30 and 70 min. In experiments, the limit was determined as 100 °C assuming that about 88% of geothermal energy in Turkey is at low and medium temperatures (30–100 °C). 4.5. Evaluation of data Table 4 shows the experimental parameter design order and the corresponding responses for L27 (313) given in Table 1 in accordance with the study run. The material (A) is shown in the first parameter column, the motor speed is in the second parameter column (B), and the pressure (C) is in the last parameter column. As the reaction values, the power and their S/N ratios take place on the right side of the table for TEG06 and TEG10 separately.
The Taguchi method prefers the signal / noise ratio (S / N) to measure variations in experimental design. It is used to measure the quality characteristic deviates from the desired value. In the S/N ratio, signal refers to the desired real value, whereas noise refers to the undesired factors in measured values [23]. To obtain the optimum process parameters, the larger S/N ratio denotes better performance. However, the signal and noise do not represent the realistic quantities in a thermoelectric generator system. As a result, the optimal combination of operating conditions can be obtained from the profiles of the S/N ratio [3]. There are three basic categories to seek the best results of experiments; they are the ‘‘nominal-the-better”, the ‘‘larger-the-better”, and the ‘‘smaller-th e-better”. The aim of the present study is to maximize output power of a TEG system and the larger-the-better criterion is thus
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Fig. 9. Images of (a) heating (b) Cooling systems in Designed system.
Fig. 10. Image of the expansion tanks in the system.
adopted. The S/N ratio in terms of output power or efficiency is written as:
S=N ¼ 10log10
1 y2
ð1Þ
where y is the measured output value (P) from the TEG system. The optimal level of the process parameters can be found by considering the highest S/N ratio value. When the power obtained from TEG06 by 27 tests is examined, it is determined that the smallest value is 1.038 W for test combination 2 and that the maximum power value is 2.347 W for test combination 13. When the power value obtained from TEG10 is considered, it is determined that the smallest value is 0.912 W for test combination 2 and that the maximum power value is 3.638 W for test combination 21. This pronounced difference between the maximum and the minimum power reveals that the combination of the 3 factors with appropriate levels plays an important role on the performance of the TEG system. 4.6. Analysis of S/N ratios In light of the predicted values of power, a response table was developed shown in Table 4. First, the obtained S/N values were
grouped by the factor level for each design parameter column in the array. For example, the mean S/N ratio of Factor A at Level 1 in terms of output power is equal to (2.09 + 0.327 + 1.114 + 2.32 + 2.21 + 0.704 + 3.164 + 4.132 + 3.952)/9 = 2.224. Consequently, the grouped values were summed and divided by the number of responses, two. The absolute differences between the highest and the lowest average were calculated. These procedures were repeated for each of the other columns and the response table was developed as shown in Table 5. The response graphs were plotted as shown in Fig. 11 reflecting these results. The effect of each factor is the difference between the maximum mean S/N ratio and the minimum one. A higher effect of a factor corresponds to a higher impact of the factor on the output power. Fig. 11(a–b) depicts that Factor A (material) has the greatest effect on the output power in that its effects are 6.827 dB and 10.419 dB, respectively. This behavior is consistent with the observation of Hsiao et al [24]. Other factors contribute almost equally with scatter ranging from 7 to 3. The optimal parameter and their levels for TEG06, as seen from Fig. 11(a), are A1B3C1. From the S/N ratio analysis in Fig. 11(b), the optimal parameters for TEG10 were determined as A2B3C1. This result is determined according to ‘’S/ N’’ response values. The highest S/N values in Fig. 11 are considered and the levels with these values are expressed writing next to the parameters in numbers. The number 2 in the term A2B3C1 is the most ideal level of A factor, the number 3 is the most ideal level of B factor and the number 1 is the most ideal level of C factor. A, B and C factors and the terms mentioned by the numbers are given in Table 2.
