Optimization Models for Reducing the Air Pollutants Emission in the Production of Insulation Bituminous

Document Type : Research Article


1 Faculty of Environment, University of Tehran, Tehran, Iran

2 Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran


Delijan, the main production center production of bitumen insulation, is one of the most polluted cities in Iran. Therefore, the measurement and analysis of emissions and the optimization of energy consumption, and, thus the reduction of emissions of these pollutants are particularly important. This study aimed to measure the gaseous pollutants emitted from the chimneys of one of the bitumen insulation production units in the industrial city of Delijan and also to improve the performance of the exhaust gas cleaning systems. For this purpose, first, the flow parameters of the chimney are measured with the KIMO gas analyzer, model KIGAS300, and then the pollutants Carbon monoxide, Hydrocarbons, and Nitrogen oxides are optimized of two methods in the MATLAB software using the Genetic Algorithm method and python software using the multiple regression with Sklearn and Statsmodels approach. An objective function was formed that has a non-linear relationship between the preheater outlet air temperature, the percent excess air, and the production of CO, CxHy and NOX pollutants. Statsmodels python regression were presented showing the effect of increasing or decreasing the preheater air temperature and the percent excess air (parameters for which the optimization is carried out) on the amount of pollutants production. According to the optimization results, the most suitable air temperature and percent excess air were selected to achieve the lowest pollutant emissions. Moreover, R2 values are at the 0.89 level and above. Finally, the proposed method can solve optimization problems and support environmental management decisions in the insulation industry.


