Dynamic system employed for predicting noise emission at new constructed mineral ore processing plant
-
Edi Karyono Putro
,
Nieke Karnaningroem
Abstract
The impact of noise can arise from the operation of a mineral ore processing plant in the mining industry, such as PT Freeport Indonesia. The use of advanced technology in modern industry, like SAG#3, has increased production productivity but can also lead to noise emission issues that may endanger workers’ health. This research aims to project the impact of noise in the workplace at a new mineral ore processing plant using dynamic system analysis. Noise sampling was conducted using a sound level meter instrument following SNI 7231:2009. After data collection, dynamic system analysis was performed using Vensim Pro software, involving steps such as causal loop design, stock flow diagram formation, and model verification testing. The analysis results indicate that the noise level exceeds the permissible exposure limit, measuring at 85 dBA. Therefore, the implementation of a Hearing Conservation Program (HCP), personal protective equipment (PPE) policies, and periodic noise control policy evaluation are crucial steps in reducing the impact of noise on employees. This study highlights the need for concrete actions by the company, such as improving the effectiveness of the HCP, enforcing stricter PPE policies, and conducting regular evaluations. Consequently, the company can create a safer work environment and reduce the health risks associated with noise exposure in the workplace.
1 Introduction
Industrial activities require advanced and sophisticated technology to streamline production processes, increase productivity, and improve efficiency. However, these technologies can have less favorable impacts, one of which is causing pollution [1]. One of the technologies that can lead to pollution is production or processing machinery. These machines can generate noise pollution, which is unwanted sound and can be harmful to the health of workers in the industry [2].
One of the industries that relies on advanced technology to support its processing is the mining industry, such as PT Freeport Indonesia (PTFI). PTFI is a company engaged in the mining and processing of copper and gold ore. PTFI has been in operation since 1972 in Tembagapura, Mimika Regency, Central Papua Province, Indonesia [3,4]. The mineral ore processing techniques at PTFI currently involve two mining methods: open-pit mining located in Grasberg and underground mining. The mineral ore processing takes place in the processing plant at Mile Post 74, where the Concentrating Division operates.
The ore processing activities are carried out in Concentrator #3 and Concentrator #4, with each Concentrator having Semi-Autogenous Grinding (SAG) Mill and Ball Mill units. According to a survey conducted by the Metallurgy Department of the Concentrating Division, it was found that SAG#1 and SAG#2 models were no longer able to meet the production capacity requirements of the mine. This inability resulted in a significant decrease in ore processing, especially from the Grasberg Block Caving underground mine. The mineral ore from the underground mine has a higher level of hardness and abrasiveness compared to the open-pit Grasberg mine, necessitating the addition of an SAG Mill plant. This plant is referred to as SAG#3, which aims to increase production capacity in the crushing and grinding process of ore from the underground mine. SAG#3 is expected to provide sufficient capacity to allow the plant to operate at a capacity of 240 thousand tons per day (ktpd) [5].
The construction of SAG#3 is located around the old limestone mixing station and the deactivated and reconfigured fluidized bed reactor-acid rock drainage, consisting of the SAG Mill unit. SAG#3 differs from SAG#1 and SAG#2 in term of pebbles regrinding process where the pebbles are directly returned to the SAG#3 milling unit without going though pebble crushing process. The operation of SAG#3 is estimated to commence in December 2023 [6]. This process involves the operation of grinding equipment and other machinery that will generate noise. The potential noise generated will include environmental noise, especially indoor noise within the processing plant, similar to what occurs in SAG#1 and SAG#2.
One of the modeling approaches used to monitor noise is dynamic systems modeling. This modeling utilizes Vensim PRO software. Vensim software is employed for creating concepts, documentation, simulation, analysis, and optimization of dynamic system models, starting with the development of causal loop diagrams or stock and flow diagrams [7]. Dynamic systems have four fundamental elements defined within the system: (a) Feedback loop (causal loop) represents a feedback loop that states cause-and-effect relationships among variables in a circular manner, not statistical correlation relationships. (b) Stock/level accumulates all inflows and also serves as the source from which outflows come. (c) Flow/rate functions as a vehicle for delivering resources to or draining resources from the stock. Flow values can be positive or negative. Positive flow is an inflow that fills the stock, while negative flow is an outflow that depletes the stock. (d) The delay represents the time required in a process to achieve something. (e) Nonlinearity is said to exist when there are parts that do not change, but other parts can change [8].
Simulation based on dynamic system principles is widely used to gain an understanding of complex and dynamic systems. Typically, these dynamic systems consist of variables that interact nonlinearly [9]. Such systems help facilitate an understanding of the impacts of various dynamic factors. The effectiveness and practicality of these systems have been demonstrated, providing appropriate solutions in problematic situations [10]. These systems provide a framework for analyzing dynamic feedback for complex project management. Developing models using dynamic systems is useful in evaluating project management procedures. The application of dynamic system models is beneficial for decision-making compared to other statistical systems. These models can be used to assess changes in land use due to development [11]. Based on the description above, the research objectives include analyzing dynamic systems using simulation models to predict noise emissions in a new mineral ore processing plant, namely SAG#3.
2 Method
This research was conducted at the new SAG#3 plant, which is currently under construction. The data source used for this research consists of primary data collected from the comparable facilities of SAG#1 and SAG#2. These comparable facilities were chosen because they have the same features as SAG#3. The research employed noise modeling with dynamic system analysis using the Vensim Pro software application. In the noise mapping process, data collection was conducted through surveys. Sampling was performed using a sound level meter (SLM), with sample points taken at machinery and equipment that are sources of noise. The sampling process adhered to the SNI 7231:2009 standard [12].
Upon noise sampling completion, analysis using a dynamic system was employed. Steps in the analysis are shown in Figure 1 [13]:
Dynamic system analysis steps.
The research implementation began with the collection of variables related to noise pollution. The dynamic hypothesis formulation involved the process of designing a causal loop. This process illustrates the interactions between variables and can formulate a model based on conditions [14]. The model formulation involved the creation of stock flow diagrams and a systematic model using Vensim Pro. Model testing is the process where the previously developed model undergoes verification testing by checking it within the Vensim Pro software. When Vensim Pro doesn’t display error messages, the model can be considered verified (error-free), and it’s ready to be executed or run [15].
3 Result and discussion
3.1 Noise sampling
Noise sampling was carried out in the areas of SAG#1 and SAG#2, with a total of ten sampling points in each area. Figure 2 depicts the layouts of SAG#1(a) and SAG#2(b), respectively. In addition, the layout of SAG#3, as the new plant in the work area, is shown in Figure 3. The selection of sampling points in SAG#1 and SAG#2 was based on the locations of machinery that are sources of noise. These machines include grinding machines, screens, and conveyors. Noise sampling was conducted using a device called a SLM. The sampling process adhered to the SNI 7231:2009 standard. Sampling was divided into four categories: daytime rainy, daytime non-rainy, nighttime rainy, and nighttime non-rainy. These categories were determined because there is research indicating that rain affects noise levels. The results of noise sampling in SAG#1 and SAG#2 indicated that rainfall increased noise levels.
SAG#1 and SAG#2 layout. (a) SAG#1 Layout with 1 SAG Mill and two Ball Mills. (b) SAG#2 Layout with 1 SAG Mill and four Ball Mills.
SAG#3 layout.