4.7. Analysis of variance The purpose of the analysis of variance (ANOVA) is to investigate which design parameters significantly affect the quality characteristic. This is accomplished by separating the total variability of the S/N ratios, which is measured by the sum of the squared deviations from the total mean S/N ratio, into contributions by each of the design parameters and the error. Tables 6 and 7 show ANOVA tables for TEG06 and TEG10, respectively. The contribution of each factor on total variation is indicated in percentage (PC%) in the rightmost column of the charts. In the ANOVA analysis, it is decided based on the P (significance) value that a parameter or interaction is effective on the response or not. Taking the 95% confidence interval into
H. Terziog˘lu / Measurement 149 (2020) 106992
8 Table 4 Experimental factor and measured response. Trial No
Factor
Response
S/N Ratio
A
B (rpm)
C (Pa)
TEG06 P (W)
TEG10 P (W)
TEG06 (dB)
TEG10 (dB)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
1.272 1.038 1.137 1.306 1.290 1.084 1.439 1.609 1.576 2.255 2.118 2.308 2.347 2.111 2.044 2.256 2.181 2.148 1.339 1.294 1.255 1.377 1.255 1.216 1.379 1.255 1.358
1.106 0.912 1.023 1.254 1.274 1.273 1.392 1.485 1.600 2.503 1.936 1.674 2.476 2.807 2.180 2.507 2.386 2.665 3.244 3.056 3.638 3.174 3.379 3.403 3.066 3.522 3.434
2.090 0.327 1.114 2.320 2.210 0.704 3.164 4.132 3.952 7.063 6.517 7.266 7.411 6.491 6.211 7.068 6.772 6.641 2.533 2.236 1.969 2.781 1.969 1.698 2.791 1.969 2.658
0.874 0.800 0.197 1.969 2.102 2.097 2.870 3.435 4.082 7.969 5.736 4.473 7.876 8.965 6.767 7.984 7.554 8.514 10.221 9.702 11.217 10.032 10.576 10.636 9.731 10.935 10.717
Table 5 Response table of the TEG06 and TEG10. Level
1 2 3 Delta Rank
TEG06
TEG10
A
B
C
A
B
C
2.224 6.827 2.290 4.603 1
3.457 3.533 4.350 0.893 2
4.136 3.625 3.579 0.556 3
1.869 7.315 10.419 8.549 1
5.510 6.780 7.314 1.804 2
6.614 6.467 6.522 0.147 3
Bold values indicate the levels of the significant factors for which the best result is obtained and the optimal design are calculated.
consideration, it is concluded that the parameter is effective on the response when P < 0.05 (5% significance level). Accordingly, it is seen that the most effective parameters for the TEG06 power are the material at a rate of 85.22%, the material and the motor speed interaction at a rate of 4.77% and the motor speed at a rate of 3.09%, depending on the analysis values given in Table 6. Based on the values given for the TEG10 power in Table 7, it was determined that the most effective parameter was material (89.9%). Following that, the other effective parameters are the motor speed (4.12%), the material and motor speed interaction (2.37%) and the motor speed and pressure interaction (1.46%). For each given ANOVA table it is possible to determine from the ratios that other parameters besides the effective parameters mentioned are also somewhat effective. However, since these effects are rather low compared to the rest, they are neglected and interpreted as if they were not effective.
5. Conclusion
Fig. 11. Profiles of mean S/N ratio for TEG06 and TEG10.
