Types Safety Alloys M
Alloys S
Alloys N
Alloys F
Alloys W
Alloys B
Alloys E
Alloys T
Alloys L
Alloys Y
Alloys R
Alloys O
Alloys A
Alloys G
Alloys D
Figure 2. Production lines 1
Figure 3. Production lines 2
Due to the high investment in infrastructure and the purchase of
machinery, the engineers needed to be advised through data outputs on the
layouts of the production lines and identify which layout stood out. With this in mind, the concep=
tual
model was diagrammed in BPMN, starting with the arrival of inputs at the
furnaces and continuing through to the output of the sub-alloys in ingots.<=
o:p>
Figure 4. Production lines 3
With the conceptual model created in BPMN, the face-to-face validati=
on
process began, during which the conceptual system was presented to the
company's engineers and managers. If any flaws were identified in the model=
, it
was adjusted to arrive at a satisfactory reference.
4.2 Implementation
Based on the conceptual
model, the basic computational model was created using PROMODEL® software.
Figure 5 shows the design of the base computational model, built in PROMODE=
L®,
from which variations in production lines can be applied simply by selecting
the desired production line scenario in the cockpit. Additionally, the cock=
pit
contains all the data processed by the computational model, such as process
times, rates, quantity of resources, among others, making it a control tower
for data inputs to the model.
Figure 5. Base computer model in PROMODEL
During the construction of t=
he
computational model, each stage was checked for errors. If errors were foun=
d,
they were corrected through observations in the model animation and by
reviewing the values assumed by the variables.
Model validation is a proces=
s of testing
a simulation model to find out whether the system test created has presented
and provided an image of the real system that exists and is currently runni=
ng. This
test is conducted by comparing the output results of the real system with t=
he
output results of the simulation system. The validation test will be declar=
ed
valid if the output results of the real system are the same or there is no
difference with the results of the simulated system (Fajriah et al., 2023).=
Statistical validation of th=
e base
model was carried out, comparing the simulation results with a sample of
historical data from the old refining sector, using the “T” test for the al=
loy
refining process, which tests for equality between two means. The tests were
conducted using Excel software, and the result of the P-value of the test
applied to the alloy refining process was 0.238, which is above the
significance level of 0.05. This indicates that there is no difference betw=
een
the test averages, indicating that the simulated values are close to the re=
al
ones.
In addition to the statistical validation of the computacional
model, a second validation was carried out, in which the simulation results
were analyzed together with the engineers responsible for the industry's
refining sector. Using the OUTPUT VIEWER® software, dashboards were created
with the data obtained from the simulation, as shown in Figure 6, and the
simulation data was analyzed by exporting matrices, as shown in Figure 7. If
the results showed a margin of error below the 10% stipulated by the battery
company, the model was considered valid for the scenarios.
Figure 6. Simulation data dashboard by=
OUTPUT
VIEWER
Figure 7. Data exported from the simulation using matrices <=
/span>
4.3 Characterization of the scenarios
The construction of the scenarios was based on the conceptual models=
and
four variables, which included two capacities for the holding pot, two
capacities for the refining pot, three production lines, and two quantities=
of
ingot mills, resulting in a total of 24 scenarios. Table 4 presents the
characteristics of these 24 scenarios and their differences.
Table 4
Scenarios
Scenarios Production Quantity Scenario =
=
Stockouts
=
Suballoys =
Mole
=
(Tons)
=
Stockouts
=
Suballoys =
Ca
=
(Tons)
=
Stockouts
Suballoys =
Sb
=
(Tons)
1
=
2
=
3
=
4
=
5
=
6
=
7
=
8
=
9
=
10
=
11
=
12
=
13
=
14
=
15
=
16
=
17
=
18
=
19
=
20
=
21
=
22
=
23
=
24
=
0
=
0
=
253
=
0
=
0
=
245
=
0
=
0
=
0
=
0
=
0
=
0
=
0
=
0
=
270
=
0
=
0
=
287
=
0
=
0
=
0
=
0
=
0
=
0
=
291
=
268
=
293
=
288
=
273
=
290
=
1
=
0
=
0
=
0
=
0
=
0
=
301
=
262
=
297
=
302
=
263
=
296
=
9
=
0
=
0
=
8
=
0
=
0
=
430
=
34
=
171
=
1083
=
32
=
193
=
1
=
0
=
0
=
2
=
0
=
0
=
767
=
1
=
1
=
468
=
8
=
1
=
1
=
0
=
0
=
1
=
0
=
0
Table 5 shows that out of 24 scenarios, only 8 had a higher alloy
productivity than the daily output, so they managed to keep a stock of allo=
ys
and didn't develop stockouts. Therefore, only scenarios 8, 9, 11, 12, 20, 2=
1,
23 and 24 passed the main filter and will be analyzed further. However, a b=
rief
analysis will be conducted to understand why the other scenarios had stocko=
uts.
