Showing 16 results for Modeling
A. Alizadehdakhel, A. Ghavidel, M. Panahandeh,
Volume 3, Issue 1 (4-2010)
Abstract
Backgrounds and Objectives: The dispersion of particulate matter has been known as the most serious environmental pollution of cement plants. In the present work, dispersion of the particulate matter from stack of Kerman Cement Plant was investigated using Computational Fluid Dynamics (CFD) modeling.
Materials and Methods: In order to study the dispersion of particulate matter from the stack, a calculation domain with dimensions of 8000m × 800m × 400m was considered. The domain was divided to 936781 tetrahedral control volumes. The mixture two-phase model was employed to model the interaction of the particulate matter (dispersed phase) and air (continuous phase). The Large Eddy Simulation (LES) method was used for turbulence modeling.
Results: The concentration of particulate matter in the whole calculation domain was computed. The predicted concentrations were compared to the measured values from the literature and a good agreement was observed. The predicted concentration profiles at different cross sections were analyzed.
Conclusion:The results of the present work showed that CFD is a useful tool for understanding the dispersion of particulate matter in air. Although the obtained results were promising, more investigations on the properties of the dispersed phase, turbulent parameters and the boundary layer effect is needed to obtain more accurate results.
R Noori, F Jafari, D Forman Asgharzadeh, A Akbarzadeh,
Volume 4, Issue 2 (9-2011)
Abstract
Backgrounds and Objectives: The Atrak River is an important water supply resource in the Razavi Khorasan, Northern Khorasan and Golestan provinces. This river is the line border of Iran and Turkistan countries. Unfortunately, lack of water quality and quantity data due to nonexistence of a proper surface water quality monitoring station network is one of the main problems for water quality evaluation in the Atrak River. The main objective of the research is to offer a proper framework for surface water quality evaluation regarding to the mentioned limitations.
Materials and Method: In the first step, proper surface water quality monitoring stations along Atrak River are selected and water quality conditions are indicated using water quality index (WQI) model. The second step is allocated for determining trophic states of the river. Finally, the river water quality modeling is carried out for one of the most important index of water quality in the Atrak River i.e. total dissolved solids (TDS) based on proposed method by Oconnor (1976).
Result: Results of WQI model showed that most of the stations were in the moderate class. The result also showed that most parts of this river had trophic condition. Finally, based on findings of O'Conor model it is demonstrated that the salinity status observed in these four stations originated from the base flow and therefore, salinity is affected by the natural sources.
Conclusion: This methodology in the research can be used in rivers which don't have the proper surface water quality monitoring stations and therefore encountered with lack of water quality data. It can provide the proper strategy and management tasks to reach the good water quality conditions.
M Shafiepourmotlagh, M Kalhor, F Khalil Arya,
Volume 4, Issue 2 (9-2011)
Abstract
Background and Objectives: This study presents an evaluation between IAQX 1.0f and Fluent 6.3.26 in modeling of NOx dispersion in an indoor residential environment. Modeling predictions are compared with sampling results.
Materials and Methods: Aresidential building with about 84 m2 area is modeled. In IAQX 1.0f the building is divided into five zones. Emission factors and absorption rate of sinks is estimated with US.EPA suggested factors. On the other hand, In the Fluent 6.3.26 model, the building was divided into 1777 cells, and the openings are defined by the boundary conditions of the inflow. In this model, pollution sources were simulated by boundary conditions of the mass inflow.
Results:Compared to IAQX 1.0f, Fluent 6.3.26 showed higher estimation of the concentrations in the zones of 1, 2 and 3. In comparison with the measurements, both models had underestimated results.
Conclusion: The results of Fluent 6.3.26 were closer to the sampling results in the zones.
Mohammad Esmaeilzadeh, Edris Bazrafshan, Mahnaz Nasrabadi,
Volume 6, Issue 1 (5-2013)
Abstract
Background and Objectives: Tous gas power plant as an emission source of gas pollutants is located in the northwest of Mashhad City. This power plant is located in the neighborhood of various (linear, point and area) sources of pollution including Tous thermal power plant, Tous industrial town and motor vehicles hence, it is not possible to determine precisely and accurately the share of these gases emission contribution at this power plant using conventional instruments. Therefore, we used modeling in order to estimate the dispersion of the pollutants emitted from this power plant.
