Comments continued
4. Materials and methods
This section should mention only about the way the research was carried out:
theoretical methods, software and hardware, conditions of the experiment and validation of the proposed solutions (adequacy of the proposed models, etc.). Thus, everything should be noted about obtaining results, but no result is given in this section.
This section is irrelevant to the problem statement in section 3, as the problem in section 3 is relevant to section 5. "Research results...".
The dosing pump is designed to drain the PAC coagulant flow fluid into the water fluid. This pump generates a coagulant flow rate by the mixing method set from the valve to flow the liquid PAC discharge into the dosed reservoir. This pump is very helpful in the process of mixing clean water treatment for coagulant variations which are controlled internally to vary the flow rate and dosage amount.
Taguchi's method is used for parameter setting and integration of the most influential waste output to improve the quality of clean water products, reduce turbidity levels with variations in coagolan doses, optimal mixing process. The target of this method is to make clean water products less sensitive to noise, so it is called a robust design [26] like the concept below:
1. 1. Clean water quality must be designed from the start, not just checking it.
2. The best quality is achieved by minimizing the deviation from the target, the product must be designed so that it is robust against environmental factors that cannot be controlled.
3. The cost of quality should be measured as a function of deviation from a certain standard and losses should be measured at all stages of the product.
According to [27], the advantages of the Taguchi method compared to other experimental designs are because it is more efficient in experiments involving many factors but the number of experimental units required is relatively small, the product is more consistent and less sensitive (robust) to the variability caused by different factors. uncontrollable (noise). The objective function of the Taguchi method for a robust design that is resistant to noise is derived from measuring product quality using a quadratic loss function in a broader definition to produce an optimal design or parameter setting. Signal to ratio (SNR) in the experimental design system of this method provides a comparison of quantitative values for response variations. To
maximize the results of SNR is the minimization of response variations and performance parameter settings that are firmly on target for the best quantification of the mean squared deviation (MSD) in the performance of the mixing process.
The most important hope in the robust design of the Taguchi method, especially parameter setting, is to examine the effect of variability on factors and experimental levels using statistical tools as a tool. To help fully factorial large, time-consuming and costly experiments, Taguchi suggests using orthogonal arrays (OA) to represent the range of possible experiments. After conducting the experiment, all experimental data were evaluated using analysis of variance (ANOVA) to determine the optimum parameter settings [28, 29].
This method is to determine the factors that influence an optimum response with its characteristics. The Smaller The Better (STB) quality characteristic indicates that the smaller the characteristic parameter, the better the quality. Larger The Better (LTB) indicates that the larger the characteristic parameter, the better the quality will be, and the Nominal The Better (NTB) quality characteristic means that the quality will be said to be getting better if it is close to the nominal (target) that has been set through parameter design and response. for clean water with various concentrations and levels [30, 31].
The conceptual development of the model structure based on the setting of clean water parameters and the optimization model for the mixing process can be seen in Fig. 1 below.
Fig. 1. Setting parameter model in process mixing
In the parameter settings there is coagulation to remove and separate colloidal particles from natural organic minerals such as mud. These colloidal particles cannot settle naturally having a diameter of less than 1 mm causing color and turbidity. The particles attract each other is called the Van Der Waals force and the repulsive particles are called zeta potential forces which have the same charge mass which clump together to form flocks. The attractive force tends to form aggregates while the repulsive force causes the stability of the colloidal dispersion. Stability of colloids can be removed by adding a dose of PAC coagulant whose electrolyte charge is opposite to that of the colloid. Coagulation is a process of deterioration of stability by the addition of a coagulant followed by rapid stirring to neutralize the colloid charge and agglomerate to form a large precipitate, see Fig. 2 below on clean water treatment plants.
Fig. 2. Research Installation
ENDING THE SECTION BY THE FIGURE IS INCORRECTLY, NEED AN ANY INTERPRETATION
5. Results
Structure the "Research results" section according to the task at hand (section 3):
5.1….