Table 1 displays the measurements of noise levels, SAG load in tons, and daily rainfall in millimeters at the SAG#1 and SAG#2 locations. Each section records data related to screening activities, grinding, and the use of conveyor equipment under various conditions. These observations reveal significant fluctuations in noise levels at SAG#1 and SAG#2, influenced by weather conditions and the type of activities being performed. The results indicate variations in measurement values at both locations, indicating the complexity of the operational environment at SAG#1 and SAG#2, which contributes to the total noise levels.
The results of noise level measurements during nighttime and daytime, both in rainy and non-rainy conditions
| SAG | Point | Remarks | Daytime, non-rainy condition | Daytime, rainy condition | Nighttime, non-rainy condition | Night time, rainy condition | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Noise (dBA) | Load SAG (tons) | Noise (dBA) | Load SAG (tons) | Rain rate (mm/day) | Noise (dBA) | Load SAG (tons) | Noise (dBA) | Load SAG (tons) | Rain rate (mm/day) | |||
| SAG#1 | 1 | Grinding | 86.1 | 563.78 | 92.5 | 555.08 | 4.8 | 90.2 | 575.78 | 90.7 | 574.30 | 9.6 |
| 2 | Grinding | 94.7 | 565.82 | 95.5 | 528.13 | 9.6 | 90.7 | 540.74 | 91.4 | 538.80 | 14.4 | |
| 3 | Grinding | 87.2 | 484.82 | 89.7 | 422.34 | 4.8 | 87.0 | 559.69 | 87.9 | 567.64 | 4.8 | |
| 4 | Screen | 87.3 | 432.92 | 92.6 | 399.99 | 43.2 | 92.2 | 461.20 | 93.1 | 461.81 | 19.2 | |
| 5 | Screen | 89.9 | 437.28 | 93.0 | 455.64 | 57.6 | 93.0 | 458.30 | 92.2 | 471.06 | 14.4 | |
| 6 | Screen | 90.4 | 556.34 | 91.6 | 415.19 | 67.2 | 90.0 | 429.12 | 90.2 | 508.46 | 9.6 | |
| 7 | Screen | 92.4 | 557.04 | 93.6 | 374.93 | 91.2 | 92.4 | 568.93 | 93.8 | 483.52 | 19.2 | |
| 8 | Conveyor | 88.3 | 452.17 | 88.2 | 389.07 | 4.8 | 89.1 | 478.86 | 88.3 | 379.50 | 4.8 | |
| 9 | Conveyor | 85.9 | 450.20 | 86.2 | 397.50 | 4.8 | 88.4 | 478.12 | 89.2 | 436.78 | 9.6 | |
| 10 | Conveyor | 89.7 | 471.11 | 93.1 | 405.66 | 9.6 | 85.4 | 468.84 | 85.6 | 492.06 | 4.8 | |
| SAG#2 | 1 | Grinding | 90.1 | 853.63 | 93.6 | 836.95 | 14.4 | 92.1 | 905.16 | 92.3 | 897.27 | 14.4 |
| 2 | Grinding | 88.9 | 812.20 | 93.0 | 883.02 | 28.8 | 93.9 | 887.14 | 95.5 | 740.36 | 24 | |
| 3 | Grinding | 92.3 | 827.57 | 92.9 | 786.53 | 9.6 | 93.2 | 871.09 | 93.3 | 769.50 | 14.4 | |
| 4 | Screen | 90.4 | 663.92 | 91.6 | 893.11 | 9.6 | 89.9 | 733.98 | 94.4 | 715.43 | 14.4 | |
| 5 | Screen | 90.4 | 646.21 | 93.9 | 678.92 | 38.4 | 90.2 | 722.61 | 94.3 | 687.34 | 14.4 | |
| 6 | Screen | 92.3 | 943.04 | 93.2 | 689.19 | 28.8 | 93.2 | 766.00 | 93.8 | 701.75 | 19.2 | |
| 7 | Screen | 91.6 | 819.41 | 90.9 | 706.91 | 19.2 | 91.5 | 690.17 | 92.0 | 730.74 | 14.4 | |
| 8 | Conveyor | 91.3 | 902.06 | 90.2 | 887.25 | 19.2 | 92.0 | 710.58 | 92.1 | 766.00 | 9.6 | |
| 9 | Conveyor | 90.5 | 725.11 | 87.5 | 881.50 | 4.8 | 88.3 | 719.45 | 91.5 | 738.96 | 9.6 | |
| 10 | Conveyor | 88.1 | 682.64 | 85.8 | 892.75 | 4.8 | 87.2 | 722.61 | 88.6 | 714 | 4.8 | |
Specifically, concerning noise levels (dBA), the maximum recorded result at SAG#1 occurred during the daytime with rainfall measuring 9.6 mm/day, resulting in a noise level of 95.5 dBA. During this measurement, the SAG load reached 528.13 tons. Then, at SAG#2, the maximum result occurred during the nighttime with 24 mm/day of rainfall. At the time when the noise reached its maximum value, the SAG#2 load was at 740.36 tons. Under these conditions, the maximum noise level at SAG#2 matched the maximum measurement result of SAG#1, which was 95.5 dBA. Meanwhile, the minimum noise level occurred during daytime without rain at SAG#1, measuring 85.9 dBA, with a SAG load of 450.2 tons, and during daytime with 4.8 mm/day of rainfall at SAG#2, measuring 85.8 dBA, with a SAG load of 892.75 tons. These noise values exceeded the permissible exposure limit of 85 dBA, as specified in the Ministry of Manpower and Transmigration Regulation No. 13 of 2011 [16]. Noise levels exceeding the threshold limit value can increase health risks for workers, thus requiring appropriate measures to reduce noise levels.
Table 2 provides information on the coefficient of determination, which represents the contribution or influence of the variables rain and load tonnage ore simultaneously on the variable noise. The obtained coefficient of determination (R 2) is 0.251, indicating that the variables night, rain, and load contribute 25.1% to the noise level. The remaining 74.9% is influenced by other variables outside this regression equation or variables not examined.
Model summary of Table 1 regression analysis
| Model summary | ||||
|---|---|---|---|---|
| Model | R | R 2 | Adjusted R 2 | Std. error of the estimate |
| 1 | 0.501a | 0.251 | 0.202 | 2.3478 |
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aPredictors: (Constant), night, rain, and load.
Based on regression analysis employed to understand what the most significant variables from Table 1 are, it provides information about the research variables and the methods used. The independent variables included in this analysis are night, rain, and load tonnage ore, with noise as the dependent variable. The regression analysis was conducted using the Enter method, meaning that no variables were excluded.
Based on the analysis of variance (ANOVA) depicted at Table 3, the significance value (Sig.) in the F-test is 0.011. Since 0.011 < 0.05, according to the decision-making basis in the F-test, it can be concluded that the variables rain and load tonnage ore simultaneously have a significant effect on the noise level.
ANOVA based on Table 1 on variables of night, rain, and load
| ANOVAa | ||||||
|---|---|---|---|---|---|---|
| Model | Sum of squares | df | Mean square | F | Sig. | |
| 1 | Regression | 57.177 | 2 | 28.589 | 5.187 | 0.011b |
| Residual | 170.872 | 31 | 5.512 | |||
| Total | 228.050 | 33 | ||||
aDependent variable: Noise.
bPredictors: (Constant), rain, and load.
3.2 Hearing Conservation Program (HCP)
Noise production can be controlled through the implementation of policies aimed at reducing noise at the source. The activation of noise reduction policies can be applied through an HCP designed to prevent occupational diseases [17]. In its implementation, HCP can include formulation and supervision aspects such as periodic noise exposure monitoring, the use of hearing protection devices (HPD), audiometric evaluations, employee and management training, record keeping, administrative controls, and engineering controls [18]. Noise control through such programs can reduce the risk of noise-induced hearing loss (NIHL), for example, through the use of equipment or materials that can absorb machine noise to help reduce noise levels in the workplace [19]. The implementation of HCP can have a positive impact on the operation of mineral processing facilities that generate noise from various sources, such as crushers, grinders, and mills [20].
