Diesel engines are important and effective in many services, but it is no friendly for environmentally. Since liquefied petroleum gas is prolific in Iraq at a lower price than other fuels and is clean and environmentally friendly, one of the important research topics is the use of liquefied petroleum gas in diesel engines because LPG has a high heat value, and the state of its gas makes mixing with air a burning issue simple and improved, reduces emissions and helps to harvest total energy. A new electronic control unit (ECU) is designed and used to inject liquefied petroleum gas via intake manifold as air enters the combustion chamber and a sensor is installed over a single-cylinder diesel engine, air-cooling. The test engine operation for fuel modes that were initially used is the D- 100, after which the LPG-25, LPG-50 and LPG-100 fuel were used. The test under loads was 0%, 25%, 50%, 75% and 100% at different speeds of 1000, 1500 and 2000 rpm. At engine speeds, 1000, 1500, 2000 rpm compared to D- 100 fuel, thermal efficiency was better in using LPG-50 fuel and improved by (4%, 3.6%, 4.9 %), and bsfc (9.81%, 9.4% and 9.68%) respectively, A decrease in emissions, NO x , HC, CO and CO 2 was observed in all operating modes with liquefied petroleum gas and the best emission reduction situation is LPG -50.
Keywords: Dual fuel, diesel engine, liquefied petroleum gas, emissions.
 M. E. L. M. S. Y. Yue and M. J. Lee (1997): “A Study On LPG As A Fuel For Vehicles,” no. March.
 R. Dhakar, A. Tripathi, and J. Raj (2016): “Use of Lpg in Internal Combustion Engines-a State of Art Review,” vol. 3, no. 8,
 Saleh, H.E. (2008): “Effect of variation in LPG composition on emissions and performance in a dual fuel diesel engine,” vol.
 Qi, D.H., Bian, Y. Z. H., MA, Z. H. Y., Zhang, C. H. and Liu, S.Q.(2007): “Combustion and exhaust emission characteristics
of a compression ignition engine using liquefied petroleum gas – Diesel blended fuel,” vol. 48, pp. 500-509.
 Tomar, C. S. and Randa, R.(2015): “Performance Evaluation of a Diesel Engine Running in Dual Fuel Mode with Karanja
Bio Diesel (Kome) and Liquified Petroleum Gas,” vol. 6, no. 11, pp.213-228, 2015.
 Tiwari, D. R. and Sinha, G. P. (2014): “Performance and 180 MAY 2020 Emission Study of LPG Diesel Dual Fuel Engine,”
Int. J. Eng. Adv. Technol., vol. 3, no. 3, pp. 198-203.
 Aydin, M., Irgin, A. and Celik, M. B. (2018): “The impact of diesel/LPG dual fuel on performance and emissions in a single
cylinder diesel generator,” Appl. Sci., vol.8, no.5, pp.1-14.
 Chiriac, R.,N. Apostolescu, N. and Niculescu, D. (2018): “An Experimental Study of Knock in a Spark Ignition Engine
Fueled with LPG,” no.724. Bhuiyan, M. S. A. and N. Naznin, N.(2003): “Multi-Fuel Performance of a Petrol Engine for Small Scale Power Generation,”
 D. Jian, D., Xiaohong, G., Gesheng, L.and Xintang, Z. (2018): “Study on Diesel-LPG Dual Fuel Engines,” no. 724.
 Combustion, Annamalai University, Chidarnbaram, (2005): pp.125-130, December 2005., no. December.
 Salman, S., Çinar, C., Ha, Mo, C., S ∫., Tolga, G. L. U. and Murat, T. (2004): “The Effects of Dual Fuel Operation on
Exhaust Emissions in Diesel Engines,” vol.7, no.3, pp.455-460.
 Qi, D.H., Bian, Y. Z., Ma, Z. Y., Zhang. C.H. and Liu, S. Q. (2007): “Combustion and exhaust emission characteristics of a
compression ignition engine using liquefied petroleum gas- Diesel blended fuel,” Energy Convers. Manag., vol. 48, no. 2, pp.
