Green Design
IV. RESULTS AND DISCUSSION
Fig 17. Image of ancient Greek characters with a spacing Letter Spacing> 2 Pixels.
This experiment was carried out 10 times as shown in Table 2.
TABLE 2. System test on case study I Test
No.
Expect ed Output
Syste m Outpu
t
Accu racy
Prec ision
Rec all
FM
A1 APhR
ODIT H
APhR ODIT H
100
% 100
% 100
% 100
%
A2 APOL
LWN
APOL LWN
100
% 100
% 100
% 100
%
A3 ARTE
MIS TART
EMTS 87,5
% 85.7 1 % 100
% 100
%
A4 AThH
NA AThH
NA 100
% 100
% 100
% 100
%
A5 HPhAI
STOS HPhL TSTO S
75% 75% 100
% 85,7 1%
A6 KRON
OS
KRO NOS
100
% 100
% 100
% 100
%
A7 POSEI
DWN POSE
TDW N
87,5
% 87,5
% 100
% 93,3 3%
A8 REA RRA 66,7
% 66,6
7%
100
% 79,5
3%
A9 ZEUS ZEUS 100
% 100
% 100
% 100
%
A10 ERMH
S
ERM HS
100
% 100
% 100
% 100
% Average = 91,67
% 91,4
9%
100
% 95,0
9%
To explain the system performance results of each experiment, below is one example of calculations taken from the experiments in Table 2 of test number A7.
2) Case Study (II) Spacing Between Letters 1-5 Pixels Case studies using ancient Greek alphabet test images with a distance between letters 1-5 pixels are represented in Figure 18.
Fig 18. Image of ancient Greek characters with spacing of 1- 5 pixels
This experiment was carried out 10 times as shown in Table 3.
TABLE 3. System test on case study II Tes
t No.
Expect ed Output
System Output
Acc urac y
Prec ision
Rec all
FM
B1 AKhIL
LES AKhHR
LES 75
% 85,7 1% 85,7
1% 85,71
% B2 KhLEO
PATR A
KhLEO PATRA 100
% 100
% 100
% 100%
B3 LEONI DAS
LEONT DAS
87,5
% 87,5
% 100
% 93,33
%
B4 MARS MARS 100
% 100
% 100
%
100%
B5 ODISS
EUS ODISSE
US 100
% 100
% 100
% 100%
B6 PARIS PARTS 80
% 80% 100
% 88,89
% B7 PhILO
SOPH PhROS
OPh 71
% 85,7 1% 85,7
1% 85,71
% B8 PsiKhE
ANG
TKhEA NG
83,3 3 %
83,3 3%
100
% 90,91
% B9 SPART
A
SPAHA 66
%
80% 80% 80%
B1 0
ZEPPE LIN
ZEPPE LM
75
% 85,7
1%
85,7 1%
85,71
%
Average 84% 88,8
% 93,7
1%
91,03
% 3) Case Study (III) Number of rows> 1
Case Study using the image of the ancient Greek alphabet test with the same letter spacing as Case Study (II) but having more than one line is represented in Figure 19.
Fig 19. Image of ancient Greek characters with rows > 1 This experiment was carried out 10 times as shown in Table 4.
TABLE 4. System test on case study III Test
No. Expecte d Output
System Output Acc
urac y
Prec ision Rec
all FM
C1 AKhRO
POLIS ARGOS
AKhRO POATS ARGOS
85,7 1%
85,7 1%
100
%
92,31
%
C2 ALEAI
AKhTA EST
AAEAR KhTAE ST
69
% 81,8 2% 81,8
2% 81,82
%
C3 ANKHI
ENTGR EEK
ANKHE NTGRE EK
91,6
% 100
% 91,6
%
95,62
%
C4 DEIMO
SKRAT OS
DETM OSKRA
TOS 91,6
% 91,6
% 100
% 95,64
%
C5 HIKhR HODUS
HIKhS ALTUS
HTKhR HODUS HTKhS AATUS
83,3
% 83,3
% 100
%
90,89
%
C6 KhOLO
SUSRH OS
KhOAO SUSRH OS
90,9
% 90,9
% 100
%
95,23
%
C7 MAGN
AKhHA RTA
MAGN AKhHA AA
81,8 1 %
90% 90% 90%
C8 MAGN
UMOP US
MAGN UMOP US
100
% 100
% 100
%
100%
C9 OLIMP
USORO KH
OLTMP USORO KH
90,9
% 90,9
% 100
% 95,23
% C10 PRIMO
RDIAL PRMOR
DRL 70
% 87,5
% 77,7 8% 98,44
%
Average 85,4
8 % 90,1
7%
77,7 8%
98,44
% 4) Case Study (IV) Separating Training Data
The next case study is to separate 20% of the training data to serve as test data with the purpose of knowing the possible letters that can be exchanged with each other. This test is done by comparing a character with 80% of the characters in the training data on each label. This experiment was carried out 456 times, and obtained 24 values of accuracy, precision, recall and F Measure on each label as shown in Table 5, Table 6 and Table 7.
