Different sources of ground water contamination lead to non-uniform distribution of contaminant concentration in the aquifer. If elimination or containment of pollution sources was not possible, the distribution of contaminant concentrations could be modified in order to eliminate peak concentrations using OPTIMAL water PUMPING discharge plan. In the present investigation Visual MODFLOW model was used to simulate the flow and transport in a hypothetic aquifer. Genetic Algorithm (GA) also was applied to optimize the location and PUMPING flow rate of wells in order to reduce contaminants peak concentrations in aquifer.

Optimum control of upstream PUMPING station in open channels with given constraint in downstream end is presented in this paper. The upstream control is capable of minimizing water level fluctuations in the channel in which the downstream PUMPING station causes an undesirable wave. The proposed method combines an unsteady non-uniform flow solver with shock-capturing capability, Fourier series and metaheuristic firefly algorithm. Fourier series is used to estimate the optimum inflow control and firefly algorithm is utilized to determine the unknown coefficients in the series. With a suitable objective function, the procedure generates the optimum inflow hydrograph that can effectively cancel destructive downstream waves. The results have been compared with the results obtained by a variational approach and show satisfactory improvement both in simplicity and the value of objective function.

This paper presents a relatively new management model for the OPTIMAL design and operation of irrigation water PUMPING systems. The model makes use of the newly introduced particle swarm optimization algorithm. A two step optimization model is developed and solved with the particle swarm optimization method. The model first carries out an exhaustive enumeration search for all feasible sets of pump combinations able to cope with a given demand curve over the required period. The particle swarm optimization algorithm is then called in to search for OPTIMAL operation of each set. Having solved the operation problem of all feasible sets, one can calculate the total cost of operation and depreciation of initial investment for all the sets and the OPTIMAL set and the corresponding operating policy is determined. The proposed model is applied to the design and operation of a real-world irrigation PUMPING system and the results are presented and compared with those of a genetic algorithm. The results indicate that the proposed mode in conjunction with the particle swarm optimization algorithm is a versatile management model for the design and operation of real-world irrigation PUMPING systems.

Improper wells location and excessive groundwater extraction can cause severe damage to aquifers and lead to numerous economic consequences. The purpose of this study is to develop a simulation-optimization model for determination of OPTIMAL wells' placement and PUMPING rates while satisfying the requirement of quantitative and qualitative considerations. The proposed model integrates the groundwater flow simulation model MODFLOW and the Particle Swarm Optimization algorithm. The coupled PSO-MODFLOW model has been applied to the Sarakhs aquifer in the north-east of Iran to minimize the total cost of drilling, transferring, and water treatment for supplying the drinking demand of the Sarakhs city. Discharges as well as locations of the PUMPING wells were taken as the decision variables. Also the maximum PUMPING rate and maximum water-table drawdown were incorporated as the problem constraints. The results show that the proposed approach not only satisfies the constraints but also reduces the total cost of water withdrawal by 5% from 5980 million Rials in the present plan to 5720 million Rials in the optimum plan. Sensitivity analyses indicates that results are not significantly sensitive to changes in aquifer hydraulic conductivity while the maximum PUMPING rate directly affects the number of required wells and can therefore make a considerable change in the final cost.

Increasing the depth of mining leads to placing the level of the mine pit in the area of mineral extraction below the groundwater level. The entry of groundwater into the mining pit increases the costs and also reduces the efficiency and safety level of the work. Gohar Zamin Iron Ore Mine with a reserve of approximately 600 million tons is one of the largest open-pit iron mines in the Middle East in the southwest of Sirjan city, located in Kerman province, which currently has a problem with groundwater entering the mine pit. In this research, MODFLOW software has been used in the complex environment of the Gohr Zamin Iron Ore Mine to model and predict the movement of underground water and the proper management of the drainage process. Due to the heterogeneity of the underground layers, which causes many changes in the hydrodynamic parameters in each place, an innovative Pilot Point network has been used to interpolate the value of the hydrodynamic parameters required by the model. After the calibration and validation of the model, the direction of the groundwater flow in the studied area is from the north side of the pit to the west. The amount of inflow water from the northern part of the pit and the outgoing water from the western part of the pit is about 679 and 1448 cubic meters per day, respectively. In the end, by performing optimization by the model, the position of seven new wells and the OPTIMAL pumped flow rate for existing and new wells in a proposed drainage scenario was determined. The prediction results of the model show a decrease in water level by 14 meters in the northern part and 10 meters in the eastern part of the studied area within one month.

