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Utilization of color change and image processing to evaluate the Waste Foundry Sand reclamation level
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Original Article
Utilization of color change and image processing to evaluate the Waste Foundry Sand reclamation level Mohammad Reza Sabour a,b,∗ , Mohammadamin Akbari a , Ghorbanali Dezvareh a a b
Civil Engineering Faculty, K. N. Toosi University of Technology, Tehran, Iran Civil Engineering Faculty, K. N. Toosi University of Technology, No. 1364, Valiasr Street, Mirdamad Intersection, Tehran, Iran
a r t i c l e
i n f o
a b s t r a c t
Article history:
Waste Foundry Sand (WFS) is the by-product of casting industries and is utilized to make
Received 8 June 2019
molds. Some minerals, such as clay particles or chemical admixtures such as phenolic resins,
Accepted 15 November 2019
are used to bond silica sand grains and shape the mold. Preventing environmental impacts
Available online xxx
such as WFS landfilling, many researches have been conducted to find an appropriate way
Keywords:
image processing is helpful for an estimation of reclamation progress. In this paper, thermal
for reclamation and evaluation of its cleaning. Due to the color changes during this process, Reclamation
reclamation method and Response Surface Methodology (RSM) are used as the most appro-
Waste Foundry Sand
priate way of reclamation and design of experiments, respectively. Samples are heated at
Response surface methodology
different temperatures for a variety of time durations. The color changes of samples are
Waste management
measured using image processing technique by detection of RGB (Red, Green, Blue) color
RGB
coordinate system parameters changes (RGB). The results of TCLP (Toxicity Characteristic
Image processing
Leaching Procedure) tests reveal that phenolic compounds are removed during the reclamation process, which causes WFS color changes from black to almost white. On this basis, a linear model is developed to predict RGB values relative to time or temperature variations. Results approve that an investigation on RGB values provides a precise estimation of WFS grains reclamation level (less than 10 % error) to guide its reuse within the foundry industries. © 2019 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1.
Introduction
Foundry industries produce different ferrous and non-ferrous metals. Most products are used in several industries such as aerospace and automobile. In order to produce these objects, different mold designs (to shape external surfaces) and core
∗
designs (to form internal void spaces) are required. Also, due to high temperatures during the casting process, the molding material should be able to resist the remarkable heat of the molten metal. Considering abovementioned issues, one of the most common materials to make molds in the casting process is uniform-size-distributed high-quality silica sand. Silica sand can take different shapes by mixing with some minerals or chemical admixtures, because the sand grains cannot naturally bond to each other [1].
Corresponding author. E-mail: [email protected] (M.R. Sabour). https://doi.org/10.1016/j.jmrt.2019.11.041 2238-7854/© 2019 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). Please cite this article in press as: Sabour MR, et al. Utilization of color change and image processing to evaluate the Waste Foundry Sand reclamation level. J Mater Res Technol. 2019. https://doi.org/10.1016/j.jmrt.2019.11.041
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Waste Foundry Sand (WFS) is the by-product of ferrous and non-ferrous metals casting process. During the casting process, foundry sand is recycled and reused several times, but eventually it cannot be utilized anymore. The result is a vast amount of a left over as WFS [2]. Landfilling, as the most common way to dispose WFS, causes considerable cost and environmental impacts. Consequently, researches were conducted towards introducing new ways of this material utilization in different industries such as road and building construction, foundry industry, and concrete production [3]. Also, it will be difficult to access new resources of raw sand in the near future, and regulations will restrict the extraction of sand from the land. As a result, the use of new sands in the casting industry will be reduced or prohibited, and industries are to use reclaimed sand for mold making shortly [4].
The chemical composition of WFS is related to the type of chemical admixture. Research articles revealed that different heavy metals (e.g.: Cr, As, Cd, Hg), phenol, formaldehyde, and other PAHs are found in WFS [8,9]. Different experimental methods are introduced to identify chemical composition and its quantity within the sample. Toxicity Characteristic Leaching Procedure (TCLP) [8,10], Synthetic Precipitation Leaching Procedure (SPLP) [8,11], and Extraction Procedure Toxicity (EP) [12,13] are the most common methods in this regard. In aforementioned methods, the sample is washed with a solution, then the concentration of each chemical composition is determined through GasChromatography/Mass-Spectrometry (GC/MS) or other similar methods [10,11,13].
1.1.