TABLE 5. System test on case study IV
Test No. Label
A B D E G H I K
1 √ √ E √ √ √ √ √
2 √ Th √ √ √ √ √ E
3 √ Th √ √ √ A T √
4 √ √ O √ √ L √ √
5 √ √ √ √ √ √ Kh √
6 Th √ √ S √ √ √ √
7 G E √ √ √ K √ √
8 W √ √ √ √ √ T G
9 S √ √ √ √ √ √ √
10 √ √ √ √ √ R √ √
11 √ √ E √ √ M √ √
12 √ √ √ R √ √ √ √
13 √ R √ U M √ √
14 √ √ √ √ √ √ √
15 √ E √ U M √ √
16 √ G √ B √ √ M
17 √ √ √ √ √ √ A
18 √ √ √ √ T √ √
19 W √ A √ G √ √
20 √ √ S √ √ T √
21 √ R √ R
Accurac y (%)
76,1 9 66,6
7 75 80,9
5 85 55 80 76,1
9 Precisio
n (%) 76,1
9%
66,6 7
75 80,9 5
85 55 80 76,1
9 Recall
(%)
100 100 100 100 10 0
100 100 100 FM (%) 86,1
4 80,0
02 85,7
1 89,4
7 91
,9 70,9
7 88,8
9 86,4
9
Table 6. System test on case study IV Test
No Label
Kh L M N O P Ph Ps
1 Z √ H √ √ N √ M
2 √ D √ √ √ √ √
3 √ √ √ H √ √ √
4 √ √ √ √ √ √ √
5 √ √ N Th √ √ √
6 √ √ √ √ √ √ √
7 √ √ W √ √ √ √
8 √ √ √ √ √ √ √
9 √ A √ √ √ √ √
10 √ √ √ √ √ G √
11 √ √ √ √ √ √ √
12 √ √ √ √ √ T I
13 √ √ N √ √ √ √
14 √ √ √ √ √ T I
15 L √ N √ S T √
16 L A √ W √ T √
17 G √ √ W √ T √
18 √ √ H √ T √
19 G A √ U T √
20 √ √ √ √ √
21 √ D √
Accur acy (%)
76,1 9
76,1 9
71, 42
75 94 ,1 1
52,6 3
90 0
Precis ion (%)
76,1 9
76,1 9
71, 42
75 94 ,1 1
52,6 3
90 0
Recall (%)
100 100 10 0
100 10 0
100 100 100 FM
(%) 86,4
9 86,4
9 83,
33 85,7
1 96 ,9 7
68,9 6
94,7 3
0
TABLE 7. System test on case study IV No
Uji
Label
R S T Th U W X Z
1 √ √ √ √ Kh √ √ √
2 √ √ √ √ √ √ √ √
3 √ √ √ √ N √ √ √
4 √ √ √ √ √ √ √ √
5 I √ √ √ I √ √ √
6 √ √ √ √ √ √ √ √
7 √ √ √ √ √ √ E √
8 √ √ √ √ O √ B √
9 I √ √ √ O √ B √
10 √ Z √ √ √ √ E S
11 √ Z √ √ √ √ √ √
12 √ √ √ √ √ √ S √
13 Th √ √ √ √ √ B
14 √ √ √ √ √ √ √
15 √ Z √ √ O N √
16 T Z √ X O √ √
17 √ Z √ √ O √ √
18 B √ √ √ L √ √
19 √ √ √ √ √ √ √
20 E √ √ √ I √ √
21 √ N √ N √ M
22 √ √
Akuras i (%)
72, 72
71,4 2
10 0
95 47,6 1
95,2 3
53,8 4
90,4 7 Presisi
(%) 72, 72 71,4
2 10
0 95 47,6 1 95,2
3 53,8 4 90,4
7 Recall 100 100 10 10 100 100 100 100
(%) 0 0 FM
(%) 83, 63 83,3
3 10
0 97 ,4 3
64,5 1 97,5
6 69,9 9 94,9
9
Tables 9, Table 10, and Table 11 represent how the system can convert each letter by separating 20% of the training data into test data, so that from 456 experiments 24 different accuracy values are generated according to the letter label. From this case study obtained an average value of accuracy and precision of 73.20%, an average value of recall of 100% and an F Measure value of 81.94%.
