Journal of Powder Metallurgy & Mining
Open Access

Coal mining; Steam coal; Metallurgical coal; Appalachian coal; Polylactic acid

Introduction

To meet energy needs, the eastern United States' Appalachian area is a significant supply of fossil fuel. Two thirds of the coal produced in the area is produced on the surface, and the remaining one third is produced primarily underground. Steam coal, which is used in the production of electricity, and (to a lesser extent) metallurgical coal, which is utilised in industrial operations, are two types of regional coal resources.

The long-term viability of Appalachian coal resource extraction is becoming more and more doubtful. The cost of coal, the cost of substitute resources (especially natural gas), and future regulations to cut greenhouse gas emissions that would diminish the demand for coal are all interconnected in a complicated, dynamic way [3]. Natural gas is increasingly challenging coal as a source of energy for generating electricity, and depending on future oil and gas prices, regulations addressing greenhouse gas emissions, the cost of coal production, and other factors, natural gas may match or perhaps overtake coal in the near future. Due to growing environmental restrictions, demand for coal with a reduced sulphur content and cleaner burn has increased, resulting in a regional change in the region's coal output.

There is a need to model and spatially predict where surface coal mining is anticipated in Appalachia due to the possible environmental implications of surface coal mining, even if coal is predicted to play a smaller and smaller role in America's energy mix in the future. The effects of coal mining on biodiversity, hydrology, human health, and water quality have all been the subject of numerous researches. Multiple surface mine impacts on streams have been studied in relation to the additive effects, and ecological stress has been studied in relation to the significance of spatial location and network position with other preexisting factors (other surface mines, deep mines, and residential development) [4]. The potential environmental effects on delicate ecosystems can be detected, and context-dependent conservation priorities can be defined in complex river systems by more accurately forecasting the likely locations for surface coal extraction.

Materials and Methods

Four distinct 3D printed polymers were tested for extractability: polylactic acid (PLA), FDA PLA, polyethylene terephthalate glycol (PETG), and polycarbonate (PC). The word "FDA-approved" was used to describe one of the PLA materials, but this term simply refers to the usage of an FDA compliant resin, according to the manufacturer, and does not really have approval from the U.S. Food and Drug Administration. Only an extractable study was examined because a real leachable study under simulated use settings was not feasible because the 3D printed devices in this study were not created for a specific medicinal therapy [5].

3D Printing parameters

Fusion deposition modelling (FDM) additive manufacturing with polymer filaments was used to manufacture the 3D printed gadgets. To balance print time and vertical resolution using a 0.4 mm nozzle, all objects were printed at a layer height of 0.2 mm. For consistency, all features on all objects were printed at a speed of 50 mm/s (infill, walls, and solid surfaces). The utilisation of a rectilinear design with a 35% infill percentage produced strong mechanical strength while balancing printing time and material consumption. All models' toolpaths were created using Simplify3D v3.1.0.

The printed materials on clean aluminum foil were placed in a temperature-controlled oven at 100°C for 18 minutes to anneal one set of FDA-approved PLA (FDA PLA) devices. The annealing process was carried out to enable a more thorough crystallization of the polymer substance. An orthopaedic implant application can benefit from polymer annealing since it can increase the material's tensional and flexural elastic moduli, Izod impact strength, and heat resistance.

Extraction procedure

Three different solvents with varied polarities—water, isopropyl alcohol (IPA), and hexane—were used to extract the devices. 50 mL of the pure solvent were used to extract each device. According to ISO 10993-12, extraction was carried out by placing a single device in an adequate extraction vessel with the associated solvent for more than 72 hours at 50°C. To make sure that the devices were completely wetted throughout the extraction, the incubator shaker was set to 100 rpm. Several industry standards advise employing aggressive extraction conditions and a variety of solvents that do not degrade the product, which was taken into account for this investigation even though there is presently no global standard for medical device extraction operations. The smallest amount required for chemical analysis guided the choice of solvent volumes.

The matching polymer filaments were extracted using the same methods as the printed devices in order to provide an appropriate comparison between printed and unprinted materials, with the exception that only 35 mL of solvent was utilised in the extractions [6].

A variety of analytical techniques targeting elemental and particulate matter, as well as volatile, semi-volatile, and low-volatility organics, were used to examine the recovered extracts. Except as otherwise specified, all reagents were analytical grade products acquired from Fisher Scientific [7].

While extractions intended for all other analytical testing were carried out in glass wide-mouth jars with PTFE caps (FisherBrand, Fisher Scientific) that had been triple rinsed with the solvent of interest before use, extractions intended for elemental composition testing were carried out in polypropylene DigiTUBES (SCP Science) that had previously been triple rinsed with 1% nitric acid (HNO3, TraceMetal Grade, Fisher Scientific). Only water was used to remove the elements from the analysis [8].

The extracted materials were then separated into portions for examination after allowing them to cool to room temperature. A 20 mL sample of the extract was used for the particle analysis. 9.85 mL of the extract were transferred to a plastic recovery vessel for ICP analysis, where they were mixed with 0.10 mL of HNO3 and 0.05 mL of HCl before the analysis. 3.0 mL was directly transferred to glass headspace vials for headspace GC/MS. Except for the GC/MS water analysis, the extracts were directly added to auto sampler vials for LC/MS and GC/MS studies. In a separatory funnel, 5 mL of the water sample was extracted in duplicate using liquid-liquid extraction using 5 mL dichloromethane for this analysis (DCM). The final DCM extracts were gathered and evaluated using GC/MS [9].

