Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms.

In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler's laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. The optimization problem is implemented in three optimization scenarios based on real and forecasted data as well as the investigation of the battery's depth of discharge in the HMG optimization in the distribution network and its effects on the different objectives. The results including energy losses, voltage deviations, and purchased power from the HMG have been presented. Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization. Also, the outcomes proved that increasing the battery depth of discharge causes the BES to have more participation in the HMG effectiveness on the distribution network objectives and affects the network energy losses and voltage deviation reduction.

Dynamic stability of the euler nanobeam subjected to inertial moving nanoparticles based on the nonlocal strain gradient theory.

This research studied the dynamic stability of the Euler-Bernoulli nanobeam considering the nonlocal strain gradient theory (NSGT) and surface effects. The nanobeam rests on the Pasternak foundation and a sequence of inertial nanoparticles passes above the nanobeam continuously at a fixed velocity. Surface effects have been utilized using the Gurtin-Murdoch theory. Final governing equations have been gathered implementing the energy method and Hamilton's principle alongside NSGT. Dynamic instability regions (DIRs) are drawn in the plane of mass-velocity coordinates of nanoparticles based on the incremental harmonic balance method (IHBM). A parametric study shows the effects of NSGT parameters and Pasternak foundation constants on the nanobeam's DIRs. In addition, the results exhibit the importance of 2T-period DIRs in comparison to T-period ones. According to the results, the Winkler spring constant is more effective than the Pasternak shear constant on the DIR movement of nanobeam. So, a 4 times increase of Winkler and Pasternak constants results in 102 % and 10 % of DIR movement towards higher velocity regions, respectively. Furthermore, the effect of increasing nonlocal and material length scale parameters on the DIR movement are in the same order regarding the magnitude but opposite considering the motion direction. Unlike nonlocal parameter, an increase in material length scale parameter shifts the DIR to the more stable region.

Technical and financial feasibility of a chemicals recovery and energy and water production from a dairy wastewater treatment plant.

Due to the high volume of wastewater produced from dairy factories, it is necessary to integrate a water recovery process with the treatment plant. Today, bipolar membrane electrodialysis units (BMEUs) are increasingly developed for wastewater treatment and reutilizing. This article aims to develop and evaluate (technical and cost analyses) a combined BMEU/batch reverse osmosis unit (BROU) process for the recovery of chemicals and water from the dairy wastewater plant. The combined BROU/BMEU process is able to simultaneously produce water and strong base-acid, and reduce power consumption due to the injection of concentrated feed flow into the BMEU. A comprehensive comparative analysis on the performances of two combined and stand-alone BMEU configurations are developed. The proposed combined technology for dairy factory wastewater treatment is designed on a new structure and configuration that can address superior cost analysis compared to similar technologies. Further, the optimal values of permeate flux and current density as two vital and influencing parameters on the performance of the studied dairy wastewater treatment process were calculated and discussed. From the outcomes, the total cost of production in the combined configuration has been reduced by approximately 26% compared to the stand-alone configuration. Increasing the feed concentration rate using the batch reverse osmosis process for the dairy wastewater treatment process can be an ideal solution from an economic point of view. Moreover, point (current density, feed concentration rate, total unit cost) = 328.9 , 7 , 14.37 can be considered as an optimal point for the economic performance of the studied wastewater treatment process.

The remarkable potential of machine learning algorithms in estimating water permeability of concrete incorporating nano natural pozzolana.

This paper aims to estimate the permeability of concrete by replacing the laboratory tests with robust machine learning (ML)-based models. For this purpose, the potential of twelve well-known ML techniques was investigated in estimating the water penetration depth (WPD) of nano natural pozzolana (NNP)-reinforced concrete based on 840 data points. The preparation of concrete specimens was based on the different combinations of NNP content, water-to-cement (W/C) ratio, median particle size (MPS) of NNP, and curing time (CT). Comparing the results estimated by the ML models with the laboratory results revealed that the hist-gradient boosting regressor (HGBR) and K-nearest neighbors (KNN) algorithms were the most and least robust models to estimate the WPD of NNP-reinforced concrete, respectively. Both laboratory and ML results showed that the WPD of NNP-reinforced concrete decreased with the increase of the NNP content from 1 to 4%, the decrease of the W/C ratio and the MPS, and the increase of the CT. To further aid in the estimation of concrete's WPD for engineering challenges, a graphical user interface for the ML-based models was developed. Proposing such a model may be effectively employed in the management of concrete quality.