Further explanation from Tables 9, Table 10, and Table 11 are represented by using one of the test examples on the label with the letter "D" in Table 9. Label "D" on test numbers 1, 4 and 11 there is an error in the system during the classification process so that the image recognizable by the letter "E", while the test number 2,3,5,6,7,8,9,10 and 12 systems can convert images in accordance with the expected label.
C. Average System Performance from the Four Case Studies
Based on the test data in Tables 2 to 7, a table of the average percentage of the introduction results shown in Table 8.
TABLE 8. Average System Performance
Case Study Accuracy Precissio
n Recall FM
I 91,67 % 91,49 % 100 % 95,09 %
II 84 % 88,8 % 93,71
% 91,03 %
III 85,48 % 90,1 % 77,68 98,44
IV 73,20 % 73,20% 100 % 81,94 %
Average in
Total 83,59 % 85,89 92,85 91,625
D. Analysis of Results
In the case study of ancient Greek characters with a spacing of > 2 pixels, the system can recognize letters with an average accuracy value of 10 experiments of 91.67%.
Some letters cannot be converted according to labels due to the non-maximum normalization process and the presence of noise that is detected as an object during the classification process.
Furthermore, in a case study of ancient Greek characters with letters spacing between 1-5 pixels, the average accuracy of 10 experiments was 84%. In this case study several letters are segmented into one object and classified into a label that has the closest correlation value of the object.
In a case study of ancient Greek characters that have a sentence pattern of more than one line, the average accuracy of 10 experiments was 85.48%. In this case study the system succeeded in segmenting each line in sequence but as in case study (ii) some characters are still segmented into one object.
The next case study is to separate 20% of the training image to be used as a test image that produces an average accuracy value of 24 letter labels of 73.20%. From this case study 20% of the fonts on each training image label were lost so that during the testing process the system converted the images into labels with the closest correlation value.
Based on the four case studies the system produces an average accuracy value of 83.59%, the success rate is quite good because the type and size of the letters used as input are different from the template.
V. CONCLUSION
The conclusions that can be drawn in making this final project are :
Based on testing on four case studies that have been done, the Template Matching Correlation algorithm is able to classify ancient Greek characters with an average total value of accuracy percentage of 83.525%, precision of 85.89%, recall of 92.85%, and F Measure of 91.625%.
Based on testing in case studies ii & iii (Table 3 &
Table 4) in ancient Greek characters tend to have the characteristics where the letters / characters in each word and sentence have a narrower distance from the Latin letters so that Greek characters that have a distance <= 2 pixels segmented into one object this thing affect the success of the classification process.
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Production of Nano Asphalt Emulsion from Asbuton with Microemulsion Method
Dr. Riny Yolandha Parapat Institut Teknologi Nasional (Itenas- Bandung), PHH. Mustopha 23, 40124
Bandung-Indonesia Technische Universität Berlin (TU- Berlin), Straße des 17 Juni 124, 10623
Berlin-Germany
Dr. Imam Aschuri
Institut Teknologi Nasional (Itenas- Bandung), PHH. Mustopha 23, 40124
Bandung-Indonesia
Prof. Dr. Reinhard Schomäcker2 Technische Universität Berlin (TU- Berlin), Straße des 17 Juni 124, 10623
Berlin-Germany
Abstract—Research on nano asphalt offers a promising prospect because it can significantly enhance the performance of asphalts at both low and high temperature. Nanoparticles are the key materials that can improve mechanical and physical properties also durability in road pavement. It has been reported that adding of nanomaterials such as nanosilica, nano calcium carbonate, nanotubes, and nanoclay in asphalts mixture will increase the viscosity of asphalt binders and improves the rutting and fatigue resistance of asphalt mixtures. Several attempts have been conducted to make nano asphalt i.e. by mixing mineral nanoparticles or oxides into the asphalt. However, this method has some disadvantages such as the tendency of the nanoparticles to agglomerate and the difficulty to distribute them evenly in the asphalt mixture because of the high viscosity of asphalt. Also, the price of those nanoparticles is still expensive.