WFS is mainly categorized as a hazardous waste based on the presence of some harmful compounds such as phenol and formaldehyde in many kinds of resins, [3]. Phenol is a solid or dense liquid, which melts down at 45 ◦ . Its boiling point is 181.8 ◦ and has an 87 g/l solubility in water at 25 ◦ . It can remain about 2–5 days in soil without any change and more than 7 days in water in a high concentration [14]. As a result, these compounds should be removed before landfilling or any other usage [3]. Thermal reclamation is a method in which WFS is heated up to 500–900 ◦ , and chemical compounds are evaporated or burned. This heat is produced by electricity or ignition of different fossil fuels such as gas and oil via rotary kiln, fluidized bed, and shaft furnaces as the most common equipment for this purpose [15]. Andrade et al. [16] drew the mass reduction percentage diagram of WFS due to the decomposition of phenolic resins. They concluded that this process is optimal by heating between 450 ◦ and 550 ◦ in 60 min, while higher temperatures lead to more energy consumption. Another research on thermal reclamation of WFS is implemented by Lucarz [17], indicating that this method can fully substitute the mechanical reclamation. The author investigated the efficiency of thermal reclamation method for 30 g WFS samples of furan molding sand through heating at 5 different temperatures for 2 h. The results show that thermal treatment at 600–700 ◦ temperature range has an acceptable efficiency.
Types of foundry sand
Referring to the casting methods, various chemical compositions are used to bond sand grains. Thus, different kinds of WFS can be produced during the casting process, being claybonded (green), air-setting bonded, and thermosetting bonded sands the most dominant produced ones [5]. The clay-bonded sand system is the oldest method for mold making, in which water and clay are mixed to bond sand grains. Clay-bonded (green) sand consists of high-quality silica sand (85–95 %), bentonite (4–10 %), water (2–5 %), and hydrocarbon compounds (2–10 %). This hydrocarbon admixture is coal dust, which produces lustrous carbon under the influence of high temperature while exposing to molten metal during the casting process. Lustrous carbon protects sand grains from entering to molten metal and improves the final surface. Therefore, a high-quality final surface of the product is obtained [5]. Chemically bonded materials are developed as a result of technology progress and introduction of new bonding materials. Chemically bonded sands consist of silica sand (93–99 %) and chemical pastes (1–3 %). Different kinds of chemical pastes, including phenolic-urethanes and epoxy resins, are utilized. Some materials, which are mainly named as thermosetting resins, are hardened by heating at a temperature above 200 ◦ . This kind of mold making materials is used in shell molding process. Another method for mold making, named as Cold-Box method, uses a substance such as sodium silicate solution or phenolic resins to mix with sand, and the mixture is hardened by blowing gas (CO2 or amine). This process produces a weak acid, which hydrolyzes sodium silicate and produces amorphous silica. This amorphous silica bonds sand grains to each other [6].
1.2.
Physical and chemical properties of WFS
Referring to previous sections, WFS consists of uniform-sizedistributed grains [1]. According to the presence of different hydrocarbon compounds in different types of foundry sand, this material has a variety of colors (e.g.: red, brown, and black). After the casting process, hydrocarbon compounds are mainly burned and the color turns into dark brown or black [7].
1.3.
1.4.
Thermal reclamation method
Image processing
Colorimetric analysis of a special point is carried out by determining RGB (Red, Green, Blue) color coordinate system (RGB−CCS) values. These values are determined through an appropriate software (e.g.: Photoshop) by calculating differences between the values of each component before and after the reclamation process (R, G, B). RGB−CCS uses 256bit color scale with white and black corresponding to color intensity of “255,255,255” and “0,0,0”, respectively [18]. Many researches are conducted using this system to determine color changes in order to demonstrate changes in organic compounds or removal of contaminants from a substance [18–20]. Endo et al. [21] developed a colloidal crystal-based chemical sensor with a reversibly tunable structural color. They detected a volatile organic compound using colorimetric
Please cite this article in press as: Sabour MR, et al. Utilization of color change and image processing to evaluate the Waste Foundry Sand reclamation level. J Mater Res Technol. 2019. https://doi.org/10.1016/j.jmrt.2019.11.041
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methods. Indeed, color changes indicate the type of target molecules and their concentrations. Thus, chemical sensors to detect the type and color changes of a specific material are able to be widely used for environmental pollution evaluation. In a recent research on detection of mercury ion (Hg2+ ) contamination, a digital image scanner based on the change in RGB values of a bio-functionalized gold nanoparticle (A-GNP) solution upon the addition of different Hg2+ concentrations is used. Poornima et al. [22] reported that a Digital Image Scanometer (DIS) simplifies contamination detection, produces results with good precision, and involves the use of fewer amounts of chemicals. Therefore, this system reduces the cost project and improves analysis accuracy.
2.