Examination of the vessel's shape, resistive force and volumetric-aqueous efficiencies to optimize the vessels' foil under noise propagation.

There are several parameters in designing undersurface vessel forms, the most important of which is the hull's total strength, which includes the strength of the hull and its attachments. According to studies, 70 % of the total strength of the vessels is related to their hull only without attachments. The hull has three major parts: nose, cylinder, and heel. The advanced vessels' architecture has a parallel shape (cylinder shape). This cylindrical part is important in examining the used volume by pilots and vessel equipment. This paper uses the CFD method to examine the vessel's shape, and the resistive force and volumetric-aqueous efficiencies are extracted. An optimum profile is extracted by the values of resistive force and volumetric-aqueous efficiencies. The results indicate the significant effect of the hull form on the hydro-acoustic noise of the hull. In other words, by optimizing the hydrodynamic form of the hull, the noise propagation can be reduced as much as possible. Also, the linear slope of the optimized hull is not optimized more than the hull. This means that the turbulence caused by the optimized hull has a higher damping potential.

A fuel gas waste heat recovery-based multigeneration plant integrated with a LNG cold energy process, a water desalination unit, and a CO2 separation process.

Development of the multigeneration plants based on the simultaneous production of water and energy can solve many of the current problems of these two major fields. In addition, the integration of fossil power plants with waste heat recovery processes in order to prevent the release of pollutants in the environment can simultaneously cover the environmental and thermodynamic improvements. Besides, the addition of a carbon dioxide (CO2) capturing cycles with such plants is a key issue towards a sustainable environment. Accordingly, a novel waste heat recovery-based multigeneration plant integrated with a carbon dioxide separation/liquefaction cycle is proposed and investigated under multi-variable assessments (energy/exergy, financial, and environmental). The offered multigeneration system is able to generate various beneficial outputs (electricity, liquefied CO2 (L-CO2), natural gas (NG), and freshwater). In the offered system, the liquified natural gas (LNG) cold energy is used to carry out condensation processes, which is a relatively new idea. Based on the results, the outputs rates of net power, NG, L-CO2, and water were determined to be approximately 42.72 MW and 18.01E+03, 612 and 3.56E+03 kmol/h, respectively. Moreover, the multigeneration plant was efficient about 32.08% and 87.72%, respectively, in terms of energy and exergy. Economic estimates indicated that the unit product costs of electricity and liquefied carbon dioxide production, respectively, were around 0.0466 USD per kWh and 0.0728 USD per kg-CO2. Finally, the total released CO2 was about 0.034 kg per kWh. According to a comprehensive comparison, the offered multigeneration plant can provide superior environmental, thermodynamic, and economic performances compared to similar plants. Moreover, there was no need to purchase electricity from the grid.

Using shock generator for the fuel mixing of the extruded single 4-lobe nozzle at supersonic combustion chamber.

The importance of the fuel injection configuration on the propulsion efficiency of high-speed vehicles is apparent. In this article, the use of an annular extruded 4-lobe nozzle for the injection of fuel jet in a supersonic combustor of a scramjet engine in the existence of a shock generator is examined. The main aim of this study is to obtain the efficient jet arrangement for efficient fuel mixing inside the engine of hypersonic vehicles. A numerical approach is used to model the supersonic air stream and cross-jet flow with the SST turbulence model. The role of nozzle altitude and internal air jet on the fuel mixing of the hydrogen within the high-speed domain are disclosed. The importance of the horseshoe vortex and counter-rotating vortex on the fuel distribution is also presented. Our results show that the usage of a coaxial jet instead of an annular jet would increase fuel mixing by more than 40% in the combustion chamber.

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid.