In this study, the effect of the speed and the pressure of water and the type of material in which the thermal water is transmitted on the energy obtained from the TEG was observed. As a result of the experiments carried out, it was seen that the material in which the thermal water was transmitted has an effect on the power
H. Terziog˘lu / Measurement 149 (2020) 106992
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Table 6 Results of the ANOVA for TEG06. Symbol
Parameter
Degrees of freedom
Sum of squares
Mean square
F
P
PC (%)
A B C AxB AxC BxC Residual Error Total
Material Engine Speed Pressure Material*Engine Speed Material*Pressure Engine Speed*Pressure
2 2 2 4 4 4 8 26
125.330 4.409 1.718 6.804 0.176 1.975 2.219 142.631
62.665 2.205 0.859 1.701 0.044 0.494 0.277
225.910 7.950 3.100 6.130 0.160 1.780
0.000 0.013 0.101 0.015 0.953 0.226
87.87 3.09 1.20 4.77 0.12 1.38 1.56
Table 7 Results of the ANOVA for TEG10. Symbol
Parameter
Degrees of freedom
Sum of squares
Mean square
F
P
PC (%)
A B C AxB AxC BxC Residual Error Total
Material Engine Speed Pressure Material*Engine Speed Material*Pressure Engine Speed*Pressure
2 2 2 4 4 4 8 26
337.133 15.454 0.099 8.902 4.285 5.484 3.672 375.028
168.566 7.727 0.050 2.225 1.071 1.371 0.459
367.23 16.83 0.11 4.85 2.33 2.99
0.000 0.001 0.899 0.028 0.143 0.088
89.90 4.12 0.03 2.37 1.14 1.46 0.98
produced by TEG. As a result of the tests, the best result in TEG06 was 3.43 W when the speed of the motor was at 3 rd level and the pressure was 3.5 bar using Aluminum material; and the best result in TEG10 was 2.43 W when the speed of the motor was at 3 rd level and the pressure was 2.5 bar using copper material. This shows that according to the TEG to be used, the material in which the thermal water is conveyed also varies. In summary, we employed numerical techniques along with Taguchi optimization method using L27 (313) orthogonal array to optimize segmented TEGs and analysis of variance (ANOVA). Three operating parameters (i.e., material, engine speed and pressure) along with 3 levels were considered to optimize power output of TEG system. Finally, Taguchi results were validated against the results obtained using traditional optimization technique and a TEG configuration for simultaneous optimization of power was obtained. According to the results, the following summaries can be made: A L27 (313) orthogonal array is built to figure out the sensitivity of the performance to the variations of the 3 factors. The analysis the signal-to-noise ratio suggests that the influences of the 3 factors on the power of TEG06 and TEG10 are ranked as: material > engine speed > pressure, and their values for optimum operation are 6.827 dB, 4.350 dB, 4.136 dB, 10.419 dB, 7.314 dB and 6.614 dB, respectively. This reflects that the material is the most important factor in determining the performance of the TEG06 and TEG10 systems. The effects of the variables on power of TEG systems were determined by the ANOVA. The most significant variable on power for TEG06 and TEG10 systems were found to be the material with 87.87% and 89.9%, respectively. As it is shown in this study, the Taguchi method provides a systematic and efficient methodology for the design optimization of the system parameters with far less effect than would be required for most optimization techniques. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References [1] Z. Niu et al., Investigation and design optimization of exhaust-based thermoelectric generator system for internal combustion engine, Energy Convers. Manage. 85 (2014) 85–101, https://doi.org/10.1016/j. enconman.2014.05.061. [2] S. Yu et al., Start-up modes of thermoelectric generator based on vehicle exhaust waste heat recovery, Appl. Energy 138 (2015) 276–290, https://doi. org/10.1016/j.apenergy.2014.10.062. [3] W.-H. Chen, S.-R. Huang, Y.