3.3 Dynamic system analysis of noise
The dynamic system analysis of noise was conducted through five sub-models described in Figures 4–8 that influence noise in the work area of the Concentrating Division at PTFI. These sub-models encompass noise generated from grinding machine components, screening machines, and rain. Furthermore, a noise control sub-model was employed to examine preventive factors in personal noise control and the consequences of noise exposure, particularly the potential for occupational diseases, specifically NIHL. Each sub-model includes specific variables and units to quantify the modeling results of noise in the workplace environment. The parameters for each model can be observed in Table 4.
Sub-model of a grinding machine.
Sub-model of screening machinery.
Sub-model of noise originated from rain condition.
Sub-model of personal noise control.
NIHL sub-model.
Parameters of noise modeling
| No. | Quantity/Variable | Unit of measurement | Definition | Equation |
|---|---|---|---|---|
| Grinding machine | ||||
| 1 | Noise from grinding machine | dBA | Noise produced by operating grinding machine | Number of operating grinding machine * Noise production from grinding machine |
| 2 | Number of operating grinding machines | Machine | Number of operating grinding machines | 1 |
| 3 | Grinding machine production capacity | ton/h | Maximum number of materials that could be processed in grinding machine per hour | INTEG (grinding machine production capacity investment − Depreciation from grinding machine production capacity |
| 4 | Initial grinding machine production capacity | ton/h | Maximum number of materials that could be processed in grinding machine per hour at its initial year | Number of operating grinding machine * Grinding machine productivity |
| 5 | Depreciation from grinding machine production capacity | ton/(h*h) | Production capacity reduction in grinding machine | Grinding machine production capacity/depreciation time of grinding machine production capacity |
| 6 | Depreciation time from grinding machine production capacity | jam | Time required for depreciated production machine | 3,600 |
| 7 | Grinding machine productivity | ton/jam/mesin | Material produced by 1 grinding machine per hour | 4,583 |
| 8 | Investment of grinding machine production capacity | ton/(jam*jam) | Production capacity addition of grinding machine | Depreciation of grinding machine capacity |
| 9 | Throughput plan | ton/h | Corrected throughput target compared with real production capacity of grinding machine | MIN (grinding machine production capacity and throughput target) |
| 10 | Throughput target | ton/h | Targeted materials to be processed in grinding machine | 4,583 |
| 11 | Throughput supply for grinding | ton/h | Material supplies to be processed at grinding machine | Input: 1; 3; 5; 7; 9; 11; 13; 15; 17; 19; 21 |
| Output: 4,139; 4,582; 4,444; 4,193; 4,439; 4,440; 4,379; 4,263; 4,151; 4,531; 4,282 | ||||
| 12 | Grinding machine throughput | ton/h | Amount of actual materials processed in grinding machine | MIN (throughput plan and throughput supply for grinding) |
| 13 | Production percentage relative to grinding machine throughput capacity | Dmnl | Actual throughput compared with actual capacity of grinding machine | Throughput mesin grinding/grinding machine production capacity |
| 14 | Effect of production percentage relative to noise originating from grinding machine | Dmnl | Influence of the production percentage of grinding machines on the generated noise | Input: 0.5; 0.85; 0.9; 0.95; 1 |
| Output: 1.4; 1.1; 1.09; 1.05; 1 | ||||
| 15 | Noise production originating from grinding machine | dBA/machine | Noise generated from single grinding machine | Effect of grinding machine quality relative to noise generation * “Noise generation from each grinding machine with normal policy” * Effect of production percentage relative to grinding machine noise |
| 16 | Effect of grinding machine quality relative to noise production | Dmnl | Influence of the quality of grinding machines on grinding machine noise | A unit percent decrease in machine quality contributes to an increase in noise production from each machine |
| 17 | Grinding machine quality | Quality unit | Relative quality of grinding machines. 1 or 100% means optimal quality; 0 or 0% means minimal quality | INTEG (machine grinding maintenance − grinding machine quality depreciation, desired grinding machine quality) |
| 18 | Desired quality of grinding machine | Quality unit | Quality target of desired grinding machine (100%) | 1 |
| 19 | Depreciation from grinding machine quality | Quality unit/h | Quality reduction of grinding machine | Quality of grinding machine/grinding machine’s lifespan |
| 20 | Grinding machine lifespan | Hour | Depreciation time of grinding machine quality | 24 |
| 21 | Grinding machine maintenance | Quality unit/h | Grinding machine maintenance to achieve a 100% quality level of the grinding machine | (Grinding machine quality gap/time required for grinding machine repair) + grinding machine quality depreciation |
| 22 | Gap of grinding machine quality | Quality unit | Difference in actual grinding machine quality compared to the desired grinding machine quality | Desired grinding machine quality − grinding machine quality |
| 23 | Time required for grinding machine maintenance | Hour | Length of operating time required by grinding machine for maintenance | 1 |
| 24 | Noise production from each grinding machine with normal policy implementation | dBA/machine | Noise generation from each grinding machine that has been intervened with policy implementation | IF THEN ELSE (Activation of grinding machine noise reduction policy = 1, Production of noise from each normal grinding machine * (1 − Percentage reduction of noise from each total machine), Production of noise from each normal grinding machine) |
| 25 | Noise production from each normal grinding machine | dBA/mesin | Noise generation on grinding machine based on condition: (1) Machine quality 100% and (2) Production capacity fulfillment at 100% | 80 |
| 26 | Activation of grinding machine noise reduction policy | Dmnl | Variable for policy activation |
|
| 27 | Percentage reduction of grinding noise from each total machine | Dmnl | Total noise reduction after policy intervention of strong bag | Percentage total reduction of grinding noise from vibration control + percentage total reduction of grinding noise from shield and barrier engineering |
| 28 | Total percentage reduction of grinding noise from vibration control | Dmnl | Percentage of total grinding noise reduction from aspects of vibration controls | Number of grinding vibration controls * percentage reduction of grinding noise from vibration control |
| 29 | Percentage reduction of grinding noise from vibration control | Dmnl/unit | Percentage of grinding noise reduction from aspects of vibration controls for each installed strong bag |
|
| 30 | Number of grinding vibration controls | Unit | Number of installed strong bag | Installed strong bag |
| 31 | Installed strong bag | Unit | Number of installed strong bag | 5 |
| 32 | Total percentage reduction of grinding noise from barrier engineering | Dmnl | Percentage of grinding noise reduction from barrier engineering aspect | Total grinding barrier engineering * Percentage reduction in grinding noise from barrier engineering |
| 33 | Percentage reduction of grinding noise from barrier engineering | Dmnl/unit | Percentage of grinding noise reduction from barrier engineering aspect for each installed strong bag |
|
| 34 | Number of grinding machine noise barrier engineering | Unit | Number of installed Strong Bag | Installed strong bag |
| Screening machine | ||||
| 35 | Noise from screening machine | dBA | Noise generated from all operating screening machines | (Noise generation from screening machine + 10 * LOG(number of screening machine operated/machine unit, 10)) * machine unit |
| 36 | Noise generated from screening machine | dBA/machine | Noise generated by one screening machine | 47 |
| 37 | Machine unit | Machine | Machine unit | 1 |
| 38 | Number of screening machine operated | Machine | Number of operating screening machine | 2 |
| 39 | Total machine noise | dBA | Total sum of noise generated from grinding machine and screening machine | IF THEN ELSE (screening machine noise > noise from grinding machine, noise from screening machine is greater, noise from grinding machine is greater) |
| 40 | Noise from screening machine is greater | dBA | Calculation parameter of total machinery noise (to determine which machinery is greater in noise generation) | Noise from screening machine + (LOG (“Noise ratio between those of Grinding and Screening,” 10)) |
| 41 | Noise from grinding machine is greater | dBA | Noise from grinding machine + (LOG (noise ratio of grinding machine and screening, 10)) | |
| 42 | Sum of machinery noise | dBA | Total machinery noise (normal sum). Employed for total machinery noise calculation | Noise from grinding machine + noise from screening machine |
| 43 | Ratio of noise generated by grinding and screening machines | Dmnl | Comparison of noise between grinding machine and screening machine | IF THEN ELSE (Noise of screening machine > noise of grinding machine, (sum of machine noise/noise of screening machine), (sum of machine noise/noise of grinding machine)) + 1 |