Materials and Methods: we used Screen 3 software using data of exhaust fume concentration, mass emission, chimney features of each unit, meteorology data, and fuel types consumed in order to model dispersion of NOx and SO2 emitted from Tous gas power plant having V94.2 turbine equipped with DLN torches.
Results: Maximum concentration of NOx and SO2 at the distance about 30 km from the power plant was 1.08 and 3.69 µg/L respectively. The results of dispersion modeling of pollutants indicated that in most cases emission of air pollutants towards southeast. Conclusion: The NOx and SO2 concentration measured revealed that the concentration of these pollutants is lower than the standards of Clean Air Act.
Ali Reza Keshtkar, Hossein Dastebashi, Morteza Ghasemi Torkabad , Mohammad Ali Moosavian,
Volume 6, Issue 4 (3-2014)
Abstract
Background and Objectives: Biosorption is a new and inexpensive technique in heavy metals removal and recovery from aqueous solutions. In order to evaluate the potential of this method for the removal of nickel ions, biosorption of nickel ions from aqueous solution was studied using Cystoseira indica biomass in a packed bed column. Materials and Methods: The uptake capacity of nickel ions was investigated using protonated biomass at different influent concentrations and flow rates. In addition, the experimental breakthrough curve obtained under definite experimental conditions was modeled using Thomas, Yoon & Nelson, Dose-Response, and Belter models. Results: It was found that increasing influent concentration from 58 to 100 mg/l led to the increase of driving force for mass transfer and uptake capacity raised from 55.84 to 95.69 mg/g. The investigation of flow rate effect showed when the process is intraparticle mass transfer controlled, a slower flow rate favors the sorption. In the case of external mass transfer control, a higher flow rate decreases the film resistance and leads to an increase in mass transfer. Modeling the experimental data revealed that the abovementioned models were suitable to predict the breakthrough curves, especially Dose-Response. Measurement of pH of the effluent solution indicated that ion exchange is one of the main mechanisms of nickel biosorption using this biosorbent. Conclusion: The results of this study are complementary of the batch equilibrium sorption experiments. Therefore, from process viewpoint, this biomass can be proposed in the sorption columns as a sorbent for nickel ions.
F Mohammadi, S Rahimi, Z Yavari,
Volume 8, Issue 4 (3-2016)
Abstract
Background and Objectives: In this work, biosorption of hexavalent chromium from aqueous solution with excess municipal sludge was studied. Moreover, the performance of neural networks to predict the biosorption rate was investigated.
Materials and Methods: The effect of operational parameters including initial metal concentration, initial pH, agitation speed, adsorbent dosage, and agitation time on the biosorption of chromium was assessed in a batch system. A part of the experimental results was modeled using Feed-Forward Back propagation Neural Network (FFBP-ANN). Another part of the test results was simulated to assess the model accuracy. Transfer function in the hidden layers and output layers and the number of neurons in the hidden layers were optimized.
Results: The maximum removal of chromium obtained from batch studies was more than 96% in 90 mg/L initial concentration, pH 2, agitation speed 200 rpm and adsorbent dosage 4 g/L. Maximum biosorption capacity was 41.69 mg/g. Biosorption data of Cr(VI) are described well by Freundlich isotherm model and adsorption kinetic followed pseudo-second order model. Tangent sigmoid function determined was the most appropriate transfer function in the hidden and output layer. The optimal number of neurons in hidden layers was 13. Predictions of model showed excellent correlation (R=0.984) with the target vector. Simulations performed by the developed neural network model showed good agreement with experimental results.
Conclusion: Overall, it can be concluded that excess municipal sludge performs well for the removal of Cr ions from aqueous solution as a biological and low cost biosorbent. FFBP-ANN is an appropriate technique for modeling, estimating, and prediction of biosorption process If the Levenberg-Marquardt training function, tangent sigmoid transfer function in the hidden and output layers and the number of neurons is between 1.6 to 1.8 times the input data, proper predication results could be achieved.
A Ebrahimi, M.h Ehrampoush, H Hashemi, M Dehvari,
Volume 9, Issue 1 (6-2016)
Abstract
Background and Objective: Predicting municipal solid waste generation has an important role in solid waste management. The aim of this study was to predict municipal solid waste generation in Isfahan through time series method and system dynamics modeling.
