- Email: [email protected]
Modeling and optimization of the molten salt cleaning process
Journal of Cleaner Production 68 (2014) 243e251
Contents lists available at ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Modeling and optimization of the molten salt cleaning process Yangyang Long a, Jianzhi Li a, *, Douglas H. Timmer a, Robert E. Jones b, Miguel A. Gonzalez a a b
Department of Manufacturing Engineering, University of Texas-Pan American, Edinburg, TX 78541, USA Department of Mechanical Engineering, University of Texas-Pan American, Edinburg, TX 78541, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 April 2013 Received in revised form 16 December 2013 Accepted 29 December 2013 Available online 6 January 2014
Molten salt cleaning is a very efficient and effective cleaning method for removing paints, oils and greases, ceramics, and oxide scales from the surface of metal substrates. Although this cleaning method has been used for decades, very limited research work can be found in literature to help remanufacturers in process modeling and parameter optimization. In order to fill the gap, the effects of molten salt on different types of metal substrates are investigated. The impact on the hardness and tensile strength of the substrates is studied. The relationship between the cleaning performances and processing parameters including salt ingredients and temperature, are explored. Finally, mathematical models are suggested to provide remanufacturers directions in selection of the optimal parameters for the molten salt cleaning process. Published by Elsevier Ltd.
Keywords: Molten salt cleaning Cleaning process Remanufacturing Central composite design
1. Introduction Remanufacturing can be defined as “the transformation of an end-of-life product into a product with an ‘as good as new’ condition” (Seitz, 2007). It is regarded as an important operation in today’s manufacturing, bringing benefits both to the industry and to the society. In the remanufacturing processes, cleaning plays an essential role. The components have to be well cleaned so that contaminants can be completely removed to reveal defects and allow the following refurbishing process to be successfully conducted. However, parts to be cleaned generally have varied conditions. The contaminants to be removed have different compositions, structures, densities and thicknesses. This often leads to inconsistencies and difficulties in the cleaning process. Molten salt cleaning method that uses hot salts as the cleaning medium is a promising method for effective cleaning. The thermal shock, solvent activity and other cleaning mechanisms make it very effective in removing contamination. Furthermore, this cleaning method has the capability to remove nearly all types of contamination with different structures and densities. Typically, a molten salt cleaning system consists of a sludge collection tank, a salt bath, a water rinse tank, an acid tank and a secondary water rinse tank. The salt bath is the central component where the salts are heated above the melting point, then circulated and used to wash the metals. The sludge tank is used to collect and * Corresponding author. Tel.: þ1 956 292 7329; fax: þ1 956 381 3527. E-mail address: [email protected] (J. Li). 0959-6526/$ e see front matter Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.jclepro.2013.12.075
remove solid sludge. The acid and water tanks are used to remove residual oxide scale and remaining salts attached to the parts. Compared with other cleaning methods, molten salt cleaning demonstrates many advantages. Firstly, there is virtually no evaporation due to the low vapor pressure of the cleaning medium. As a result, less cleaning medium is consumed and less energy is lost throughout the cleaning process. Secondly, because of the high heat transfer rate and the high heat capacities of molten salt, the parts immersed in the cleaning medium can be heated up to the process temperature in very short time. This, as a result helps to protect the parts from overheating (Malloy, 2009). The third benefit comes from the re-salt process of sodium nitrate. Typically, sodium nitrate used in the molten salt can regenerate salt by reaction with oxygen supplemented from the ambient air, permitting the cleaning medium to be reused. Last but not the least, as an anhydrous cleaning method, molten salt has the capability of cleaning any complex parts. When environmental impact is considered, since no evaporation of salts is expected, there is no need to consider the salts as an air contamination source. Most of the gas produced in the salt bath reactions is carbon dioxide while most of carbon dioxide could be converted into alkali carbonate (Kopietz, 1994; Shoemaker, 1971) by alkali hydroxide. The bubbles that can be observed in the cleaning process are mostly water vapor from the part and a minor amount of gas product. Although molten salt cleaning has many advantages, very little research work has been conducted to characterize the process parameters. Based on conversation with industry, the research questions are defined as follows: 1) Since molten salts are very
244
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
active in general and are operated at a high temperature, will this damage the part substrate under certain process parameters? 2) What are the individual and joint effects of processing parameters to the cleaning effectiveness? 3) The cleaning cycle time for molten salt process can go from less than 1 min to an hour (Kopietz, 1994; Malloy, 2010) and tends to be longer when cleaning heavily contaminated components with complex geometry. Are there ways to reduce the cycle time when processing such parts? Solutions to these research questions could not only help remanufacturers better understand this cleaning technique, it can also contribute to the optimal design and control of the process to further reduce cycle time and improve cleaning performance, which are two major concerns of the remanufacturers. Thus the objectives of the paper can be summarized as follows: 1) understand impact of molten salt process to typical metals by study of the potential damage effects; 2) enhance the cleaning efficiency of molten salt process through analysis, modeling and optimization of the cleaning process. With a brief review of the history of molten salt cleaning, detailed experimental and modeling work targeting these two goals are discussed in the rest of paper. 2. Literature review Molten salt has been used for nearly a century in a variety of areas, such as heat treatment, fuel cells, chemical weapons, and nuclear reactors (Hoglund, 1997). As a cleaning medium, it has been widely applied in automobile and military remanufacturing and already has a history of decades. The start of molten salt cleaning can be traced back to the late 1930s. In 1938, Vincent-Daviss (1940) heated sodium-lead alloy up to 800 C to remove the oxide scale from metal substrates. This descaling bath was patented in 1940. In 1942, Forsberg (1945) introduced electrical current into the salt bath to remove contamination. A low temperature salt bath used to clean aluminum parts was created by White (1947) in 1942. Later in 1944, a more general salt bath that was able to remove a variety of contaminants was invented by Webster et al. (1949). Dunlevy et al. (1953) established a molten salt cleaning system in 1950. It was not until the 1970’s that articles on molten salt cleaning were published in the literature. Shoemaker (1971) first introduced the cleaning process and revealed the reactions which occurred in the salt bath. Then different molten salt systems were briefly described by Shoemaker and other researchers (Shoemaker and Wood, 1971; Shoemaker, 1973a,b; Willing and Faulkner, 2001). In these systems, different high processing temperature ranges are applied (Kopietz, 1994; Shoemaker and Wood, 1971) while the range must be adjusted for corresponding materials. At this high temperature range, molten salt could remove nearly all types of contamination. For example, most coatings with a high chemical resistance would become active at a high temperature and start to react with the highly active hot salts. Ceramics that are impervious to most forms of chemical attack can also easily dissolve in molten salt (Shoemaker, 1973a,b). The removal mechanisms were also studied by Shoemaker et al. and five cleaning mechanisms including thermal shock, wetting, deflocculation, emulsification and solvent activity are involved (Sandia Laboratories, 1969; Shoemaker, 1971, 1973a, b). Thermal shock happens since molten salt cleaning is operated at a high temperature that can destroy certain contaminants. The wetting mechanism that delivers the salts to the contaminants as well as the interface between contaminants and substrates plays an essential role. Deflocculation allows salts to break down a solid or semi-solid mass into small particles while emulsification causes the detachment of oily film from substrates (Sandia Laboratories, 1969). The solvent activity refers to the thermochemical or