5.2…..
number of tasks (Section 3) = number of subsections Result
In the mixing process to decrease turbidity with the addition of coagulant variations by setting the parameters of the Taguchi method with Low level (5-10 ppm), medium level (11-15 ppm) and high level (16-20 ppm) as shown in Tables 1, 2 below.
The table should not consist of a single row of values
Tabel Table 1 Parameter respon
Response Response Quality
Concentration PAC ( % ppm)
Fresh water quality
Tabel 2 Level factor
Parameter Code Level 1 Level 2 Level 3 Level 4
Conc.alum ( % ppm) Water supply (lt/sec)
Pump stroke ( %)
A B C
5 5 15
10 10 20
15 15 25
20 20 30
The change of concentration level of PAC coagulant starts from low level, medium level, and high level simultaneously with the setting of water supply and pump stroke. The decrease of turbidity level seems better as the raise of coagulant concentration level has positive ions. The positive and negative ions depict the effect and response of variable concentration, water supply, and pump stroke as shown in
Fig. 3 below. The plot effect of S/N ratio is in the optimum process of parameter design of mixing process at the various concentration level.
The Fig. should be in one piece
Response Table for Means
Conc. Water Pump Level PAC Supply Stroke 1 4.793 4.775 4.813 2 4.877 4.900 4.782 3 4.775 4.848 4.868 4 4.865 4.787 4.848 Delta 0.102 0.125 0.085 Rank 2 1 3
Fig 3. Main effect and resoonse table variable conc PAC, water supply, pump stroke
20 15 10 5 4.90 4.88 4.86 4.84 4.82 4.80 4.78
4.76
20 15 10
5 15 20 25 30
Conc. PAC (%)
Mean of Means
Water Supply (lt/sec) Pump Stroke (%)
Main Effects Plot for Means Data Means
Parameter setting conducted in mixing process, as shown in Fig. 4 below, shows that the turbidity value of water supply is above 5 NTU (nephelometric turbidity unit) - which is the standard value for clean water. With the medium level of concentration (11 to 15 ppm), it in shown that the decrease of turbidity value is at the range of 4 – 5 NTU at pH 7.1. The turbidity value of water supply and mixing process can also be seen in contour plot, Figure 5 which presents the dispersion of turbidity value with a more detailed range system in the position of turbidity value at each change of PAC concentration.
Fig 4. Level turbidity water supply and turbidity mixing process
0 2 4 6 8
1 3 5 7 9 1113151719212325272931333537394143454749
Level of Turbidity
Sample of Turbidity Turbidity Water Supply and
Process Mixing
Turbidity Water Supply Turbidity Process Mixing
Turbidity Process Mixing
Turbidity Water Supply
4.8 4.7 4.6 4.5 4.4 4.3 4.2 4.1
4.8 4.7 4.6 4.5 4.4 4.3 4.2 4.1
6.8 6.6 6.4 6.2 6.0 5.8 5.6 5.4 5.2 6.8
6.6 6.4 6.2 6.0 5.8 5.6 5.4 5.2
>
– – – –
< 4.20 4.20 4.35 4.35 4.50 4.50 4.65 4.65 4.80 4.80 Mixing Process Turbidity
Turbidity water supply vs Turbidity process mixing
Fig. 5. Contour plot turbidity water supply and turbidity mixing process
Turbidity level of water supply is averagely 5,8 NTU and the reduce of turbidity of mixing process is averagely 4.5 NTU as the addition of medium level in coagulant concentration, but the fluctuation approximates the normal distribution as shown in Fig. 6 below. The variation of turbidity of water supply is influenced by the condition of downstream river, weather and environment of the forest. Meanwhile, the decrease of turbidity in the mixing process is due to the coagulant setting process.
As the ppm coagulant is getting higher, the turbidity would reduce until reaching the optimum concentration level at the medium level in the condition of water supply which is of 5-7 NTU.
a b Sign by example:
Fig. 2. The main signature: a – ...; b – ...