Testing procedure
This study is conducted on the casting unit WFS of IKCO, which is one of the most prominent Iranian automakers. The waste is the by-product of Cold-box casting process in which a phenolic resin is mixed with sand, and the mold is made by blowing amine. Due to the use of a phenolic resin, this waste has the potential of being hazardous for the environment. As a result, it is necessary to implement reclamation on it prior to any application or landfilling. The presence of phenol or other hydrocarbon compounds was assessed by TCLP test method. In this regard, sample 11 and a sample before reclamation process are tested. Gallenkamp electrical kiln is utilized for thermal reclamation process, while temperatures raised up to 1000 ◦ with an increment of 20 ◦ . A set of experiments are designed using Design Expert V.7 and Response Surface Methodology (RSM) based on the color changes during the heating procedure. RSM is a combination of different statistical and mathematical techniques, which explores the relationship between several input parameters and the responses. Central Composite Design (CCD) method, which was first introduced by Box and Wilson in 1951 [23], is used to design experiments. Time and temperature are defined as input variables in the range of 10 minutes–120 minutes and 150 ◦ –600 ◦ , respectively, and R, G, and B of each sample are defined as responses. The results are 13 different situations of experiments, which are reported in Table 1. In order to evaluate color changes on the surface of samples before and after the heating process, a CANON EOS 70D camera is used to take photos of the samples upper surface. Photos are taken in a closed room with a constant light, which is not exposed to external light. Adobe Photoshop CS3 Portable and MATLAB R2014a are utilized to extract average R, G, and B values of the whole surface of sample before and after reclamation. This process is performed manually using Photoshop by gaining R, G, and B values of 16 randomly selected points, and automatically through scanning and gaining R, G, and B values of all pixels using MATLAB. Finally, the average value of each parameter and differences of R, G, and B values before and after reclamation process are calculated and reported as R, G, and B responses for each sample.
3.
3
Results and discussion
RGB values are reported in Table 1. Results show that by increasing both time and temperature, RGB values increase (Fig. 2). As discussed, high and low values of RGB represent light and dark colors, respectively. Color changes indicate that the reclamation method applied to WFS is very effective and hydrocarbon contaminants are removed at the exposure of high temperatures or increasing heating duration, although each of the two conditions leads to higher energy consumption. Scrutinizing RGB values of samples 1,5,7,8, and 12 highlights the following 2 issues. 1 RGB values obtained by Photoshop show a great difference to the same values obtained by MATLAB. Fig. 1 shows the photos of samples 11, 5, and an unreclaimed sample (left to right). Sample 5 was heated at 375 ◦ in 65 min and hydrocarbon contaminants are removed out of some WFS particles, while other particles remain contaminated. As a result, manual point selection through a software such as Photoshop leads to an error. This error is the result of selecting just white points (the points related to reclaimed particles) or black points (contaminated points). Thus, if the black points are chosen, RGB values report an unreclaimed surface, and if the white points are chosen, RGB values report a completely reclaimed surface. In this case, although reclamation process occurred partly, the results show an unreclaimed surface or a completely reclaimed surface. 2 The difference among RGB values of sample 8 (which are low and approximately 0), 7, 1, 5, and 12, (Fig. 2) is the result of the location of each sample inside the kiln. Sample 7 was close to the source of heat, so it has faced higher heat compared to samples 1, 5, and 12, which were at the midpoint of the kiln, while sample 8 was far from the source of heat. Fig. 3 shows 2 photos of color changes occurred for sample 8. Obviously, there is no significant color change after the reclamation process. In order to correct errors in samples 7 and 8, thermal reclamation process was repeated again. Samples 7 and 8 were heated at 375 ◦ in 65 min and RGB values were measured. The results are reported in Table 1. The values obtained by MATLAB are used in Design Expert V.7 for extracting a model on the basis of precision and accuracy of results. RGB values of samples 7 and 8 were used in order to generate the model instead of samples 7 and 8. This model predicts the result of reclamation process based on temperature and heating duration. In Central Composite Design method, P-value and F-Value parameters are investigated. Lower P-value and higher F-value indicate significance of the results, validity of population, which is tested, and the sensitivity of input variables. The lower the P-value and the higher the F-value, the stronger the model. Results obtained by Design Expert show that R, G, and B values follow a great linear equation. Statistical parameters, P-value, and F-value are reported in Table 2, and the
Please cite this article in press as: Sabour MR, et al. Utilization of color change and image processing to evaluate the Waste Foundry Sand reclamation level. J Mater Res Technol. 2019. https://doi.org/10.1016/j.jmrt.2019.11.041
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Table 1 – Design of experiments and RGB values. RGB values obtained by MATLAB R2014a
RGB values obtained by photoshop CS3 portable
Design of experiments
B
G
R
B
G
R
Time (minute)
Temperature (◦ )
No.
15.9 1.7 5.0 9.0 29.3 55.5 37.3 1.9 54.5 73.0 77.8 18.9 0.0 19.0 17.2
20.3 1.6 7.2 6.3 32.2 59.9 42.9 2.3 67.6 84.6 90.1 20.1 0.9 21.7 20.9
26.4 1.9 9.9 7.0 38.1 65.6 50.2 0.2 73.5 94.4 106.0 25.1 0.6 27.1 25.7
35.3 1.1 4.1 7.1 27.1 55.8 21.8 13.9 49.6 69.8 80.5 37.0 3.9 17.3 19.1
39.4 1.8 5.4 5.0 30.1 60.7 24.0 17.2 66.1 87.3 93.8 39.2 2.3 20.2 23.2
47.5 1.9 8.3 5.1 36.3 66.6 29.1 21.6 73.8 96.6 108.8 43.1 3.1 25.1 27.6
65 10 26 104 65 26 65 65 120 104 65 65 65 65 65
375 375 216 216 375 534 375 375 375 536 600 375 150 375 375
1 2 3 4 5 6 7 8 9 10 11 12 13 7 8
Fig. 1 – WFS samples (left to right: sample 11, sample 5, unreclaimed sample).