| 44 | Total noise | dBA | Total noise generated from grinding machine and screening machine | IF THEN ELSE (Ratio of Total Machine Noise and Rain Noise = 0, Total Machine Noise, Machine Noise Affected by Rain) |
| 45 | Ratio of total machine noise to rain noise | Dmnl | Comparison of noise generated by grinding + screening with noise from rain | Noise from rain/total machine noise + 1 |
| 46 | Machine noise influenced by rain | dBA | Calculation parameter of total noise | IF THEN ELSE (ratio of total machine noise and rain noise > 0, total machine noise + (LOG (ratio of total machine noise and rain noise, 10)), 0) |
| 47 | Noise threshold limit value | dBA | Noise threshold limit value | 85 |
| 48 | Difference in total noise from the noise threshold | dBA | Difference in total noise from the noise threshold | IF THEN ELSE (total noise–noise threshold < 0, 0, total noise–noise threshold) |
| Rain noise | ||||
| 49 | Noise originated from rain | dBA | Noise originated from rain based on discrete rain intensity | Noise pollution index from rain * noise unit |
| 50 | Noise pollution index from rain | Dmnl | Noise production index per rain intensity (Rain Intensity Index) | |
| 51 | Noise production index per rain intensity | Dmnl | Table to express the relationship between rain intensity and noise generated by rain | Input: 0; 20; 50 |
| Output: 0; 10; 50 | ||||
| 52 | Rain Intensity Index | mm/h | Rain intensity | Input: 1; 3; 5; 7; 9; 11; 13; 15; 17; 19; 21 |
| Output: 0; 0; 0; 0; 0; 0; 5; 57; 7; 0; 0 | ||||
| Personal noise controls | ||||
| 53 | Employees at SAG#3 site | Employee | The number of employees working daily at site SAG#3 who are potentially exposed to noise | INTEG (increase in employees at site SAG#3 and initial employees at site SAG#3) |
| 54 | Employees at SAG#3 site when initial condition | Employee | The number of employees working daily at site SAG#3 who are potentially exposed to noise (initial year). | 15 |
| 55 | Addition of employees at SAG#3 site | Employee/year | Increase in employees at site SAG#3 | Fraction of increase in employees at site SAG#3 * employees at site SAG#3 |
| 56 | Fractional increase of employees at site SAG#3 | 1/year | Percentage increase in employees at site SAG#3 | 0 |
| 57 | Employee compliance | Compliance unit * employee | Accumulation of compliance figures from a certain number of employees | INTEG (increase in employee compliance − decrease in employee compliance, initial employee compliance level) |
| 58 | Level of initial employee compliance | Compliance unit * employee | Compliance figures from a certain number of employees in the initial year | 10 |
| 59 | Decrease of employee compliance | Compliance unit * employee | Reduction in the accumulation of compliance figures from a certain number of employees | Employee compliance/delay in employee compliance reduction time |
| 60 | Delay time of decrease in employee compliance | Year | Time | 5 |
| 61 | Compliance of supervisory effectiveness at field | Dmnl | Percentage of successful field supervision for employees to use personal protective equipment (PPE) such as earplugs and earmuffs. For example, out of ten people who were reprimanded, only nine immediately complied by using PPE after being reprimanded | 1 |
| 62 | Compliance supervision at field | Compliance unit * employee/year | Number employees reprimanded/supervised in the field | Effectiveness of field compliance supervision * Reduction in employee compliance |
| 63 | Increase of compliance with field supervision | Compliance unit * employee/year | Number of potential employees become more compliant after being reprimanded/supervised in the field | Field compliance supervision |
| 64 | Increase of employee compliance | Compliance unit * employee/year | Increase in the accumulation of compliance scores from a certain number of employees | Increase in compliance from field supervision + increase in employee compliance from HCP |
| 65 | Increase of employee compliance from HCP | Compliance unit * employee/year | Reduction in the accumulation of compliance scores from a certain number of employees due to HCP | ((Average employee compliance gap/delay in increase in employee compliance) * employees participating in hearing conservation) * effectiveness of HCP |
| 66 | Delay time of employee compliance increase | Year | Time span it takes for employees to truly comply with using PPE after the HCP | 1 |
| 67 | Effectiveness of HCP | Dmnl | Percentage effectiveness of the HCP in increasing employee compliance | 0.8 |
| 68 | Average target of employee compliance | Compliance unit | Target average employee compliance. 1 or 100% means optimal compliance; 0 or 0% means minimal compliance | 1 |
| 69 | Average gap of employee compliance | Compliance unit | Difference between target employee compliance and average employee compliance | Target average employee compliance − average employee compliance |
| 70 | Employee compliance average | Compliance unit | Average employee compliance | Employee compliance/employees at site SAG#3 |
| 71 | Employee following HCP | Employee | Number of employees participating in the HCP | INTEG (increase in employees participating in hearing conservation − employees completed HCP, initial employees participating in hearing conservation) |
| 72 | Employee following HCP at initial condition | Employee | Number of employees participating in the HCP in the initial year | Employees at site SAG#3 * percentage of employees participating in hearing conservation |
| 73 | Percentage of employees following HCP | Dmnl | Percentage of employees participating in the HCP (education and health check) | 0.5 |
| 74 | Employees completed HCP | Employee/year | Number of employees who complete the HCP each year | Fraction of employees participating in hearing conservation * employees participating in hearing conservation |
| 75 | Fraction of employees following HCP | Units: 1/year | Percentage of employees who complete the HCP | 0.5 |
| 76 | Increase of employees following HCP | Employee | Number of employees expected to participate in the HCP each year | Fraction of employees participating in hearing conservation * Employees at site SAG#3 |
| NIHL | ||||
| 77 | Percentage reduction of normal employee noise | Dmnl | Percentage reduction in noise perceived by employees when using PPE and ear protection | 0.1 |
| 78 | Effect of employee compliance | Dmnl | Effect of employee compliance on the average reduction in noise perceived by employees | Input: 0; 0.5; 1 |
| Output: 0; 0.4;1 | ||||
| 79 | Percentage reduction in employee noise effect of compliance | Dmnl | Percentage reduction in noise perceived by employees on average, considering the effect of employee compliance | Effect of employee compliance * percentage reduction in employee noise |
| 80 | Average noise perceived by employees | dBA | Average noise perceived by employees (out of many employees) | Total noise * (1 − percentage reduction in employee noise effect of compliance) |
| 81 | Delay in noise perceived by employees | dBA | Average noise perceived by employees and its impact on hearing impairment | DELAY1 (“Average noise perceived by employees” and hearing impairment delay time) |
| 82 | Time delay in hearing impairment | Year | On average, time it takes for someone to develop NIHL, starting from exposure to experiencing NIHL | 7 |
| 83 | Excess noise from its threshold | dBA | Difference in noise compared to the noise threshold | IF THEN ELSE (average noise perceived by employees delay − noise threshold < 0, 0, (average noise perceived by employees delay − noise threshold)) |
| 84 | Potential employees affected by NIHL | Employee * dBA | Potential employees affected by NIHL | High-risk NIHL employees * excess noise from threshold |
| 85 | Percentage of high-risk employees for NIHL | Dmnl | Percentage of employees at PTFI at high risk of NIHL | 0.7 |
| 86 | High-risk employees for NIHL | Employee | Number of employees at high risk of NIHL | Employees at site SAG#3 * Percentage of high-risk NIHL employees |
| 87 | Percentage of potential prevalence of NIHL | Percent | Percentage of employees potentially affected by NIHL out of the many employees working at site SAG#3 | Potential employees affected by NIHL/(employees at site SAG#3 * noise threshold) * percentage |
3.4 Analysis of dynamic system on the grinding machine sub-model
Grinding machines are one of the significant sources of noise that have a substantial impact on the sound levels experienced by workers in the Concentrating Division’s workplace environment. Grinding machines are used to reduce the size of mineral ore, and this process generates noise [21]. The impact of grinding machines on occupational health has prompted further analysis to understand noise-related issues, allowing for the mapping of key variables, interrelationships between variables, and causal relationships between variables. This structure explains how grinding machine noise is formed from other elements [22]. The noise levels in the workplace have been shown to impair the hearing function of industrial workers [23]. The causal loop diagram mapping for the grinding machine sub-model can be seen in Figure 4.
