عباس سیفی

Today, the rapid development of industries and the use of hundreds of new chemical compounds in them have led to the creation of huge amounts of industrial wastewater, the discharge of which into the environment, especially watercourses, has caused severe pollution. The discharge of colored wastewater into natural ecosystems has created serious risks such as carcinogenesis, mutagenicity, etc. for aquatic life due to adverse functions. Meanwhile, Reactive Black 5 has many industrial applications, especially in the textile industry worldwide. Fe (VI) was synthesized electrochemically using iron and steel electrodes in potassium hydroxide solution at 65 ° C and then used to remove the reactive Black 5 in the batch removal system. In this study, the surface response method (RSM), the effect of various parameters including initial color pH, Fe (VI) dose and time and then the central composite design (CCD) were used to find the best removal conditions. The optimal values for the three variables of pH, Fe (VI) dose and time were obtained 4.5, 24.5 mg and 25 minutes, respectively. In the present study, by increasing the Fe (VI) dose and after 25 minutes from the start of the reaction, the removal efficiency increased, if the removal was better in acidic conditions than in alkaline conditions. Also, under the optimal conditions of Fe (VI) solution, it was able to remove 97% of reactive black 5. Finally, under the same conditions, real wastewater was also worked on and the removal rate was 95%. The findings of this study showed that Fe (VI) can be used as a suitable, inexpensive and high-performance oxidizing agent in the removal of reactive Black 5 from textile industry effluents.

Infectious diseases significantly distress the physical and mental health as well as economic conditions of the societies. Understanding how a virus spreads, especially the dynamics of an epidemic is critical in order to make proper decisions and take proper measures. System dynamics approach is a scientific and appropriate method for modeling and studying the behavior of epidemics. There are many studies on system dynamics modeling of infectious diseases in the literature. However, in case of Coronavirus, there are few extensive studies on the behavior of this outbreak especially calibrated to the actual situation in a specific region. In this study, we develop an SEIR model of Covid-19 epidemic in Iran that is actually calibrated to the real data. More Importantly Most of the previous studies are focused only on projecting the number of infected people and deaths. In this study, in addition to projecting the simulation results under different scenarios, sensitivity analysis of the influential parameters are performed. Also studying the effect of quarantine measure for patients on the results and isolating the susceptible population as two different yet indirectly connected sections is not largely considered in previous studies. The developed model in this study includes Susceptible and Exposed population, identification and quarantine of infected individuals, number of Recovered cases and Deaths. The effect of asymptomatic infected people has been included as well. The quarantine itself has been expressed by several sub-variables such as required time for a person to be quarantined, the percentage of the infected who are quarantined (testing capabilities) and the efficiency of the process. There is a same decomposition for other variables such as Isolation of susceptible population and contact rate. Contact rate is defined by sub-variables: media impact, initial contact rate and a slope which determines the increase in contact rate based on the population density.  The media Impact also starts at a minimum level and gradually reaches its maximum. This definition demonstrates the gradual growth in public awareness which improves the hygiene and social distancing commitment among the individuals. Isolation process is highly depended on economic pressure. As isolation period last longer, the more resources are required to keep people fed and healthy in isolation. A simple variable to illustrate the economic pressure is defined which based on an assumed function table limits the isolation duration and percentage of the healthy people who remain isolated. Although this relationship requires more research to indicates the economic impact more accurately it is still useful and enough in order to demonstrate a limitation for isolation process.  After developing the model, it is validated through applying it for a previous real epidemic situation of H1N1 and the results are valid. Also, running the model in extreme conditions returns the expected results. It is assumed that infectivity of the virus is constant (the virus does not mutate during the period of the study) so that through a calibration process it can be obtained based on other parameters. The asymptomatic rate (the percent of people who get exposed to the infection but do not develop any symptoms) and the incubation period (average required time for a person from getting exposed to infection until developing the symptoms) are also assumed constant. After validating the model, it is used to study the spread of Covid-19 in Iran and is calibrated to official data. This stage generates a basic simulation in which the results for target variables produce real data pattern and numbers. As the official data are usually lower than the real cases, a variable to introduce the official data is considered. The official data is a portion of the real data and this portion is a given value for the model. Having the basic calibrated model, developing scenarios and performing sensitivity analysis is the next stage. In particular, we study the impacts of people isolation as well as media, quarantine rate and other given variables on spread of the virus. We investigate and advise the required conditions for controlling the spread of the disease mainly based on the testing capabilities and quarantine of the infected people and contact rate. This study includes several sensitivity analysis for important target variables such as number of daily deaths, total deaths, daily new infected, total infected, required hospital beds and ICU rooms. This analysis is based on changes in entry factors such as quarantine of patients, contact rate, media impact, asymptomatic rate, averaged required time for quarantine, recovery time, isolation of susceptible population and quarantine efficiency. The results show that total number of death and infected are highly sensitive to changes in two key factors: Quarantine of patients and contact rate. The sensitivity of mentioned target variables to isolation of susceptible population, however, is considerably less than those two key factors. We developed several scenarios with different values of isolation rate while other variables are constant. The results indicate that in absence of either reliable quarantine measure or desired contact rate level, isolating the susceptible population will only lead to continues oscillation in the target variables unless the isolation remains for a very long time. However, in scenarios where the quarantine rate or contact rate or both are below the desired level the sensitivity of deaths or infected to isolation rate is higher. Therefore, Isolation could help in providing time for applying proper quarantine measures or to reduce contact rate to desired level. These results indicate the priority level of launching the controlling measures. Also, through performing the sensitivity analysis a minimum level of quarantine rate is obtained. The results show a required contact rate level as well. In fact for both contact rate and quarantine, a lower level than the minimum level will cause the epidemic to get out of control. Another important outcome of the study is an important guidance about the contact rate controlling policy. The results show that the contact rate should be sustained at a desired level through media and public awareness. Most models link the contact rate to the daily deaths or total deaths. We developed two scenarios where in the first one contact rate is controlled by policy making and media and in the other one it is defined based on the feedback of number of deaths. The results show that in the latter the outbreak goes out of control and takes a very long time while in the first scenario it will be controlled in an acceptable time. Therefore, although this is true that practically feedbacks from deaths influences the contact rate, the model shows that if this behavior remains uncontrolled, the epidemic will not be controlled. In a third combined scenario the results show by securing the required contact rate through public awareness and media, feedbacks from deaths could be helpful for policy makers to control the outbreak even faster. Therefore, through performing several sensitivity analysis and developing different scenarios, controlling conditions for the epidemic are obtained. There are two main requirements: first a proper testing and quarantine practice in which more than 87 percent of infected are identified and quarantined within a day and a quarantine efficiency level more than 95 percent. Secondly, contact rate must be level downed and sustained at least to the desired level through media and policy making. This is especially important because reaching and sustaining these conditions will suppress the disease and prevent the next waves of the outbreak.

