فهرست مطالب سید سعید کیخسروی

Nowadays, air pollution is now considered to be the largest environmental health threat. This study was conducted with the aim of determining occupational exposure to chemical pollutants, including sulfur dioxide (SO2) and hydrogen sulfide (H2S) and assessing the health risk of exposure to these compounds using a combination of AERMOD and SQRA methods.

The present study is considered as a descriptive-analytical and cross-sectional research, which was conducted in 2002 in one of the gas air refineries of South Pars in the Persian Gulf region, in such a way that the amount of emissions coming out of refinery chimneys was measured by the Testo 350- XL. AERMOD model was used to simulate the dispersion of H2S and SO2 chemical pollutants. Respiratory exposure and health risk assessment of refinery personnel and nearby residents were performed using the recommended method by the Singapore Occupational Health Services Pte Ltd.

Hydrogen sulfide and sulfur dioxide were introduced as the most dangerous chemicals. According to the results, the highest risk value for sulfur dioxide among the exposure groups was related to the sulfur recovery unit (SRU), the west side of the Train Gas unit and the gate pass building of the refinery, and the highest risk values for sulfur dioxide among the exposure groups were related to the HSE building, security door, fire stations building, tanks, steam generating unit, west side of Train Gas unit, dining hall and gate pass building of the refinery. Hydrogen sulfide obtained a low to medium risk level, and sulfur dioxide a low to high risk level in terms of frequency.

This model can be considered as a suitable and quick solution in the superior management of the concentration of pollutants and also a promising solution in order to increase the ability of decision makers to assess the health risk of industries’ personnel. Also, ensuring quality   monitoring results and reducing sampling costs are discussed.

 Nowadays, the cement industry is regarded as one of the most important air pollution industries globally. This study aimed to simulate the emission of NOx, CO, SO2, and PM pollutants caused by the Sabzevar Cement Factory chimney by SCREEN3 software. 

 In this study, the SCREEN3 software was employed for the distribution of NOx, CO, SO2, and PM pollutants. The inputs of the model include the concentration and emission of pollutant gases, physical factors associated with the cement factory chimney, wind speed and direction, ambient temperature, and stability classes. 

 The results of this study indicated that the maximum concentrations of NOx, CO, SO2, and PM by the SCREEN3 software occurred in unstable weather conditions (B) and wind speed of 5 m.s. The highest concentrations of NOx, CO, and PM (use of gas) were at a distance of 1400 meters from the factory chimney with the rates of 0.9, 0.32, 6.2 μg.m³, respectively. Moreover, the highest concentrations of NOx, CO, SO2, and PM (using fuel oil) were predicted at a distance of 1100 m from the factory chimney with 19.5, 360, 9, and 7.9 μg.m³, respectively. A comparison of the obtained results with the standard of the Environmental Protection Agency of Iran (EPA) revealed that the concentrations of NOx, CO, SO2, and PM were not higher than the standards. 

 The comparison of results with EPA standard and Iranian clean air standard showed that NOX, CO, SO2, and PM concentrations were not higher than standards during the sampling period.