In this research, noise originating from grinding machines is measured through three key factors: noise production from each normal grinding machine, the quality of grinding machines, and the influence of production percentage on their capacity. First, the noise production from each normal grinding machine describes the noise generated by grinding machines under conditions of prime machine quality (100%) and when the production percentage relative to their capacity is 100%. This means that noise production and noise levels are inversely proportional, with noise increasing if the quality of the grinding machine is <100% and the production percentage relative to its capacity is <100%. Second, the quality of grinding machines also plays a crucial role in determining noise production. Machine quality is essential because of its long-term use [24]. Machine quality that falls short of 100% will result in increased noise generated by the machine. Third, the influence of the production percentage on the capacity of grinding machines also has a significant impact on noise levels. If the capacity of the grinding machines is not optimal, it will lead to increased noise.
This research also takes into account that the quality of grinding machines depreciates with use, typically due to the aging of the machines. This depreciation can be compensated for through the maintenance and upkeep of the grinding machines to ensure that the grinding quality meets the required standards. Machine maintenance is scheduled periodically, approximately every 5–7 months. Regarding the management of production percentages relative to machine capacity, efforts are made to maintain the production percentage of the grinding machines close to 100%. However, in some scenarios, the target throughput and supply throughput can be adjusted to not reach the maximum capacity of the grinding machines in order to evaluate sensitivity to the level of noise generated. This strategic adjustment allows for a better understanding of noise-related variables and their impact on machine performance.
3.5 Analysis of dynamic system on-screen machinery sub-model
Another process with the potential to generate noise is the screening machine. Screening machines are essential technology in mineral processing [25]. This process is required after grinding to ensure that the size of mineral ore meets the specifications before being processed in the subsequent stages. In the screening process, there are holes for separating or sieving mineral ore, and the machine has a vibrating mechanism for this separation [26]. This mechanism generates noise, so noise components in screening machines are influenced by three key factors: noise production from screening machines, machine units, and the number of machines in operation. When noise occurs, other machines in operation, such as grinding machines, contribute to the overall increase in machine noise, leading to noise levels exceeding the prescribed threshold. The dynamic system analysis of the screening machine model can be seen in Figure 5.
The noise calculation method for screening machines takes into account the noise production from each screening machine as the basis for calculation. Each screening machine produces a similar level of noise, and the total noise calculation from these machines is done using formula (1) as follows:
where TI2 is the combined noise intensity level from both machines, TI1 is the noise intensity level from one machine, and n is the number of machines emitting sound. The total noise is also calculated from one grinding machine, two screening machines, and the noise factor caused by rain.
3.6 Analysis of dynamic system on rainy condition noise sub-model
Noise generated from sources other than the operational activities of machines, such as natural events like rain, is another consideration. The work location of the Concentrating Division at PTFI experiences a wide range of rainfall, from low to high. As shown in Table 1, the rainfall in the Concentrating Division during the noise sampling period ranged from 4.8 to 91.2 mm/day. Daily noise potential is produced when rain hits objects, such as the roofs of mineral processing plant. To calculate noise originating from rain, the approach used is to consider the intensity of rainfall as the determining factor. The noise level generated will be related to the intensity of rainfall, assuming that a certain intensity of rainfall will result in noise with a specific decibel (dBA) value. The analysis of the rain sub-model is depicted in detailed dynamic system modeling in Figure 6.
3.7 Dynamic system analysis on personal noise control sub-model
The noise hazards can be controlled through personal noise control measures. This control program is carried out through the HCP, which involves educational efforts and health examinations. The program is considered a preventive measure in addressing noise in the workplace. Employee-controlled activities fall under administrative control, where compliance with regulations is implemented to reduce the risk of NIHL due to noise emitted. Some of these control measures include training processes, job rotation implementation, and regular health check-ups for employees. In its development, the level of employee compliance with this program can vary. Improvement occurs when there is an effective HCP and strict supervision in the field from all levels of supervision. However, a decrease in compliance may occur due to reduced individual awareness of the dangers of noise, with the assumption that they are already immune to loud noises. The analysis of the personal noise control sub-model is detailed and depicted clearly in Figure 7.
The HCP is mandatory for all employees across the site, although in practice, some employees may not always participate in the program regularly. Therefore, there is a variable called “Fraction of Employees Following HCP,” which refers to the level of employee compliance with the program. If all employees strictly adhere to the HCP, this fraction will reach its maximum value, which is 1% or 100%. Every employee who participates in the HCP is expected to experience an increase in compliance. However, full participation in the program does not always guarantee an increase in compliance due to the presence of other factors that play a role in the effectiveness of the HCP.
Another significant contributing factor is the accumulation development of the compliance stock supported by the effectiveness of the HCP itself. Besides the HCP, direct field supervision is also a crucial factor in improving employee compliance. If there is no commitment to supervising the use or maintenance of PPE, the compliance level will decrease [27]. The supervision initiative can enhance employee compliance with the presence of field supervision, influenced by the effectiveness of that supervision.
3.8 Dynamic system analysis on NIHL sub-model
The danger of noise exposure can lead to damage to the hearing structure after being exposed to loud sounds in the workplace [28]. The NIHL sub-model can be seen in Figure 8. In the influence of noise perceived by employees, several factors can have a significant impact on the level of noise received. Noise received by workers can decrease significantly compared to the overall noise level generated by machines and rain. This can occur with the activation of policies that can be implemented through the use of PPE.