Main Subjects

Akram, A., Khanali, M., Mohammadnia Galeshklamei, M., and Hosseinzadeh-Bandbafha, H. (2019). Optimization of energy consumption and reduction of environmental emissions in cake production using data envelopment analysis and genetic algorithm. Environmental Sciences, 17(2), 103-124.
Beckerman, B. S., Jerrett, M., Martin, R. V., van Donkelaar, A., Ross, Z., and Burnett, R. T. (2013). Application of the deletion/substitution/addition algorithm to selecting land use regression models for interpolating air pollution measurements in California. Atmospheric environment, 77, 172-177.
Borhani, F., Noorpoor, A., and Khalili, K. (2016). measuring and evaluation of non-hydrocarbon air pollutants emitted in the production of insulation bituminous (Isogam) exhaust flue gas. education, 2016.
Borhani, F., and Noorpoor, A. (2017). Cancer risk assessment Benzene, Toluene, Ethylbenzene and Xylene (BTEX) in the production of insulation bituminous. Environmental Energy and Economic Research, 1(3), 311-320.
Borhani, F., Mirmohammadi, M., and Aslemand, A. (2017). Experimental study of benzene, toluene, ethylbenzene and xylene (BTEX) concentrations in the air pollution of Tehran, Iran. Journal of Research in Environmental Health, 3(2), 105-115.
Borhani, F., Zahed, F., and Noorpoor, A. (2019). Modeling and evaluating the contribution of NOX and CO pollutants emitted in the insulation Bituminous units (Isogam) exhaust flue gas on the around area (Case study: Delijan City). New Science and Technology, 1(2), 91-100.
Borhani, F., and Noorpoor, A. (2020). Measurement of Air pollution Emissions from Chimneys of Production Units Moisture Insulation (Isogam) Delijan. Journal of Environmental Science and Technology, 21(12), 57-71.
Borhani, F., Motlagh, M. S., Stohl, A., Rashidi, Y., and Ehsani, A. H. (2021). Changes in short-lived climate pollutants during the COVID-19 pandemic in Tehran, Iran. Environmental Monitoring and Assessment, 193(6), 1-12.
Borhani, F., Shafiepour Motlagh, M., Stohl, A., Rashidi, Y., and Ehsani, A. H. (2022a). Tropospheric Ozone in Tehran, Iran, during the last 20 years. Environmental Geochemistry and Health, 44(10), 3615-3637.
Borhani, F., Shafiepour Motlagh, M., Rashidi, Y., and Ehsani, A. H. (2022b). Estimation of short-lived climate forced sulfur dioxide in Tehran, Iran, using machine learning analysis. Stochastic Environmental Research and Risk Assessment, 1-14.
Borhani, F., Shafiepour Motlagh, M., Ehsani, A. H., and Rashidi, Y. (2022c). Evaluation of short-lived atmospheric fine particles in Tehran, Iran. Arabian Journal of Geosciences, 15(16), 1-10.
Borhani, F., Shafiepour Motlagh, M., Ehsani, A. H., Rashidi, Y., Maddah, S., and Mousavi, S.M. (2022d). On the predictability of short-lived particulate matter around a cement plant in Kerman, Iran: machine learning analysis. International Journal of Environmental Science and Technology.
Cheraghi, A., and Borhani, F. (2016a). Assessing the effects of air pollution on Four Methods of pavement by using Four Methods of Multi-Criteria Decision in Iran. Journal of Environmental Science Studies, 1(1), 59-71.
Cheraghi, A., and Borhani, F. (2016b). Evaluation of Environmental and Sustainable Development of Four Pavements in Iran by Four Method of Multi-Criteria Analysis. Journal of Environmental Science Studies, 1(2), 51-62.
Daraie, H., Motasadi Zarandi, S., and Piraste, M. (2011). Study of monitoring, maintenance, and problems of electrostatic precipitators in some cement plants in Iran. Knowledge Horizons. Journal of the Medical and Health Sciences University Gonabad, 3(17), 66-74.
Dasgupta, D., and McGregor, D. R. (1993). sGA: A structured genetic algorithm. Glasgow: Department of Computer Science, University of Strathclyde.
Delavar, M. R., Gholami, A., Shiran, G. R., Rashidi, Y., Nakhaeizadeh, G. R., Fedra, K., and Hatefi Afshar, S. (2019). A novel method for improving air pollution prediction based on machine learning approaches: a case study applied to the capital city of Tehran. ISPRS International Journal of Geo-Information, 8(2), 99.
Eiben, A. E., Michalewicz, Z., Schoenauer, M., and Smith, J. E. (2007). Parameter control in evolutionary algorithms. In Parameter setting in evolutionary algorithms (pp. 19-46). Springer, Berlin, Heidelberg.
Elavarasan, D., Vincent, D. R., Sharma, V., Zomaya, A. Y., and Srinivasan, K. (2018). Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers and Electronics in Agriculture, 155, 257-282.
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D. W., et al. (2007). Changes in atmospheric constituents and in radiative forcing. Chapter 2. In Climate Change 2007. The Physical Science Basis.
Ghaffarizadeh, A. (2006). Investigation on evolutionary algorithms emphasizing mass extinction. Shiraz University, Shiraz, Iran.
Goap, A., Sharma, D., Shukla, A. K., and Krishna, C. R. (2018). An IoT based smart irrigation management system using Machine learning and open source technologies. Computers and electronics in agriculture, 155, 41-49.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, addison-wesley, reading, ma, 1989. NN Schraudolph and J, 3(1).
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
Hong, T. P., and Wang, H. S. (1996, October). A dynamic mutation genetic algorithm. In 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No. 96CH35929) (Vol. 3, pp. 2000-2005). IEEE.
Hoveidi, H., Aslemand, A., Borhani, F., and Naghadeh, S. F. (2017). Emission and Health Costs Estimation for Air pollutants from Municipal Solid Waste Management Scenarios, Case Study: NOx and SOx Pollutants, Urmia, Iran. Journal of Environmental Treatment Techniques, 5(1), 59-64.
Liu, S. C., Trainer, M., Fehsenfeld, F. C., Parrish, D. D., Williams, E. J., Fahey, D. W., et al. (1987). Ozone production in the rural troposphere and the implications for regional and global ozone distributions. Journal of Geophysical Research: Atmospheres, 92(D4), 4191-4207.
Maddah, S., Bidhendi, G. N., Borhani, F., and Taleizadeh, A. A. (2022). Resilient-Sustainable Supplier Selection Considering Health-Safety-Environment Performance Indices: A Case Study in Automobile Industry.
Man, K. F., Tang, K. S., and Kwong, S. (1996). Genetic algorithms: concepts and applications [in engineering design]. IEEE transactions on Industrial Electronics, 43(5), 519-534.
Mansouri, N., and Khairi, M. (2014). Air Emission Factors and Emission Rates in Asphalt Roofing Manufacturing. International Journal of occupational hygiene, 6(4), 175-183.
Markazi Province Meteorogicat Organization, MPMO. (2021). http://markazimet.ir
Michalewicz, Z., and Michalewicz, Z. (1996). Genetic algorithms + data structures= evolution programs. Springer Science and Business Media.
Moray, S., Throop, N., Seryak, J., Schmidt, C., Fisher, C., and D’Antonio, M. (2006, May). Energy efficiency opportunities in the stone and asphalt industry. In Proceedings of the Twenty-Eighth Industrial Energy Technology Conference (pp. 71-83).
Murao, N., Ohta, S., Furuhashi, N., and Mizoguchi, I. (1990). The causes of elevated concentrations of ozone in Sapporo. Atmospheric Environment. Part A. General Topics, 24(6), 1501-1507.
Penkett, S. A., and Brice, K. A. (1986). The spring maximum in photo-oxidants in the Northern Hemisphere troposphere. Nature, 319(6055), 655-657.
Pérez-Uresti, S. I., Ponce-Ortega, J. M., and Jiménez-Gutiérrez, A. (2019). A multi-objective optimization approach for sustainable water management for places with over-exploited water resources. Computers and Chemical Engineering, 121, 158-173.
Scovronick, N., Dora, C., Fletcher, E., Haines, A., and Shindell, D. (2015). Reduce short-lived climate pollutants for multiple benefits. The Lancet, 386(10006), e28-e31.
Seinfeld, J. H. (1986). ES&T books: atmospheric chemistry and physics of air pollution. Environmental science and technology, 20(9), 863-863.
Shamshirband, S., Khoshnevisan, B., Yousefi, M., Bolandnazar, E., Anuar, N. B., Wahab, A. W. A., and Khan, S. U. R. (2015). A multi-objective evolutionary algorithm for energy management of agricultural systems—a case study in Iran. Renewable and Sustainable Energy Reviews, 44, 457-465.
Vares, S., Häkkinen, T., Ketomäki, J., Shemeikka, J., and Jung, N. (2019). Impact of renewable energy technologies on the embodied and operational GHG emissions of a nearly zero energy building. Journal of Building Engineering, 22, 439-450.
Wang, F., Lai, X., and Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision support systems, 51(2), 262-269.