Results

The original nine predictor variables were incorporated in the final Random Forests model scenario. Based on each variable's contribution to the final outcome, we tried deleting low-performing variables from the model to see what happened. Alternative models that had fewer variables, however, underperformed the entire model and had greater classification error rates. Model significance was compared to models created at random and was determined to be significant with a p value of 0.01 [10].

The middle Appalachian region, notably in southwestern West Virginia and eastern Kentucky, has the highest likelihood of future surface mining, according to the results. Other areas with higher probabilities include western Kentucky, central Alabama, north central West Virginia, and the bituminous coal region of Pennsylvania and Ohio, to a lesser extent [11].

According to the criteria used in this study, Table 1 lists the entire area within each EIA coal supply zone with a relatively high likelihood (above 0.90). For the four regions, the central Appalachian region contains the highest high probability areas, whereas only a very small portion of the northern Appalachian and eastern interior/Illinois regions' total area has high likelihood. While the eastern interior/Illinois coal supply region also includes production in parts of western and central Illinois and Mississippi that are not included in the Appalachian LCC study area for this project, the northern, central, and southern Appalachian regions are entirely within the current study boundary (Appalachian LCC). Six counties in the eastern interior/Illinois region generate coal, but they are not included in the project research area, according to the most recent information on coal output from 2011 [57]. The eastern interior/Illinois region's surface coal output for the year 2011 was accounted for by these six counties at 11.7%, hence about 11–12% of this region's coal production will not be taken into account in our model's predictions and findings [12].

Conclusion

This study illustrates the need for creating a clever application to optimise the whole mining procedure in underground mining. It elaborates the input/output and algorithms of a decision support tool that is suggested to help miners arrange mobile mining equipment. Examples and a case study are used to show the method in more detail. The main goal of algorithms is to reduce the total mining time for a given workload within a predetermined time frame while still meeting other requirements. This tool has the potential to be useful for the mining sector, particularly for the production and excavation of underground mining. It clearly indicates to operators when each machine should work on each working face and at what time. It can be used to increase operational performance through more precise machine scheduling, quicker response to unforeseen events, and more accurate budget forecasting for both capital expenditures and operating expenses.

The research presented in this publication serves as a foundation for future studies. In order to verify and validate this strategy, this instrument will first be placed and tested continually with more cases in the Kittilä mine. The instrument's suggested timespan and the actual timespan from the foremen's schedule will be compared in terms of time savings. The study will concentrate on the control of the underground mining process to ensure that the mining activities are finished within the stipulated timeframe because there are significant uncertainties in underground mining. Thirdly, since the approach is heuristic and nearoptimal, it is essential to obtain the true optimal schedules for numerous examples by employing a more powerful, high-performing computer in order to fully assess the approach with a real optimal solution.

Acknowledgement

None

Conflict of Interest

None

1. Weintraub A, Barros L, Magendzo A, lbarra F, Ortiz C (1987) A truck dispatching system for a large open pit mine. Proceedings of the 11th International Conference of Operation Research. North Holland, Amsterdam 650-662.

Google Scholar

2. Beaulieu M, Gamache M (2006) An enumeration algorithm for solving the fleet management problem in underground mines. Computers & Operations Research 33: 1606-1624.

Indexed at, Google Scholar, Crossref

3. Saayman P, Craig IK, Camisani-Calzolari FR (2006) Optimization of an autonomous vehicle dispatch system in an underground mine. J South Afr Inst Min Metall 106: 77-86.

Indexed at, Google Scholar

4. McKenzie P, Newman A, Tenorio L (2008) Front Range Aggregates optimizes feeder movements at its quarry. Interfaces 38: 436- 447.

Indexed at, Google Scholar, Crossref

5. Nehring M, Topal E, Knights P (2010) Dynamic short term production scheduling and machine allocation in underground mining using mathematical programming. Mining Technology 119: 212-220.

Indexed at, Google Scholar, Crossref

6. Yu S, Ding C, Zhu K (2011) A hybrid GA–TS algorithm for open vehicle routing optimization of coal mines material. Expert Systems with Applications 38: 10568-10573.

Indexed at, Google Scholar, Crossref

7. Salvador MS (1973) A solution to a special class of flow shop scheduling problems, Symposium on the Theory of Scheduling and Its Applications. Springer 83-91.

Indexed at, Google Scholar, Crossref

8. Sawik T (2002) Balancing and scheduling of surface mount technology lines. Int J Prod Res 40: 1973-1991.

Indexed at, Google Scholar, Crossref

9. Kurz ME, Askin RG (2004) Scheduling flexible flow lines with sequence-dependent setup times. Eur J Oper Res 159: 66-82.

Indexed at, Google Scholar, Crossref

10. Gupta JND (1988) Two-stage, hybrid flowshop scheduling problem. J Oper Res Soc 39: 359-364.

Indexed at, Google Scholar, Crossref

11. Sriskandarajah C, Sethi SP (1989) Scheduling algorithms for flexible flowshops: worst and average case performance. Eur J Oper Res 43: 143-160.

Indexed at, Google Scholar, Crossref

12. Guinet A, Solomon MM, Kedia PK, Dussauchoy A (1996) A computational study of heuristics for two-stage flexible flowshops. Int J Prod Res 34: 1399-1415.

Indexed at, Google Scholar, Crossref