Efforts to reduce the impact of received noise can be achieved through the use of PPE. The use of PPE is intended to reduce noise that enters the ear, whether from the outside, before entering the middle ear, or from inside the ear. The use of PPE such as ear muffs and earplugs has been proven to reduce sound intensity by 20–30 dBA [29]. Each PPE has its own Noise Reduction Rating, which is an average representation of sound reduction achieved by a device through laboratory testing, so the noise reduction value can be adjusted based on the equipment used [24]. In the model used, this is represented by the variable percentage of normal employee noise reduction, which indicates to what extent ear protection devices can reduce the noise level experienced by employees. However, noise reduction cannot be achieved by equipment alone; it also requires employee compliance with established policies.
The noise perceived by employees currently will not immediately lead to NIHL. There is a certain time gap between noise exposure and the onset of NIHL. This time gap exists because of the delay between noise exposure and the effects of NIHL. The number of employees potentially at risk of NIHL is determined by the percentage of potential NIHL prevalence, which is the ratio between the number of potential employees who may experience NIHL and the total number of employees working across the entire site. Not all employees in the workplace have the same risk of NIHL, and only a small portion of them are assumed to be potentially affected. Therefore, the variable representing the percentage of employees at high risk of NIHL becomes one of the contributors to the number of potential employees affected by NIHL.
3.9 Model validation
The available model was then validated using the total noise variable. The total noise, which has been modeled, can be validated against historical data of total noise in the workplace. Historical total noise data are obtained from measurements at all sampling points. The behavioral patterns and magnitude of total noise from the modeling results show similarity to the levels of noise in historical data. Therefore, the model formulation has been validated, indicating that the model is error-free. This demonstrates that the parameter estimates in the dynamic model have effectively produced results that represent the real system. The results of the total noise validation can be seen in Figure 9.
Model validation for total noise.
3.10 Baseline scenario
The baseline scenario refers to the initial simulation scenario used to compare various outcomes. Policy simulations are applied to observe the results of several scenarios, which will later be compared to the baseline scenario. In this model, the baseline scenario is defined as the scenario based on the validated model structure, which aligns with the behavioral patterns and magnitude of total noise from historical data. The baseline scenario assumptions encompass several components, as outlined in Table 5.
Assumption usage on various aspects of causal loop on baseline scenario
| Aspect | Assumption | Remarks |
|---|---|---|
| Grinding machine throughput | Fluctuated historically, but the range of throughput rate minimal 600 ton/cycle | The minimum figure of 600 tons/cycle is taken to create dynamics of grinding machine noise above 85 dBA |
| Grinding machine quality | At the most prime condition, 100% | Same as historical data |
| Grinding machine noise generation | 70 dBA, constant until the end of the simulation | Same as historical data |
| Rain intensity | Fluctuated historically | Same as historical data |
| Effectiveness of HCP | 50% | Same as assumed historical data |
| Effectiveness of compliance supervision at field | 50% | Same as assumed historical data |
| Percentage of employees following HCP | 50% | Same as assumed historical data |
| Policy | No policies yet for reducing noise from grinding machines, policies for reducing noise from rain, HCP policies, and PPE usage policies |
In the analyzed baseline scenario, it can be concluded that the total noise levels in the industrial environment exceed the established threshold of 85 dBA. This indicates that workers in PTFI’s Concentrating Division who are exposed to noise may have potential risks associated with the negative effects of environmental noise. The results of the baseline scenario analysis can be seen in Figure 10a. Noise perceived by employees is predominantly above this threshold because, in the baseline scenario, there are no implemented policies to reduce noise. Noise control policies such as the use of personal protective equipment, HCP, and on-site compliance supervision have not been applied in the baseline scenario. The results of the noise scenario perceived by employees can be seen in Figure 10b.
Baseline scenarios. (a) Baseline of total noise exceeds the noise permissible limit value, (b) noise perceived by employees exceeds PEL value, (c) number of employees potentially affected NIHL – baseline, and (d) percentage of NIHL prevalence potential – baseline (%).
Policies that have not been implemented will impact the number of employees potentially affected by NIHL and the percentage of potential NIHL prevalence. The number of employees potentially having NIHL fluctuates each year. In the modeling conducted, a significant increase occurred in the 20th year, as shown in Figure 10c. This increase leads to a similar pattern in the percentage of potential NIHL prevalence. In the 20th year, the prevalence of NIHL increased from 1% in the previous year to 8%. This value rises and then fluctuates for the next 2 years. The potential prevalence of NIHL can be seen in Figure 10d.
3.11 Policy scenario
The policy scenario refers to simulating various scenarios that implement the HCP policy to assess its impact. This simulation is done by comparing the results of various policy scenarios with the baseline scenario. Policy scenarios involve assumptions that affect various components within the system. The policies applied include the noise reduction policy for grinding machines, the noise reduction policy for rain, APD policy 1 (assuming the policy is not working optimally), and APD policy 2 (assuming the policy is working optimally). These assumptions cover several aspects listed in Table 6.
Assumption usage on various aspects of causal loop on policy scenario
| No. | Aspect | Description | Assumption | Policy applied time |
|---|---|---|---|---|
| 1 | Policy on noise reduction of grinding machine | Reduction in grinding noise from barrier engineering by 5 dBA | Year 21st | |
| 2 | Policy on noise barrier from rain | Rain noise suppressors can reduce 80% of the noise generated by rain | Year 21st | |
| 3 | Policy on PPE 1 | Maximum 29 dBA noise reduction by hearing protection devices is already active but with some suboptimal assumptions | HCP effectiveness, 50% | Year 21st |
| Field compliance supervision effectiveness, 50% | ||||
| Percentage of employees participating in HCP, 50% | ||||
| 4 | Policy on PPE 1 | Maximum 29 dBA noise reduction by hearing protection devices | HCP effectiveness gradually increases to 100% | Year 21st |
| Field compliance supervision effectiveness gradually increases to 100% | ||||
| Percentage of employees participating in hearing conservation gradually increases to 100% |
3.12 Result of policy scenario in reducing noise at grinding machine
In the implementation of the noise reduction policy for grinding machines, the noise reduction efforts applied to the grinding machines have yielded positive results. As shown in Figure 11a, from the results of the policy scenario’s sub-model, the total noise level has successfully decreased compared to the baseline scenario. The impact is also evident in the noise levels perceived by employees who have not used hearing protection devices (APD), as seen in Figure 11b. As a result, more employees experience noise levels below the established safe threshold of less than 85 dBA. The steps taken in this policy demonstrate that noise reduction is an effective measure for creating a safer working environment for employees.
Policy scenario in reducing noise at grinding machine. (a) Total noise – result of policy scenario in reducing noise at grinding machine, (b) noise perceived by employees – result of policy scenario implementation on reducing noise at grinding machine, (c) percentage of NIHL prevalence potential (%) – result of policy scenario in reducing noise at grinding machine, and (d) number of employees potential affected NIHL – result of policy scenario in reducing noise at grinding machine.
The noise reduction policy for grinding machines can also have a significant impact on reducing the percentage potential prevalence of NIHL in the workplace, as shown in Figure 11c. According to the modeling conducted, this policy can reduce the potential risk for employees to experience NIHL, which was previously a major concern. The results are also evident in the percentage potential prevalence of NIHL among employees, which is now significantly lower. In fact, the modeling shows that the number of employees does not exceed 13 individuals with the implementation of this policy. The percentage potential prevalence also remains below 8% after the policy is initiated in year 21 and reaches 0% in some periods. The results of the percentage potential prevalence of NIHL can be seen in Figure 11d.