Materials and Methods: Verified data of solid waste generation was collected from Waste Management Organization and population information was collected from the National Statistics Center, Iran for the period 1996-2011. Next, the effect of factors on solid waste generation such as population, urbanization, gross domestic product was investigated. Moreover, the relationship between each of these factors was identified using generalized estimating equation model. Finally, the quantity of the solid waste generated in Isfahan city was predicted using system dynamics modeling by Vensim software and time series method by ARMA technique.
Results: It was found that population and gross domestic product have a significant relationship with the amount of solid waste with P value 0.026 and 0 respectively. The annual average of municipal solid waste generation would be 1501.4 ton/day in 2021 estimated by the time series method and 1436 ton/day estimated by the system dynamics modeling. In addition, average annual growth rate achieved was 3.44%.
Conclusion: According to the results obtained, population and gross domestic product have a significant effect on MSW generation. Municipal solid waste generation will increase in future. Increasing solid waste is not the same in different areas and methods. The prediction of the time series method by ARMA technique gives precise results compared with other methods.
H Cheraghi, A Soltanzadeh, S Ghiyasi,
Volume 11, Issue 2 (9-2018)
Abstract
Background and Objective: Ethylene oxide (EO) is a very toxic and dangerous substance with a high potential for explosion and fire. Ethylene oxide units are among the most hazardous units in petrochemical industries. This study was designed to analyze and model the consequences of ethylene oxide storage tanks explosion in one of Iran's petrochemical industries.
Materials and Methods: In this study, the consequences of the ethylene oxide storage tanks explosion in a petrochemical industry was identified and analyzed. This study was conducted in 2017 using PHAST software version 6.54. For this study, two climate conditions including the first climate conditions (spring and summer) and the second climate conditions (autumn and winter) were considered.
Results: The results of the modeling for the first and second climate conditions showed that there were possibility of severe damages due to the explosion consequences up to 204 and 256 meters, respectively. In addition, based on the criteria for assessing the consequences of accidents associated with damage levels, such as the explosion wave, the wind speed and direction due to the sudden release scenario and the numerical results related to the modeling, the consequence of this scenario in the second climate conditions (autumn and winter) was higher than the first climate conditions (spring and summer).
Conclusion: The findings of the study indicated that, in addition to the high risk of explosion of ethylene oxide storage tanks, the modeling scenarios in different climate conditions have different consequences. Thus, more attention should be paid to safety of these equipment as risk centers in the petrochemical industry and similar industries.
M Kalhor, S Ghaleh Askari, M Bozorgi,
Volume 11, Issue 3 (12-2018)
Abstract
Background and Objective: Concentration prediction with Gaussian dispersion models is highly sensitive to meteorological data. The lack of sounding data station in developing countries may lead to large error and uncertainty in air pollution modeling results. In this paper, the effects of estimated upper air data on the model output concentration values were investigated.
Materials and Methods: AERMOD model was executed once with real upper air data and also with estimated upper air data separately. T-Student and LEVENE tests were used to evaluate the significant differences between concentrations in two modes of using actual and estimated upper air data.
Results: The results showed that large differences in concentration between the two methods. In long term modeling, there was up to 33% differences between real and estimated upper meteorological data and up to 63% differences for short term modeling. A large difference was also observed between boundary layer parameterization values in each case. The statistical analysis showed a meaningful difference (p=0.00) between the cases. The differences between ZICNV, DT/DZ, W* were 7.1%, 48%, and 19%, respectively.
Conclusion: The use of estimated upper meteorological data in comparison with measured data may lead to a large error. The AERMOD modeling results with estimated meteorological data must be expressed with appropriate uncertainties and confidence interval.
Milad Ghaffariraad, Mehdi Ghanbarzadeh Lak,
Volume 13, Issue 2 (8-2020)
Abstract
Background and Objective: One of the major challenges facing landfill operation is the pollution caused by leachate infiltration beneath the landfill site. Comprehensive leachate management requires knowledge of production rate and factors affecting it Therefore, in this study, HELP software was used to calculate leachate quantity and analyze input data.
Materials and Methods: After designing a landfill by the existing conditions in Urmia city, the quantity of leachate was calculated using HELP software. Then, in different scenarios, the effects of precipitation, Curve number, and removal of the geomembrane layer on leachate production -were investigated. Finally, the impact of similar layer aggregation on the simulation process was discussed.