Fig. 6. Normality turbidity water supply and process mixing
By looking at the fluctuation of water supply turbidity and mixing process, the model of mixing process can be predicted at the medium level of 14 and 15 ppm
7.0 6.5 6.0 5.5 5.0 99
95 90 80 70 60 50 40 30 20 10 5
1
Mean 5.826 StDev 0.4561
N 50
AD 0.793 P-Value 0.037
Turbidity Water Supply
Percent
Probability Plot of Turbidity Water Supply Normal
5.0 4.8 4.6 4.4 4.2 4.0 99
95 90 80 70 60 50 40 30 20 10 5
1
Mean 4.559 StDev 0.1802
N 50
AD 0.227 P-Value 0.805
Turbidity Process Mixing
Percent
Probability Plot of Turbidity Process Mixing Normal
coagulant. Averagely, the model of prediction of mixing process collects the maximum at medium level which is 15 for the condition of 5-7 NTU.
Optimum process mixing =
15 % ppm conc. PAC + 15 lt/sec + 15 % pump stroke
Optimal model can be decreased or increased fluctuatively at each 2 point range change of turbidity, for instance 5-7 NTU, 7-9 NTU, and so on. For the more detail, every change of ppm concentration, water supply and pump stroke can be seen in Fig. 7 below. At the position of medium level, it can be seen that the turbidity value of mixing process is at 4.8 NTU and 4.9 NTU which are close to the health standard.
The higher % ppm of PAC causes the turibidity to decrease and the water color turn to transparent. The effect of over PAC would not be harmful for human body but with a continuous excessive dosage in a long time, it would cause side effect in form of toxin within human body.
Fig. 7. The variation of PAC concentration at each level
Each change of PAC concentration level causes the change of water turbidity value, colloid behavior and floc formation in mixing process. Coagulant characteristic whose charge is positive ion would attract negatively charged colloid that is often called as the effect of Van der Waals’ attractive force. A proper addition of coagulant concentration would reduce the zeta potential repulsive force in the colloid, thus there is more positive ions than negative ions which causes faster floc formation process. With sedimentation process and flock filterization, it would
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Conc. PAC ( % ppm) 5 5 5 5 5 10 10 10 10 10 15 15 15 15 15 20 20 20 20 20 25 25 25 25 25 Turbidity Water Supply 6.8 7.2 6.9 7.3 7.2 6.7 7.4 7.4 7.3 7.4 7.1 6.9 7.2 6.6 6.7 6.9 7.3 7.2 6.8 6.6 7.0 7.1 7.3 6.8 7.2 Turbidity process mixing 6.4 6.6 5.5 5.8 5.5 5.4 5.3 5.3 5.2 5.3 4.9 4.8 4.8 4.9 4.9 4.3 4.2 4.4 4.2 4.3 4 3.9 3.9 4 3.9
Standart (max 5) 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
5 5 5 5 5
10 10 10 10 10
15 15 15 15 15
20 20 20 20 20
25 25 25 25 25
0 5 10 15 20 25 30
Concentration (ppm)
Turbidity standart Concentration PAC vs Turbidity
Op mal process mixing
gravitationally percipate at the bottom surface to furthermore be thrown away since the sedimentation is known as waste in the industry of clean water.
Before the Fig. should be a link to the Fig. (in the same section)
Split into 6 separate figures, signing them with the text a, b, c, d…
each of the subpictures should be a separate picture
a b c…
Sign by example:
Fig. 2. The main signature: a – ...; b – ...
Fig. 8. SEM image of flock or colloid (arrow sign) turbidity < 5 NTU after being mixed with coagulant (PAC): 5 % ppm, (b) 10 % ppm, (c) 15 % ppm, (d) 20 % ppm, (e,f) 25 % ppm
ENDING THE SECTION BY THE FIGURE IS INCORRECTLY, NEED AN ANY INTERPRETATION