3.13 Result of policy scenario on noise barriers from rain
Unlike the previous grinding machine policy results, the rain noise reduction policy seems to have not yet provided a substantial impact on reducing the total noise levels in the workplace. Implementing a rain noise reduction policy can be done through engineering techniques involving roof angles or materials that can reduce noise caused by rain [30,31]. Figure 12a shows that rain-induced noise has been significantly reduced, with noise levels below 2 dBA in year 21 and beyond. However, as indicated in Figure 12b, the total noise still does not seem to be fully addressed by this policy, thus not mitigating the overall noise. Further evaluation of policy effectiveness and consideration of more efficient adjustments may be needed.
Policy scenario on noise reduction from rainfall. (a) Noise originating from rain – result of policy scenario on noise barrier from rain and (b) total noise – result of policy scenario on noise barrier from rain.
3.14 Result of PPE policy scenario implementation
The policy of using PPE has proven to be one of the most significant policies in reducing the noise levels perceived by employees in the industrial environment, as shown in Figure 13a. In the simulation scenario with assumptions of 50% effectiveness of HCPs, 50% effectiveness of field compliance supervision, and 50% of employees participating in HCP, there is a noticeable decrease in the noise levels perceived by employees. As seen in Figure 13b and c, the potential employees at risk of NIHL due to noise also decreased drastically, so after year 21, the number of employees is below five, with a prevalence of less than 3.1%. This indicates that this policy has a substantial impact on reducing workplace noise.
Result of PPE policy scenario implementation. (a) Noise perceived by employees – result of PPE policy scenario implementation, (b) number of employees potential wit NIHL, and (c) percentage of NIHL prevalence potential (%).
In the next scenario, assuming gradual increases in the effectiveness of HCPs, field compliance supervision effectiveness, and the percentage of employees participating in HCPs until it reaches 100%, the results are even more significant compared to the previous scenario. The number of employees potentially at risk of NIHL falls below three after year 21, with a potential prevalence below 1.8%. The modeling results show that by increasing these three variables, the noise perceived by employees can be well below the established threshold. This highlights the effectiveness of HCP, field compliance supervision effectiveness, and the percentage of employees participating in HCP as significant factors in the dynamic system model for managing and reducing the impact of workplace noise. Therefore, improvement efforts in these three variables can be the primary focus for a company striving to create a safer work environment. This modeling aligns with the benefits of HCP policies that provide preventive measures against the health effects of noise exposure [32].
4 Conclusion
In the dynamic system analysis regarding the prediction of noise emissions in the new mineral ore processing plant’s environment, it was found that noise levels can exceed the safety threshold of 85 dBA. Noise levels exceeding this safe limit increase health risks for workers, necessitating the implementation of a HCP to reduce noise exposure in that area.
Within the HCP, policies aimed at reducing noise from grinding and screening machines, as well as the use of PPE, have a significant impact on reducing the perceived noise levels by employees. However, it appears that the rain attenuation policy has not yet effectively addressed the overall noise levels, and therefore, the total noise has not been adequately addressed. Further measures may be needed to manage total noise emissions.
Based on these findings, the company is recommended to take concrete steps to enhance the safety and health of its employees. First, the HCP can be effectively implemented with a focus on reducing noise from grinding and screening machines. Improving the effectiveness of the HCP, field supervision levels, and employee participation are three crucial factors in implementing PPE policies. Second, the use of PPE such as earplugs and earmuffs should be mandatory for workers with potential noise exposure. Earplugs have several advantages, including portability, compatibility with safety glasses and equipment, and comfort in hot conditions. However, they require careful installation, are difficult to remove, and can irritate the ears. On the other hand, earmuffs are easy to install, can be easily monitored, and provide maximum noise attenuation, but they are less comfortable in hot conditions and difficult to use with helmets. In addition, the company can conduct periodic evaluations to measure the effectiveness of policies and the HCP. This ongoing assessment can help ensure that the measures in place continue to provide adequate protection for employees.
Acknowledgement
The authors are thankful to the Resource Management Department and all departments working in the Concentrating Division, PT Freeport Indonesia, for providing the necessary equipment to carry out the experiments and for their support throughout the study.
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Funding information: There has been no funding provided for the research work.
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Author contributions: Conceptualization, Edi Karyono Putro; methodology, Edi Karyono Putro; monitoring, Edi Karyono Putro; supervision, Nieke Karnaningroem, and Arie Dipareza Syafei; formal analysis, Edi Karyono Putro; data interpretations, Edi Karyono Putro; writing – original draft preparation, Edi Karyono Putro; writing – review and editing, Edi Karyono Putro, Nieke Karnaningroem, and Arie Dipareza Syafei. All authors have read and agreed to the published version of the manuscript.
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Conflict of interest: The authors declare no competing interests.
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Ethical approval and consent to participate: Consent for publication informed consent was obtained from all subjects involved in the study.
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Data availability statement: The data used for the study are mostly shown in the tables, and the required data will be available on request.
References
[1] Chusna RRR, Rudiyanti S, Suryanti S. The relation of dominant substrate to Gastropods abundance in the mangrove forest of Kulonprogo, Yogyakarta. Saintek Perikanan: Indones J Fish Sci Technol. 2017;13(1):19–23.10.14710/ijfst.13.1.19-23Search in Google Scholar
[2] Sumardiyono S, Wijayanti R, Hartono H, Sri Budiastuti MTS. Pengaruh kebisingan terhadap tekanan darah, dengan stres kerja sebagai variabel mediator. J Kesehat Vokasional. 2020;5(2):124–31. (in Indonesian)10.22146/jkesvo.54088Search in Google Scholar
[3] Susanto A, Mulyono NB. Risk assessment method for identification of environmental aspects and impacts at ore processing industry in Indonesia. J Ecol Eng. 2018;19(2):72–80.10.12911/22998993/81781Search in Google Scholar
[4] Susanto A, Setyawan D, Setiabudi F, Savira Y, Listiarini A, Putro E, et al. GIS-based mapping of noise from mechanized minerals ore processing industry. Noise Mapp. 2021;8(1):1–15.10.1515/noise-2021-0001Search in Google Scholar
[5] Freeport Indonesia (PTFI). SAG#3 Management of Change. Unpublished Report; 2023.Search in Google Scholar
[6] Freeport Indonesia (PTFI). SAG#3 Feasibility Study. Unpublished Report; 2022.Search in Google Scholar
[7] Almamalik L. Buku sistem dinamik Vensim PLE. Guepedia; 2021. (in Indonesian)Search in Google Scholar
[8] Primadian D. Pengembangan model sistem dinamik terhadap ketersedian air bersih di Kabupaten Kutai Timur Provinsi Kalimantan Timur. J Ind Eng Manag. 2016;1(2):16–20. (in Indonesian)10.33536/jiem.v1i2.79Search in Google Scholar