Results: According to the results, 7.67% of precipitation is converted to leachate. NO significant correlation was observed between precipitation and leachate production in a short period of time due to the absorption of rain by landfill layers. However, for the long term, as the absorption capacity was reached leachate produced. With increasing the Curve number from 70 to 90, leachate production decreased by 23%. Also, the removal of geomembrane from the final coating increased the amount of leachate by 78.46%. Furthermore, by replacing a 76cm dense clay layer instead of capping geomembrane layer, the same leachate generation rate was observed. Re- running the software after layer aggregation showed a slight difference in leachate estimation compared to the baseline state.
Conclusion: Leachate generation modeling and identifying influential parameters with the aim of HELP software, may be helpful in landfill leachate management prior to its construction.
Azad Mollaei, Reza Rafie, Mazaher Moeinaddini, Sayyed Hossein Khazaei,
Volume 14, Issue 2 (9-2021)
Abstract
Background and Objective: The purpose of this study was to use the HELP model to estimate the leachate generation rate and its pattern in a landfill located in the semi-arid region of Iran.
Materials and Methods: The input data for the model were collected through fieldwork. To evaluate the accuracy of outputs, the actual amount of leachate production has been measured on-site for 10 months. In addition, sensitivity analysis was conducted to find out the most important parameters in leachate generation in the landfill.
Results: The results showed that the model was able to estimate the rate of leachate generation with an accuracy of 75.5% and the correlation between the model's estimated values and actual values was 60%. In addition. the sensitivity analysis showed that the most important factors affecting the leachate generation in the landfill were waste moisture content and rainfall, respectively.
Conclusion: The model showed satisfactory performance in the prediction of leachate generation in the arid area. The model showed that the moisture content of the waste significantly contributes to leachate generation in Karaj landfill and therefore, it is suggested to identify and implement procedures to reduce the moisture content of the waste at the source of generation.
Zeinab Mousania, Seyed Hassan Mousavi, Farzane Mirza Bayati, Reza Rafiee,
Volume 14, Issue 3 (12-2021)
Abstract
Background and Objective: Various aspects including the environmental burdens, social and economic consequences of the waste management(WM) scenarios must be considered to come up with a comprehensive WM plan. Life Cycle Assessment (LCA) approach is a systematic method to quantify the environmental burdens of each WM scenario.
Materials and Methods: This study used an LCA approach to develop a decision support system to analyze different scenarios of WM. Local and global databases were used to develop a comprehensive life cycle inventory database. The model comes with a graphical user interface in Persian to make it easier to use by a wide range of customers. Finally, to evaluate the model, three scenarios were assessed in Karaj city, Iran.
Results: To the best of our knowledge, this model is the first attempt to automate the process of waste management scenarios evaluation in Iran. The model enables users to easily and quickly simulate a wide range of scenarios. All calculations will be carried out by the software in the background and the user only needs to determine his/her scenario of concern which is very easy owing to the user-friend GUI of the software. The model was evaluated by analyzing the current WM in Karaj. The results showed that the waste collection system is the main source of environmental pollution of the WM in Karaj city due to the inefficient system of the waste collection and poor fleet fuel efficiency.
Conclusion: This tool allows users to gather detailed information about the waste management systems. In this way, the user can make informed decisions about the most suitable waste management scenario in a city.
Hedieh Chorom, Nabiollah Mansouri, Mohammad Hassan Behzadi,
Volume 15, Issue 3 (12-2022)
Abstract
Background and Objective: This study aims to develop a quantitative model for the performance evaluation of urban green buildings using exploratory and confirmatory factor analysis.
Materials and Methods: Criteria and sub-criteria related to green building were collected, then to content validity and reliability of the primary questionnaire were confirmed by a panel of 11 experts. The final questionnaire with 8 main criteria and 26 sub-criteria was provided to 295 green building users to model the performance of the green buildings. Content validity and Cronbach's alpha were used for validity and reliability of the initial questionnaire, Expletory Factor Analysis was employed to identify factor structure and Confirmatory Factor Analysis was utilized to examine factor loadings and goodness of fit.
Results: The final questionnaire included 8 main criteria and 26 secondary criteria. The internal consistency of the test was adequate (alpha>0.6); the chi-square test for EFI analysis was equal to 0.09 and RMSEA<0.05 and the CFI index was equal to =0.98.
Conclusion: The results showed the designed 8-factor model could predict the impact of green building performance by 81.64%. EFI and CFI analysis confirmed the fitting of the model too.
