This study introduces a pioneering method to enhance the efficiency and effectiveness of three-phase five-level reduced switch cascaded H-bridge multilevel inverters (CHB MLI) by employing the Henry Gas Solubility Optimization (HGSO) Algorithm. Targeting the selective harmonic elimination (SHE) technique, the research emphasizes the Optimization of switching angles to significantly reduce total harmonic distortion (THD) and align the fundamental output voltage closely with the reference voltage. Central to this exploration are three distinct objective functions (OFs), meticulously designed to assess the HGSO Algorithm’s performance across various modulation indices. Simulation results, facilitated by PSIM software, illustrate the impactful role these objective functions play in the Optimization process. OF1 demonstrated a superior ability in generating low OF values and maintaining a consistent match between reference and fundamental voltages across the modulation index spectrum. Regarding the reduction of THD, it is crucial to emphasize that all OFs can identify the most effective switching angle to minimize THD and eliminate the fifth harmonic to a level below 0.1%. The findings highlight the potential of HGSO in solving complex Optimization challenges within power electronics, offering a novel pathway for advancing modulation strategies in CHB MLIs and contributing to the development of more efficient, reliable, and compact power conversion systems.

Gas supply cost minimization by selecting the appropriate pipe diameters consid- ering the limitations of pressures in the nodes and the volume of Gas flow is one of the major challenges in the oil and Gas industry, which can be designed as a con- strained Optimization problem. Nowadays, many approaches such as genetic al- gorithms and ant colony Algorithm, which have achieved remarkable success, are proposed to solve this problem using heuristic methods. Despite the work done, yet the convergence speed and accuracy of convergence to the optimal point are con- sidered as two of the major challenges. In this paper one solution method based on imperialistic competitive Algorithm is presented. Test results show that the pro- posed approach compared to GPNet software, which is used in the National Gas Company, has achieved a 20% reduction in costs and it has better performance in comparison to genetic Algorithm, which has a 12% cost reduction.

The prediction of economic series with high volatility and high uncertainty-such as natural Gas prices-is always a challenge in econometric models, because the use of traditional linear modeling models does not allow us to predict complex and nonlinear time series. Regarding the prediction of natural Gas prices, findings point to superiority of the neural network compared to regression models. Nevertheless, the main challenge of this method-the possibility of overlapping and noise of data from the system-has kept the choice for an optimal method open. In this study we use the Kriging interpolation to predict the price of natural Gas. For this purpose, after identifying the effective parameters, sampling and normalizing them, we created a Kriging predicting functions and improved it with the Nelder-Mead Optimization technique. The results of the study show that the Kriging metamodel provides a more accurate prediction than the artificial neural network prediction model. Our research findings also suggest that the Neldar-Mead Optimization Algorithm has somewhat improved the predicted results. However, theextent of this improvement is not remarkable.

The purpose of this paper is to find the optimum design of a typical Gas turbine exhaust diffuser. In order to access the maximum overall static pressure recovery at the condition of swirling flow, an evolutionary Algorithm is used. The Optimization process is studied in three independent cases. Firstly, the Optimization is done for a single profile of strut cover from hub to shroud. Secondly, two profiles are selected for the strut covers, one in the hub section and the other in the shroud section. Finally, the Optimization process is done for the strut cover and diffuser channel geometries simultaneously. In order to produce the strut cover profiles the PARSEC parameterization method is used. The turbulent 3D flow is solved using computational fluid dynamic (CFD). The Optimization process starts with the initial sampling of solution domain and subsequently the genetic Algorithm (GA) is used to find the global optimum. The swirling flow at the turbine exit with the Reynolds number of 1.7 ×105 based on the hydraulic diameter of the diffuser inlet is optimized. All steps of GA and corresponding processes of model creation, mesh generation by TurboGrid, flow simulation by ANSYS CFX and goal function calculation for all members of each generation are coded in the MATLAB platform. As a result of the Optimization, the pressure recovery coefficients increased 1.94%, 3.1% and 7.42% in the first, second and third cases of the Optimization process respectively.

The main aim of this study is to simulate and optimize an integrated industrial natural Gas (NG) to polypropylene (PP) plant (NGTPP). The Optimization in this study aimed to increase the PP productivity as an objective function of the Optimization problem. This plant consisted of four subunits: NG to synthesis Gas, synthesis Gas to methanol, MTP , and PTPP  units. After sensitivity analysis of all possible effective parameters, reformer temperature(TRef.Gas), methanol reactor temperature(TMeOH), methanol reactor pressure (PMeOH), hydrocarbon return flow ratio to methanol reactor (RHC), PP reactor temperature(TPPR), PP reactor pressure(PPPR), Ticl4(MTicl4) and TEA(MTEA) inlet flow to PP reactor have been selected. These parameters were optimized using the Sinus-Cosine Algorithm(SCA). Optimal obtained results showed that the TRef.Gas, TMeOH, PMeOH, RHC, TPPR, PPPR, MTicl4, and MTEA equal 875.53 0C, 225.91 0C, 140.87 bar, 0.7, 55.19 0C, -1.96 bar, 107.97 kg/h, and 3.93 kg/h, consequently. Maximum PP productivity was 7058.85 kg/hr at the optimum point.