 Vijayabalan, P. and Nagarajan, G. (2009): “Performance , Emission and Combustion of LPG Diesel Dual Fuel,” vol. 3, no.
2, pp. 105-110.
 Karim, G. A. (1980); “A review of combustion processes in the dual fuel engine – the gas diesel engine,” vol. 6, pp. 277
 Lee, K. and Ryu, J. (2005): “An experimental study of the flame propagation and combustion characteristics of LPG fuel,”
Fuel, vol. 84, no. 9, pp. 1116-1127.
 Abd Alla, G.H., Soliman, H.A., Badr, O.A.and Abd Rabbo, M.F. (1999): “Effect of pilot fuel quantity on the performance of
a dual fuel engine,” SAE Tech. Pap., vol. 41, pp. 559-572.
 Anye Ngang, E. and Ngayihi Abbe, C.V. (2018): “Experimental and numerical analysis of the performance of a diesel
engine retrofitted to use LPG as secondary fuel,” Appl. Therm. Eng., vol. 136, pp. 462-474.
 Vinoth, T. Vasanthakumar, P., Krishnaraj,J., Arunsankar, S.K., Hariharan, J. and Palanisamy, M. (2017): “Experimental
Investigation on LPG + Diesel Fuelled Engine with DEE Ignition Improver,” Mater. Today Proc., vol. 4, no. 8, pp. 9126-9132.
 Rao, G. (2010): “Performance evaluation of a dual fuel engine (Diesel + LPG),” vol. 29, no. 14, pp. 235-246.
 Oester, U. and Wallace, J.S. (1987): “Liquid propane injection for diesel engines,” SAE Tech. Pap.,
 Ashok, B., Denis Ashok,S. and Ramesh Kumar, C.(2015): “LPG diesel dual fuel engine – A critical review,” Alexandria
Eng. J., vol. 54, no. 2, pp. 105-126.
 Yuvaraj, M. (2018): “Performance and Emission Characteristics of a Diesel-LPG Duel Fuel in Greeves Engine,” no.
 Rosha, P., Bharj, R.S. and Gill, K.J.S. (2014): “Performance and emission characteristics of Diesel + LPG dual fuel engine
with exhaust gas recirculation,” Int. J. Sci. Eng. Technol. Res., vol. 3, no. 10, pp. 2570-2574.
 Wei, M., Li, S., Liu, J., Guo, Sun, Z. and Xiao,H. (2017): “Effects of injection timing on combustion and emissions in a
diesel engine fueled with 2, 5-dimethylfuran-diesel blends,” Fuel, vol. 192, no. 8, pp. 208-217.
 Anye Ngang, E. and Ngayihi Abbe, C.V (2018): “Experimental and numerical analysis of the performance of a diesel engine
retrofitted to use LPG as secondary fuel,” Appl. Therm. Eng., vol. 136, pp. 462-474.
 Goto, S. and Lee, D. (2018): “Sae Technical Development of an LPG DI Diesel Engine using Cetane Number Enhancing
Additives,” no. 724.
 Cao, J.,Bian, Y., Qi, D., Cheng, Q. and Wu,T. (2004): “Comparative investigation of diesel and mixed liquefied petroleum
gas/diesel injection engines,” Proc. Inst. Mech. Eng. Part D J. Automob. Eng., vol. 218, no. 5, pp. 557-565.
Selection of cut off grade in long-term open pit mine planning is a tough research challenge now-a-days. The subsequent operational planning for the selected cut off grade decides the economic factor in mine production
scheduling. The distribution of grade, sequence of mining operation, economic parameters, the capacities of mining operations are influencing points for deciding the model. In any given period of time the dynamic cut off grade is a
function of the availability of ore and the capacity of stockpile as well as the process plant. The extraction sequence and cut off grade strategy should be considered simultaneously in order to achieve the optimum result. By keeping these points in first row, various attempts have been made to develop an electronic technique for the extraction sequence of open pit mines. Because of the numerous variables involved for getting the optimum result, different approaches have been made is not sufficient to widespread acceptance. A new model has therefore been proposed to overcome this shortcoming. The optimum sequences of extraction in each period are recognized by optimum processing decisions. To examine the applicability of the model developed, a case study is offered to validate.
