جستجوی مقالات مرتبط با کلیدواژه « بهینه سازی » در نشریات گروه « علوم پایه »

The growing demand for reliable and sustainable electricity has led to increased interest in photovoltaic-wind-diesel hybrid systems based on distributed generation sources. These systems combine renewable energy sources with distributed generation sources to provide electricity, especially in remote areas or microgrids. However, it is crucial to optimize the performance of such systems to maximize their benefits and achieve a balance between cost, reliability and environmental impact. Therefore, in this article, a multi-objective optimization method has been modeled to reduce energy production costs, improve the network voltage level with reference to the power changes of renewable power plants, and finally increase the reliability of the network with the aim of reducing the amount of interruptions of network loads. Also, due to the different structure of the objective functions, the fuzzy method has been used to make the objective functions fuzzy, and the optimization method used is based on a meta-heuristic optimization algorithm called multi-faceted optimization, which is based on the behavior of the emergence of new stars. Inspired. Finally, the results are implemented on the 33-bus test network in IEEE standard island mode.

The process of diphtheria toxoid production was designed by using SuperPro Designer and the effect of the applied changes in process on the yield and costs of the manufacturing was investigated. First, giving the information of the real process of the toxoid production, a bioreactor with improved operational conditions and a disc stack centrifuge instead of the filter press, which is applied for the bacterial debris separation, were utilized. Such alterations followed the addition of a pump between the bioreactor and centrifuge. The results indicated that improvement of the bioreactor operational conditions can lead to the 25% increase in the toxin production, i.e., the increase of toxoid production from 7,000,000 doses to 8,750,000 doses. The toxin waste through filter press (14%) may be remarkably reduced by using the centrifuge, which in turn resulted in the 44% enhancement in the toxoid production. Such alterations can result in the 16% reduction in the separation operation time, 29% reduction in water consumption and 32% increase in the energy consumption. Overall, the simulation results showed that the costs of the new equipment suggested to be used in the improved process can be recoverable through running two batches.

The pulp and paper industry has historically been one of the world‘s largest consumers of freshwater resources and producers of wastewater, which, can cause serious damage to the water and soil ecosystem if directly discharged into the environment. On the other hand, the use of traditional treatment techniques including biological treatment and coagulation is restricted by the presence of non-biodegradable organic compounds with high molecular masses. The photo-Fenton process is an environmentally friendly, simple, and fast method with low operational cost which could oxidize polluting substances by producing powerful hydroxyl radicals. The most important variables in the effectiveness of this process are the concentration of the oxidant agent, the concentration of Ferro ions, and pH. With three variables; the dimensionless concentration of hydrogen peroxide, the concentration of Ferro ions, and the pH, the experiment's design was carried out by the central composite method based on the response surface method in 18 experiments. pH was adjusted, Ferro ion and hydrogen peroxide were added, and the solution was then immediately exposed to UV light in order to apply the Photo Fenton treatment procedure to the sample. COD was defined as an indicator of wastewater pollution, and sodium sulfite was used to eliminate the effect of hydrogen peroxide on COD test. Effective factors were also optimized for the best performance at the most economical cost. The maximum value of COD removal = 71.51% was obtained experimentally. In the economic optimal state with the initial concentration of dimensionless oxidant dose = 0.369, ferro ion concentration = 98.99 mg/L, and pH = 3.28, 65.21% of COD removal was achieved and 1.37 riyals for removal of 1 COD unit were obtained.

Sparse representation has many applications in signal and image processing, including applications in medical image reconstruction, image enhancement and compression, signal separation, array and radar signal processing. This importance has caused the researchers to benefit from a variety of sparse representation method and to use different norms to solve optimization problems. In this article, firstly, different methods of solving the sparse representation are reviewed, and then a new and efficient method is presented to recover noisy sparse signals with the benefit of sequential quadratic programming and smoothed L0 norm. The results of the experiments show the high success rate of the proposed method compared to other sparse representation optimization methods.

Redundancy allocation to the system is one way to improve the reliability and efficiency of the system. Adding a standby component to the system  is an important factor in improving system performance and usually will incur some costs to the system. In this paper, we investigate the stress-strength reliability of a k-out-of-n: G system in a Weibull-H model. Also, in order to increase the stress–strength reliability and decrease the costs simultaneously, we obtain the optimal time to activate  the standby component to the working state,in some cases related to the Weibull-H model.