Sayyed Hossein Khazaei, Mazaher Moeinaddini, Reza Rafiee, Nematollah Khorasani, Melanie L. Sattler,
Volume 15, Issue 3 (12-2022)
Abstract
Background and Objective: Various models have been developed to predict methane generation and emissions from landfills. Due to their simplicity, the minimum number of required data, and the accuracy of the outputs, First-order decay are the most common models to predict methane generation in landfill,. Three important parameters in modeling landfill gas generation using a first-order model are the total weight of waste buried in the landfill, the methane generation potential, and the methane generation rate constant. The purpose of this research was to accurately estimate the parameters of the first-order model and to optimize it for estimating methane generation in the landfill and also to develop the ILGAM software.
Materials and Methods: ILGAM model consists of two submodels: 1) the gas generation sub-model and 2) the methane oxidation sub-model. The methane oxidation sub-model is based on the MOT model. The gas generation sub-model is based on a first-order equation with an emphasis on the contribution of the aerobic process in the estimation of the ultimate methane potential of waste. The parameters of the equation were modeled using the latest available results in the literature. To evaluate the model, the actual methane emission and methane oxidation were measured in the Karaj landfill. The results of the model, along with a few common models, were compared with actual data obtained from the Karaj landfill.
Results: The ILGAM model predicted the gas emission from the Karaj landfill with an error of 5.8%. In contrast, LandGem, IPCC and CLEEN models predicted the methane gas emission from the Karaj landfill with an error of 74.4%, 40.2%, and 27.1%, respectively.
Conclusion: When compared to other models, the ILGAM model estimated the closest values to actual measurements for methane emission and methane oxidation in the Karaj landfill. Owing to its user-friend Graphical User Interface (GUI), the model can be easily executed in a wide range of landfills by entering a few easy-to-measure data in the field.
Ehsan Mohammad Hassani, Reza Rafei, Mazaher Moeinaddini, Niki Aghapour,
Volume 17, Issue 1 (6-2024)
Abstract
Background and Objective: One of the largest sources of methane emissions is landfills, and various models have been developed to predict landfill methane production and emissions. The main goal of this research is to utilize the inverse Gaussian model to estimate g methane greenhouse gas emissions and model it using field data. This study introduces a simple method to estimate the amount of methane emissions based on ambient methane concentrations.
Materials and Methods: In this research, the methane emission rates from landfill were estimated for warm (July) and cold (February) seasons using a sampling campaign from 27 stations and standard inverse Gaussian dispersion equations. Monte Carlo simulation was also employed. To determine the model, an optimization-based method, along with inverse scattering modeling, was utilized to process surface emission monitoring data.
Results: The model results indicated during the cold (February) and warm (July) seasons, the methane emission rates were estimated at 1696.99 and 16.53 g/s, respectively. These findings confirm that the methane production and emission during the cold season were lower than in the warm season, likely due to reduced temperature and bacterial activity.
Conclusion: The method used in this study, the inverse Gaussian dispersion model, can be applied to estimate methane gas emission rates from other landfills. However, it necessitates the permanent recording of data and the use of daily or weekly averages in calculations to mitigate potential errors and enhance the accuracy of modeling.
Gholamreza Shaghaghi, Amir Hossein Javid, Sara Allahyaribeik, Ali Mashinchian Moradi,
Volume 17, Issue 3 (12-2024)
Abstract
Background and Objective: The discharge of seawater concentrate from desalination plants into the sea causes irreparable effects on the environment. The purpose of this study is to identify the effects of this discharge, model methods for optimizing it, and design an effective outlet that minimizes environmental impacts and costs.
Materials and Methods: This study discusses impacts of seawater concentrate discharged into the sea, numerical modeling of diffusion, and outlet design based on discharge standards.A review of articles and sources from databases such as Google Scholar, Academia, Scopus, Civilica and Irandak was conducted using keywords such as “brine discharge”, “numerical modeling”, and “outlet design.” Out of 132 reviewed articles, 45 articles were consistent with the objectives of the study.
Results: The effects of seawater discharge can be observed in the discharge area and at greater distances. Numerical modeling is employed to predict pollutant concentrations at various distances and to determine the optimal discharge point while considering established standards. The design of the diffuser and the use of multiple nozzles at an angle of 60 degrees result in the greatest dilution at the discharge point.
Conclusion: The use of desalination systems necessitates addressing the effects of climate change. Appropriate modeling and design of the outlet are essential for complying with environmental standards and optimizing costs. Further research in this field is needed.