In this work, we are using a facile and economical method to produce nano asphalt. The nanoparticles are produced directly during the process from the natural asphalt in the Asbuton rocks which are found in Buton Island (Indonesia). The nanoparticles are produced and mixed evenly in the system by combining the microemulsion method and ultrasonication. Therefore, it is not necessary to purchase the expensive nanoparticles for making nano asphalt. The influence parameters such as the concentration and kind of solvent, concentration and kind of surfactant, and mixing temperature were studied. The little amount of both solvent and surfactant that are used in this method (less than 5%) with the high yield of each variation (around 99%), make this process worth to be developed and applied in the road pavement.
Keywords—Nano Asphalt, Microemulsion, Asbuton, Nanoparticles
I. INTRODUCTION
Asphalt is a binding material in a road construction although the proportion is only 4-10% of the total weight of the mixture. Asphalt is obtained from petroleum cannot meet the increasing demand for bitumen globally. One way to overcome this is the use of natural asphalt such as asphalt from rocks found on the Buton island, or known as Asbuton.
Asphalt deposits on Buton Island are very large, reaching 700 million tons which is the largest natural asphalt in the world. Not only that, the asphalt content in Asbuton, which ranges from 10 - 40%, is a bitumen level that is quite large compared to the natural asphalt levels of other countries.
Although the natural resource is large, Indonesia still cannot afford domestic asphalt needs. Some researchers have attempted to extract the asphalt from Asbuton, but the process is still expensive and involves dangerous solvents.
Pavements consist of a combination of layers of engineered materials that generally provide all-weather access to vehicles to travel in a safe en economical way. The layers of materials used are selected and engineered to provide a structure which can withstand the applied vehicular loads under a range of environmental conditions for a defined minimum life. Natural soils and gravels can also be termed unbound materials, and they consist of soil material selected for its specific properties. Typically, water is added to these materials to ensure optimal moisture contents, and then the material is compacted and forms the subgrade, subbase or (in selected cases) base layer of the pavement structure. The behavior of these materials is typically affected by changes in moisture content.
The main objectives of pavements are to provide a safe and strong surface for vehicles to travel, while protecting the underlying layers of material under any kind of environmental conditions. Although good pavements can be constructed using existing materials and techniques, there are a number of areas where the thoughtful application of nanotechnology techniques should be able to improve the longevity and performance of the service provided by the pavement facility. These include improved and smart materials and characterization of materials. In this chapter the specific current needs that are addressed through these applications are discussed.
Nanotechnology has been increasingly intruded into the field of asphalt modification. In fact, bitumen is actually classified as a nano-material. The morphology as well as the interfaces between organic and inorganic materials are intriguing and may be of importance for bitumen and aggregate composition. Kotlyar et al [1998] has shown that the solids associated with bitumen in crude oil can be described as mainly ultrafine (nano-sized) aluminosilicate clays coated with a strongly bound toluene insoluble organic material having asphaltene characteristics. Investigation of the colloidal structure of bitumen indicates that bitumen can also be described as a complex mixture of mostly hydrocarbons whose structure is well described by a colloidal model with solid particles (asphaltenes) with a radius of a few nano-meters dispersed in an oily liquid
matrix (maltenes). The critical shear rate of bitumen is an intrinsic property of a given bitumen, directly related to its nanostructure [Lesueur 2009].
Nanomaterial exhibits specific features novel properties compared to the bulk material due to its large surface area.