In recent years, the need for L-asparaginase in different industries has increased. L-asparaginase obtained from microorganisms is cost-effective, has no harmful effects on the environment, and improves the quality of products. In the present study, L-asparaginase-producing microorganisms were isolated and optimized from different sources, and selected strain enzymes were produced. L-asparaginase-producing yeasts were randomly isolated and purified from different parts of Isfahan. Strains were screened for L-asparaginase production. Then, the superior yeast strain with the highest enzymatic activity was selected for optimization. Under suitable pH, temperature, time, carbon source, and nitrogen source, multi-factor optimization was performed using response-level statistical software to investigate the simultaneous effect of several factors on production and the highest enzyme activity. In the present study, 5S2 yeast isolate led to the formation of the highest halo of L-asparaginase. This strain was first introduced as the L-asparaginase-producing strain and was registered in the NCBI database under Diutina mesorugosa strain SBG-IAUF-2 with access number MZ901211. Yeast 5S2 with enzyme activity equivalent to 1722.8 u / ml was selected as the selected strain for optimization. The results of multivariate optimization of enzyme activity showed that at pH 7, a concentration of 2% glucose as carbon source, the attention of 10% L-asparagine, a temperature of 35°C, and a time of 120 hours, there is the highest enzyme production activity and curve RSM is located in the optimization area. In the present study, after recording 5S2 yeast isolate for the first time as an L-asparaginase-producing strain in the NCBI database, extracellular L-asparaginase produced by the 5S2 yeast strain was introduced due to its high enzymatic activity as a potent strain in the production of L-asparaginase enzyme.

Greenhouse refers to a limited space that has the ability to control the appropriate environmental conditions for the growth of plants in different areas during different seasons of the year. Rapid economic and cultural growth, population growth, soil and water constraints, the community's need for food, the existence of large consumer markets and the interest in producing off-season crops in recent years have led to the development of greenhouse crops. But choosing the wrong place to build a greenhouse leads to a waste of capital and the failure of these goals. The purpose of this study is to find the optimal location of the greenhouse in Asadabad plain with a sustainable development approach.

Due to the existence of numerous and effective parameters –for the performance of greenhouses, the technique of multi-criteria analysis has been used to find a suitable location. Also, due to the uncertainty in the behavior of natural parameters, fuzzy logic has been used to model the effect of the parameters.

The results show that of the total area of Asadabad plain, 10% are in a very good condition, 39% are in a good condition (suitable with restrictions) and 51% are in a bad condition in terms of greenhouse construction.

Relying on spatial analysis can reduce the investment risk for greenhouses and lead to environmental sustainability.

In this research, the optimization of the geometric parameters of the hovercraft skirt is investigated using the PSO algorithm in order to reduce the range of vibrations on the structure and increase the ride quality. At first, the dynamic equations governing the hovercraft in two states of contact with the ground surface and non-contact are presented. Based on the presented nonlinear dynamic equations, the equilibrium point of the hovercraft was extracted and by linearizing the nonlinear dynamic equations governing the heave motion of the vessel around the equilibrium point, the linear form of the governing dynamic equations was extracted. In order to reduce the vibrations and increase the ride quality with the vessel, an objective function is presented, and based on this objective function and using both forms of linear and non-linear dynamic equations, the geometric parameters of the skirt are obtained based on the intelligent PSO algorithm. Finally, it is shown that comparing the frequency and time behavior of the optimized hovercraft with the original hovercraft, the range of vibrations is reduced and the ride quality with the optimized hovercraft is greatly improved.

The aim of this study was to model the removal efficiency of cefixime by the Fenton method using a neural network. This model predicts experimental results well. In this model, the amount of hydrogen peroxide, iron catalyst, cefixime removal time, initial concentration of cefixime, and pH are the input parameters. The output variable is the removal percentage of cefixime. Total error squares (SSE), mean the square root of error (RMSE), adjusted coefficient of determination (), and coefficient of determination   in determining the number of optimal neurons in the middle of the performance index. According to the obtained results, the neural network model was able to predict the absorption efficiency with the sigmoid tangent transfer function in the hidden layer and the linear stimulus transfer function in the output layer. Also, the results of modeling the neural network with org-art showed that the grid with a 1-13-5 arrangement (5 neurons in the input layer, 13 neurons in the hidden layer, and 1 neuron in the output layer) had the best result in predicting the output. The correlation coefficients of all the levels of training, validation, and test 0.3 were 0.99436, 0.9993, and 0.96901, respectively. To predict the trend of changes, neural network tools have been used in MATLAB software.