This paper aims to propose a knowledge sharing optimization model based on the game theory that optimizes both employer and employee(s) decisions simultaneously. This model is a bi-level programming model. The upper-level problem includes employer decision about the reward, and the lower-level problem contains employee(s) decisions about time and effort allocation to knowledge sharing activity. Mathematical formulation of the model designed based on previous literature and in the framework of Motivation-Opportunity-Ability. The proposed bi-level programming model provides a foundation to investigate more different parameters comparing with previous models introduced in the literature. This model considers opportunity and ability factors in addition to the motivation. Also, payoff functions in this model are non-linear and therefore is more consistent with real cases relative to previous linear models. Additionally, this model analyzes the effects of available time as a key factor. The bi-level model coded in GAMS using EMP syntax and solved for a set of randomly generated data using BARON algorithm. Results indicated that the increase of applicability of codified knowledge and impact coefficient of social comparison could improve organizational performance and also save the cost of reward system. Therefore, neglecting these two parameters in designing a reward system could lead to under optimized decision making. This research provides a basis to consider more parameters simultaneously and help to improve organizational decisions. However, based on the results, BARON algorithm is not efficient to solve big problems, so developing a more efficient algorithm is needed.

This paper investigates the Stackelberg equilibrium for pricing and ordering decisions in a dominant retailer dual-channel supply chain. In a dual-channel sales network, the products are sold through both a traditional, physical retailer and a direct Internet channel. It is assumed that the retailer is the leader and the powerful member of the supply chain has the market power and acts as a leader and proposes his/her inventory policies and dollar-markup and the manufacturer, as a follower, will decide on the wholesale price and the price of Internet channel as well as the inventory quantity of online store based on the retailer’s decisions. The situation is formulated as a bi-level programming problem, and it is converted to a single level model using Karush-Kuhn-Tucker (KKT) conditions. The single level problem is solved using the α-Branch and Bound (α-BB) algorithm. We investigate the significance of customers’ channel preference on adopting an online channel by the manufacturer. We show that an online channel is not always detrimental to a retailer, but in a Pareto-zone, a range or zone of customers’ channel preference, both supply chain members benefit from newly added sales channel.

Production-injection optimization has been the subject of various researches due to its complicated and expensive computations. The main reason for this complexity is number of reservoir simulation runs is needed to predict reservoir performance. These numerical reservoir simulations are computationally expensive and time consuming. Therefore, finding a way to reduce the computational burden of reservoir simulation will facilitate the optimization process. One of the methods for reducing the complexity of reservoir simulation is Reduced Order Modeling (ROM) which has been recently introduced for improving efficiency of open source reservoir simulators. In this paper, an ROM method based on Artificial Neural Networks (ANN) and Discrete Empirical Interpolation Method (DEIM) is proposed to resolve the curse of dimensionality while simulating reservoir dynamics with acceptable accuracy. This method is also applicable to black box reservoir simulators. The performance of the suggested ANN-DEIM algorithm has been investigated on a case study on Brugge field. The reduced model well represent the reservoir dynamic behavior while reducing run time by a factor of eight comparing with that of a full order reservoir simulator. ANN-DEIM also has been applied in production-injection optimization of Brugge filed using a Pattern Search optimization algorithm. The proposed method can reduce optimization time by 7 times while leading to %11 improvement in Net Present Value (NPV) over the initial solution used in the optimization process.