electrochemical reactions between the hot salts and the contaminants. Reaction equations are summarized in Appendix A. Recently, Cheng et al. (2005) and Holm and Pettersson (2005) analyzed the contaminants’ transformation during the descaling process on stainless steel and stressed that the longer the work piece is pretreated in the salt bath, the more effective the following pickling process would be. The low cost salt bath cleaning was also discussed in the literature (Malloy, 2000; McCardle, 1988). 3. Damage study Both the high corrosivity and the high temperature of the hot salts have the potential to damage the substrate surfaces. In this study, the damage effect of hot salts on three different metal substrates was tested to detect if the metal substrates are suitable for molten salt cleaning. Tests included hardness testing and tensile strength testing. 3.1. Materials Samples to be tested were 6-inch long cylindrical bars that were made of 1145 carbon steel, 2024-T6 aluminum and G2 grey iron. 1145 carbon steel and 2024 aluminum bars were purchased from Onlinemetal.com and G2 grey iron bars were provided by Lokey Metal Corp. The nominal chemical compositions of these bars are shown in Table 1. The salts used in the study are sodium nitrate with 98% purity and sodium hydroxide from Bscientific Corp. 3.2. Equipment and molten salt treatment A Rockwell hardness tester produced by Wilson Instruments Division of Instron Corp. was used for the hardness testing. The Rockwell B scale which uses a 1.59 mm ball was chosen. The tensile strengths of the metal bars were measured by a Sintech 65/G machine from MTS System Corp. Each sample contained six specimens and three of them were placed in the salt bath for an hour’s treatment. The 1145 carbon steel and G2 grey iron were treated at 482 C (900 F), while 2024 aluminum bars were treated at 427 C (800 F) due to their low melting point. All these bars were treated by salts composed of 50% sodium nitrate and 50% sodium hydroxide by weight. The salt bath container was made of stainless steel 316 and heated inside the Thermolyne Premium large Furnace from Thermo Scientific Corp. 3.3. Results and discussion Five different points on each specimen were selected for the hardness tests before and after the salt treatment, respectively. The average hardness values as well as the variances were calculated and compared in Table 2. Table 1 Chemical composition (by weight) of bars. Material
1145 Carbon steel
2024 Aluminum
G2 grey iron
Fe Al C Cr Si Cu Mg Mn S P Other
Balance 0.006% 0.42e0.49% 0.03% 0.2% 0.06%
0.5% Balance
Balance
0.70e1.00% 0.04e0.07% 0.037% 0.064%
2.60e3.75% 0.1% 0.5% 3.8e4.9% 1.2e1.8% 0.3e0.9%
0.55%
1.80e3.00%
0.60e0.95% 0.07% 0.12%
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
From this test, the effect of the hot salts on the surfaces of different substrates can be determined. The surface hardness of 1145 steel after the salt treatment is statistically smaller than that before the treatment since all the P-values for the three specimens are less than 0.05. However, this gap is so trivial that it can be ignored. The tester was unable to measure the hardness of the aluminum bars after the salt treatment because the surfaces of the bars became too soft to measure on the B scale. The soft surface is expected due to the fact that the bath temperature is in the high end of the annealing temperature range for these alloys. This inference was further confirmed by the tensile test. Our observation from the tests differs from the results provided in reference (McCardle, 1988) where the authors stated that the salts had no detrimental effects on the aluminum aircraft parts. This is mainly due to different types of aluminum (casting aluminum in (McCardle, 1988)) that were analyzed. As a result, the 2024 aluminum parts cannot be cleaned by molten salt at the current treatment settings. Data in the last row indicate that no adverse effect on G2 grey iron was caused by the molten salt treatment. The tensile strengths of three specimens for each sample were tested without any treatment while the other three specimens were tested after the salt treatment. The results of the tensile test are shown in Table 3. The data for 1145 carbon steel shows that molten salt had virtually no influence on the tensile strengths of the steel parts. Together with the results of hardness test, it can be concluded that molten salt has no detrimental effect on steel parts. Similar results were observed with grey iron. A large difference in peak stresses could be observed in 2024 aluminum. The low peak stresses of the bars after the salt treatment imply that the temper of the aluminum was removed. Since the tensile strength is less sensitive to surface changes, the effect of the salt bath was rather a reduction in the bulk strength. Therefore the bath temperature, not chemical composition, is predominantly responsible for the change in the aluminum bars. Thus it can be concluded that when aluminum parts are being treated in molten salt bath, the temperature should not exceed the annealing temperature of aluminum and the cleaning cycle time must be carefully controlled.
4. Process modeling and optimization With the results from the damage study, the next step of the research was to analyze the impact of the processing parameters to the cleaning performance of the salt bath, so that a relationship model could be created and optimized to further improve the Table 2 Hardness of each specimen. Materials
Specimen
Mean (five readings)
1145 Carbon steel
2024 T6 aluminum
G2 Grey iron
1 2 3 1 2 3 1 2 3
P-valuec
Hardness (Rockwell B) a
StDev (five readings)
Before
After
Before
After
89.64 89.22 91.64 55.36 54.56 53.02 95.12 94.16 95.34
88.34 87.74 88.98 N/Ab N/A N/A 94.16 94.42 95.6
0.559 0.259 0.740 0.462 0.462 0.377 0.460 0.805 0.773
0.871 0.940 0.572 N/A N/A N/A 0.358 0.295 0.604
0.015 0.014 0.000 0.000 0.000 0.000 0.004 0.736 0.714
a StDev is calculated as the standard deviation of the corresponding five hardness values. b N/A means it is impossible to obtain the value as the specimen had been softened to the point that it registered a negative value on the “B” scale. c A p-value that is less than 0.05 means that the hardness before the treating is statistically greater that that after the treating.
245
Table 3 Peak stress of each bar in tensile testing. Materials
Peak stress (MPa) No-treating
1145 Steel 2024 Aluminum G2 Gray iron
P-Value Post-treating
Mean
StDev
Mean
StDev
635.7 344.0 248.0
18.5 25.0 17.8
648.6 140.4 255.3
36.6 3.4 6.4
0.640 0.005 0.569
performance of the process and reduce cycle time. A specific focus was given to heavily contaminated complex parts, since molten salt process serves as the most effective method for such applications. Thus, for the purpose of this research, grey iron parts were selected, which are almost always casted into complicated geometries and are usually contaminated with oxide scale, lubricant oil and grease, and carbonized particles after several years’ usage. Although molten salt is capable to remove virtually all contaminants no matter how they are distributed, the cleaning efficiency need to be further improved in term of cleanness and cycle time, through a designed experiment and process optimization process. The whole procedure was described in the following sections. 4.1. Specimens The specimens chosen in this research are center housings in a turbo-charger, which are typical complex parts made of grey iron. Specimens that have seen five to ten years of service were obtained from a heavy equipment manufacturer located in U.S. As shown in Fig. 1, due to high pressure, high temperature and other extreme conditions, the contaminant layers on the surfaces of these parts are dense, thick, and highly chemical resistant. Furthermore, a center housing has a large ratio of surface area to volume, which results into a large amount of contaminants to be removed. The black layer on the center housing is primarily consisting of lubricants and greases underneath which is a layer of oxide scale. The thickness of the oxide scale layer that is directly in contact with the metal substrate ranges from 10 to 100 mm. While in places such as the corners and flanges, this layer could go up to several hundred micrometers or even more than 1 mm thickness. 4.2. Experiment procedure The procedure of the experiment is summarized in Fig. 2. The first step was to immerse the specimen in the salt bath to fully remove the particles, lubricant oil and grease and part of oxide scale. The experiment parameters include the variation of sodium nitrate (SN) and the bath temperature. This was followed by a water rinse process. The center housing was then put into an acid solution comprised of 12% HNO3 and 6% HCl to remove any remaining scales. This pickling process was designed to last 15 min. The last process is the final water rinse to remove any debris and chemical residuals. The part was then inspected to evaluate the cleaning performance under different experimental settings. For the purpose of this study, the process parameter settings of the last three steps were maintained at the same level in the experiment, only the parameters of the salt bath were varied. A dry-out and rust prevention step is recommended after the last water rinse since the parts can be quickly re-rusted, which would bring difficulty in evaluation of the cleaning performance. 4.3. Experiment design The experiment was then conducted under different process conditions. To reduce the number of experimental runs, a central
246
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
Fig. 1. Appearance of contaminated center housings. These center housings received from Caterpillar Inc. have been used for more than 5 years and heavily contaminated by carbonized particles, oils and greases and oxide scales.
composite design used in response surface methodology was applied in this study. This design is the most popular design for fitting a second-order model and is a very useful tool to not only model but also optimize processing parameters (Montgomery, 2009). The design had a total of 13 runs with 5 replicates at the center point as shown in Fig. 3 and Appendix B. As mentioned earlier, the main processing parameters of the process include: portion of SN and temperature. The portion of SN generally ranges from 50 to 98 wt% in the sodium nitrate/sodium hydroxide composition. Since pure sodium nitrate would decompose at 380 C, 98% was set as the maximum limit instead of 100%. And the mixture containing 98% sodium nitrate is tested to work at 399 C. The overall range of temperature was from 316 C to 482 C (600 Fe900 F). Two responses were recorded for each run of experiment: cleaning cycle time and cleanness of the surface. Cycle time was recorded from the time the part was immersed into the bath to the time when no
bubbles were observed in the bath. As shown in Appendix A, all reactions in the salt bath would generate water vapor. As a result, the cleaning reaction would be completed when bubbles stop in the experiment. The cleanness was visually analyzed and recorded after the final water rinse. This is a subjective response and the evaluation is
Fig. 2. Molten salt cleaning procedure. All settings other than processing parameters (portion of SN and temperature) kept the same through all experiments.
Fig. 3. Central composite design. The design has totally 13 runs, including 5 replicates at the center point and 8 runs at other points as in the figure.