Outstanding effects of nanomaterials are being brought to improve the performance of asphalt. Several nanomaterials used in asphalt modification have been studied by some researchers. Adding nanomaterials such as nanoclay, nanosilica, and nanotubes in asphalts will increase the viscosity of asphalt binders and improves the rutting and fatigue resistance of asphalt mixtures. Partl et al [2003]
anticipates nanotechnology to provide great potential in advancing asphalt pavement technology in the fields of materials design, manufacturing, properties, testing, monitoring and modeling. Specifically, focus areas in asphalt pavement analysis should include the bonds between aggregate, bonds between layers, properties of the mastic, self-repair and rejuvenation of binder, ageing (oxidation) effects and improvements in surface to tire properties.
Nanoparticles for pavement materials is required to be non- hazardous low-cost products, due to them being spread over large volumes of material and being in almost direct contact with human activities. The reduction of energy requirements during construction of asphalt pavements through development of improved emulsions and reduction in binder viscosity at ambient temperatures through the introduction of micro-bubbles will not only lead to a potential energy cost saving, but also assist in the lowering of emissions during construction. The typical bitumen binder thickness coating around aggregate is in the order of a few microns. However, most studies on binder properties do not focus on this small dimension.
As generally known, there are some common problems caused by the complexity of asphalt materials and their behavior, such as aging and moisture damage. This work focuses on introducing the nanomaterials to produce nano asphalts. The nanoparticles that are used were not purchased at high prices, instead nanoparticles are produced directly (in situ) from the mineral of asbuton and mixed directly in the system. Asphalt found on the island of Buton has different mineral composition depending on the area where Asbuton was obtained. The difference is due to the formation process in nature which is heterogeneous and typical for each region.
However, Asbuton minerals generally consist of limestone which is derived from marine animal deposits, is very porous and relatively light (Table 1).
Properties of nanomaterials can be changed through controlling the size, regulating chemical composition, surface modification and controlling interactions between particles. Related to the fact that bitumen materials such as those used on a large scale, nano asphalt research has considerable prospects for reinforcement applications that improve the physical and nanoscale physical properties and durability of groups of construction materials. Nanocarbons (CNT), Silica, Alumina, Magnesium, Calcium, and Titanium Dioxide (TiO2) nanoparticles can also have a significant effect on the performance of asphalt. In general, nanotechnology will give some benefits such as making existing products and processes more cost effective, durable and efficient.
Nanoclays have very large aspect ratio (Carl et al, 2011) with non-uniform size and shape. Adding 6% of nanoclays improves the permanent deformation or rutting behavior and enhance the resistance to aging of asphalt. Using nano calcium carbonate in asphalt mixtures can reduce the permanent deformation on asphalt pavement (Elochukwu et al, 2014). The optimum performance is reached by adding 5% of these nanomaterials to the asphalt mixture. In Asbuton rock, there are naturally occurring minerals and subjected to natural variation in their formation. Various physical properties (such as stiffness and tensile strength, tensile modulus, flexural strength and modulus thermal stability) of the bitumen can be enhanced when it is modified with small amounts of nano-clay, on condition that the clay is dispersed at nano-scopic level. Generally, the elasticity of the nanoclay modified bitumen is much higher and the dissipation of mechanical energy much lower than in the case of unmodified bitumen (Mansoori et al, 2010).
In the nanoemulsion mixture, the asphalt is fully dispersed into the water phase. In order to disperse non-polar asphalt into the polar water phase, emulsifiers are needed whose molecules have polar and non-polar parts (Center for Research and Development of roads, 1996). The function of this emulsifier is to change the composition of the asphalt particles in the emulsion asphalt to separate it from the water and attach to the aggregate surface. In case of the emulsion asphalt in Indonesia, the largest source of the aggregate is SiO2 (silica). This shows that the aggregate needed is negatively charged materials. For the type of road pavement construction with asphalt binding material, the emulsion will be better if it is used positively charged emulsion asphalt, namely asphalt cationic.
TABEL 1. Mineral composition of rock containing bitumen (~30%) located in different places in Buton Island
Mineral Composition (%)
Kabungka Lawele
CaCO3 86.66 72.90
MgCO3 1.43 1.28
CaSO4 1.11 1.94
CaS 0.36 0.52
H2O 0.99 2.94
SiO2 5.64 17.06
Al2O3 + Fe2O3 1.52 2.31
Residu 0.96 1.05
II. RESEARCH METODOLOGY
In this research, nanoemulsion was used as a method to produce a modified nano asphalt emulsion. Nanoemulsions are stable of isotropic dispersions which have a droplet size of 100 – 1000 nm, obtained from the spontaneous formation of surfactants from the hydrophilic parts of their lipophiles.