Fig. 2 – RGB values of samples 1–13.
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Fig. 3 – Surface of sample 8 before (left) and after (right) reclamation process.
Table 2 – Statistical parameters used for modeling. F-Value
P-Value
Std. Dev.
Mean
Maximum
Minimum
– – 29.43 26.85 28.96
– – <0.0001 <0.0001 <0.0001
124.808 30.509 33.794 30.178 25.937
375 65 38.562 33.338 28.985
600 120 106.0 90.1 77.8
150 10 0.6 0.9 0.0
Fig. 4 – Predicted vs. actual graph of R values.
Temperature(◦ C) Time (min) R G B
Fig. 5 – Predicted vs. actual graph of G values.
related linear equations are presented as Eq. 1,2, and 3, as follow. R = − 74.08189 + 0.22954 ∗ Temperature + 0.4087 ∗ Time
(1)
G = − 66.89670 + 0.20203 ∗ Temperature + 0.3765 ∗ Time
(2)
B = − 57.25867 + 0.17640*Temperature + 0.3091*Time
(3)
These equations are applicable within the introduced time and temperature ranges. In order to analyze the differences between real values and the values produced by the model, “Predicted vs. Actual” graphs are drawn. Figs. 4–6 show “Predicted vs. Actual” graph for R, G, and B values, respectively. As demonstrated, in lower values of time and temperature, error increases. Hence,
Fig. 6 – Predicted vs. actual graph of B values.
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Table 3 – Verification of the model (RGB values of sample 14). B
G
R
No.
44.6 40.21 9.8 %
49.1 45.41 7.5 %
57.9 52.95 8.54 %
14 predicted % Error
Fig. 9 – TCLP analysis of WFS after the reclamation process (sample 11).
Table 4 – TCLP analysis results of WFS samples.
Fig. 7 – Predicted vs. actual graph of RGB values for sample 14 (Model Verification).
Sample 11
Unreclaimed sample
<25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <10 ppb
625 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb 49 ppb <25 ppb <10 ppb
4.
Fig. 8 – TCLP analysis of WFS before the reclamation process (unreclaimed sample).
the model functions accurately in higher ranges of time and temperature. As discussed, many researches imply that due to the presence of carbon and some other chemical contents, WFS has a variety of colors from black to medium tan [7,24,25]. Color changes during reclamation process prove the correctness of the hypothesis that those chemical contents are burned or evaporated while being heated. Consequently, investigating color changes could be a reliable and applicable method for evaluation concerning reclamation level. In order to verify the obtained model, sample 14 is heated at 500 ◦ in 30 min and RGB values are compared with the result of the model (Table 3). Referring to Fig. 7, differences between actual values and predicted ones are less than 10 % and the accuracy of the model is quite acceptable. The results of TCLP test indicate the presence of phenol and some other phenolic compounds before the reclamation process (Fig. 8). Fig. 9 illustrates that after the reclamation process on sample 11 at 600 ◦ in 65 min pulses become weak. Thus, the concentration of phenolic compounds decreases to almost zero (Table 4).
Phenol 2-chlorophenol 2,4-dimethyl phenol 4-chloro-3-methyl phenol 2,4-dichlorophenol 2-nitrophenol 2,4,6-trichlorophenol 4-nitrophenol Pentachlorophenol Formaldehyde
Conclusion
Waste Foundry Sand (WFS) consists of different contamination based on the type of casting process and mold making method. Thermal reclamation process is a method for removing contaminants from the sand and preparing it to be reused in various industries. The results of TCLP tests before and after thermal reclamation process indicate that this method is an appropriate cleaning for WFS grains. During thermal reclamation process, burning or evaporation of hydrocarbon contaminants leads to color changes, from almost black to almost white. Quantification of these changes is a good index for determination of contaminants removal level. In order to quantify color changes, photos should be taken of the samples surface in a room with a constant light before and after the reclamation process and RGB values should be determined. MATLAB, Photoshop, and other software are useful for measuring RGB values, but MATLAB is more accurate because it reports an average of all pixels RGB values. The enhancement of temperature leads to breaking chemical bonds through providing higher levels of energy. Therefore, hydrocarbon compounds burn or evaporate easier, and higher levels of reclamation process are achieved. As same as temperature, the enhancement of heating time duration increases the exposure of hydrocarbon contaminants to thermal energy. Thus, more hydrocarbon compounds burn or evaporate, and reclamation process progresses in a more efficient manner. Due to the costs of chemical tests (TCLP, SPLP, etc.) and the preparation time of their results, image processing helps to quickly evaluate the reclamation level. A linear model is obtained in order
Please cite this article in press as: Sabour MR, et al. Utilization of color change and image processing to evaluate the Waste Foundry Sand reclamation level. J Mater Res Technol. 2019. https://doi.org/10.1016/j.jmrt.2019.11.041
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to predict RGB values based on reclamation temperature and heating duration. The values out of the model indicate less than 10 % error in comparison with actual ones of RGB.