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251 Table 4 Standards for cleanness evaluation. Cleanness
Description
10
Completely clean; no contaminants are left on the inner or outer surfaces Most part is clean; very little carbonized residue remains at the inner corner Most part is clean; some carbonized residue remains at the inner corner after the center housing is split; very little oxide scale remains at outer corners Most part is clean; some carbonized residue remains at the inner corner after the center housing is split; some oxide scale remains at outer corners More than half part is clean; carbonized residue is distributed at the inner corner; certain amount of oxide scale remains at the outer surface Around half part is clean; carbonized residue is distributed on the inner surface; around half of the outer surface is covered by oxide scale Slight amount of contaminants is removed
9 8
7
6
5
1e4
247
based on the scores described in Table 4. Note that when it is difficult to obtain objective measures, subjective estimates are frequently used in process models. As found in literature, subjective estimates have been used in optimization design (Florez and Castro-Lacouture, 2013), group decision (Singh et al., 1992) and multi-criteria decision making (Yeh and Xu, 2013) et al. To incorporate subjective estimates in the optimization models, they are generally converted to scores, similar to the system used in this research as shown in Table 4. It should be noted that, for this research, microscopic techniques could be more appropriate for the evaluation of cleanness. However, there are several issues that make these methods inapplicable: 1) Specimens of the experiment (i.e. contaminated parts studied in this research) are impossible to be prepared or created in a lab environment. The samples used in the experiment are big engine parts provided by a heavy equipment manufacture that were returned from its customers after around 5 years of service on site. Due to the size and quantities of the part processed in the experiment, it is neither allowed by the
Fig. 4. Main effects plot for cleaning cycle time and cleanness. Ignoring the other processing parameter, the mean of experimental results at different settings of portion of SN (or temperature) are connected to show the influence of portion of SN (or temperature) on cleaning effects.
248
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
Table 5 Estimated regression coefficients for cycle time. Term
Coefa
SE Coefb
Tc
Pd
Constant Portion of SN Temp Portion of SNPortion of SN TempTemp Portion of SNTemp
9.745 55.307 7.385 18.675 1.053 24.300
48.027 15.241 41.443 2.724 8.826 6.466
0.203 3.629 0.178 6.856 0.119 3.758
0.845 0.008 0.864 0.000 0.908 0.007
a b c d
Coef. ¼ coefficients. SE coef. ¼ standard error for coefficients. T ¼ T ratio test. P ¼ probability.
Table 6 Estimated regression coefficients for cycle time after removing TempTemp. Term
Coef
SE coef
T
P
Constant Portion of SN Temp Portion of SNPortion of SN Portion of SNTemp
4.090 55.307 12.314 18.717 24.300
7.189 14.272 3.027 2.529 6.055
0.569 3.875 4.068 7.402 4.013
0.585 0.005 0.004 0.000 0.004
manufacturer nor feasible to cut the part into smaller pieces so that microscope can be used to evaluate the cleanness. 2) After molten salt cleaning, these parts are quickly re-oxidized in around 10 min. Antirust spray is always used after the cleaning to avoid re-rusting. However to better evaluate the surface cleanness, anti-rust spray is not used in the present work. As a result, the evaluation of cleanness has to be done in a short time by visual assessment. 4.4. Results and analysis Thirteen experiments were conducted according to the central composite design and the run order was randomly generated to minimize systematic error. The level of the process parameters and its corresponding results in each run are shown in Appendix B. Based on the results shown in Appendix B, several observations were apparent: When the portion of sodium nitrate is very high, the cycle time tends to be short with a poor cleanness. This indicates that under this setting, the molten salt has a poor capability of penetrating into the contamination layers. Therefore, only a small portion of the contaminants can react with the salts and after a short period of time, reaction would stop. The second observation is that when both the portion of sodium nitrate and temperature are at their mid level, a high level of cleanness is achieved with a relatively long cycle time. Finally when the portion of sodium nitrate is low while the temperature is high, both responses show good values. In addition, violent reactions between salts and contaminations were clearly observed. Table 7 Analysis of variance for cycle time. R-Sq ¼ 91.7% R-Sq(adj) ¼ 87.5%. Source
DFa
Seq SSb
Adj SSc
Adj MSd
F
P
Regression Linear Square Interaction Residual Error Lack-of-Fit Pure Error Total
4 2 1 1 8 4 4 12
999.18 197.10 619.83 182.25 90.51 69.71 20.80 1089.69
999.18 357.11 619.83 182.25 90.51 69.71 20.80
249.795 178.555 619.835 182.249 11.314 17.428 5.200
22.08 15.78 54.78 16.11
0.000 0.002 0.000 0.004
3.35
0.134
a b c d
DF ¼ degree of freedom. SS ¼ sum of squares. Seq.SS ¼ the sequential sum of squares. Adj.SS ¼ the adjusted sum of squares.
Observations discussed above suggest significant impacts of individual main factors on both cycle time and cleanness, which are displayed in Fig. 4. For the effect of temperature on cycle time, the path nearly fits an exponential curve, which indicates that the more energy (the higher temperature) is involved in the cleaning process, the shorter the cycle time. This regulation also coincides with the physical law. As to cleaning effectiveness, the observed cleanness increases along with the decrease of the potion of SN and the increase of temperature. Another observation from the experiment was that a very small amount of carbonized contaminants was observed on the inner surface of the center housings during the 12th and 13th run. This is due to the lack of circulation in the salt bath used in this study. It has been proved that circulation mechanism can export waste and make the molten salt cleaning more effective for the internal surfaces. 4.5. Modeling of cycle time With experiment results in Appendix B, a quadratic model for cleaning cycle time was estimated using regression analysis. The model was expressed in coded units in equations (1) and (2). X and Y represent the level of portion of SN and temperature in coded units, respectively. The estimated regression coefficients are listed in Table 5.
X ¼ ðPortion of SN 0:74Þ=0:24;
(1)
Y ¼ ðTemp 399Þ=83:
(2)
As demonstrated in the table, the P-values for terms of X, XX and XY are much smaller than 0.05, which indicates that they are significant. However, the P-value for YY was much greater than 0.05, which indicates that this insignificant term should be deleted from the quadratic model. Term Y cannot be removed because of the significance of XY requiring this term to be kept in the model. After removing the insignificant term TempTemp, the coefficients were reanalyzed and shown in Table 6. The revised model is expressed in uncoded units as the following:
Cycle Time ¼ 91:81 þ 205:13$x 0:58$y 324:95$x2 þ 0:68$xy þ ε
(3)
where, x stands for the portion of sodium nitrate, y for the temperature and ε for the regression error. The Analysis of Variance (ANOVA) is summarized in Table 7. The R-Sq value implies that 91.7% of the variability for the response variable (cycle time) can be explained by the regressor variables. The adjusted R-Sq value, which is reasonably identical to the R-Sq value, indicates that extra variables are not included in the model. The P-values for linear, square and interaction regression are all less than 0.05, which means they are all statistically significant. Furthermore, the data model does not have any lack of fit since the P-value for lack of fit is much larger than 0.05. The residual analysis for the revised model is also conducted and shows no abnormalities. The response surface and contour plots of the model are demonstrated in Fig. 5 and Fig. 6, respectively. Two observations are apparent. The first observation is that the lower the portion of sodium nitrate and the higher the temperature, the shorter the cycle time. The other observation is that the higher the portion of sodium nitrate and the lower the temperature, the shorter the cycle time with a high level of residual contaminants, which indicates a low performance in cleaning. The problem of low cleanness under this condition is further discussed in Section 4.6.
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
249
Table 9 Estimated regression coefficients for cleanness after removing TempTemp. Term
Coef
SE coef
T
P
Constant Portion of SN Temp Portion of SNPortion of SN Portion of SNTemp
17.652 6.706 4.411 1.185 4.050
1.035 2.055 0.436 0.364 0.872
17.049 3.263 10.119 3.253 4.645
0.000 0.011 0.000 0.012 0.002
Table 10 Analysis of variance for cleanness. R-Sq ¼ 97.1% R-Sq(adj) ¼ 95.6%. Fig. 5. Surface plot of cycle time vs Temp and portion of SN.
Fig. 6. Contour plot of cycle time vs Temp and portion of SN. Different cycle time ranges are clearly shown by different boundaries whose specific time are stated. The grey area is optimum for molten salt cleaning.
4.6. Modeling of cleanness Similar analysis was performed on modeling of cleanness. Table 8 shows the results of the regression model for cleanness. All terms except YY play significant roles in the model. So term YY was removed from the model and the model was then reanalyzed. The same as in modeling of cycle time, term Y cannot be removed as term XY is significant. The new result was shown in Table 9. The revised model can be expressed in uncoded units as the following:
Source
DF
Seq SS
Adj SS
Adj MS
F
P
Regression Linear Square Interaction Residual Error Lack-of-Fit Pure Error Total
4 2 1 1 8 4 4 12
62.3535 54.8076 2.4835 5.0625 1.8772 1.1772 0.7000 64.2308
62.3535 26.5242 2.4835 5.0625 1.8772 1.1772 0.7000
15.5884 13.2621 2.4835 5.0625 0.2347 0.2943 0.1750
66.43 56.52 10.58 21.57
0.000 0.000 0.012 0.002
1.68
0.313
Fig. 7. Surface plot of Cleanness vs Temp. and Portion of SN.
Table 10 shows the ANOVA analysis for the model created. The difference between R-Sq and the adjusted R-Sq value is extremely small and the P-value for lack of fit is much greater than 0.05. Both of them demonstrate a good fit for the model expressed in equation (4). The validation of this model is also proved by residual analysis.