-L. Lin, Performance analysis and optimum operation of a thermoelectric generator by Taguchi method, Appl. Energy 158 (2015) 44–54, https://doi.org/10.1016/j.apenergy.2015.08.025. [4] X. Gao et al., Numerical model of a thermoelectric generator with compact plate-fin heat exchanger for high temperature PEM fuel cell exhaust heat recovery, Int. J. Hydrogen Energy 37 (10) (2012) 8490–8498, https://doi.org/ 10.1016/j.ijhydene.2012.03.009. [5] X. Zheng et al., Experimental study of a domestic thermoelectric cogeneration system, Appl. Therm. Eng. 62 (1) (2014) 69–79, https://doi.org/10.1016/j. applthermaleng.2013.09.008. [6] B. Xiong et al., Modeling and performance analysis of a two-stage thermoelectric energy harvesting system from blast furnace slag water waste heat, Energy 77 (2014) 562–569, https://doi.org/10.1016/j. energy.2014.09.037. [7] L. Tayebi, Z. Zamanipour, D. Vashaee, Design optimization of micro-fabricated thermoelectric devices for solar power generation, Renewable Energy 69 (2014) 166–173, https://doi.org/10.1016/j.renene.2014.02.055. [8] W.-H. Chen et al., Modeling and simulation for the design of thermalconcentrated solar thermoelectric generator, Energy 64 (2014) 287–297, https://doi.org/10.1016/j.energy.2013.10.073. [9] Y.-X. Huang et al., Geometry optimization of thermoelectric coolers using simplified conjugate-gradient method, Energy 59 (2013) 689–697, https://doi. org/10.1016/j.energy.2013.06.069. [10] J.-H. Meng, X.-X. Zhang, X.-D. Wang, Multi-objective and multi-parameter optimization of a thermoelectric generator module, Energy 71 (2014) 367– 376, https://doi.org/10.1016/j.energy.2014.04.082. [11] R.V. Rao, V. Patel, Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm, Eng. Appl. Artif. Intell. 26 (1) (2013) 430–445, https://doi.org/10.1016/j. engappai.2012.02.016. [12] C. Favarel et al., Numerical optimization of the occupancy rate of thermoelectric generators to produce the highest electrical power, Energy 68 (2014) 104–116, https://doi.org/10.1016/j.energy.2014.02.030. [13] R.J. Stevens, S.J. Weinstein, K.S. Koppula, Theoretical limits of thermoelectric power generation from exhaust gases, Appl. Energy 133 (2014) 80–88, https:// doi.org/10.1016/j.apenergy.2014.07.075. [14] R.-S. Chen, C. Kung, G.-B. Lee, Analysis of the optimal dimension on the electrothermal microactuator, J. Micromech. Microengineering 12 (3) (2002) 291. [15] C. Denneval et al., Prediction of photophysical properties of pyrimidine chromophores using Taguchi method, Dyes Pigm. 110 (2014) 49–55, https:// doi.org/10.1016/j.dyepig.2014.05.030. [16] N. Yusoff, M. Ramasamy, S. Yusup, Taguchi’s parametric design approach for the selection of optimization variables in a refrigerated gas plant, Chem. Eng. Res. Des. 89 (6) (2011) 665–675, https://doi.org/10.1016/j.cherd.2010.09.021.
10
H. Terziog˘lu / Measurement 149 (2020) 106992
[17] D. Zhao, G. Tan, A review of thermoelectric cooling: materials, modeling and applications, Appl. Therm. Eng. 66 (1–2) (2014) 15–24, https://doi.org/ 10.1016/j.applthermaleng.2014.01.074. [18] C.-H. Cheng, S.-Y. Huang, T.-C. Cheng, A three-dimensional theoretical model for predicting transient thermal behavior of thermoelectric coolers, Int. J. Heat Mass Transf. 53 (9–10) (2010) 2001–2011, https://doi.org/10.1016/j. ijheatmasstransfer.2009.12.056. [19] M. Kotsur, Optimal control of distributed parameter systems with application to transient thermoelectric cooling, Adv. Electr. Computer Eng. 15 (2) (2015) 117–122. [20] C.-T. Hsu et al., An effective Seebeck coefficient obtained by experimental results of a thermoelectric generator module, Appl. Energy 88 (12) (2011) 5173–5179, https://doi.org/10.1016/j.apenergy.2011.07.033.
[21] G. Yalçın, M. Selek, H. Terziog˘lu, Termoelektrık jeneratör ile maksimum enerji elde edilmesi için levha tasarimi, Organizing Committee Members (2016) 909–916. [22] A.C. Ag˘açayak et al., The impact of different electrical connection types in thermoelectric generator modules on power, Int. J. Eng. Res. Sci. 3 (12) (2017) 46–55. [23] T. Canel, A.U. Kaya, B. Celik, Parameter optimization of nanosecond laser for microdrilling on PVC by Taguchi method, Opt. Laser Technol. 44 (8) (2012) 2347–2353, https://doi.org/10.1016/j.optlastec.2012.04.023. [24] Y. Hsiao, Y. Tarng, W. Huang, Optimization of plasma arc welding parameters by using the Taguchi method with the grey relational analysis, Mater. Manuf. Processes 23 (1) (2007) 51–58, https://doi.org/10.1080/10426910701524527.