Keywords: NPV, production scheduling, economic loss, mining sequence, cut off grade
1. Akaike, A., and Dagdelen, K. (1999): A strategic production scheduling method for an open pit mine. Proceedings of the
28th Application of Computers and Operation Research in the Mineral Industry, 729-738.
2. Asad, M. W. A. (2002): Development of generalized cutoff grade optimization algorithm for open pit mining
operations. Journal of Engineering and Applied Sciences, 21(2).
3. Ataei, M., and Osanloo, M. (2003): Methods for calculation of optimal cutoff grades in complex ore deposits. Journal of
Mining Science, 39(5), 499-507.
4. Ataei, M., and Osanloo, M. (2004): Using a combination of genetic algorithm and the grid search method to determine
optimum cutoff grades of multiple metal deposits. International Journal of Surface Mining, Reclamation and Environment,
5. Boland, N., Dumitrescu, I., Froyland, G., and Gleixner, A. M. (2009): LP-based disaggregation approaches to solving the open
pit mining production scheduling problem with block processing selectivity. Computers & Operations Research, 36(4), 1064-
1089.6. Cairns, R. D., and Shinkuma, T. (2003): The choice of the cutoff grade in mining. Resources Policy, 29(3-4), 75-81.
7. Dagdelen, K. (1985): Optimum multi-period open pit mine production scheduling. PhD thesis, Colorado School of
Mines, Golden, Colorado.
8. Dagdelen, K. (1986): Optimum open pit mine production scheduling by Lagrangian parameterization. Proc. of the 19
9. Dagdelen, K. (1993): An optimization algorithm for open pit mine design. In 24th International Symposium on the Application
of Computers and Operations Research in the Mineral Industry (APCOM) (pp. 157-165). 10. Dagdelen, K. (1993): An NPV
optimization algorithm for open pit mine design. In Proceedings of the 24th International Symposium on Application of
Computers and Operations Research in Minerals Industries (pp. 257-263).
11. Gershon, M. E. (1983): Optimal mine production scheduling: evaluation of large-scale mathematical
programming approaches. International Journal of Mining Engineering, 1(4), 315-329.
12. Gholamnejad, J. (2009): Incorporation of rehabilitation cost into the optimum cut-off grade determination. Journal of the
Southern African Institute of Mining and Metallurgy, 109(2), 89-94.
13. Gleixner, A. M. (2009): Solving large-scale open pit mining production scheduling problems by integer programming.
14. Halls, J. L., Bellum, D. P., and Lewis, C. K. (1969): Determination of optimum ore reserves and plant size by incremental
financial analysis. Transactions of the Institute of Mining and Metallurgy, 78, A20-A26.
15. Johnson, T. B. (1968): Optimum open pit mine production scheduling (No. ORC-68-11). California Univ Berkeley Operations
16. Johnson, T. B. (1969): Improving returns from mine products through use of operations research techniques (Vol. 7230). US
Dept. of the Interior, Bureau of Mines.
17. Kawahata, K. (2006): New algorithm to solve large scale mine production scheduling problems by using the Lagrangian
relaxation method, A (Doctoral dissertation, Colorado School of Mines. Arthur Lakes Library).
18. Lane, K. F. (1964): Choosing the optimum cut-off grade. Q. Colorado Sch. Min., 59, pp-811.
19. Lane, K. F. (1988): The economic definition of ore: cut-off grades in theory and practice (pp. 149). London: Mining Journal
20. Mogi, G., Adachi, T., Akaike, A., and Yamatomi, J. (2001): Optimum Production Scale and Scheduling of Open Pit Mines Using
Revised 4-D Network Relaxation Method. Journal-mining and materials processing institute of Japan, 117(7), 599-603.