Cleanness ¼ 26:09 25:99$x 0:05$y 20:57$x2 þ 0:11$xy þ ε (4) where, x denotes the actual portion of sodium nitrate, y is the actual temperature and ε denotes the error term. Table 8 Estimated regression coefficients for cleanness. Term
Coef
SE coef
T
P
Constant Portion of SN Temp Portion of SNPortion of SN TempTemp Portion of SNTemp
13.954 6.706 1.188 1.213 0.689 4.050
6.777 2.151 5.848 0.384 1.245 0.912
2.059 3.118 0.203 3.154 0.553 4.439
0.078 0.017 0.845 0.016 0.598 0.003
Fig. 8. Contour plot of Cleanness vs Temp. and Portion of SN. Different cleanness ranges are clearly shown by different boundaries whose specific cleanness are stated.
250
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
Both Fig. 7 and Fig. 8 demonstrate the effect of portion of SN and temperature on surface cleanness. As the portion of SN and temperature increase, the cleanness performance improves. The upperleft area in Fig. 8 shows a good cleaning result.
4.7. Process optimization As demonstrated in prior analysis, it is difficult to find parameter values that could simultaneously optimize both response variables. Considering that in industry applications, the parameter values are rather regulated in a range, it is more meaningful to recommend an optimized area instead of a point. The idea of optimized area could also reduce the sensitivity of the model to errors that might be involved in subjective assessments used in evaluation of the cleanness response. As a result, the upper-left corner area (in grey) in Fig. 6 is recommended based on the two models. The diagonal boundary line is expressed as the following equation:
result, the impact of higher temperature on cleaning efficiency is still unknown; 2) In this research, only the effects of sodium nitrate and sodium hydroxide are analyzed. Other types of salts, such as sodium chloride, potassium hydroxide, etc., may be studied and analyzed in future research; 3) Other cleaning mechanisms such as ultrasonic cleaning could be integrated with molten salt cleaning, which would result in a new cleaning system. And the new cleaning system may greatly enhance the cleaning performance of the molten salt process. Acknowledgment Authors would like to thank Dr. A. Han for her help in chemical analysis, and Caterpillar Inc. for its support in providing the samples. Appendix A. Reactions in molten salt bath
Contaminant
Reaction equations
Reference
Carbonized residue
C þ 2NaNO3CO2 þ 2NaNO2 CO2 þ 2NaOH Na2CO3 þ H2O 2NaNO2 þ O22NaNO3 2AO þ 2MO þ 2NaNO3A2O3 þ M2O3 þ 2NaNO2 2M2O3 þ 4NaOH 2Na2MO4 þ 2H2O 2NaNO2 þ O22NaNO3 (CxHyOz)n þ OH- þ O2CO2 þ H2O CO2 þ 2NaOH Na2CO3 þ H2O 2Cr3C2 þ 20NaOH þ 13O26Na2CrO4 þ 4Na2CO3 þ 10H2O MoS2 þ 6NaOH þ 9NaNO3Na2MoO4 þ 2Na2SO4 þ 9NaNO2 þ H2O
(Shoemaker, 1971; Shoemaker and Wood, 1971; Shoemaker, 1973a,b)
Oxide scales
Organic coatings Inorganic films
205:13$x 0:58$y 324:95$x2 þ 0:68$xy þ 71:81 ¼ 0
(5)
Processes that fall into this area generally produce a shorter cleaning cycle time (<20 min) and a cleanness higher than 8. Within this area, most contaminants can be quickly removed. It is expected that when circulation is introduced, contaminants would be completely removed and the cleaning effectiveness could be further enhanced. 5. Conclusions In this paper, the impacts of molten salts on metal parts are studied. The results show no detrimental effects for 1145 carbon steel and G2 grey iron samples. When 2024 aluminum parts are treated with the molten salt, however, the processing parameters should be carefully controlled in order to avoid part damage. The impact of molten salt to the cleaning performance was then analyzed with an aim to characterize and optimize the molten salt cleaning process. It is shown that with a low percentage of SN and high process temperature, the process performance in terms of reaction time and cleaning effectiveness is enhanced. Two mathematical models were established for cycle time and cleanness, respectively. Finally, process parameters located in the upper-left corner area in Fig. 6 are recommended for processing heavily contaminated cast-iron parts using molten salt cleaning process. It is worth noting that the current research is not intended to be a complete work. Future research is needed so that the application of molten salt cleaning method in industry can be better instructed. This includes: 1) Due to the equipment limitation, the highest temperature that can be reached in current research is 482 C. As a
(Shoemaker, 1971; Shoemaker, 1973a,b; Willing and Faulkner, 2001)
(Malloy, 2009; Malloy, 2010) (Shoemaker, 1971; Shoemaker and Wood, 1971)
Appendix B. The central composite design and corresponding results
RunOrder
Portion of SN (wt%)
Temperature ( C)
CycleTime (min)
Cleanness
1 2 3 4 5 6 7 8 9 10 11 12 13
0.74 0.74 0.91 0.74 0.74 0.98 0.74 0.91 0.74 0.57 0.57 0.50 0.74
316 399 340 399 399 399 399 458 399 340 458 399 482
44 32 19 30 34 11 32 28 36 32 14 18 23
5.0 7.5 2.0 7.0 8.0 3.0 7.0 8.0 7.5 7.5 9.0 9.5 9.5
References Cheng, S.Y., Kuan, S.L., Tsai, W.T., 2005. Effect of molten salt treatment on descaling behavior of 410 SS in sulfuric acid solution. Mater. Sci. Eng. A 393, 352e360. Dunlevy, R., Frick, H., Shoemaker, J.H., 1953. Apparatus and method for processing of steel strip continuously. U.S. Patent 2,635,062. Florez, L., Castro-Lacouture, D., 2013. Optimization model for sustainable materials selection using objective and subjective factors. Mater. Des. 46, 310e321. Forsberg, F., 1945. Cleaning oxides from steel. U.S. Patent 2,378,761. Hoglund, B.N., 1997. What is Molten Salt & its Technology?. http://home.earthlink. net/wbhoglund/whatsMoltenSal.html (accessed 15 07.10.). Holm, B., Pettersson, R., 2005. Descaling of stainless steels in an oxidizing salt bath. In: Stainless Steel World 2005 Conference & Expo., Maastricht, the Netherlands, pp. 346e350.
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251 Kopietz, K.H., September 1994. Salt bath cleaning of synthetic fiber spinning equipment. In: Fiber & Yarn Technology. Carpet & Rug Ind, Dalton, pp. 62e67. Malloy, J.C., 2000. Stripping organic coatings from parts and part carriers using molten salt bath technology. Met. Finish 98, 507e508, 510-513. Malloy, J.C., 2009. Extreme Cleaning. Gardner Publications Inc.. http://www. pfonline.com/articles/060901.html (accessed 19.07.10.). Malloy, J.C., 2010. Molten Salt Cleaning of Engine Components e an Alternative Cleaning Technology. Kolene Corp. http://www.kolene.com/A55B6F/ Kolene2Website.nsf/Pages/CCABE1047EB75E1285257363002E50E9/$File/ engclean.pdf (accessed 12.02.11.). McCardle, T., 1988. Stripping Aluminum Parts in Molten Salt Bath. In: Products Finishing, 52. Gardner Publications Inc., pp. 96e101 Montgomery, D.C., 2009. Design and Analysis of Experiments, seventh ed. Wiley Inc., New York. Sandia Laboratories, 1969. Contamination Control Handbook. Technology Utilization Division, National Aeronautics and Space Administration, Washington, D.C. Seitz, M.A., 2007. A critical assessment of motives for product recovery: the case of engine remanufacturing. J. Clean. Prod. 15, 1147e1157. Shoemaker, R.H., 1971. Molten salt process. Wire J. 4, 33e38.
251
Shoemaker, R.H., Wood, W.G., 1971. Production cleaning systems involving molten salt baths. Met. Eng. Q. 11, 38e41. Shoemaker, R.H., 1973a. Molten salt baths reduce finishing time and cost. Met. Eng. Q. 13, 22e25. Shoemaker, R.H., 1973b. New molten salt systems for cleaning stainless steels. In: Cleaning Stainless Steel: a Symposium. ASTM Int, West Conshohocken, pp. 106e 117. Singh, M.G., Bennavail, J.C., Chen, Z.J., 1992. A group decision tool for combining subjective estimates based on an optimisation approach. Decis. Support Syst. 8, 541e549. Vincent-Daviss, C.A., 1940. Metal Cleaning Process. U.S. Patent 2,188,930. Webster, H.G., Falter, C.L., Shoemaker, J.H., 1949. Process of Cleaning Metal Surfaces and Compositions there for. U.S. Patent 2,458,661. White, W.E., 1947. Method for Cleaning Aluminum. U.S. Patent 2,431,595. Willing, R., Faulkner, C., 2001. Salt bath technology e a better way to clean metals. Adv. Mater. Process 159, 61e64. Yeh, C.-H., Xu, Y., 2013. Sustainable planning of e-waste recycling activities using fuzzy multicriteria decision making. J. Clean. Prod. 52, 194e204.