Conflicts of interest The authors declare no conflicts of interest.
references
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Please cite this article in press as: Sabour MR, et al. Utilization of color change and image processing to evaluate the Waste Foundry Sand reclamation level. J Mater Res Technol. 2019. https://doi.org/10.1016/j.jmrt.2019.11.041
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Original Article
Utilization of color change and image processing to evaluate the Waste Foundry Sand reclamation level Mohammad Reza Sabour a,b,∗ , Mohammadamin Akbari a , Ghorbanali Dezvareh a a b
Civil Engineering Faculty, K. N. Toosi University of Technology, Tehran, Iran Civil Engineering Faculty, K. N. Toosi University of Technology, No. 1364, Valiasr Street, Mirdamad Intersection, Tehran, Iran
a r t i c l e
i n f o
a b s t r a c t
Article history:
Waste Foundry Sand (WFS) is the by-product of casting industries and is utilized to make
Received 8 June 2019
molds. Some minerals, such as clay particles or chemical admixtures such as phenolic resins,
Accepted 15 November 2019
are used to bond silica sand grains and shape the mold. Preventing environmental impacts
Available online xxx
such as WFS landfilling, many researches have been conducted to find an appropriate way
Keywords:
image processing is helpful for an estimation of reclamation progress. In this paper, thermal
for reclamation and evaluation of its cleaning. Due to the color changes during this process, Reclamation
reclamation method and Response Surface Methodology (RSM) are used as the most appro-
Waste Foundry Sand
priate way of reclamation and design of experiments, respectively. Samples are heated at
Response surface methodology
different temperatures for a variety of time durations. The color changes of samples are
Waste management
measured using image processing technique by detection of RGB (Red, Green, Blue) color
RGB
coordinate system parameters changes (RGB). The results of TCLP (Toxicity Characteristic
Image processing
Leaching Procedure) tests reveal that phenolic compounds are removed during the reclamation process, which causes WFS color changes from black to almost white. On this basis, a linear model is developed to predict RGB values relative to time or temperature variations. Results approve that an investigation on RGB values provides a precise estimation of WFS grains reclamation level (less than 10 % error) to guide its reuse within the foundry industries. © 2019 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1.
Introduction
Foundry industries produce different ferrous and non-ferrous metals. Most products are used in several industries such as aerospace and automobile. In order to produce these objects, different mold designs (to shape external surfaces) and core
∗
designs (to form internal void spaces) are required. Also, due to high temperatures during the casting process, the molding material should be able to resist the remarkable heat of the molten metal. Considering abovementioned issues, one of the most common materials to make molds in the casting process is uniform-size-distributed high-quality silica sand. Silica sand can take different shapes by mixing with some minerals or chemical admixtures, because the sand grains cannot naturally bond to each other [1].
Corresponding author. E-mail: [email protected] (M.R. Sabour). https://doi.org/10.1016/j.jmrt.2019.11.041 2238-7854/© 2019 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). Please cite this article in press as: Sabour MR, et al. Utilization of color change and image processing to evaluate the Waste Foundry Sand reclamation level. J Mater Res Technol. 2019. https://doi.org/10.1016/j.jmrt.2019.11.041
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Waste Foundry Sand (WFS) is the by-product of ferrous and non-ferrous metals casting process. During the casting process, foundry sand is recycled and reused several times, but eventually it cannot be utilized anymore. The result is a vast amount of a left over as WFS [2]. Landfilling, as the most common way to dispose WFS, causes considerable cost and environmental impacts. Consequently, researches were conducted towards introducing new ways of this material utilization in different industries such as road and building construction, foundry industry, and concrete production [3]. Also, it will be difficult to access new resources of raw sand in the near future, and regulations will restrict the extraction of sand from the land. As a result, the use of new sands in the casting industry will be reduced or prohibited, and industries are to use reclaimed sand for mold making shortly [4].
The chemical composition of WFS is related to the type of chemical admixture. Research articles revealed that different heavy metals (e.g.: Cr, As, Cd, Hg), phenol, formaldehyde, and other PAHs are found in WFS [8,9]. Different experimental methods are introduced to identify chemical composition and its quantity within the sample. Toxicity Characteristic Leaching Procedure (TCLP) [8,10], Synthetic Precipitation Leaching Procedure (SPLP) [8,11], and Extraction Procedure Toxicity (EP) [12,13] are the most common methods in this regard. In aforementioned methods, the sample is washed with a solution, then the concentration of each chemical composition is determined through GasChromatography/Mass-Spectrometry (GC/MS) or other similar methods [10,11,13].
1.1.