Lipases are a group of hydrolases capable of catalyzing the hydrolysis of triacylglycerols to free fatty acids and glycerol. They are very important industrial enzymes because of the potential to be used in many industrial applications. In this research, potent lipase producer fungi were isolated and screened from olive mill wastewater and its disposal pond. In the qualitative test, 7 strains were identified as lipase producer fungi. One of the 7 isolated fungi, was identified based on macroscopic and microscopic examination as Aspergillus sp. that detected good lipase activity. The L25 Taguchi orthogonal array design of experiments with four factors in five levels was used to reach optimal fermentation conditions and medium composition for lipase production using Minitab 19 software. The optimum conditions derived were: WS/OF content ratio (0.5), fermentation time (7 days) and moisture content 1:2 (66%), glucose concentration as a co-substrate (0 g/L). Using optimal fermentation condition, maximum lipase activity (2754.73 U/g of the dry substrate) was obtained. The results demonstrated that the fungus isolated is a promising alternative for lipase production. This study aimed to better understand the microorganisms in OMW and take them to the potential candidates for the conversion of agricultural by-products into valuable secondary metabolites like lipase.

In this study, hydrogen production was simulated and optimized using the coal gasification in the moving-bed reactor. In this reactor, coal from the top and the combination of oxidizing gas and steam from below is in contact with solid and gas produced comes out from the top of reactor. The results of the simulation have been compared with the experimental values and the average relative error of 2.47% indicated acceptable accuracy of simulation results. The effects of inlet gas temperature, reactor pressure, ratio of steam to oxygen and coal to oxygen are investigated on the hydrogen production efficiency. Although some studies have been published regarding the modeling of the moving-bed reactor in the coal gasification, limited parametric studies are available to investigate the effect of different variables on the reactor performance with Pittsburgh coal as food. Also, in this study, simulation with discrete temperature for gas and solid to determine the exact maximum temperature of the solid phase, the kinetics of all possible reactions, simultaneous use of shrinking core and volumetric models in different areas of reactor and considering the distribution of gas components in the pyrolysis stage, happened that it is useful to find out more precisely the composition of the product gas. Finally, the reactor performance for generating maximum hydrogen yield is optimized using the particle swarm optimization method. The optimization results show that there is a 24.3% increase in hydrogen production yield compared to the basic mode.

Gas recycling is a generally accepted method to improve production from condensate gas reservoirs and has been welcomed due to its low cost compared with drilling new wells. The use of the net present value, which is both a factor of time and cost, is a reliable way of evaluating this process which considers the costs of injection facilities and production costs. It is important to find new methods for planning and optimizing gas injection. In this research, it has been tried to identify the optimal amount of gas recycling as well as the amount of gas produced by combining optimizers and reservoir simulators and using evolution optimization algorithms. It is worth noting that the method used in this study is based on a case study of gas recycling. The study shows that with the recycling of the entire production gas in the studied reservoir, the net present value could be increased from $ 126 million to $ 40 million. The production and recycling of a portion of the gas in an optimal form also increases the reservoir's life due to economic constraints from 10 years to 18 years. In addition, with a closer examination, it was concluded that due to the higher price of injected gas in cold seasons compared to the warm seasons, 65 percent of the gas produced can be returned to the reservoir in these seasons. By sensitivity analysis of the optimization results, the gas prices as well as the amount of gas condensate richness have significant effects on the optimal amount of recovered gas.

The use of membrane reactors to distribute one or more components through the membrane (also known as side-feeding strategy) is an efficient method for controlling reactions pathways and achieving the highest performance. In this study, an optimization of three types of membrane reactor and a conventional tri-reformer was carried out to maximize H2 yield subject to produce the proper syngas for the next common processes, including methanol, Fischer-Tropsch, and dimethyl ether (DME) direct synthesis. In this regard, a sensitivity analysis was performed to identify significant parameters affecting H2 yield in O2, CO2, and H2O membrane reactors. A comparison between these configurations under optimal conditions shows that O2 side-feeding strategy was the most favorable strategy in terms of CH4 conversion, H2 yield, and catalyst lifetime due to no formation of hot spot temperature. Also in this strategy, the H2 yield was increased by 8% and 10% compared to the conventional tri-reformer to produce suitable syngas for the methanol and DME direct synthesis processes, respectively.