Contents lists available at ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Modeling and optimization of the molten salt cleaning process Yangyang Long a, Jianzhi Li a, *, Douglas H. Timmer a, Robert E. Jones b, Miguel A. Gonzalez a a b
Department of Manufacturing Engineering, University of Texas-Pan American, Edinburg, TX 78541, USA Department of Mechanical Engineering, University of Texas-Pan American, Edinburg, TX 78541, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 April 2013 Received in revised form 16 December 2013 Accepted 29 December 2013 Available online 6 January 2014
Molten salt cleaning is a very efficient and effective cleaning method for removing paints, oils and greases, ceramics, and oxide scales from the surface of metal substrates. Although this cleaning method has been used for decades, very limited research work can be found in literature to help remanufacturers in process modeling and parameter optimization. In order to fill the gap, the effects of molten salt on different types of metal substrates are investigated. The impact on the hardness and tensile strength of the substrates is studied. The relationship between the cleaning performances and processing parameters including salt ingredients and temperature, are explored. Finally, mathematical models are suggested to provide remanufacturers directions in selection of the optimal parameters for the molten salt cleaning process. Published by Elsevier Ltd.
Keywords: Molten salt cleaning Cleaning process Remanufacturing Central composite design
1. Introduction Remanufacturing can be defined as “the transformation of an end-of-life product into a product with an ‘as good as new’ condition” (Seitz, 2007). It is regarded as an important operation in today’s manufacturing, bringing benefits both to the industry and to the society. In the remanufacturing processes, cleaning plays an essential role. The components have to be well cleaned so that contaminants can be completely removed to reveal defects and allow the following refurbishing process to be successfully conducted. However, parts to be cleaned generally have varied conditions. The contaminants to be removed have different compositions, structures, densities and thicknesses. This often leads to inconsistencies and difficulties in the cleaning process. Molten salt cleaning method that uses hot salts as the cleaning medium is a promising method for effective cleaning. The thermal shock, solvent activity and other cleaning mechanisms make it very effective in removing contamination. Furthermore, this cleaning method has the capability to remove nearly all types of contamination with different structures and densities. Typically, a molten salt cleaning system consists of a sludge collection tank, a salt bath, a water rinse tank, an acid tank and a secondary water rinse tank. The salt bath is the central component where the salts are heated above the melting point, then circulated and used to wash the metals. The sludge tank is used to collect and * Corresponding author. Tel.: þ1 956 292 7329; fax: þ1 956 381 3527. E-mail address: [email protected] (J. Li). 0959-6526/$ e see front matter Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.jclepro.2013.12.075
remove solid sludge. The acid and water tanks are used to remove residual oxide scale and remaining salts attached to the parts. Compared with other cleaning methods, molten salt cleaning demonstrates many advantages. Firstly, there is virtually no evaporation due to the low vapor pressure of the cleaning medium. As a result, less cleaning medium is consumed and less energy is lost throughout the cleaning process. Secondly, because of the high heat transfer rate and the high heat capacities of molten salt, the parts immersed in the cleaning medium can be heated up to the process temperature in very short time. This, as a result helps to protect the parts from overheating (Malloy, 2009). The third benefit comes from the re-salt process of sodium nitrate. Typically, sodium nitrate used in the molten salt can regenerate salt by reaction with oxygen supplemented from the ambient air, permitting the cleaning medium to be reused. Last but not the least, as an anhydrous cleaning method, molten salt has the capability of cleaning any complex parts. When environmental impact is considered, since no evaporation of salts is expected, there is no need to consider the salts as an air contamination source. Most of the gas produced in the salt bath reactions is carbon dioxide while most of carbon dioxide could be converted into alkali carbonate (Kopietz, 1994; Shoemaker, 1971) by alkali hydroxide. The bubbles that can be observed in the cleaning process are mostly water vapor from the part and a minor amount of gas product. Although molten salt cleaning has many advantages, very little research work has been conducted to characterize the process parameters. Based on conversation with industry, the research questions are defined as follows: 1) Since molten salts are very
244
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
active in general and are operated at a high temperature, will this damage the part substrate under certain process parameters? 2) What are the individual and joint effects of processing parameters to the cleaning effectiveness? 3) The cleaning cycle time for molten salt process can go from less than 1 min to an hour (Kopietz, 1994; Malloy, 2010) and tends to be longer when cleaning heavily contaminated components with complex geometry. Are there ways to reduce the cycle time when processing such parts? Solutions to these research questions could not only help remanufacturers better understand this cleaning technique, it can also contribute to the optimal design and control of the process to further reduce cycle time and improve cleaning performance, which are two major concerns of the remanufacturers. Thus the objectives of the paper can be summarized as follows: 1) understand impact of molten salt process to typical metals by study of the potential damage effects; 2) enhance the cleaning efficiency of molten salt process through analysis, modeling and optimization of the cleaning process. With a brief review of the history of molten salt cleaning, detailed experimental and modeling work targeting these two goals are discussed in the rest of paper. 2. Literature review Molten salt has been used for nearly a century in a variety of areas, such as heat treatment, fuel cells, chemical weapons, and nuclear reactors (Hoglund, 1997). As a cleaning medium, it has been widely applied in automobile and military remanufacturing and already has a history of decades. The start of molten salt cleaning can be traced back to the late 1930s. In 1938, Vincent-Daviss (1940) heated sodium-lead alloy up to 800 C to remove the oxide scale from metal substrates. This descaling bath was patented in 1940. In 1942, Forsberg (1945) introduced electrical current into the salt bath to remove contamination. A low temperature salt bath used to clean aluminum parts was created by White (1947) in 1942. Later in 1944, a more general salt bath that was able to remove a variety of contaminants was invented by Webster et al. (1949). Dunlevy et al. (1953) established a molten salt cleaning system in 1950. It was not until the 1970’s that articles on molten salt cleaning were published in the literature. Shoemaker (1971) first introduced the cleaning process and revealed the reactions which occurred in the salt bath. Then different molten salt systems were briefly described by Shoemaker and other researchers (Shoemaker and Wood, 1971; Shoemaker, 1973a,b; Willing and Faulkner, 2001). In these systems, different high processing temperature ranges are applied (Kopietz, 1994; Shoemaker and Wood, 1971) while the range must be adjusted for corresponding materials. At this high temperature range, molten salt could remove nearly all types of contamination. For example, most coatings with a high chemical resistance would become active at a high temperature and start to react with the highly active hot salts. Ceramics that are impervious to most forms of chemical attack can also easily dissolve in molten salt (Shoemaker, 1973a,b). The removal mechanisms were also studied by Shoemaker et al. and five cleaning mechanisms including thermal shock, wetting, deflocculation, emulsification and solvent activity are involved (Sandia Laboratories, 1969; Shoemaker, 1971, 1973a, b). Thermal shock happens since molten salt cleaning is operated at a high temperature that can destroy certain contaminants. The wetting mechanism that delivers the salts to the contaminants as well as the interface between contaminants and substrates plays an essential role. Deflocculation allows salts to break down a solid or semi-solid mass into small particles while emulsification causes the detachment of oily film from substrates (Sandia Laboratories, 1969). The solvent activity refers to the thermochemical or
electrochemical reactions between the hot salts and the contaminants. Reaction equations are summarized in Appendix A. Recently, Cheng et al. (2005) and Holm and Pettersson (2005) analyzed the contaminants’ transformation during the descaling process on stainless steel and stressed that the longer the work piece is pretreated in the salt bath, the more effective the following pickling process would be. The low cost salt bath cleaning was also discussed in the literature (Malloy, 2000; McCardle, 1988). 3. Damage study Both the high corrosivity and the high temperature of the hot salts have the potential to damage the substrate surfaces. In this study, the damage effect of hot salts on three different metal substrates was tested to detect if the metal substrates are suitable for molten salt cleaning. Tests included hardness testing and tensile strength testing. 3.1. Materials Samples to be tested were 6-inch long cylindrical bars that were made of 1145 carbon steel, 2024-T6 aluminum and G2 grey iron. 1145 carbon steel and 2024 aluminum bars were purchased from Onlinemetal.com and G2 grey iron bars were provided by Lokey Metal Corp. The nominal chemical compositions of these bars are shown in Table 1. The salts used in the study are sodium nitrate with 98% purity and sodium hydroxide from Bscientific Corp. 3.2. Equipment and molten salt treatment A Rockwell hardness tester produced by Wilson Instruments Division of Instron Corp. was used for the hardness testing. The Rockwell B scale which uses a 1.59 mm ball was chosen. The tensile strengths of the metal bars were measured by a Sintech 65/G machine from MTS System Corp. Each sample contained six specimens and three of them were placed in the salt bath for an hour’s treatment. The 1145 carbon steel and G2 grey iron were treated at 482 C (900 F), while 2024 aluminum bars were treated at 427 C (800 F) due to their low melting point. All these bars were treated by salts composed of 50% sodium nitrate and 50% sodium hydroxide by weight. The salt bath container was made of stainless steel 316 and heated inside the Thermolyne Premium large Furnace from Thermo Scientific Corp. 3.3. Results and discussion Five different points on each specimen were selected for the hardness tests before and after the salt treatment, respectively. The average hardness values as well as the variances were calculated and compared in Table 2. Table 1 Chemical composition (by weight) of bars. Material