[9] Wang W, Zeng W, Yao B. An energy-economy-environment model for simulating the impacts of socioeconomic development on energy and environment. Sci World J. 2014;2014:353602.10.1155/2014/353602Search in Google Scholar PubMed PubMed Central
[10] Ovtavariani D, Sofyan A. System dinamics for oprimizing the use of energy in the domestic sector at two villages in Bandung Barat Regency. J Tek Lingkung. 2015;21(1):66–76.10.5614/jtl.2015.21.1.8Search in Google Scholar
[11] Axella O, Suryani E. Aplikasi model sistem dinamik untuk menganalisis permintaan dan ketersediaan listrik sektor industri (studi kasus: Jawa Timur). J Teknik ITS. 2012;1(1):339–44. (in Indonesian)Search in Google Scholar
[12] Standar Nasional Indonesia. SNI 7231:2009 Pengukuran Kebisingan; 2009. (in Indonesian)Search in Google Scholar
[13] Turban E, Aronson JE, Liang TP. Decision support systems and intelligent systems. 7th ed. Yogyakarta. New Delhi: Prentice Hall of India Private Limited; 2005.Search in Google Scholar
[14] Wirijadinata JE, Afriany D. Analisis peningkatan kualitas pelayanan izin usaha perdagangan dengan menggunakan causal loop diagram (CLD). J Ilmu Administrasi: Media Pengemb Ilmu dan Praktek Administrasi. 2017;14(2):152–66. (in Indonesian)10.31113/jia.v14i2.110Search in Google Scholar
[15] Kesuma ZM, Fradinata E, Fitri A. Optimalisasi tempat tidur menggunakan model sistem dinamik di Rumah Sakit Zainal Abidin Kota Banda Aceh. J Stat dan Matematika. 2021;3(1):58–70. (in Indonesian)10.32493/sm.v3i1.7859Search in Google Scholar
[16] Menteri Tenaga Kerja dan Transmigrasi Republik Indonesia. Peraturan Menteri Tenaga Kerja dan Transmigrasi Nomor Per.13/MEN/X/2011 Tahun 2011 Tentang Nilai Ambang Batas Faktor Fisika dan Faktor Kimia di Tempat Kerja; 2011. (in Indonesian)Search in Google Scholar
[17] Haryandi H, Setiawati VR, Mayasisca M. Implementasi Hearing Conservation Program industri pertambangan sebagai upaya pencegahan penyakit akibat kerja (PAK) akibat risiko kebisingan; studi kasus di area grinding, process plant di PT. ABC, Sumbawa Barat, Nusa Tenggara Barat. Hexag J Teknik dan Sains. 2020;1(2):1–8. (in Indonesian)Search in Google Scholar
[18] Madahana MC, Nyandoro OT, Moroe NF. Engineering noise control for mines: Lessons from the world. S Afr J Commun Disord. 2020;67(2):684.10.4102/sajcd.v67i2.684Search in Google Scholar PubMed PubMed Central
[19] Setyaningrum I, Widjasena B. Analisa pengendalian kebisingan pada penggerindaan di area fabrikasi perusahaan pertambangan. J Kesehat Masy. 2014;2(4):267–75. (in Indonesian)Search in Google Scholar
[20] Sun K, Azman AS, Camargo HE, Dempsey PG. Risk assessment of recordable occupational hearing loss in the mining industry. Int J Audiol. 2019;58(11):761–8.10.1080/14992027.2019.1622041Search in Google Scholar PubMed PubMed Central
[21] Saldaña MR, Gálvez E, Navarra A, Toro N, Cisternas LA. Optimization of the SAG grinding process using statistical analysis and machine learning: a case study of the Chilean copper mining industry. Materials. 2023;16(8):3220.10.3390/ma16083220Search in Google Scholar PubMed PubMed Central
[22] Aryani RP, Siallagan M. Causal loop diagram for better understanding of customer satisfaction in the Indonesian public service. J Int Conf Proc. 2021:4(2):76–88.10.32535/jicp.v4i2.1228Search in Google Scholar
[23] Li X, Dong Q, Wang B, Song H, Wang S, Zhu B. The influence of occupational noise exposure on cardiovascular and hearing conditions among industrial workers. Sci Rep. 2019;9(1):11524.10.1038/s41598-019-47901-2Search in Google Scholar PubMed PubMed Central
[24] Isliko V, Budiharti N, Adriantantri E. Analisa kebisingan peralatan pabrik dalam upaya meningkatan kesehatan keselamatan kerja dan meningkatkan kinerja karyawan. J Valtech. 2022;5(1):101–6. (in Indonesian)Search in Google Scholar
[25] Wang W, Pan M, Duan C, Jiang H, Zhao Y, Lu H. Dry deep screening of spodumene and its mineral processing technology. Miner Eng. 2022;179:107445.10.1016/j.mineng.2022.107445Search in Google Scholar
[26] Rónai L, Lénárt J, Szabó T. Vibration measurement of a screening machine. Int J Eng Manag Sci. 2020;5(3):116–23.10.21791/IJEMS.2020.3.12Search in Google Scholar
[27] Fitriani ZA. Hearing loss due to noise and factors associated with X company workers. (Evaluation of a Hearing Conservation Program). Maj Kesehat PharmaMedika. 2019;11(1):50–7.Search in Google Scholar
[28] Natarajan N, Batts S, Stankovic KM. Noise-induced hearing loss. J Clin Med. 2023;12(6):2347.10.3390/jcm12062347Search in Google Scholar PubMed PubMed Central
[29] Mauliya D, Putra G. Evaluasi tingkat kebisingan peralatan pabrik dalam upaya meningkatkan kesehatan keselamatan kerja dan meningkatkan kinerja karyawan. J Sains Teknol Ind. 2022;20(1):98–107. (in Indonesian)Search in Google Scholar
[30] Qiram I, Rubiono G. Pengaruh sudut kemiringan atap seng dan plastik gelombang terhadap tingkat kebisingan akibat air hujan. J Dinamika Teknik Mesin. 2016;6(2):99–106. (in Indonesian)10.29303/d.v6i2.12Search in Google Scholar
[31] Krismani AY, Pambudi YS. Pelapis atap metal sebagai peredam suara. J Ekon Sos Hum. 2021;3(04):108–17. (in Indonesian)Search in Google Scholar
[32] Mayasari D, Khairunnisa R. Pencegahan noise induced hearing loss pada pekerja akibat kebisingan. J Kesehat Agromed. 2018;4(2):354–60. (in Indonesian)Search in Google Scholar
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Articles in the same Issue
- Regular Articles
- Statistical modeling of traffic noise at intersections in a mid-sized city, India
- Framework for urban sound assessment at the city scale based on citizen action, with the smartphone application NoiseCapture as a lever for participation
- Case study on the audibility of siren-driven alert systems
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- Three-dimensional visualisation of traffic noise based on the Henk de-Klujijver model
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- Exploring relationships among soundscape perception, spatiotemporal sound characteristics, and personal traits through social media
- Dynamic system employed for predicting noise emission at new constructed mineral ore processing plant
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- Montreal soundscapes during the COVID-19 pandemic: A spatial analysis of noise complaints and residents’ surveys
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Articles in the same Issue
- Regular Articles
- Statistical modeling of traffic noise at intersections in a mid-sized city, India
- Framework for urban sound assessment at the city scale based on citizen action, with the smartphone application NoiseCapture as a lever for participation
- Case study on the audibility of siren-driven alert systems
- Noise pollution and associated health impacts at Ganeshpeth Bus Terminus in Nagpur, India
- Reliability of smart noise pollution map
- Three-dimensional visualisation of traffic noise based on the Henk de-Klujijver model
- Assessment of noise pollution and associated subjective health complaints in Jharia Coalfield, India: A structural equation model analysis
- Exploring relationships among soundscape perception, spatiotemporal sound characteristics, and personal traits through social media
- Dynamic system employed for predicting noise emission at new constructed mineral ore processing plant
- Special Issue: Living with the Pandemic – Reflections on the Urban Sound Consequences of 2 Years of the COVID-19 Pandemic
- Montreal soundscapes during the COVID-19 pandemic: A spatial analysis of noise complaints and residents’ surveys
- Sound complexity as a strategy for livable and sustainable cities: The case of an urban waterfront
- Influence of soundscape on quality of work from home during the second phase of the pandemic in Brazil
- Special Issue: Latest Advances in Soundscape - Part I
- Prediction of the acoustic comfort of a dwelling based on automatic sound event detection