Toxic heavy metal contamination of industrial water is a significant universal problem. They accumulate in living tissues throughout the food chain which has humans at its top. These toxic metals can cause accumulative poisoning, cancer, and brain damage. Uranium is one of the most serious heavy metals because of its high toxicity and radioactivity. Excessive amounts of uranium have found their way into the environment through the activities associated with the nuclear industry (1). Conventional methods for removing uranium from wastewater include precipitation, evaporation, ion exchange, membrane processing, and adsorption. Nevertheless, these methods have several disadvantages, such as high installation and operating costs, the requirement of preliminary treatment steps, the difficulty of treating the subsequently generated solid waste, and low efficiency at low metal concentration (2,3). Owing to an increase in environmental awareness, there has been an emphasis on the development of new environmentally friendly ways to decontaminate waters using low-cost methods and materials. In this study, microbial biomass has emerged as a complementary, economic, and eco-friendly device for controlling the mobility and bioavailability of metal ions (2,4). The present work evaluates the performance of the Citrobacter freundii biomass to remove uranium ions from aqueous solutions. The effect of pH, temperature, initial concentration, and sorbent dose on biosorption capacity is also studied.

Materials: Citrobacter freundii bacteria used in this research with PTCC No. 1772 was purchased from the Scientific and Industrial Research Organization of Iran. Uranyl nitrate salt (UO2(NO3)2.6H2O) was obtained from the Research Institute of Nuclear Sciences and Technologies. Nutrient Broth culture medium, sulfuric acid, and sodium hydroxide and other materials used in this research were supplied from the Merck Company.Preparation of uranium solutions and biomass: A stock solution containing 1000 mg L-1 of U(VI) was prepared of UO2(NO3)4.6H2O. The working solutions were prepared daily from stock solutions. In this study, the biomass of Citrobacter freundii bacteria was heat treated in an autoclave at a temperature of 121°C for 15 minutes at a pressure of 1.5 atmospheres. Experimental design and batch biosorption studies: The design of the experiment was done using the response surface method by Design Expert software. Four variables, including initial uranium concentration (mg/l), temperature (°C), pH, and biosorbent dose (g/l), in five levels α-, -1, 0, +1, α+, 1 were used to design the experiment (Table 1). Therefore, 27 experiments were presented using a central composite design. The values of the variables and the obtained answers are shown in Table 2. Uranium biosorption experiments were performed by adding specified amounts of bacterial biomass in 20 ml Erlenmeyer flasks containing uranium solution with the concentration and pH corresponding to each experiment, with the specified temperature in the Shaker. After 90 minutes, each sample was centrifuged at 4500 rpm for 15 minutes at 4°C. Then, the remaining uranium in the solution was measured by ICP (Perkin Elmer/Optima 7300DV). The percentage of uranium removal (R) was calculated by equation 1:  Where C0 and Cf are the initial and the final concentrations of the metal ion solutions (mg/l), respectively.Table 1- Variables and Levels of the Central Composite Design Method

By using the RSM-CCD method, the optimization of the biosorption process was carried out. Table 2 shows the experimental results based on each point of the experimental design. Then, using analysis of variance (ANOVA), the obtained results were evaluated.The equation obtained for the biosorption efficiency of uranium by Citrobacter freundii is expressed as follows:Removal= +68.97045-1.43160 * C (ppm)+12.81296 * pH+1.08935 * T (0C)+2.89856 * M (g/l)+0.55737 * C (ppm) * pH+0.011459* C (ppm)* T (0C)+0.014961* C (ppm)* M (g/l)-0.32111 * pH * T (0C)-0.62783 * pH * M (g/l)-0.037633* T (0C) * M (g/l)-6.93488E-003* C (ppm)2-4.06361 * pH2Table 2- Values of Variables and Experimental Responses in the Response Surface Method.

The F-value and p-value of the proposed model are equal to 5.03 and 0.0027, respectively, reflecting the accuracy of the proposed model. This model with R2 equal to 0.81 shows that the proposed model can well predict the experimental values. The results showed that the factor of initial uranium concentration and pH statistically affect (p-value‹ 0.05) the uranium biosorption process. In contrast, temperature and sorbent dose factor (p-value› 0.05) have no statistically effect on uranium removal by Citrobacter freundii. With increasing uranium concentration from 10 mg/l to 77.5 mg/l, the removal increases from %66.5 to %99/92. Then, with increasing uranium concentration from 77.5 mg/l to 100 mg/l, the removal decreases to %97.34. On the other hand, one of the most important effective parameters in biosorption is the pH of the solution. With increasing the pH from 2 to 5, the removal decreased from %96.82 to %79.01 due to the formation of uranyl complexes (5). In this research, the results indicated that the pre-treated biomass under the conditions suggested by Design Expert software (19.84 g/l of biomass, temperature 28.92 OC, pH 2.89 and initial uranium concentration 53.71 mg/l) is able to remove approximately 99.99 percent of uranium from the contaminated area, which shows valuable potential Citrobacter freundii in bioremediation applications of uranium from contaminated wastewaters.