1145 Carbon steel
2024 Aluminum
G2 grey iron
Fe Al C Cr Si Cu Mg Mn S P Other
Balance 0.006% 0.42e0.49% 0.03% 0.2% 0.06%
0.5% Balance
Balance
0.70e1.00% 0.04e0.07% 0.037% 0.064%
2.60e3.75% 0.1% 0.5% 3.8e4.9% 1.2e1.8% 0.3e0.9%
0.55%
1.80e3.00%
0.60e0.95% 0.07% 0.12%
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
From this test, the effect of the hot salts on the surfaces of different substrates can be determined. The surface hardness of 1145 steel after the salt treatment is statistically smaller than that before the treatment since all the P-values for the three specimens are less than 0.05. However, this gap is so trivial that it can be ignored. The tester was unable to measure the hardness of the aluminum bars after the salt treatment because the surfaces of the bars became too soft to measure on the B scale. The soft surface is expected due to the fact that the bath temperature is in the high end of the annealing temperature range for these alloys. This inference was further confirmed by the tensile test. Our observation from the tests differs from the results provided in reference (McCardle, 1988) where the authors stated that the salts had no detrimental effects on the aluminum aircraft parts. This is mainly due to different types of aluminum (casting aluminum in (McCardle, 1988)) that were analyzed. As a result, the 2024 aluminum parts cannot be cleaned by molten salt at the current treatment settings. Data in the last row indicate that no adverse effect on G2 grey iron was caused by the molten salt treatment. The tensile strengths of three specimens for each sample were tested without any treatment while the other three specimens were tested after the salt treatment. The results of the tensile test are shown in Table 3. The data for 1145 carbon steel shows that molten salt had virtually no influence on the tensile strengths of the steel parts. Together with the results of hardness test, it can be concluded that molten salt has no detrimental effect on steel parts. Similar results were observed with grey iron. A large difference in peak stresses could be observed in 2024 aluminum. The low peak stresses of the bars after the salt treatment imply that the temper of the aluminum was removed. Since the tensile strength is less sensitive to surface changes, the effect of the salt bath was rather a reduction in the bulk strength. Therefore the bath temperature, not chemical composition, is predominantly responsible for the change in the aluminum bars. Thus it can be concluded that when aluminum parts are being treated in molten salt bath, the temperature should not exceed the annealing temperature of aluminum and the cleaning cycle time must be carefully controlled.
4. Process modeling and optimization With the results from the damage study, the next step of the research was to analyze the impact of the processing parameters to the cleaning performance of the salt bath, so that a relationship model could be created and optimized to further improve the Table 2 Hardness of each specimen. Materials
Specimen
Mean (five readings)
1145 Carbon steel
2024 T6 aluminum
G2 Grey iron
1 2 3 1 2 3 1 2 3
P-valuec
Hardness (Rockwell B) a
StDev (five readings)
Before
After
Before
After
89.64 89.22 91.64 55.36 54.56 53.02 95.12 94.16 95.34
88.34 87.74 88.98 N/Ab N/A N/A 94.16 94.42 95.6
0.559 0.259 0.740 0.462 0.462 0.377 0.460 0.805 0.773
0.871 0.940 0.572 N/A N/A N/A 0.358 0.295 0.604
0.015 0.014 0.000 0.000 0.000 0.000 0.004 0.736 0.714
a StDev is calculated as the standard deviation of the corresponding five hardness values. b N/A means it is impossible to obtain the value as the specimen had been softened to the point that it registered a negative value on the “B” scale. c A p-value that is less than 0.05 means that the hardness before the treating is statistically greater that that after the treating.
245
Table 3 Peak stress of each bar in tensile testing. Materials
Peak stress (MPa) No-treating
1145 Steel 2024 Aluminum G2 Gray iron
P-Value Post-treating
Mean
StDev
Mean
StDev
635.7 344.0 248.0
18.5 25.0 17.8
648.6 140.4 255.3
36.6 3.4 6.4
0.640 0.005 0.569
performance of the process and reduce cycle time. A specific focus was given to heavily contaminated complex parts, since molten salt process serves as the most effective method for such applications. Thus, for the purpose of this research, grey iron parts were selected, which are almost always casted into complicated geometries and are usually contaminated with oxide scale, lubricant oil and grease, and carbonized particles after several years’ usage. Although molten salt is capable to remove virtually all contaminants no matter how they are distributed, the cleaning efficiency need to be further improved in term of cleanness and cycle time, through a designed experiment and process optimization process. The whole procedure was described in the following sections. 4.1. Specimens The specimens chosen in this research are center housings in a turbo-charger, which are typical complex parts made of grey iron. Specimens that have seen five to ten years of service were obtained from a heavy equipment manufacturer located in U.S. As shown in Fig. 1, due to high pressure, high temperature and other extreme conditions, the contaminant layers on the surfaces of these parts are dense, thick, and highly chemical resistant. Furthermore, a center housing has a large ratio of surface area to volume, which results into a large amount of contaminants to be removed. The black layer on the center housing is primarily consisting of lubricants and greases underneath which is a layer of oxide scale. The thickness of the oxide scale layer that is directly in contact with the metal substrate ranges from 10 to 100 mm. While in places such as the corners and flanges, this layer could go up to several hundred micrometers or even more than 1 mm thickness. 4.2. Experiment procedure The procedure of the experiment is summarized in Fig. 2. The first step was to immerse the specimen in the salt bath to fully remove the particles, lubricant oil and grease and part of oxide scale. The experiment parameters include the variation of sodium nitrate (SN) and the bath temperature. This was followed by a water rinse process. The center housing was then put into an acid solution comprised of 12% HNO3 and 6% HCl to remove any remaining scales. This pickling process was designed to last 15 min. The last process is the final water rinse to remove any debris and chemical residuals. The part was then inspected to evaluate the cleaning performance under different experimental settings. For the purpose of this study, the process parameter settings of the last three steps were maintained at the same level in the experiment, only the parameters of the salt bath were varied. A dry-out and rust prevention step is recommended after the last water rinse since the parts can be quickly re-rusted, which would bring difficulty in evaluation of the cleaning performance. 4.3. Experiment design The experiment was then conducted under different process conditions. To reduce the number of experimental runs, a central
246
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
Fig. 1. Appearance of contaminated center housings. These center housings received from Caterpillar Inc. have been used for more than 5 years and heavily contaminated by carbonized particles, oils and greases and oxide scales.
composite design used in response surface methodology was applied in this study. This design is the most popular design for fitting a second-order model and is a very useful tool to not only model but also optimize processing parameters (Montgomery, 2009). The design had a total of 13 runs with 5 replicates at the center point as shown in Fig. 3 and Appendix B. As mentioned earlier, the main processing parameters of the process include: portion of SN and temperature. The portion of SN generally ranges from 50 to 98 wt% in the sodium nitrate/sodium hydroxide composition. Since pure sodium nitrate would decompose at 380 C, 98% was set as the maximum limit instead of 100%. And the mixture containing 98% sodium nitrate is tested to work at 399 C. The overall range of temperature was from 316 C to 482 C (600 Fe900 F). Two responses were recorded for each run of experiment: cleaning cycle time and cleanness of the surface. Cycle time was recorded from the time the part was immersed into the bath to the time when no
bubbles were observed in the bath. As shown in Appendix A, all reactions in the salt bath would generate water vapor. As a result, the cleaning reaction would be completed when bubbles stop in the experiment. The cleanness was visually analyzed and recorded after the final water rinse. This is a subjective response and the evaluation is
Fig. 2. Molten salt cleaning procedure. All settings other than processing parameters (portion of SN and temperature) kept the same through all experiments.
Fig. 3. Central composite design. The design has totally 13 runs, including 5 replicates at the center point and 8 runs at other points as in the figure.