One of the most crucial variables in the study of heat transport is thermal conductivity and methods for measuring this variable have long been sought after. In this paper, to achieve the equation for approximation of the thermal conductivity coefficient, 61 experimental data were collected for pure Gases in P=1 bar and variable temperature (91.88-1500 K). The proposed model was then obtained using the Particle Swarm Optimization (PSO) Algorithm in MATLAB V2015. It includes a variety of hydrocarbon and non-hydrocarbon compounds. The physical properties of pure Gases including temperature, critical temperature, critical pressure, molecular weight, viscosity, and heat capacity at constant volume were obtained for pure components and used for prediction of the conductivity of these Gases. Also, during the validation phase, the suggested model attained the most accurate prediction withR^2=0.9995. This model is capable of predicting the thermal conductivity coefficient of Gases with a mean relative error percentage of 4.67% and mean square error percentage of 2.4210×10-4% compared to actual data. These results are significantly better than those obtained from other models.

Phosphorus is one of the macronutrient that its deficiency severely restricts plant growth. One of the simplest and least costly methods of providing phosphorus is direct application of rock phosphate but in calcareous soils it is not very effective due to its low Solubility. The use of rock phosphate mixed with sulfur and organic matter along with phosphate solubilizing microorganisms is considered as a method for increasing rock phosphorus Solubility. This study aimed to model the effect of different ratios of vermicompost, rock phosphate and sulfur on dissolution and release of phosphorus by Aspergillus sp and to optimize the levels of these variables for efficient biofertilizer preparation. Accordingly, 20 experiments were designed using response surface methodology based on central composite design. The effects of different values of vermicompost, rock phosphate and sulfur variables encoded in the constraint (+1, 0,-1) on the dissolution rate of phosphorus were modeled. The results showed a high efficiency (R 2 = 0. 8841) of the central composite design model in estimating P dissolution. The results also indicated that vermicompost interaction with sulfur (p <0. 05) and interaction of rock phosphate with sulfur (p <0. 05) were significant. The results of the statistical analysis of the central composite model coefficients indicated that the vermicompost, vermicompost*sulfur and rock phosphate*sulfur additives had a positive and incremental effect on the phosphorus Solubility. According to prediction of optimum conditions for phosphorus solubilization, 58% vermicompost, 23. 3% rock phosphate and 18. 7% sulfur resulted in maximum phosphorus solubilization (773. 04 mg / kg) by Aspergillus sp. in microbial fertilizer.

In the absence of satellite ephemeris data and inner geometry of satellite’ s sensor, utilization of Rational Function Models (RFMs) is one of the best approaches to georeferencing satellite images and extracting spatial information from them. However, since RFMs have high number of coefficients, then usually high number of control points is needed for their estimation. In the other hand, RFM terms are uninterpretable and all of them causes over-parametrization error which count as the most important weakness of the terrain-dependent RFMs. Utilization of Optimization Algorithms is one of the best approaches to eliminate these weaknesses. Therefore, various Optimization Algorithms have been used to discover the optimal composition of RFM’ s terms. Since the mechanism of these Algorithms is different, the performance and feature characteristics of these Algorithms differ in the discovery of the optimal composition train-dependent RFM’ s terms. But the existing differences not comprehensively analyzed. In this paper, in order to comprehensive assessment the abilities of Genetic Optimization Algorithm (GA), Genetic modified Algorithm (GM), and a modified Particle Swarm Optimization (PSO) in terms of accuracy, quickness, number of control points required, and reliability of results, are evaluated. These methods are evaluated using for different datasets including a GeoEye-1, an IKONOS-2, a SPOT-3-1A, and a SPOT-3-1B satellite images. In terms of accuracy achieved, difference between these methods was less than 0. 4 pixel. In terms of speed of evaluation of parameters, GM was 10 to 12 time more quickly in comparison with two other Algorithms. In terms of control points required, degree of freedom of modified PSO was 45. 25 percent and 27 percent more than GM and GA respectively, and finally in terms of reliability, the dispersion of RMSE obtained in 10 runs of three Algorithms are relatively same. These results indicated that accuracy and reliability of all three methods are almost the same, speed of GM is higher and modified PSO needs less control points to optimize terrain-dependent RFM.