The occurrences of unexpected failures in heavy earthmoving machines (HEMMs) lead to the downtime
that reduces the productivity, safety, and reliability of the machines. Unwanted failures increase the likelihood of
unplanned maintenance activities. Dragline is an HEMM used in the opencast coal mines for removal of the
overburden and its failure is undesirable as the capital invested on draglines are very high. This paper utilises the
failure mode, effects and criticality analysis (FMECA) to identify the critical failure components of the dragline
system and their root causes. Seven subsystems and thirty components for failure have been identified in the two-
year maintenance record 2014-16 of dragline. Risk estimation has been carried out for the dragline components to
estimate the risk priority number (RPN) considering four factors: failure occurrence, production loss, degradation in
performance, and detectability. The RPN is used to categorise the components into three groups: high, medium, and
low risk components. Based on the risk groups of component, the inspection interval and inspection time can be
optimised to avoid the unexpected failure of the component and eventually improving the productivity of the
Keywords: Dragline; FMECA; risk priority number; maintenance; HEMM
1. Adar, E., Ince, M., Karatop, B., and Bilgili, M. S. (2017): The risk analysis by failure mode and effect analysis (FMEA) and
fuzzy-FMEA of supercritical water gasification system used in the sewage sludge treatment. Journal of Environmental Chemical
Engineering, 5(1), 1261–1268. https://doi.org/ 10.1016/j.jece.2017.02.006
2. Arabian-Hoseynabadi, H., Oraee, H., and Tavner, P. J. (2010): Failure Modes and Effects Analysis (FMEA) for wind turbines.
International Journal of Electrical Power and Energy Systems, 32(7), 817–824. https://doi.org/10.1016/j.ijepes. 2010.01.019
3. Arunraj, N. S. and Maiti, J. (2007): Risk-based maintenance- Techniques and applications. Journal of Hazardous Materials,
142(3), 653–661. https://doi.org/10.1016/j.jhazmat. 2006.06.069
4. Balaraju, J., Govinda Raj, M., and Murthy, C. S. (2019): Fuzzy-FMEA risk evaluation approach for LHD machine-A case
study. Journal of Sustainable Mining, 18(4), 257–268. https://doi.org/10.1016/j.jsm.2019.08.002
5. Carmignani, G. (2009): An integrated structural framework to cost-based FMECA: The priority-cost FMECA. Reliability
Engineering and System Safety, 94(4), 861–871. https://doi.org/10.1016/j.ress.2008.09.009
6. Chang, C. L., Liu, P. H., and Wei, C. C. (2001): Failure mode and effects analysis using grey theory. Integrated Manufacturing
Systems, 12(3), 211–216. https://doi.org/ 10.1108/095760601103911747. Chang, W. L., Pang, L. M. and Tay, K. M. (2017): Application of self-organizing map to failure modes and effects analysis
methodology. Neurocomputing, 249, 314–320. https://doi.org/10.1016/j.neucom.2016.04.073
8. Chen, K. S., Wang, C. C., Wang, C. H. and Huang, C. F. (2010): Application of RPN analysis to parameter optimization of
passive components. Microelectronics Reliability, 50(12), 2012–2019. https://doi.org/10.1016/j.microrel.2010.06.014
9. Demirel, N.and Golbasi, O. (2016): Preventive Replacement Decisions for Dragline Components Using Reliability
Analysis. Minerals, 6(2), 51. https://doi.org/10.3390/min6020051
10. Feili, H. R., Akar, N., Lotfizadeh, H., Bairampour, M. and Nasiri, S. (2013): Risk analysis of geothermal power plants using
Failure Modes and Effects Analysis (FMEA) technique. Energy Conversion and Management, 72, 69–76.