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251 Table 4 Standards for cleanness evaluation. Cleanness
Description
10
Completely clean; no contaminants are left on the inner or outer surfaces Most part is clean; very little carbonized residue remains at the inner corner Most part is clean; some carbonized residue remains at the inner corner after the center housing is split; very little oxide scale remains at outer corners Most part is clean; some carbonized residue remains at the inner corner after the center housing is split; some oxide scale remains at outer corners More than half part is clean; carbonized residue is distributed at the inner corner; certain amount of oxide scale remains at the outer surface Around half part is clean; carbonized residue is distributed on the inner surface; around half of the outer surface is covered by oxide scale Slight amount of contaminants is removed
9 8
7
6
5
1e4
247
based on the scores described in Table 4. Note that when it is difficult to obtain objective measures, subjective estimates are frequently used in process models. As found in literature, subjective estimates have been used in optimization design (Florez and Castro-Lacouture, 2013), group decision (Singh et al., 1992) and multi-criteria decision making (Yeh and Xu, 2013) et al. To incorporate subjective estimates in the optimization models, they are generally converted to scores, similar to the system used in this research as shown in Table 4. It should be noted that, for this research, microscopic techniques could be more appropriate for the evaluation of cleanness. However, there are several issues that make these methods inapplicable: 1) Specimens of the experiment (i.e. contaminated parts studied in this research) are impossible to be prepared or created in a lab environment. The samples used in the experiment are big engine parts provided by a heavy equipment manufacture that were returned from its customers after around 5 years of service on site. Due to the size and quantities of the part processed in the experiment, it is neither allowed by the
Fig. 4. Main effects plot for cleaning cycle time and cleanness. Ignoring the other processing parameter, the mean of experimental results at different settings of portion of SN (or temperature) are connected to show the influence of portion of SN (or temperature) on cleaning effects.
248
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
Table 5 Estimated regression coefficients for cycle time. Term
Coefa
SE Coefb
Tc
Pd
Constant Portion of SN Temp Portion of SNPortion of SN TempTemp Portion of SNTemp
9.745 55.307 7.385 18.675 1.053 24.300
48.027 15.241 41.443 2.724 8.826 6.466
0.203 3.629 0.178 6.856 0.119 3.758
0.845 0.008 0.864 0.000 0.908 0.007
a b c d
Coef. ¼ coefficients. SE coef. ¼ standard error for coefficients. T ¼ T ratio test. P ¼ probability.
Table 6 Estimated regression coefficients for cycle time after removing TempTemp. Term
Coef
SE coef
T
P
Constant Portion of SN Temp Portion of SNPortion of SN Portion of SNTemp
4.090 55.307 12.314 18.717 24.300
7.189 14.272 3.027 2.529 6.055
0.569 3.875 4.068 7.402 4.013
0.585 0.005 0.004 0.000 0.004
manufacturer nor feasible to cut the part into smaller pieces so that microscope can be used to evaluate the cleanness. 2) After molten salt cleaning, these parts are quickly re-oxidized in around 10 min. Antirust spray is always used after the cleaning to avoid re-rusting. However to better evaluate the surface cleanness, anti-rust spray is not used in the present work. As a result, the evaluation of cleanness has to be done in a short time by visual assessment. 4.4. Results and analysis Thirteen experiments were conducted according to the central composite design and the run order was randomly generated to minimize systematic error. The level of the process parameters and its corresponding results in each run are shown in Appendix B. Based on the results shown in Appendix B, several observations were apparent: When the portion of sodium nitrate is very high, the cycle time tends to be short with a poor cleanness. This indicates that under this setting, the molten salt has a poor capability of penetrating into the contamination layers. Therefore, only a small portion of the contaminants can react with the salts and after a short period of time, reaction would stop. The second observation is that when both the portion of sodium nitrate and temperature are at their mid level, a high level of cleanness is achieved with a relatively long cycle time. Finally when the portion of sodium nitrate is low while the temperature is high, both responses show good values. In addition, violent reactions between salts and contaminations were clearly observed. Table 7 Analysis of variance for cycle time. R-Sq ¼ 91.7% R-Sq(adj) ¼ 87.5%. Source
DFa
Seq SSb
Adj SSc
Adj MSd
F
P
Regression Linear Square Interaction Residual Error Lack-of-Fit Pure Error Total
4 2 1 1 8 4 4 12
999.18 197.10 619.83 182.25 90.51 69.71 20.80 1089.69
999.18 357.11 619.83 182.25 90.51 69.71 20.80
249.795 178.555 619.835 182.249 11.314 17.428 5.200
22.08 15.78 54.78 16.11
0.000 0.002 0.000 0.004
3.35
0.134
a b c d
DF ¼ degree of freedom. SS ¼ sum of squares. Seq.SS ¼ the sequential sum of squares. Adj.SS ¼ the adjusted sum of squares.
Observations discussed above suggest significant impacts of individual main factors on both cycle time and cleanness, which are displayed in Fig. 4. For the effect of temperature on cycle time, the path nearly fits an exponential curve, which indicates that the more energy (the higher temperature) is involved in the cleaning process, the shorter the cycle time. This regulation also coincides with the physical law. As to cleaning effectiveness, the observed cleanness increases along with the decrease of the potion of SN and the increase of temperature. Another observation from the experiment was that a very small amount of carbonized contaminants was observed on the inner surface of the center housings during the 12th and 13th run. This is due to the lack of circulation in the salt bath used in this study. It has been proved that circulation mechanism can export waste and make the molten salt cleaning more effective for the internal surfaces. 4.5. Modeling of cycle time With experiment results in Appendix B, a quadratic model for cleaning cycle time was estimated using regression analysis. The model was expressed in coded units in equations (1) and (2). X and Y represent the level of portion of SN and temperature in coded units, respectively. The estimated regression coefficients are listed in Table 5.
X ¼ ðPortion of SN 0:74Þ=0:24;
(1)
Y ¼ ðTemp 399Þ=83:
(2)
As demonstrated in the table, the P-values for terms of X, XX and XY are much smaller than 0.05, which indicates that they are significant. However, the P-value for YY was much greater than 0.05, which indicates that this insignificant term should be deleted from the quadratic model. Term Y cannot be removed because of the significance of XY requiring this term to be kept in the model. After removing the insignificant term TempTemp, the coefficients were reanalyzed and shown in Table 6. The revised model is expressed in uncoded units as the following:
Cycle Time ¼ 91:81 þ 205:13$x 0:58$y 324:95$x2 þ 0:68$xy þ ε
(3)
where, x stands for the portion of sodium nitrate, y for the temperature and ε for the regression error. The Analysis of Variance (ANOVA) is summarized in Table 7. The R-Sq value implies that 91.7% of the variability for the response variable (cycle time) can be explained by the regressor variables. The adjusted R-Sq value, which is reasonably identical to the R-Sq value, indicates that extra variables are not included in the model. The P-values for linear, square and interaction regression are all less than 0.05, which means they are all statistically significant. Furthermore, the data model does not have any lack of fit since the P-value for lack of fit is much larger than 0.05. The residual analysis for the revised model is also conducted and shows no abnormalities. The response surface and contour plots of the model are demonstrated in Fig. 5 and Fig. 6, respectively. Two observations are apparent. The first observation is that the lower the portion of sodium nitrate and the higher the temperature, the shorter the cycle time. The other observation is that the higher the portion of sodium nitrate and the lower the temperature, the shorter the cycle time with a high level of residual contaminants, which indicates a low performance in cleaning. The problem of low cleanness under this condition is further discussed in Section 4.6.
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
249
Table 9 Estimated regression coefficients for cleanness after removing TempTemp. Term
Coef
SE coef
T
P
Constant Portion of SN Temp Portion of SNPortion of SN Portion of SNTemp
17.652 6.706 4.411 1.185 4.050
1.035 2.055 0.436 0.364 0.872
17.049 3.263 10.119 3.253 4.645
0.000 0.011 0.000 0.012 0.002
Table 10 Analysis of variance for cleanness. R-Sq ¼ 97.1% R-Sq(adj) ¼ 95.6%. Fig. 5. Surface plot of cycle time vs Temp and portion of SN.
Fig. 6. Contour plot of cycle time vs Temp and portion of SN. Different cycle time ranges are clearly shown by different boundaries whose specific time are stated. The grey area is optimum for molten salt cleaning.
4.6. Modeling of cleanness Similar analysis was performed on modeling of cleanness. Table 8 shows the results of the regression model for cleanness. All terms except YY play significant roles in the model. So term YY was removed from the model and the model was then reanalyzed. The same as in modeling of cycle time, term Y cannot be removed as term XY is significant. The new result was shown in Table 9. The revised model can be expressed in uncoded units as the following:
Source
DF
Seq SS
Adj SS
Adj MS
F
P
Regression Linear Square Interaction Residual Error Lack-of-Fit Pure Error Total
4 2 1 1 8 4 4 12
62.3535 54.8076 2.4835 5.0625 1.8772 1.1772 0.7000 64.2308
62.3535 26.5242 2.4835 5.0625 1.8772 1.1772 0.7000
15.5884 13.2621 2.4835 5.0625 0.2347 0.2943 0.1750
66.43 56.52 10.58 21.57
0.000 0.000 0.012 0.002
1.68
0.313
Fig. 7. Surface plot of Cleanness vs Temp. and Portion of SN.
Table 10 shows the ANOVA analysis for the model created. The difference between R-Sq and the adjusted R-Sq value is extremely small and the P-value for lack of fit is much greater than 0.05. Both of them demonstrate a good fit for the model expressed in equation (4). The validation of this model is also proved by residual analysis.