By looking at natural disasters which have occurred in recent decades, we will understand that such disasters will cause financial and human costs for governments and societies. One of the most important activities which is done in every relief operation and encompass roughly 80 percent of all activities is logistics operations. Because of this matter, a new area and subject to title of humanitarian logistics is stated in the literature in recent years. Humanitarian logistics aims to minimize loss of lives by using common models in commercial logistics and considering specific situation and issues such as stochastic demand, lack of precise information aftermath of a crisis, transportation network failure, and very short and limited relief time. This review article explores every aspect of humanitarian logistics and shows its active and widespread role in preparedness and response of any relief operations. In this paper, definition of humanitarian logistics and in upper level humanitarian relief chain is presented and then, literature review of this issue is stated. Finally, the most important and unknown factors which must be considered to improve the level of humanitarian logistics operations before and the aftermath of an earthquake are discussed.

In recent decades, natural disasters imposed financial and humanitarian costs to the governments and societies. One of the most important activities which is done in every relief operation and encompass roughly 80 percent of all activities is logistics operations. Thus, a new area and subject with title of humanitarian logistics are stated in literature in recent years. Humanitarian logistics aims to minimize loss of lives by using common models in commercial logistics and considering specific situation and issues such as stochastic demand, lack of precise information aftermath of a crisis, transportation network failure, and very short and limited relief time. This study aims to utilize a mix integer linear humanitarian logistics model. Transportation network failure aftermath of an earthquake, standard relief time and online datawere obtained by the geographic information systems (GIS).These data were used in the model to locate distribution centers of relief supplies. Moreover, this study indicated vehicle routing for the response time. Then, the developed model was implemented in San Francisco city as a real life and large-scale case study by GIS's data. To solve the model, variable neighborhood search (VNS) was used. Variable neighborhood search (VNS) is used for the first time to solve a large-scale humanitarian logistics model in this study.

The application of a flight simulator with six Degree of Freedom (6DOF)) in design and analysis of rocket systems is essential. The calibration of a 6DOF flight simulator is a challenging problem. This paper focuses on the calibration of a flight simulator and intends to answer the following questions: 1) Does experimental design have significant impact on calibration quality? If so, which experimental design is suitable for calibration of a flight simulator? 2) How to determine the suitable number of runs for calibration? 3) Could DOE be used for estimation of calibration parameters? If so, how is it applied? The experiments have been conducted using a 6DOF simulator. Our investigation shows that the experimental design has significant impact on the quality of calibration. The suitable design for calibration of the 6DOF simulator is the triangular design. A new method for determining the number of runs is proposed based on entropy function. It is also used for estimation of the calibration parameters. One advantage of the proposed method is the calibration of 6DOF simulator by considering several criteria such as range error, azimuth error and combination of these errors. The quality of calibration has been investigated by hypothesis testing.

In this paper, by developing a mixed integer stochastic programming model, total cost of construction of new power generation capacity using various technologies and the cost of generating electricity during a planning horizon is minimized. The demand is considered uncertain in this model and is assumed to follow three possible scenarios. In this study, the country is divided to several regions and the investment decisions are taken for each region in an integrated model. Furthermore, a learning mechanism of technologies is considered in the model, so the cost of investment in technologies will decrease as installed capacity of each technology is increasing. Finally, in order to analyze the output of optimization model, by developing a system dynamic model, the impacts of the proposed plan on some power industry indicators such as cost and profit is shown with respect to each demand scenario.
The resulting capacity expansion plan consists of various power generation technologies among which gas turbine power plant constitutes the biggest part of the expansion plan. Other technologies such as hydro, combined cycle, nuclear, geothermal and wind power plants exist in the expansion plan.

A long-term planning model is presented in this study for optimizing Karoon-Dez reservoir system operation in Iran with hydropower generation, water supply, and environmental objectives. The matrix implementation of the large-scale resulting optimization model has been solved using dual-primal interior-point methods (DPIPMs) and multiobjective programming. These algorithms have shown to be promising especially when they take the advantage of sparsity structure of associated matrix formulation of the optimization problem with linear and quadratic functions. The computational time required for solving the Karoon-Dez reservoir system studied was less than 45 minutes. This is promising especially knowing that handling such an optimization model is very difficult using other techniques such as generalized reduced gradient (GRG) algorithm of nonlinear programming and discrete dynamic programming with adequate fine representation of state variables of the model.