4.4 Analysis of the scenarios
Analyzing the scenarios that showed stockouts (negative stock), it c=
an
be observed that those with production line configuration 1, 100 tons of
capacity in the holding pot, and 80 tons of capacity in the refining pot had
negative stockouts.
Scenarios using production
line 1 experienced stockouts in the calcium and antimony alloys due to an
overvaluation of the Pb-based alloy production lines and their mass product=
ion,
causing a shortage of lead in the waiting pans. This shortage caused the
calcium alloy pans to wait longer for material. Figure 8 shows a comparison=
of
the status of the calcium pans between similar scenarios in which only the
production line differs. Scenario 20 uses production line 2, while scenario=
19
uses production line 1.
Figure 8. Comparing scenarios 20 with 19. <=
/span>
Critiquing the scenarios w=
ith
a waiting pan capacity of 100 tons reveals the development of bottlenecks in
the crane, where crane utilization increases by 16% and waiting time rises =
by
30%. Figure 9 shows a comparison of similar scenarios (9 and 21), with the =
only
difference being the waiting pan capacity. Scenario 9 has a waiting pan
capacity of 100 tons, whereas Scenario 21 has a capacity of 50 tons.
Figure 9. Comparing scenarios 9 wi=
th
21.
=
Evaluating
the scenarios with a refining ladle capacity of 80 tons reveals that they do
not meet the demand for calcium alloys, as this represents the bottleneck in
the process. Figure 10 illustrates that the waiting ladle spends 13% more t=
ime
waiting for the refining ladle to process the material. Figure 10 also shows
that the refining pan spends 13% more time proce=
ssing
material, accounting for 93% utilization. Scenario 2 has a refining ladle
capacity of 80 tons, while Scenario 8 has a capacity of 100 tons.
Figure 10. Comparing scenarios 9 an=
d 21
After understanding the reasons why some scenarios resulted=
in
negative stock levels, the scenarios that made it through the first filter =
were
subjected to a comparative analysis based on the performance of the followi=
ng
indicators: waiting time, ladle occupancy rate, ingot occupancy rate, crane
occupancy rate, pre-furnace stock, pre-refining stock, furnace production,
refining production and refining ladle filling delay. Once the indicators w=
ere
defined, the next step involved calculating a weighted arithmetic average, =
with
weights assigned to the indicators based on a two-by-two comparison. Table =
6 presents
how the scores are divided and Figure 11 compare=
s the
indicators two-by-two in terms of their importance.
Table
6
Grade division
Division
Note
N &=
gt;
1st Quartile
1st
Quartile > N > 2nd Quartile
2nd Quartile > N =
>
3rd Quartile
N < 3rd Quartile <=
o:p>
10
7,5
5
2,5
Figure 11. Indicators compared 2 by 2
Figure 11 shows that pre-furnace
stock is the most relevant indicator, followed by furnace production, refin=
ing
production, and ladle occupancy, all with a normalized weight of 1.4. In th=
ird
place are the indicators for crane occupancy, refining ladle filling delay,=
and
pre-refining stock. Finally, refining time holds the lowest normalized weig=
ht. Figure
12 displays the score achieved by each scenario for every indicator.
=
=
=
=
=
Figure
12. Scena=
rios
scores by indicator/
Figure 12 provides an anal=
ysis
of the scores for the scenarios, which delves into the advantages and disad=
vantages
that each scenario developed during the simulation. Table 7 shows the posit=
ive
and negative characteristics of the possible scenarios for the new refining
sector.