Cleanness ¼ 26:09 25:99$x 0:05$y 20:57$x2 þ 0:11$xy þ ε (4) where, x denotes the actual portion of sodium nitrate, y is the actual temperature and ε denotes the error term. Table 8 Estimated regression coefficients for cleanness. Term
Coef
SE coef
T
P
Constant Portion of SN Temp Portion of SNPortion of SN TempTemp Portion of SNTemp
13.954 6.706 1.188 1.213 0.689 4.050
6.777 2.151 5.848 0.384 1.245 0.912
2.059 3.118 0.203 3.154 0.553 4.439
0.078 0.017 0.845 0.016 0.598 0.003
Fig. 8. Contour plot of Cleanness vs Temp. and Portion of SN. Different cleanness ranges are clearly shown by different boundaries whose specific cleanness are stated.
250
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251
Both Fig. 7 and Fig. 8 demonstrate the effect of portion of SN and temperature on surface cleanness. As the portion of SN and temperature increase, the cleanness performance improves. The upperleft area in Fig. 8 shows a good cleaning result.
4.7. Process optimization As demonstrated in prior analysis, it is difficult to find parameter values that could simultaneously optimize both response variables. Considering that in industry applications, the parameter values are rather regulated in a range, it is more meaningful to recommend an optimized area instead of a point. The idea of optimized area could also reduce the sensitivity of the model to errors that might be involved in subjective assessments used in evaluation of the cleanness response. As a result, the upper-left corner area (in grey) in Fig. 6 is recommended based on the two models. The diagonal boundary line is expressed as the following equation:
result, the impact of higher temperature on cleaning efficiency is still unknown; 2) In this research, only the effects of sodium nitrate and sodium hydroxide are analyzed. Other types of salts, such as sodium chloride, potassium hydroxide, etc., may be studied and analyzed in future research; 3) Other cleaning mechanisms such as ultrasonic cleaning could be integrated with molten salt cleaning, which would result in a new cleaning system. And the new cleaning system may greatly enhance the cleaning performance of the molten salt process. Acknowledgment Authors would like to thank Dr. A. Han for her help in chemical analysis, and Caterpillar Inc. for its support in providing the samples. Appendix A. Reactions in molten salt bath
Contaminant
Reaction equations
Reference
Carbonized residue
C þ 2NaNO3CO2 þ 2NaNO2 CO2 þ 2NaOH Na2CO3 þ H2O 2NaNO2 þ O22NaNO3 2AO þ 2MO þ 2NaNO3A2O3 þ M2O3 þ 2NaNO2 2M2O3 þ 4NaOH 2Na2MO4 þ 2H2O 2NaNO2 þ O22NaNO3 (CxHyOz)n þ OH- þ O2CO2 þ H2O CO2 þ 2NaOH Na2CO3 þ H2O 2Cr3C2 þ 20NaOH þ 13O26Na2CrO4 þ 4Na2CO3 þ 10H2O MoS2 þ 6NaOH þ 9NaNO3Na2MoO4 þ 2Na2SO4 þ 9NaNO2 þ H2O
(Shoemaker, 1971; Shoemaker and Wood, 1971; Shoemaker, 1973a,b)
Oxide scales
Organic coatings Inorganic films
205:13$x 0:58$y 324:95$x2 þ 0:68$xy þ 71:81 ¼ 0
(5)
Processes that fall into this area generally produce a shorter cleaning cycle time (<20 min) and a cleanness higher than 8. Within this area, most contaminants can be quickly removed. It is expected that when circulation is introduced, contaminants would be completely removed and the cleaning effectiveness could be further enhanced. 5. Conclusions In this paper, the impacts of molten salts on metal parts are studied. The results show no detrimental effects for 1145 carbon steel and G2 grey iron samples. When 2024 aluminum parts are treated with the molten salt, however, the processing parameters should be carefully controlled in order to avoid part damage. The impact of molten salt to the cleaning performance was then analyzed with an aim to characterize and optimize the molten salt cleaning process. It is shown that with a low percentage of SN and high process temperature, the process performance in terms of reaction time and cleaning effectiveness is enhanced. Two mathematical models were established for cycle time and cleanness, respectively. Finally, process parameters located in the upper-left corner area in Fig. 6 are recommended for processing heavily contaminated cast-iron parts using molten salt cleaning process. It is worth noting that the current research is not intended to be a complete work. Future research is needed so that the application of molten salt cleaning method in industry can be better instructed. This includes: 1) Due to the equipment limitation, the highest temperature that can be reached in current research is 482 C. As a
(Shoemaker, 1971; Shoemaker, 1973a,b; Willing and Faulkner, 2001)
(Malloy, 2009; Malloy, 2010) (Shoemaker, 1971; Shoemaker and Wood, 1971)
Appendix B. The central composite design and corresponding results
RunOrder
Portion of SN (wt%)
Temperature ( C)
CycleTime (min)
Cleanness
1 2 3 4 5 6 7 8 9 10 11 12 13
0.74 0.74 0.91 0.74 0.74 0.98 0.74 0.91 0.74 0.57 0.57 0.50 0.74
316 399 340 399 399 399 399 458 399 340 458 399 482
44 32 19 30 34 11 32 28 36 32 14 18 23
5.0 7.5 2.0 7.0 8.0 3.0 7.0 8.0 7.5 7.5 9.0 9.5 9.5
References Cheng, S.Y., Kuan, S.L., Tsai, W.T., 2005. Effect of molten salt treatment on descaling behavior of 410 SS in sulfuric acid solution. Mater. Sci. Eng. A 393, 352e360. Dunlevy, R., Frick, H., Shoemaker, J.H., 1953. Apparatus and method for processing of steel strip continuously. U.S. Patent 2,635,062. Florez, L., Castro-Lacouture, D., 2013. Optimization model for sustainable materials selection using objective and subjective factors. Mater. Des. 46, 310e321. Forsberg, F., 1945. Cleaning oxides from steel. U.S. Patent 2,378,761. Hoglund, B.N., 1997. What is Molten Salt & its Technology?. http://home.earthlink. net/wbhoglund/whatsMoltenSal.html (accessed 15 07.10.). Holm, B., Pettersson, R., 2005. Descaling of stainless steels in an oxidizing salt bath. In: Stainless Steel World 2005 Conference & Expo., Maastricht, the Netherlands, pp. 346e350.
Y. Long et al. / Journal of Cleaner Production 68 (2014) 243e251 Kopietz, K.H., September 1994. Salt bath cleaning of synthetic fiber spinning equipment. In: Fiber & Yarn Technology. Carpet & Rug Ind, Dalton, pp. 62e67. Malloy, J.C., 2000. Stripping organic coatings from parts and part carriers using molten salt bath technology. Met. Finish 98, 507e508, 510-513. Malloy, J.C., 2009. Extreme Cleaning. Gardner Publications Inc.. http://www. pfonline.com/articles/060901.html (accessed 19.07.10.). Malloy, J.C., 2010. Molten Salt Cleaning of Engine Components e an Alternative Cleaning Technology. Kolene Corp. http://www.kolene.com/A55B6F/ Kolene2Website.nsf/Pages/CCABE1047EB75E1285257363002E50E9/$File/ engclean.pdf (accessed 12.02.11.). McCardle, T., 1988. Stripping Aluminum Parts in Molten Salt Bath. In: Products Finishing, 52. Gardner Publications Inc., pp. 96e101 Montgomery, D.C., 2009. Design and Analysis of Experiments, seventh ed. Wiley Inc., New York. Sandia Laboratories, 1969. Contamination Control Handbook. Technology Utilization Division, National Aeronautics and Space Administration, Washington, D.C. Seitz, M.A., 2007. A critical assessment of motives for product recovery: the case of engine remanufacturing. J. Clean. Prod. 15, 1147e1157. Shoemaker, R.H., 1971. Molten salt process. Wire J. 4, 33e38.
251
Shoemaker, R.H., Wood, W.G., 1971. Production cleaning systems involving molten salt baths. Met. Eng. Q. 11, 38e41. Shoemaker, R.H., 1973a. Molten salt baths reduce finishing time and cost. Met. Eng. Q. 13, 22e25. Shoemaker, R.H., 1973b. New molten salt systems for cleaning stainless steels. In: Cleaning Stainless Steel: a Symposium. ASTM Int, West Conshohocken, pp. 106e 117. Singh, M.G., Bennavail, J.C., Chen, Z.J., 1992. A group decision tool for combining subjective estimates based on an optimisation approach. Decis. Support Syst. 8, 541e549. Vincent-Daviss, C.A., 1940. Metal Cleaning Process. U.S. Patent 2,188,930. Webster, H.G., Falter, C.L., Shoemaker, J.H., 1949. Process of Cleaning Metal Surfaces and Compositions there for. U.S. Patent 2,458,661. White, W.E., 1947. Method for Cleaning Aluminum. U.S. Patent 2,431,595. Willing, R., Faulkner, C., 2001. Salt bath technology e a better way to clean metals. Adv. Mater. Process 159, 61e64. Yeh, C.-H., Xu, Y., 2013. Sustainable planning of e-waste recycling activities using fuzzy multicriteria decision making. J. Clean. Prod. 52, 194e204.