Distribution of air pollutants in industrial units is one of the issues that always affects the environment and ecosystem of adjacent areas. Air pollution is an inevitable part of modern life, and the knowledge of air pollution existed in the past and must be continued to make policies. The cement manufacturing process produces millions of tons of byproducts each year, including dust, toxic gases and heavy metals, which will pose health and respiratory hazards and environmental pollution. Therefore, awareness of pollutant concentrations can be used as key information in pollution control programs. There are still many activities to complete effective control strategies for the prevention, continuous improvement and application of modern technologies to substantially reduce dust in the cement industry. The amount of dust produced can be significantly reduced using modern technologies such as cyclones, bag filters, and electrostatic filters. There are various types of dust in the cement industry including, raw materials such as limestone, marl, clay, iron ore, mixing dust, dryer outlet dust, furnace outlet dust, clinker dust, gypsum dust, and cement dust. In this study, the purpose of dust is the furnace outlet dust. Air pollution is a very complex process that depends on many factors. Therefore, it is very difficult to predict such data with nonlinear dynamics and to determine how the pollutants are to be dispersed and propagated in the atmosphere, which will also be extremely costly. Then, each section can be modeled using this data and solving the equations involving atmospheric processes in the form of data networks. Air quality modeling can be considered as a suitable tool for predicting future air quality and determining emission control strategies. In recent years, one of the best models that has shown good performance in pattern and control modeling is intelligent systems, which include neural networks. Multilayer perceptron neural networks are a model for information processing made by mimicking biological neural networks such as the human brain. The key element of this model is the new structure of its information processing system, consisting of a large number of elements (neurons) with strong internal communications that work together to solve specific problems. Artificial neural networks by empirical data processing transmit knowledge by the law behind the data into the network structure; this is called learning. Ability to learn is essentially the most important feature of an intelligent system. A system that can learn is more flexible and easier to program, so it is better responsive to new problems and equations. Artificial neural networks have been employed for various purposes such as simulation, pattern recognition, classification, prediction, and optimization in engineering sciences. The ability of these networks to map between input and output information with acceptable error has made this method a useful tool for modeling engineering processes. In the basic state, a neural network consists of three layers, input layer, hidden layer, and output layer. Also, each layer contains a number of neurons. In the neural network, neurons are active (on or 1) and inactive (off or 0) and each wing (synapse or communication between nodes) has a weight. Positive-weighted wings trigger or activate the next inactive node, and negative-weighted edges inactivate or inhibit the next inactive node (if it was active). To calculate the output, each of the previous layer inputs is multiplied by the network weights and summed with the corresponding bias. Sabzevar city is located on the west of Khorasan Razavi province in Mashhad-Tehran highway. This city is bounded by Esfarayen in the north, Neyshabur in the east, Kashmar and Bardeskan in the south, Shahroud city in Semnan province in the west. Based on the latest country divisions, Sabzevar has five Bakhsh including, Markazi, Roudab, Khoshab, Davarzan, and Sheshtamad. Sabzevar Cement Factory is one of the sources of dust emission in southwest of Sabzevar in Roudab Bakhsh. The plant started operating on an area of 150 hectares, with a capacity of 3400 tons per day, with Portland cement type 2, 1-325, and 1-425 from October 2007. The plant also uses Bag filter, Bag house, and electrostatic filters for dust, and allocates an area of 50 hectares to build a green belt to protect the environment. The purpose of this research was to predict the amount of dust from the main chimney (baking furnace of Sabzevar cement plant) using a perceptron neural network model.

In this study, first, the amount of dust emitted from the main chimney of Sabzevar cement plant was studied through field surveying in three seasons in 2017-18 by the Experts of Mashhad Environmental Trust Company (Pak Afarinan Avizheh). All sampling and measurement procedures were in accordance with the requirements of the Iranian Environmental Protection Agency. 180 dust samples were collected and then, compared with Iranian Environmental Protection Agency standards and the US Environmental Protection Agency (EPA). The parameters of output gas velocity parameters, temperature, voltage, fuel, and furnace feed were used as perceptron neural network input. Perceptron neural network with 5 neurons as input in the first layer, 10 neurons in the hidden layer, and 1 neurons in the hidden layer. The Levenberg-Marquardt algorithm was used to train the network, which is one of the most used and fastest optimization algorithms. Perceptron neural network is one of the supervised learning methods. To evaluate this method, the data were divided into two training and test groups. The model was trained on the training data. The model error was estimated on the test data. In order to include all data in both training and test groups, k-fold cross-validation method with k = 5 was used. In this way, the data were divided into 5 sections and at each run, 4 groups were considered as training and 1 groups as testing. The network with the least error over the test data was selected. The amount of dust concentration was estimated based on input parameters with the above mentioned neural network (input, hidden and output three layers) and above method. In order to observe the effect of each of the input parameters on the estimation of dust concentration with the perceptron neural network, all possible scenarios should be considered. Since five input parameters are selected, so 25, there are 32 subsets, which results in 31 cases with the removal of the null set. The perceptron neural network was run with 10 neurons in the hidden layer and one neuron in the output layer on each mode (subset). Thus, all subsets of one-member, two-member, three-member, four-member and five-member problem parameters were investigated. In each case, the number of input layer neurons is equal to the number of selected parameters. For each subset of input parameters a neural network was constructed and the amount of dust was estimated. The subset that had the least error in predicting dust concentration was selected as the influencing parameter. For each subset, 5 models were trained, validated each time, and the model with the least error mean squared on the test data was selected. Correlation coefficients and mean squared error were used to evaluate the accuracy of the model in predicting dust concentration. All implementations were performed in MATLAB software.