WFS is mainly categorized as a hazardous waste based on the presence of some harmful compounds such as phenol and formaldehyde in many kinds of resins, [3]. Phenol is a solid or dense liquid, which melts down at 45 ◦ . Its boiling point is 181.8 ◦ and has an 87 g/l solubility in water at 25 ◦ . It can remain about 2–5 days in soil without any change and more than 7 days in water in a high concentration [14]. As a result, these compounds should be removed before landfilling or any other usage [3]. Thermal reclamation is a method in which WFS is heated up to 500–900 ◦ , and chemical compounds are evaporated or burned. This heat is produced by electricity or ignition of different fossil fuels such as gas and oil via rotary kiln, fluidized bed, and shaft furnaces as the most common equipment for this purpose [15]. Andrade et al. [16] drew the mass reduction percentage diagram of WFS due to the decomposition of phenolic resins. They concluded that this process is optimal by heating between 450 ◦ and 550 ◦ in 60 min, while higher temperatures lead to more energy consumption. Another research on thermal reclamation of WFS is implemented by Lucarz [17], indicating that this method can fully substitute the mechanical reclamation. The author investigated the efficiency of thermal reclamation method for 30 g WFS samples of furan molding sand through heating at 5 different temperatures for 2 h. The results show that thermal treatment at 600–700 ◦ temperature range has an acceptable efficiency.
Types of foundry sand
Referring to the casting methods, various chemical compositions are used to bond sand grains. Thus, different kinds of WFS can be produced during the casting process, being claybonded (green), air-setting bonded, and thermosetting bonded sands the most dominant produced ones [5]. The clay-bonded sand system is the oldest method for mold making, in which water and clay are mixed to bond sand grains. Clay-bonded (green) sand consists of high-quality silica sand (85–95 %), bentonite (4–10 %), water (2–5 %), and hydrocarbon compounds (2–10 %). This hydrocarbon admixture is coal dust, which produces lustrous carbon under the influence of high temperature while exposing to molten metal during the casting process. Lustrous carbon protects sand grains from entering to molten metal and improves the final surface. Therefore, a high-quality final surface of the product is obtained [5]. Chemically bonded materials are developed as a result of technology progress and introduction of new bonding materials. Chemically bonded sands consist of silica sand (93–99 %) and chemical pastes (1–3 %). Different kinds of chemical pastes, including phenolic-urethanes and epoxy resins, are utilized. Some materials, which are mainly named as thermosetting resins, are hardened by heating at a temperature above 200 ◦ . This kind of mold making materials is used in shell molding process. Another method for mold making, named as Cold-Box method, uses a substance such as sodium silicate solution or phenolic resins to mix with sand, and the mixture is hardened by blowing gas (CO2 or amine). This process produces a weak acid, which hydrolyzes sodium silicate and produces amorphous silica. This amorphous silica bonds sand grains to each other [6].
1.2.
Physical and chemical properties of WFS
Referring to previous sections, WFS consists of uniform-sizedistributed grains [1]. According to the presence of different hydrocarbon compounds in different types of foundry sand, this material has a variety of colors (e.g.: red, brown, and black). After the casting process, hydrocarbon compounds are mainly burned and the color turns into dark brown or black [7].
1.3.
1.4.
Thermal reclamation method
Image processing
Colorimetric analysis of a special point is carried out by determining RGB (Red, Green, Blue) color coordinate system (RGB−CCS) values. These values are determined through an appropriate software (e.g.: Photoshop) by calculating differences between the values of each component before and after the reclamation process (R, G, B). RGB−CCS uses 256bit color scale with white and black corresponding to color intensity of “255,255,255” and “0,0,0”, respectively [18]. Many researches are conducted using this system to determine color changes in order to demonstrate changes in organic compounds or removal of contaminants from a substance [18–20]. Endo et al. [21] developed a colloidal crystal-based chemical sensor with a reversibly tunable structural color. They detected a volatile organic compound using colorimetric
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methods. Indeed, color changes indicate the type of target molecules and their concentrations. Thus, chemical sensors to detect the type and color changes of a specific material are able to be widely used for environmental pollution evaluation. In a recent research on detection of mercury ion (Hg2+ ) contamination, a digital image scanner based on the change in RGB values of a bio-functionalized gold nanoparticle (A-GNP) solution upon the addition of different Hg2+ concentrations is used. Poornima et al. [22] reported that a Digital Image Scanometer (DIS) simplifies contamination detection, produces results with good precision, and involves the use of fewer amounts of chemicals. Therefore, this system reduces the cost project and improves analysis accuracy.
2.