IntroductionThe growing importance of energy resources in the formation and growth of economic processes, as well as the need to exploit these resources based on environmental considerations and sustainable economic development, the issue of energy saving as an important issue in all economic infrastructures, including industry. Global warming, declining crop yields, climate change and acid rain are the result of fossil fuel consumption. Hence, in recent years, there has been a growing global emphasis on renewable energy across both developed and developing nations. The primary objective is to decrease reliance on conventional energy sources, mitigate environmental pollution, and attain sustainable energy practices. Materials and WaysIn the present study, a multi-objective mathematical planning model was designed, in which the OPTIMAL crop cultivation model in the southern grove area of Babol city, taking into account the maximum profit from the sale of crops, relying on expanding the use of renewable energy sources in the supply basket Irrigation water and reduction of fossil energy consumption used in irrigation water supply were determined. The objective functions of the study are in the form of two objectives: profit maximization and minimization of greenhouse gas emissions. The amount of profit considered in the present study is equal to the difference between income from crop production and total costs, including irrigation, planting and harvesting costs, seed costs, fertilizers and pesticides, and labor costs. The greenhouse gases considered in this study encompass CO2, N2O, and CH4, with the objective of minimizing their emissions for the determination of an OPTIMAL crop pattern. The study endeavors to resolve the proposed nonlinear multi-objective pattern utilizing the constraint epsilon method. Subsequently, employing the energy link indexes (E), the OPTIMAL solution is identified among the proposed alternatives through the TOPSIS multi-criteria decision model. The statistical population for this investigation comprises farmers in the southern forest area of Babol city. The analysis of results has been conducted using Matlab, Lingo, and Excel software. Results and DiscussionIn this study, a multi-objective model with objective functions of profit maximization and minimization of greenhouse gas emissions subject to energy, energy flow, water, fertilizer, and capital and land constraints using the method the constraint is resolved and eventually the study decision variables are obtained. In terms of recyclable energy, the model suggests that four types of Tarom rice, Shiroodi rice, soybean, and corn be grown with 0.40, 0.34, 0.14 and 0.12 ha / ha, respectively. As can be seen, in accordance with the outputs of the OPTIMAL model in terms of renewable energy, most of the cultivated land is allocated to Tarom and Shiroodi rice. The total amount of energy required in the OPTIMAL model in terms of renewable energy was 2518 kWh, of which fossil energy is 79% and solar energy is 21%. According to the calculations made in Equation 3, to provide 21% (518 kWh) of solar electricity required by the irrigation system in order to irrigate one hectare of the proposed model of the OPTIMAL model in the study area, a solar panel with a capacity of 0.22 kW will be required. The results of the model show that in terms of renewable energy, with the implementation of the proposed model, the profit of farmers in the region per hectare increases from 14776.21 to 14778.18 million rials compared to the situation of non-renewable energy in the entire growing season. In other words, in the current situation, people cultivate crops regardless of energy consumption and production costs (traditional method of rice production by local farmers), while by choosing the right model, the farmer's economic benefits can be increased by 0.013%. At the same time, it saved a significant amount of fossil energy consumption.The minimum emission target of the model in terms of renewable energy states that for the cultivation of 0.40 hectares of Tarom rice, 0.34 hectares of Shiroodi, 0.14 hectares of soybean and 0.12 hectares of corn, at the rate of 2836 kg equivalent to CO2 pollution Fertilizers, pesticides and fossil fuel consumption will be released, which is 9% less than the current situation where only fossil fuels are used to irrigate crops. ConclusionIn the present study, the interests of farmers were considered by considering the maximum profit from the sale of agricultural products by relying on the use of renewable energy sources in the energy basket of the agricultural sector (by reducing the fuel consumption of fossil energy sources used) in determining the cultivation pattern. OPTIMALly calculated and compared with current conditions. The results show that the OPTIMAL model in terms of renewable energy, shows a more appropriate achievement of goals than in the absence of renewable energy. To this end, it is suggested that agricultural policy makers, by promoting the use of solar energy in irrigation and the use of this large capacity in the country, justify farmers to the potential benefits of solar energy (in the agricultural sector) and its widespread use, reduce current agricultural subsidies in the energy sector. Fossilization and subsidizing solar energy, encouraging the private sector to invest in solar projects can help power irrigation projects.