11. Golbasi, O. and Demirel, N. (2017): A cost-effective simulation algorithm for inspection interval optimization: An
application to mining equipment. Computers and Industrial Engineering, 113, 525–540. https://doi.org/10.1016/j.cie.2017.09.002
12. Golbas1, O. and Demirel, N. (2017): Optimisation of dragline inspection intervals with time-counter algorithm.
International Journal of Mining, Reclamation and Environment, 31(6), 412–425. https://doi.org/10.1080/17480930.2017.1339168
13. Helvacioglu, S. and Ozen, E. (2014): Fuzzy based failure modes and effect analysis for yacht system design. Ocean
Engineering, 79, 131–141. https://doi.org/10.1016/j.oceaneng.2013.12.015
14. Kim, K. O. and Zuo, M. J. (2018): General model for the risk priority number in failure mode and effects analysis. Reliability
Engineering and System Safety, 169, 321–329. https://doi.org/ 10.1016/j.ress.2017.09.010
15. Li, Y. and Liu, W. (2013): Dynamic dragline modeling for operation performance simulation and fatigue life prediction.
Engineering Failure Analysis, 34, 93–101. https://doi.org/10.1016/j.engfailanal.2013.07.020
16. Lo, H.-W. and Liou, J. J. H. (2018): A novel multiple-criteria decision-making-based FMEA model for risk assessment.
Applied Soft Computing Journal, 73, 684–696. https://doi.org/ 10.1016/j.asoc.2018.09.020
17. Lo, H. W., Liou, J. J. H., Huang, C. N. and Chuang, Y. C. (2019): A novel failure mode and effect analysis model for
machine tool risk analysis. Reliability Engineering and System Safety, 183, 173–183. https://doi.org/10.1016/j.ress.2018.11.018
18. Mzougui, I. and Felsoufi, Z. El. (2019): Proposition of a modified FMEA to improve reliability of product. Procedia
CIRP, 84, 1003–1009. https://doi.org/10.1016/ j.procir.2019.04.315
19. Passath, T. and Mertens, K. (2019): Decision Making in Learn Smart Maintenance/ : Criticality Analysis as a Support Tool.
IFAC-PapersOnLine, 52(10), 364–369. https://doi.org/10.1016/j.ifacol.2019.10.058
20. Ponnusamy, M. and Maity, T. (2016): Recent advancements in Dragline control systems. Journal of Mining Science, 52(1),
21. Rai, P., Yadav, U. and Kumar, A. (2011): Productivity Analysis of Draglines Operating in Horizontal and Vertical Tandem
Mode of Operation in a Coal Mine-A Case Study. Geotechnical and Geological Engineering, 29(4), 493–504.
22. Rajput, P., Malvoni, M., Kumar, N. M. and Tiwari, G. N. (2019): Risk priority number for understanding the severity of
photovoltaic failure modes and their impacts on performance degradation. Case Studies in Thermal Engineering, 16, 100563.
23. Renjith, V. R., Jose kalathil, M., Kumar, P. H. and Madhavan, D. (2018): Fuzzy FMECA (failure mode effect and criticality
analysis) of LNG storage facility. Journal of Loss Prevention in the Process Industries, 56, 537–547.
24. Renu, R., Visotsky, D., Knackstedt, S., Mocko, G., Summers, J. D. and Schulte, J. (2016). A Knowledge Based FMEA to
Support Identification and Management of Vehicle Flexible Component Issues. Procedia CIRP, 44, 157–162.