The results showed that the perceptron model has a good accuracy in predicting the dust concentration of Sabzevar cement plant. Perceptron neural network model was able to Show the correlation coefficient as much as 0.98168 and 0.98249, and Mean square error as much as 0.709 and 280, respectively in two modes of using all parameters (output gas speed, temperature, voltage, fuel, and furnace feed) and the influential parameter (temperature). This indicates a higher correlation and lower error of the perceptron neural network model in using only the temperature parameter than in the prediction of dust concentration in all parameters.

Achieving environmental pollution standards in the cement industry requires proper design and increased efficiency of dust collecting equipment to reduce dust particles. Nowadays, due to the increasing development of cement factories to produce cement needed in the country as well as the growing importance of environmental considerations in various industries, especially the cement industry, the need for better and more efficient technologies to control pollutants and wastes from cement plants has become more and more prevalent. The cement industry is one of the industries that is always referred to as the pollutant of the environment. Although in recent years the industry has tried to meet the environmental standards required by modernizing its equipment and machinery, it is still one of the most important contributors to air pollution. The results of this study showed that the, this model can be a suitable and fast way to better manage industrial dust concentration and ensure quality monitoring results and reduce costs because of the high capability of perceptron neural network in predicting dust concentration.

Dust modeling can be considered as an appropriate tool for predicting future industrial dust and identifying pollutant emission control strategies. Perceptron (MLP) and radial base (RBF) neural networks were used as a means for predicting the outflow dust from the main cogeneration of Sabzevar cement factory located in Khorasan Razavi Province. the concentration of dust from the main cement chimney in the study area was measured through field measurements. Then, the parameters of the production line (temperature, speed of gas output, voltage, fuel, raw materials, and time of sampling) were used as input data to the nerve networks to predict the concentration of dust. The values obtained from the implementation of the models were compared with the results of field measurements as a superior model selection. The analysis of figures and statistical parameters showed that the mean squared errors for the two MLP and RBF models were as much as 1.787 and 21.263, respectively, and the correlation coefficients were as much as 0.99693 and 0.95811, respectively, which indicates a lower error and greater correlation between the MLP and RBF model in predicting the concentration of dust. Because of the high ability of perceptron nervous networks to predict dust concentration, this model can be a convenient and fast solution to predict the amount of dust in the industry.

The increasing production of waste, including glass and its unhealthy disposal into the environment, in addition to the loss of national capital, causes the loss of natural resources. Considering the importance of recycling in this research, the economic and environmental aspects of recycling of glass were evaluated from Tehran municipal waste. The total amount of dry wastes in Tehran in 2011 is 416,324,412 and the average daily amount is 1,140,615. Of the total solid waste in Tehran, 4.6% of these wastes are glass waste, with only 2% recycled glass waste. In the past, a major research effort was made at Hong Kong University Polytechnic University to find practical ways to recycle glass waste to produce various concrete products such as concrete blocks, self-compacting concrete. Some of these concrete concrete products have successfully entered the market and attracted much attention. This paper will provide a general overview of the current state of waste management and recycling and the experience of recycling recycled waste in concrete cement products in Tehran.