In this paper, a novel risk-based, two-objective (technical and economical) OPTIMAL reactive power dispatch method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

The ram pump is a device which pumps a portion of input discharge to the PUMPING system in a significant height by using renewable energy of water hammer. The complexities of flow hydraulic on one hand and on the other hand the use of simplifying assumptions in ram pumps have caused errors in submitted analytical models for analyzing running cycle of these pumps. In this study it has been tried to modify the governing analytical model on hydraulic performance of these pumps in PUMPING stage. In this study by creating a logical division, the cycle of the ram pump was divided into three stages of acceleration, PUMPING and recoil and the governing equations on each stage of cycling are presented by using method of characteristics. Since the closing of impulse valve is nonlinear, velocity loss in PUMPING stage is considered nonlinearly. Also the governing equations in PUMPING stage were modified by considering disc elasticity of impulse valve and changing volume of the pump body when the water hammer phenomenon is occurred. In order to evaluate results and determine empirical factors of the proposed analytical model, a physical model of the ram pump is made with internal diameter of 51 mm. Results of this study are divided into several parts. In the first part, loss coefficients of the impulse valve were measured experimentally and empirical equations of drag coefficient and friction coefficient of the impulse valve were submitted by using nonlinear regression. In the second part, results were evaluated by using experimental data taken from this study. Evaluation of statistical error functions showed that the proposed model has good accuracy for predicting experimental observations. In the third part, in order to validate the results in PUMPING stage, the analytical models of Lansford and Dugan (1941) and Tacke (1988) were used and the error functions resulted from prediction of experimental observations were investigated through analytical models of the previous researchers. Comparing the results indicates that in the proposed model, noticing that the recommended equations of PUMPING stage are presented based on nonlinear closing theory of the impulse valve, the model accuracy for predicting relative PUMPING rate has been increased up to 3% compared with linear closure theory (Lansford and Dugan, 1941) and has been increased up to 5% compared with rapid closure theory (Tacke, 1988).

Objective Flexible Manufacturing Systems (FMS) are integrated workstations that utilize computer-controlled equipment components for production. These systems are managed by a central computer, which significantly enhances the efficiency and productivity of the production process. Accordingly, a case study is conducted on an FMS electrical manufacturing system with complex manufacturing processes, where automation on the production line is crucial. Selecting the OPTIMAL number of advanced equipment is a challenging and vital issue for managers aiming to boost productivity and efficiently fulfill customer orders. It is a hard-to-change model, and replacing equipment incurs substantial costs.   Methods This study employs the two-stage D-OPTIMAL method to optimize the combination of FMS elements and equipment. The D-OPTIMAL response level input is derived from discrete-event simulation results. Depending on the conditions, various FMS equipment is allocated to each process. Each simulation result for element combinations serves as input for the experimental design. Additionally, the response level (y) of experiments from various FMS indexes is calculated using a weighting method. To reduce the number of experiments and increase data accuracy in a case study with hard-to-change parameters, all combinations are categorized based on the number of automated and manual equipment. The two stages of the D-OPTIMAL design are defined as follows: In the first stage, all combinations within these categories are investigated. In the second stage, the optimized combination from the first stage is analyzed to determine the best configuration. Experiments in the top category from the first stage are simulated and further evaluated in the second stage of the D-OPTIMAL method.   Results In the first phase, all advanced production equipment and FMS elements were considered. After selecting the best-calculated “y” value, which was 147,133.09 in this category, another D-OPTIMAL design was optimized in the second phase to determine the best combination. This combination yielded a “y” value of 151,317.88, representing an improvement over the best category in the first phase of the D-OPTIMAL design. Consequently, the optimized combination from the first phase was further refined. The results from the developed D-OPTIMAL method and the second phase indicate that the OPTIMAL combination of equipment involves using automated and FMS equipment for approximately 92.8% of the total components. Finally, a list of recommended FMS equipment is provided, and its productivity is compared with the productivity at the current level and a higher degree of automation for this production line.   Conclusion In summary, the results of the experimental design show that using advanced production systems does not necessarily improve system efficiency, and determining OPTIMAL combinations requires accurate calculations.

In order to study the spatial structure of oriental beech (Fagus orientalis Lipsky) stand at OPTIMAL stage, this research was carried out in the Caspian forests of Iran. The studied site consisted of a natural stand located at Sangdeh district (Mers-e-se) with three hectare area. It is located between 1900 and1950 m.a.s.l. Six sample plots each with 0.36 ha area, were systematically selected and all trees (1244 stem) within the plots were measured and recorded. Using increment borer, the mean age of the stand was estimated as 137 years; the age difference of trees was 40 years. Results showed that the stand has a closed canopy cover and distribution of stem number per diameter class was more or less homogenous (Bell shape) with a semi even-aged structure. Using Ripley's K function, the distribution of trees within the stand was random.