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Frontiers of Environmental Science & Engineering

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Front. Environ. Sci. Eng.    2019, Vol. 13 Issue (5) : 74    https://doi.org/10.1007/s11783-019-1156-x
RESEARCH ARTICLE
An integrated optimization and simulation approach for air pollution control under uncertainty in open-pit metal mine
Zunaira Asif1,2(), Zhi Chen2
1. Institute of Environmental Engineering and Research, University of Engineering and Technology, Lahore, Pakistan
2. Concordia University, Department of Building, Civil and Environmental Engineering (BCEE) in Montreal, Quebec, Canada
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Abstract

Air Pollution Control model is developed for open-pit metal mines.

Model will aid decision makers to select a cost-effective solution.

Open-pit metal mines contribute toward air pollution and without effective control techniques manifests the risk of violation of environmental guidelines. This paper establishes a stochastic approach to conceptualize the air pollution control model to attain a sustainable solution. The model is formulated for decision makers to select the least costly treatment method using linear programming with a defined objective function and multi-constraints. Furthermore, an integrated fuzzy based risk assessment approach is applied to examine uncertainties and evaluate an ambient air quality systematically. The applicability of the optimized model is explored through an open-pit metal mine case study, in North America. This method also incorporates the meteorological data as input to accommodate the local conditions. The uncertainties in the inputs, and predicted concentration are accomplished by probabilistic analysis using Monte Carlo simulation method. The output results are obtained to select the cost-effective pollution control technologies for PM2.5, PM10, NOx, SO2 and greenhouse gases. The risk level is divided into three types (loose, medium and strict) using a triangular fuzzy membership approach based on different environmental guidelines. Fuzzy logic is then used to identify environmental risk through stochastic simulated cumulative distribution functions of pollutant concentration. Thus, an integrated modeling approach can be used as a decision tool for decision makers to select the cost-effective technology to control air pollution.
Keywords Air pollution      Decision analysis      Linear programming      Mining      Optimization      Fuzzy      Monte Carlo     
Corresponding Author(s): Zunaira Asif   
Issue Date: 23 September 2019
 Cite this article:   
Zunaira Asif,Zhi Chen. An integrated optimization and simulation approach for air pollution control under uncertainty in open-pit metal mine[J]. Front. Environ. Sci. Eng., 2019, 13(5): 74.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-019-1156-x
https://academic.hep.com.cn/fese/EN/Y2019/V13/I5/74
Fig.1  A framework to conceptualize an integrated optimization and simulation approach for air pollution control model under uncertainty analysis.
Fig.2  Steps of risk evaluation using a hybrid fuzzy-stochastic risk assessment approach (modified from Chen et al., 2010).
Fig.3  Location of mine and monitoring station at mine A, Utah, North America.
Input parameters Average values
The annual production rate of mine (×105 t/a) 2.55
Grade of copper mine (g/t) 0.97
Total production of copper (×105 t) 2.68
Emission of PM2.5 produced (×104 kg/a) 7.74
Emission of PM10 produced (×104 kg/a) 6.50
Emission of NOx produced (×104 kg/a) 1.26
Emission of SO2 produced (×104 kg/a) 3.78
Carbon footprints (×106 kg CO2 eq.) 760
Tab.1  Inputs for optimal model (NPRI, 2013)
Air pollution control equipment Removal efficiency (%) Direct cost (×103 $) (DC) Indirect cost (×103 $) (IC) References
PM
Scrubbers-wet (SW) 96 159 103 USEPA (2002)
Bag house (BH) 95 56 29 Driussi and Jansz (2006)
Chemical suppressant-Magnesium chloride (CS) 85 0.37/(×103 a·d3) 0.12(×103 a·d3) Dwayne and Regensburg (2001)
Water suppressant (WS) 65 0.20/acre 0.15/acre Prostański (2013)
?Capping (Cp) 75 5.6/acre 2.1/acre Sheoran et al. (2013)
NOx
Selective catalytic reduction (SCR) 85 8.490/t 3.540/t MJ Bradley and Associates (2005)
Non-selective catalytic reduction (NSCR) 65 3.130/t 2.545/t MJ Bradley and Associates (2005)
Flue gas recirculation (FGR) 60 1.370/t 0.450/t USEPA (1999)
SO2
Dry flue gas desulfurization (FGD-dry) 94 6300 1250 USEPA (2002)
Wet flue gas desulfurization (FGD-wet) 98 7760 5600 USEPA (2002)
Greenhouse gases (GHGs)
Concentrated solar thermal technologies (CST) 15 of overall reduction 5.257/kWe 1.200/kWe Eglinton et al. (2013)
Anti-idling hauling management (AIH) 15 of overall reduction 15/unit system 10/unit system Vivaldini et al. (2012)
Biodiesel (Bio) 10 of overall reduction 1.06/L 0.72/L Bugarski et al. (2014)
Tab.2  Economic inputs for air pollution control technology (USEPA, 2002)
Fig.4  Monte Carlo simulation of wind speed.
Fig.5  Percentage distribution of stability classification.
Pollutants Treatment options Optimum cost (×103$)
PM2.5 and PM10 1. Baghouse (BH) 567
2. Scrubber-wet (Sw) 656.5
3. Chemical suppressant (CS) 100.98
4. Water suppressant (WS) 85
5. Capping (Cp) 79
NOx 1. Selective catalytic reduction (SCR) 2079
2. Non-selective catalytic reduction (NSCR) 923.93
3. Flue gas recirculation (FGR) 239.76
GHGs 1. Concentrated solar thermal technologies (CST) 7290
2. Anti-idling hauling management (AIH) 5560
3. Biodiesel (Bio) 1034
SO2 1. Dry flue gas desulfurization (FGD-dry) 4290
2. Wet flue gas desulfurization (FGD-wet) 6230
Tab.3  Optimization analysis of air pollution control technology
Fig.6  Scenario analysis of various treatment combinations for PM, SO2, NOx and GHGs.
Fig.7  Comparison of control cost with annual production.
Fig.8  Fuzzy based environmental guidelines using membership function (a) SO2; (b) NO2; (c) PM2.5; (d) PM10.
Fig.9  Fuzzy risk assessment based on pollutant concentration through MCS (a) PM2.5; (b) PM10; (c) SO2; (d) NO2.
Pollutants Before control Avg. (µg/m3) Fuzzy environmental guideline
S-M-L(µg/m3)		 Control methods After control (µg/m3)
PM2.5 8 8-10-15 (annual) BH and DS 1.813
PM10 35 25-50-150 (24 h) BH and DS 13.1
NOx 50 40-60-100 (annual) NSCR 15.34
SO2 15 30-60-80 (annual) FGD-dry 4.6
Tab.4  Average pollutant concentration before and after pollution control
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