Testing procedure
This study is conducted on the casting unit WFS of IKCO, which is one of the most prominent Iranian automakers. The waste is the by-product of Cold-box casting process in which a phenolic resin is mixed with sand, and the mold is made by blowing amine. Due to the use of a phenolic resin, this waste has the potential of being hazardous for the environment. As a result, it is necessary to implement reclamation on it prior to any application or landfilling. The presence of phenol or other hydrocarbon compounds was assessed by TCLP test method. In this regard, sample 11 and a sample before reclamation process are tested. Gallenkamp electrical kiln is utilized for thermal reclamation process, while temperatures raised up to 1000 ◦ with an increment of 20 ◦ . A set of experiments are designed using Design Expert V.7 and Response Surface Methodology (RSM) based on the color changes during the heating procedure. RSM is a combination of different statistical and mathematical techniques, which explores the relationship between several input parameters and the responses. Central Composite Design (CCD) method, which was first introduced by Box and Wilson in 1951 [23], is used to design experiments. Time and temperature are defined as input variables in the range of 10 minutes–120 minutes and 150 ◦ –600 ◦ , respectively, and R, G, and B of each sample are defined as responses. The results are 13 different situations of experiments, which are reported in Table 1. In order to evaluate color changes on the surface of samples before and after the heating process, a CANON EOS 70D camera is used to take photos of the samples upper surface. Photos are taken in a closed room with a constant light, which is not exposed to external light. Adobe Photoshop CS3 Portable and MATLAB R2014a are utilized to extract average R, G, and B values of the whole surface of sample before and after reclamation. This process is performed manually using Photoshop by gaining R, G, and B values of 16 randomly selected points, and automatically through scanning and gaining R, G, and B values of all pixels using MATLAB. Finally, the average value of each parameter and differences of R, G, and B values before and after reclamation process are calculated and reported as R, G, and B responses for each sample.
3.
3
Results and discussion
RGB values are reported in Table 1. Results show that by increasing both time and temperature, RGB values increase (Fig. 2). As discussed, high and low values of RGB represent light and dark colors, respectively. Color changes indicate that the reclamation method applied to WFS is very effective and hydrocarbon contaminants are removed at the exposure of high temperatures or increasing heating duration, although each of the two conditions leads to higher energy consumption. Scrutinizing RGB values of samples 1,5,7,8, and 12 highlights the following 2 issues. 1 RGB values obtained by Photoshop show a great difference to the same values obtained by MATLAB. Fig. 1 shows the photos of samples 11, 5, and an unreclaimed sample (left to right). Sample 5 was heated at 375 ◦ in 65 min and hydrocarbon contaminants are removed out of some WFS particles, while other particles remain contaminated. As a result, manual point selection through a software such as Photoshop leads to an error. This error is the result of selecting just white points (the points related to reclaimed particles) or black points (contaminated points). Thus, if the black points are chosen, RGB values report an unreclaimed surface, and if the white points are chosen, RGB values report a completely reclaimed surface. In this case, although reclamation process occurred partly, the results show an unreclaimed surface or a completely reclaimed surface. 2 The difference among RGB values of sample 8 (which are low and approximately 0), 7, 1, 5, and 12, (Fig. 2) is the result of the location of each sample inside the kiln. Sample 7 was close to the source of heat, so it has faced higher heat compared to samples 1, 5, and 12, which were at the midpoint of the kiln, while sample 8 was far from the source of heat. Fig. 3 shows 2 photos of color changes occurred for sample 8. Obviously, there is no significant color change after the reclamation process. In order to correct errors in samples 7 and 8, thermal reclamation process was repeated again. Samples 7 and 8 were heated at 375 ◦ in 65 min and RGB values were measured. The results are reported in Table 1. The values obtained by MATLAB are used in Design Expert V.7 for extracting a model on the basis of precision and accuracy of results. RGB values of samples 7 and 8 were used in order to generate the model instead of samples 7 and 8. This model predicts the result of reclamation process based on temperature and heating duration. In Central Composite Design method, P-value and F-Value parameters are investigated. Lower P-value and higher F-value indicate significance of the results, validity of population, which is tested, and the sensitivity of input variables. The lower the P-value and the higher the F-value, the stronger the model. Results obtained by Design Expert show that R, G, and B values follow a great linear equation. Statistical parameters, P-value, and F-value are reported in Table 2, and the
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Table 1 – Design of experiments and RGB values. RGB values obtained by MATLAB R2014a
RGB values obtained by photoshop CS3 portable
Design of experiments
B
G
R
B
G
R
Time (minute)
Temperature (◦ )
No.
15.9 1.7 5.0 9.0 29.3 55.5 37.3 1.9 54.5 73.0 77.8 18.9 0.0 19.0 17.2
20.3 1.6 7.2 6.3 32.2 59.9 42.9 2.3 67.6 84.6 90.1 20.1 0.9 21.7 20.9
26.4 1.9 9.9 7.0 38.1 65.6 50.2 0.2 73.5 94.4 106.0 25.1 0.6 27.1 25.7
35.3 1.1 4.1 7.1 27.1 55.8 21.8 13.9 49.6 69.8 80.5 37.0 3.9 17.3 19.1
39.4 1.8 5.4 5.0 30.1 60.7 24.0 17.2 66.1 87.3 93.8 39.2 2.3 20.2 23.2
47.5 1.9 8.3 5.1 36.3 66.6 29.1 21.6 73.8 96.6 108.8 43.1 3.1 25.1 27.6
65 10 26 104 65 26 65 65 120 104 65 65 65 65 65
375 375 216 216 375 534 375 375 375 536 600 375 150 375 375
1 2 3 4 5 6 7 8 9 10 11 12 13 7 8
Fig. 1 – WFS samples (left to right: sample 11, sample 5, unreclaimed sample).
