مدل‌سازی ذرات غبار خروجی از دود‌کش با شبکة عصبی مصنوعی و بررسی عملکرد الکتروفیلتر: مطالعة کارخانة سیمان زاوه

Anwar, Kh., Deshmukh, S., 2018. Assessment and mapping of solar energy potential using artificial neural network and GIS technology in the Southern part of India. International Journal of Renewable Energy Research 8)2(, 974-985.

Atabati, A., Adab, H., Zolfaghari, G., Nasrabadi, M., 2022. Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment. Water Science and Engineering 15(3), 218-227.

Chehregani, H., 2003. Environmental engineering in the cement industry. Hazeq Publications, Tehran, 1026 pages.

Chiu, M., Lin, G., 2004. Collaborative supply chain planning using the artificial neural network. Journal of Manufacturing Technology Management 15(8), 787-796.

Dorffner, G., 1996. Neural networks for time series processing. Neural Network World.

Esmaili Sari, A., Zolfaghari, G., Ghasempouri, S.M., Shayegh, S.S., Hasani Tabatabei, M., 2007. Effect of age, gender, years of practice, specialty and number of amalgam restorations on mercury concentration in nails of dentists practicing in Tehran. Journal of Iranian Dental Association 19(1), 97-104. (in Persian with English abstract).

Faradars, 2024.  https://blog.faradars.org (accessed February 2024).

Gardner, M., Dorling, S., 1998. Artificial neural networks (the multilayer perceptron) a ‎review of applications in the atmospheric sciences. Atmospheric Environment 32(14), 2627-2636.

Haq Moradkhani, H.R., Issaie, A., Shahu, A., 2013. Measuring and investigating the amount of pollutant suspended particles exiting from Ardabil cement factory and providing its control solutions. The 6th Transregional Conference on New Developments in Engineering Sciences.

Hosseini Shahpariyan, N., Firozi, M.A., Hosseini Kahnoj, S.R., 2021. Application of Artificial Neural Network and Regression Model to Predict the Phenomenon of Dust in the City of Ahvaz. Human and Environment 18(3), 13-24. (In Persian)

Huang, M., Terng, J., Chen, L., 2014. Predicting air pollutant emissions from a medical ‎incinerator using grey model and neural network. Applied Mathematical Modelling 39, 1513-1525.

Ilamathi, p., Selladurai, V., Balamurugan, K., 2012. Predictive modelling and optimization ‎of power plant nitrogen oxides emission. Institute of Advanced Engineering and Science 1(1), 11-‎‎18.

Khodaparast Shirazi, J., Sadeghi, Z., 2016. Forecasting seafood production in Iran using the ARIMA and Artificial Neural Networks. Journal of Agricultural Economics Research 8(31), 145-166. (In Persian)

Kia, M., 2011. Neural networks in MATLAB. Kian University Press, Tehran, 412 p.

Kolehmainen, M., Martikainen, H., Ruuskanen, J., 2001. Neural networks and periodic ‎components in air quality forecasting. Atmospheric Environment 35(5), 815-825.

Messikh, N., Bougdah, N., Bousba, S., Djazi F., 2020. Modeling the adsorption of chlorobenzene on modified bentonite using an artificial neural network. Current Research in Green and Sustainable Chemistry 3, 100026.

Noorani, V., Karimzadeh, H., Najafi, H., Hosseini, A., 2019. Predicting the concentration of NO₂ and SO₂ pollutants in the air of Tabriz using artificial neural network and adaptive neural-fuzzy inference system and comparing the obtained results. Conference: The 5th International Conference on Civil Engineering, Architecture & Urban Planning with Sustainable development approach, Shiraz, Iran. (In Persian)

Noori, R., Ashrafi, Kh., Azhdarpoor, A., 2008. Comparison of ANN and PCA based multivariate linear regression applied to predict the daily average concentration of CO: a case study of Tehran. Journal of the Earth and Space Physics 34(1), 135-152. (In Persian)

Noorpoor, A., Kazemi Shahabi, N., 2014. Dispersion modeling of air pollutants from the Ilam cement factory stack. Journal of Civil and Environmental Engineering 44(1), 107-116.

Onsori, H., Guderzai, G., Alavi, N.A., 2013. Investigation of air pollutants caused by elements of steam production cycle of Ramin thermal power plant using neural network model. The first national and specialized conference on environmental research in Iran.

Sadr Mousavi, M.S., Rahimi, A., 2010. Comparison of the results of multilayer perceptron neural networks and multiple linear regressions for prediction of ozone concentration in Tabriz city. Physical Geography Research 42(71), 65-72.

Shokati, H., Kaffash Charandabi, N., 2021. Evaluation and comparison of FFB, CFB, and MLP artificial neural networks for the identification of potential sites for the construction of photovoltaic solar power plants in East Azarbaijan province. Journal of Geography and Environmental Planning 31(4), 71-94. (In Persian)

Sivaneasan, B., Yu, C.Y., Goh, K.P., 2017. Solar forecasting using ANN with fuzzy logic pre-processing. Energy Procedia 143, 727-732.

Taghavi, H., Danesh, S., Mosaedi, A., 2011. Comparison of the effectiveness of artificial neural network and linear regression models in predicting daily carbon monoxide concentration based on meteorological parameters. National conference on air flow and pollution.

Zolfaghari, G., 2018. Risk assessment of mercury and lead in fish species from Iranian international wetlands. MethodsX 5, 438-447.

Zolfaghari, G., 2023. The first ecological contamination study of avian mercury and lead in southeast Iran, Hamun International Wetlands. Environmental Science and Pollution Research 30(42), 96575-96590.

Zolfaghari, G., Delsooz, M., Rajaee, S., 2016. Study of mercury pollution in water, sediments, and fish from Hamoon International Wetland. Journal of Water and Wastewater 27(5), 25-37. (In Persian)

Zolfaghari, G., Esmaili Sari, A., Ghsempoori, S.M., Ghorbani, F., Ahmadifard, N., Shokri, Z., 2006. The comparison of relation between age, sex and weight with mercury concentration in different tissues of Chalcalburnus Chalcalburnus from Anzali wetland, Iran. Journal of Marine Science and Technology 5(4-3), 23-31. (In Persian)

Zolfaghari, G., Kargar, M., 2019. Nanofiltration and microfiltration for the removal of chromium, total dissolved solids, and sulfate from water. MethodsX 6, 549-557.