Due to the change in the output characteristic of photovoltaic units under changes in radiation, temperature, load size and also the angle of placement of the panels, these systems rarely automatically turn on around the maximum power point. So far, various methods such as increasing conductivity, parasitic capacitor, neural network and fuzzy logic controller have been presented to track the maximum power point of photovoltaic systems, which have disadvantages such as high cost, high complexity. And they have low reliability. In this article, a fuzzy controller is designed to track the maximum power point so that the maximum power point tracking can be extracted under the conditions of changing temperature and radiation. In order to increase the accuracy of the proposed controller and considering the nature of temperature and radiation changes, the gray wolf optimization algorithm has been used to adjust the limits of the fuzzy functions to obtain a robust controller, and a proportional integral controller has also been used on the load side Its parameters are also calculated by GWO algorithm to set the optimal voltage. In order to validate the proposed mechanism, this structure has been implemented in the Simulink environment of MATLAB software, and the results show the optimal efficiency of the optimized fuzzy controller in tracking the maximum power point of photovoltaic sources.

In this research, integration of well logging and 2D/3D seismic data in the reservoir lithofacies modeling process has been considered. For this purpose, two methods from the so-called seismic matching loop class have been used. In the first method, the particle swarm optimization (PSO) algorithm is implemented to find the optimal value of the probability perturbation method (PPM) deformation parameter. The PPM is used to convert an N-parameter optimization problem to a problem with one parameter. In the second method, in the absence of parametrization methods, the problem of updating lithofacies models will be considered as an optimization problem with the N-unknown parameter. Obviously as the number of optimization unknown parameters increases, the optimization algorithms ability in finding the optimum solution decreases. One way to overcome this problem is to design optimization algorithms with higher capabilities. In the second method, an attempt has been made to establish a proper balance between the exploration and exploitation capabilities of the optimization algorithm. In this research, the crossover and mutation operators of the genetic algorithm (GA) optimization method have been used to improve the exploration and exploitation capabilities of the PSO and artificial bee colony (ABC) algorithms. To evaluate the performance of the proposed methods, a 3D synthetic reservoir model (reference model) has been used. The obtained results show that reservoir lithofacies models generated by "PPM-PSO", "PSO-GA" and "ABC-GA" methods have 6.65%, 10.44%, and 0.99% mismatches compared with the reference lithofacies model, respectively. To highlight the ability of the proposed algorithms in generating and updating the reservoir lithofacies models, two traditional geostatistical methods have also been applied to the specified problem. The results indicate that using the "PPM-PSO", "PSO-GA" and "ABC-GA" algorithms, respectively, leads to 18.8%, 15.27%, and 24.46% improvement on mismatch values compared to the traditional geostatistical methods. Finally, the performance of "ABC-GA" method has been evaluated on two larger and more complex synthetic reservoir models.

Due to the complexities of oil reservoir simulation models, there is a need to find an optimization method that can have good accuracy and speed while reducing computational costs. In this regard, in this study, a parallel genetic algorithm has been used to accelerate the optimizer in order to prevent the increase of the computational load of the well placement problem and the prolongation of the execution time. Instead of one population of chromosomes (i.e., the same optimization variables), this algorithm works with several populations, which exchange chromosomes with each other. The proposed mathematical model is a single-objective problem, which is to maximize the present value of the project in the location of wells. The price of oil and the cost of separating water and gas are considered in this economic function. In evaluating the objective function, the CPU of a single computer, or multiple networked computers, each in parallel, are responsible for calculating the chromosomes’ fitness of parallel populations. After calculating the target function or suitability of the chromosomes of all populations (in which each population has common genetic function), migration between populations takes place. A case study has been used to validate the implementation of this method. In the proposed model, the location of vertical wells in an oil tank has been verified. As a result of the optimization, the wells are located at five points, four injection wells at the corners, and one production well at the center of the model, and in addition to reducing optimization time, the number of simulator performances in parallel processing mode is significantly reduced. In this research, the performance of genetic and parallel genetic algorithms for location optimization on the same reservoir model is shown. The execution time of optimization between genetics and parallel genetics is 7100 seconds and 1800 seconds, respectively. As it is known, the use of PGA has quadrupled the execution time in the studied oil reservoir.