The fromation of nanoemulsions follows the principle of microemulsions system. According to the structure, microemulsions are divided into oil in water (o/w), water in oil (w/o), and bicontinuous microemulsions. The type of microemulsion can be determined based on the values of α (oil concentration) and γ (surfactant concentration). The value of α and γ can be determined by the following equation.
The principle of this process is to dissolve asphalt as an oil phase contained in the pores of Asbuton nanoparticles using solvents to form dispersions of oil in water. The aim of using surfactants in this experiment is to improve the performance of solvents so that nanoemulsions can be formed as shown in Figure 1.
Fig 1. Phase diagram of microemulsions (left) showing the one phase, two phases, and three phases microemulsions.
Nanoasphalt is produced by following the two phases microemulsion which has the excess oil (right).
Because water is not involved in this nanoemulsion system, therefore the nanoparticles which are the very fine minerals are bound by surfactants and dispersed in continuous phase which is the mixture of asphalt and solvent.
This upper layer is called nanoaspal. Diesel oil and turpentine were used as the solvent whereas lecithin and SPAN 85 were used as the surfactant.
The method of making nanoaspal includes 4 stages, namely the reduction of the size of Asbuton rocks, nanoaspal formation, separation and washing. At the stage of reduction in size, asbuton rocks are ground and mashed by using mortars to an average size of 200 micrometers. The scoured particles are then filtered with a 70 mesh filter so that the coarse particles can be collected and returned to the mortar to be refined. Asbuton fine grains measuring <200 micrometers are collected in a container until the amount of weight as needed. For each experiment, 100 g of fine Asbuton granules were taken and put in a 200 ml beaker. Then into another 200 ml beaker, SPAN 60 (as surfactant) is 6.32 g and turpentine (as a solvent) is 20 g. Turpentine was chosen as the solvent used because turpentine has 2 dominant aromatic components, namely α and β pinene with compositions reaching 80%.
Fine asbuton (<200 micrometers) 100 g is slowly inserted into a beaker containing a mixture of surfactant and solvent while stirring with spatuta slowly until it appears that the whole mixture of dough has blended well. The mixture is then closed and will be processed later by sonication. The sonifier homogenizer is then prepared with the probe sonotrodes. The sonotrode probe made of titanium metal alloy is mounted on the sonifier. A mixture of fine Asbuton, solvents and surfactants in the beaker is placed under the probe, but the probe must not touch the base of the beaker so
that the beaker does not break because of the very high frequency waves of the sonifier. Sonication generally produces ultrasonic waves with a frequency of 20 kHz (20000 cycles of waves per second) or higher
The chemical glass containing the mixture to be sonicated is placed in a container filled with water so that when the sonication process is carried out, the nano-asphalt emulsion mixture is not too hot because the water can absorb heat from the mixture. After the sonication process is complete, the beaker containing the mixture is then placed in a waterbath to be heated and kept at a constant temperature of 70 oC for 30 minutes to form 2 stable layers. The top layer is nanoaspal emulsion which is composed of extracted asphalt, mineral nanoparticles, solvents, and surfactants. The top layer is then separated while the bottom layer is a fine grain residue with the remaining asphalt still attached to the surface. Separation is done by pouring the top layer into a sample container and keeping it so that the remaining residual solids are not taken.
III. RESULT AND DISCUSSION
The most influential factors in producing nano asphalt can be analyzed by using the Factorial Design technique. The factors that are varied in the experiment are shown in Table 2.
TABLE 2. Design factors of the experiments either using turpentine or citrus oil
Level α Temperature
(oC)
HLB Surfactant Particle size (µm)
Low (-) 0.42 0.025 60 1.3 400
High (+) 0.5 0.05 120 4.7 <1
The result of analysis of Variance (ANOVA) in the experiments using turpentine and citrus oil is shown in Table 3.
Table 3. Yield of nano asphalt according to the design factors in Table 2 when using turpentine (left) and citrus oil (right)
Nanoasphalt
Mineral Residue
Surfactant Nanoparticle