Fig. 2 – RGB values of samples 1–13.
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Fig. 3 – Surface of sample 8 before (left) and after (right) reclamation process.
Table 2 – Statistical parameters used for modeling. F-Value
P-Value
Std. Dev.
Mean
Maximum
Minimum
– – 29.43 26.85 28.96
– – <0.0001 <0.0001 <0.0001
124.808 30.509 33.794 30.178 25.937
375 65 38.562 33.338 28.985
600 120 106.0 90.1 77.8
150 10 0.6 0.9 0.0
Fig. 4 – Predicted vs. actual graph of R values.
Temperature(◦ C) Time (min) R G B
Fig. 5 – Predicted vs. actual graph of G values.
related linear equations are presented as Eq. 1,2, and 3, as follow. R = − 74.08189 + 0.22954 ∗ Temperature + 0.4087 ∗ Time
(1)
G = − 66.89670 + 0.20203 ∗ Temperature + 0.3765 ∗ Time
(2)
B = − 57.25867 + 0.17640*Temperature + 0.3091*Time
(3)
These equations are applicable within the introduced time and temperature ranges. In order to analyze the differences between real values and the values produced by the model, “Predicted vs. Actual” graphs are drawn. Figs. 4–6 show “Predicted vs. Actual” graph for R, G, and B values, respectively. As demonstrated, in lower values of time and temperature, error increases. Hence,
Fig. 6 – Predicted vs. actual graph of B values.
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Table 3 – Verification of the model (RGB values of sample 14). B
G
R
No.
44.6 40.21 9.8 %
49.1 45.41 7.5 %
57.9 52.95 8.54 %
14 predicted % Error
Fig. 9 – TCLP analysis of WFS after the reclamation process (sample 11).
Table 4 – TCLP analysis results of WFS samples.
Fig. 7 – Predicted vs. actual graph of RGB values for sample 14 (Model Verification).
Sample 11
Unreclaimed sample
<25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <10 ppb
625 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb <25 ppb 49 ppb <25 ppb <10 ppb
4.
Fig. 8 – TCLP analysis of WFS before the reclamation process (unreclaimed sample).
the model functions accurately in higher ranges of time and temperature. As discussed, many researches imply that due to the presence of carbon and some other chemical contents, WFS has a variety of colors from black to medium tan [7,24,25]. Color changes during reclamation process prove the correctness of the hypothesis that those chemical contents are burned or evaporated while being heated. Consequently, investigating color changes could be a reliable and applicable method for evaluation concerning reclamation level. In order to verify the obtained model, sample 14 is heated at 500 ◦ in 30 min and RGB values are compared with the result of the model (Table 3). Referring to Fig. 7, differences between actual values and predicted ones are less than 10 % and the accuracy of the model is quite acceptable. The results of TCLP test indicate the presence of phenol and some other phenolic compounds before the reclamation process (Fig. 8). Fig. 9 illustrates that after the reclamation process on sample 11 at 600 ◦ in 65 min pulses become weak. Thus, the concentration of phenolic compounds decreases to almost zero (Table 4).
Phenol 2-chlorophenol 2,4-dimethyl phenol 4-chloro-3-methyl phenol 2,4-dichlorophenol 2-nitrophenol 2,4,6-trichlorophenol 4-nitrophenol Pentachlorophenol Formaldehyde
Conclusion
Waste Foundry Sand (WFS) consists of different contamination based on the type of casting process and mold making method. Thermal reclamation process is a method for removing contaminants from the sand and preparing it to be reused in various industries. The results of TCLP tests before and after thermal reclamation process indicate that this method is an appropriate cleaning for WFS grains. During thermal reclamation process, burning or evaporation of hydrocarbon contaminants leads to color changes, from almost black to almost white. Quantification of these changes is a good index for determination of contaminants removal level. In order to quantify color changes, photos should be taken of the samples surface in a room with a constant light before and after the reclamation process and RGB values should be determined. MATLAB, Photoshop, and other software are useful for measuring RGB values, but MATLAB is more accurate because it reports an average of all pixels RGB values. The enhancement of temperature leads to breaking chemical bonds through providing higher levels of energy. Therefore, hydrocarbon compounds burn or evaporate easier, and higher levels of reclamation process are achieved. As same as temperature, the enhancement of heating time duration increases the exposure of hydrocarbon contaminants to thermal energy. Thus, more hydrocarbon compounds burn or evaporate, and reclamation process progresses in a more efficient manner. Due to the costs of chemical tests (TCLP, SPLP, etc.) and the preparation time of their results, image processing helps to quickly evaluate the reclamation level. A linear model is obtained in order
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to predict RGB values based on reclamation temperature and heating duration. The values out of the model indicate less than 10 % error in comparison with actual ones of RGB.
Conflicts of interest The authors declare no conflicts of interest.
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Please cite this article in press as: Sabour MR, et al. Utilization of color change and image processing to evaluate the Waste Foundry Sand reclamation level. J Mater Res Technol. 2019. https://doi.org/10.1016/j.jmrt.2019.11.041