Table=
7
Advantages and disadvantages of the scenarios=
Scenario Advantages Disadvantages 8
High furnace productivity, high refining product=
ion,
low occupancy of refining pans
High stock of furnace inputs
9
High furnace productivity, high production, low
stock of lead cakes
High occupancy of refining pans, high overhead c=
rane
utilization
11<=
/o:p>
High furnace productivity, high refining output<=
span
style=3D'mso-tab-count:1'>
High stock of furnace inputs
12<=
/o:p>
High furnace productivity, high refining output,=
low
stock of lead cakes
High occupancy of refining pans, high overhead c=
rane
utilization
20<=
/o:p>
High furnace productivity, high refining product=
ion,
low refining pan occupancy, low lead cake inventory
Delay in filling refining pans =
21<=
/o:p>
Low stock of furnace inputs, high furnace
productivity, high refining production, low stock of lead cakes
High occupancy of refining pans
23<=
/o:p>
High furnace productivity, high refining output,=
low
stock of lead cakes
High refining time
24<=
/o:p>
Low stock of furnace inputs, high furnace
productivity, high refining production, low stock of lead cakes
High occupancy of refining pans
Among the best scenarios, =
20
and 21 stood out from the others, as they presented characteristics that we=
re
considered most important for the new refining unit, as shown in Table 7 and
Figure 12.
Scenario 20 demonstrated
better pre-refining stock conditions, indicating a balance between the furn=
ace,
holding pot, and refining. It also developed better conditions for the use =
of
refining pots, indicating a balance between the refining lines and dispatch.
Scenario 20 also performed better in terms of crane occupancy and use of
refining pans when compared to scenario 21.
For scenario 21, the
pre-furnace stock indicator showed superior conditions among those evaluate=
d, demonstrating
an adequately balanced operation between the furnace, holding pot, and
refining, similar to Scenario 20. In addition, t=
his
scenario provided less delay in filling the refining pans, which consequent=
ly
improved the cycle time. Comparing scenario 21 with scenario 20, it is clear that scenario 21 performed better in terms=
of
pre-furnace stock and operating time.
Scenarios 20 and 21 obtain=
ed
the highest score overall on a scale of 0 to 10 and showed an advantage over
the other scenarios. By analyzing these factors, we can conclude that the b=
est
scenarios are those that offer an optimized combination of their indicators=
and
their weights, reaching a dimensionless score of 7.9.
5. CONCLUSION
This work sought to develop
insights to assist in decision-making on the creation of a=
new
unit of a battery company in the refining sector. It involved developing
indicators to show managers what the plant will look like with certain
variables, helping to choose the best production line flow, machinery quant=
ity,
and other factors, thus reducing manufacturing costs.
In this way, discrete-event
simulation proves to be a suitable tool, as even in the absence of a real
system, it can demonstrate how this system will behave =
in a
given context, providing relevant KPIs for decision-making, thus reducing future costs f=
or
the company.
In the scenarios analyzed,
those with negative stockouts shared one or more of the following
characteristics: they were in production line configuration 1, had 100 tons=
of
capacity in the holding pot or had 80 tons of capacity in the refining pot.=
It
is therefore clear that implementing a discrete simulation model offers a w=
ide
range of possibilities for conducting experiments within a procedure,
anticipating possible scenarios and planning desired situations.
In the scenarios that stood
out (20 and 21), it was observed that they had better pre-refining stock
conditions, better crane occupancy performance, better pre-furnace stock
operation and operating time. These scenarios achieved a balance between th=
e furnace,
holding pot and refining processes.
In view of the above, it can be seen that depending on the scenario adopted,=
it
will have different characteristics and better performance in different are=
as,
being outlined by the simulation. This achieves the objective of uncovering
what was previously a 'black box' for the company's engineers and managers.=
It is therefore clear that
implementing a discrete simulation model provides a wide range of possibili=
ties
for conducting experiments within a procedure, anticipating possible scenar=
ios
and planning desired situations.
In addition, all simulatio=
ns
and analyses can be conducted without relying on an existing system, using =
only
inputs, which allows for more precise and consistent decisions regarding
changes to the system. This study was conducted with the aim of making
computational projections for the plant's implementation, which would
facilitate decision-making and create indicators aligned with business goal=
s in
a cost-effective manner.
For future work, it is
advisable to conduct additional analyses using the simulation project to
evaluate other factors, such as the cost of implementation and the feasibil=
ity
of realizing the two best scenarios, in order to
arrive at a definition.
&nbs=
p;
&nbs=
p;
&nbs=
p;
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/o:p>
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Simulation to support decision-making on project options in the refi=
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IS=
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2237-4558 •<=
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16 • p. 01- 20 • jan. /dez . 2025
20
ISSN 2237-4558
• Navus • &n=
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