25. Scheu, M. N., Tremps, L., Smolka, U., Kolios, A. and Brennan, F. (2019): A systematic Failure Mode Effects and Criticality
Analysis for offshore wind turbine systems towards integrated condition based maintenance strategies. Ocean Engineering,
176, 118–133. https://doi.org/10.1016/j.oceaneng.2019.02.04826. Sharma, R. K., Kumar, D. and Kumar, P. (2005): Systematic failure mode effect analysis (FMEA) using fuzzy linguistic
Soda lime is one of the most popular carbon dioxide absorbent materials to be used for closed-circuit life saving safety breathing apparatus in mining industries. A trained rescue person uses it during a situation such as a
fire, explosion or emission of toxic gasses in underground mines. This paper evaluates the chemical composition and physical properties of soda lime using specific parameters (moisture, carbon dioxide gas absorption, granule shape and fine particle size) which plays an important role in its application in breathing apparatus. Results indicated that soda lime moisture content, fine grains and hardness ranged between 11.6-18.3%, 0.2-1.9g, and 70-90%,
respectively. The CO 2 absorption rate was observed to be 20.0 to 57.0 minutes compared to standard UK Protosorb soda lime CO 2 (135 minutes). X-Ray Diffraction (XRD), Energy Dispersive Spectroscopy (EDS) and Scanning
Electron Microscope (SEM) analysis of the samples were carried out to understand the changes in molecular structure of the material before and after CO 2 absorption. The XRD result indicated presence of portlandite
(48.5%),calcite (49.6%) and potassium rhenium sulfide telluride cyan acetate (PRSTCA) (1.84%) before CO 2 absorption and calcite calcium carbonate (89.4%) portlandite (3.38%) and octasodium d-potassium tetra hydrogen
dihydroxo tetra telluride dipalladate (7.2%), 20-hydrate was observed after CO 2 absorption. EDS of sample 6 indicated presence of carbon (4.94%), oxygen (39.80%) sodium (3.36%) and calcium (51.90%) before CO 2 absorption and carbon (6.27%), oxygen (36 96%), sodium (1.37%) and calcium (55.40%) after CO 2 absorption.
Keywords: Carbon dioxide (CO 2 ), soda lime, hot air oven, breathing apparatus, XRD, EDS, SEM.
1. Douglas, R. K., Donald, V. T., Jeffry, S. B., O’Neill, H. J. & Gordon, S. M., (1989): Submarine abs atmospheres,
Toxically Letts 49(1989), pp. 243-251
2. Olajire, A.A., (2010): CO 2 capture and separation technologies for end-of-pipe applications a review, Energy 35(2010),
3. Hsu H. C. & Chung S. T., (2011): Removal of CO 2 from indoor air by alkanet amine in a rotating packed bed. Sep
Purify Technol, 82 (2011), pp.156-166.
4. Jonathan L.S., David G.K. and Randal J.K., (2009): Occupational hazards of carbon dioxide exposure,J. Chem. Health
Safety 16(2009), pp.18-22.
5. Cable, J., (2008): NIOSH Report Details The Dangers of Carbon Dioxide in Confined Spaces,Available online at:
http://www.occupationalhazards.com /News/ Article/ 37358 (Last access date: 24 December 2009).6. Moore, L.W and Campbell, D.L., (1983): Robert Erastus Wilson. In National Academy of Sciences (Ed.), Biographical
Memoir, National Academy of Sciences, Washington D.C. (1983), pp. 409-433
7. Renato, B., Giuseppe, S. and Marco, M., (2006): Process design and energy requirements for the capture of carbon
dioxide from the air, Chem. Eng. & Process. 45(2006), pp. 1047-1058.
8. Shunji, K, Hiromichi, B. Takasumi, K. and Shigehito, S. (2003): Effect of humidity in the circuit on the CO 2 absorption
capacity of Amsorb and Sodasorb II, J Anaesth, 17(2003), pp-145-146.
9. Klos, R. (2008): Removal of oxidable contaminations contained in the submarine atmosphere, Polish Maritime Res., 3
(2008), pp. 67-69.
10. Mazurek, W. (2005): Submarine atmosphere In Hocking, M.B. & Hocking, D. (Eds.), The Handbook of
Environmental Chemistry: Vol. 4, Part H (Air Quality in Airplane Cabins and Similar Enclosed Spaces), Springer- Verlag
Berlin Heiderberg, Germany, (2005), pp. 351-382.
11. Singh, R.S. and Tripathi, N. (2009). Occupational health and safety in coal mining industry. In Proceedings of Recent
Trends in Design, Development, Testing and Certification of Ex-equipment, organized at CIMFR, Dhanbad, 29-31