This study predicts the parameters such as viscosity and thermal conductivity in silica-alumina-MWCN/water nanofluid using the artificial intelligence method and using design variables such as solid volume fraction and temperature. In this study, 6 optimization algorithms were used to predict and numerically model the µnf and TC of silica-alumina-MWCNT/water-NF. In this study, six measurement criteria were used to evaluate the estimates obtained from the coupling process of GMDH ANN with each of these 6 optimization algorithms. The results reveal that the influence of the φ is notably higher on both µnf and TC with values of 0.83 for µnf and 0.92 for TC, while Temp has a relatively weaker impact with -0.5 for µnf and 0.38 for TC. Among various algorithms, the coupling of the evolutionary algorithm NSGA II with ANN and GMDH performs best in predicting µnf and TC for the NF, with a maximum margin of deviation of -0.108 and an R2 evaluation criterion of 0.99996 for µnf and 1 for TC, indicating exceptional model accuracy. In the subsequent phase, a meta-heuristic Genetic Algorithm minimizes µnf and TC values. Four points (A, B, C, and D) along the Pareto front are selected, with point A representing the optimal state characterized by low values of φ and Temp (0.0002 and 50.8772, respectively) and corresponding target function values of 0.9988 for µnf and 0.6344 for TC. In contrast, point D represents the highest values of φ and Temp (0.49986 and 59.9775, respectively) and yields target function values of 2.382 for µnf and 0.8517 for TC. This analysis aids in identifying the optimal operating conditions for maximizing NF performance.

The effect of the initial temperature, pressure, and shape of carbon nanopores on the separation process of SiO2 molecules from water vapor by molecular dynamics simulation.

Today, with the advancement of science in nanotechnology, it is possible to remove dust nanostructures from the air breathed by humans or other fluids. In the present study, the separation of SiO2 molecules from H2O vapor is studied using molecular dynamics (MD) simulation. This research studied the effect of initial temperature, nanopore geometry, and initial pressure on the separation of SiO2 molecules. The obtained results show that by increasing the temperature to 500 K, the maximum velocity (Max-Vel) of the samples reached 2.47 Å/fs. Regarding the increasing velocity of particles, more particles pass via the nanopores. Moreover, the shape of the nanopore could affect the number of passing particles. The results show that in the samples with a cylindrical nanopore, 20 and 40% of SiO2 molecules, and with the sphere cavity, about 32 and 38% of SiO2 particles passed in the simulated structure. So, it can be concluded that the performance of carbon nanosheets with a cylindrical pore and 450 K was more optimal. Also, the results show that an increase in initial pressure leads to a decrease in the passage of SiO2 particles. The results reveal that about 14 and 54% of Silica particles passed via the carbon membrane with increasing pressure. Therefore, for use in industry, in terms of separating dust particles, in addition to applying an EF, temperature, nanopore geometry, and initial pressure should be controlled.

Modeling and Optimal Operating Conditions of Hollow Fiber Membrane for CO2/CH4 Separation.

In this work, the capture of carbon dioxide using a dense hollow fiber membrane was studied experimentally and theoretically. The factors affecting the flux and recovery of carbon dioxide were studied using a lab-scale system. Experiments were conducted using a mixture of methane and carbon dioxide to simulate natural gas. The effect of changing the CO2 concentration from 2 to 10 mol%, the feed pressure from 2.5 to 7.5 bar, and the feed temperature from 20 to 40 °C, was investigated. Depending on the solution diffusion mechanism, coupled with the Dual sorption model, a comprehensive model was implemented to predict the CO2 flux through the membrane, based on resistance in the series model. Subsequently, a 2D axisymmetric model of a multilayer HFM was proposed to simulate the axial and radial diffusion of carbon dioxide in a membrane. In the three domains of fiber, the CFD technique was used to solve the equations for the transfer of momentum and mass transfer by using the COMSOL 5.6. Modeling results were validated with 27 experiments, and there was a good agreement between the simulation results and the experimental data. The experimental results show the effect of operational factors, such as the fact that temperature was directly on both gas diffusivity and mass transfer coefficient. Meanwhile, the effect of pressure was exactly the opposite, and the concentration of CO2 had almost no effect on both the diffusivity and the mass transfer coefficient. In addition, the CO2 recovery changed from 9% at a pressure equal to 2.5 bar, temperature equal to 20 °C, and a concentration of CO2 equal to 2 mol%, to 30.3% at a pressure equal to 7.5 bar, temperature equal to 30 °C, and concentration of CO2 equal 10 mol%; these conditions are the optimal operating point. The results also manifested that the operational factors that directly affect the flux are pressure and CO2 concentration, while there was no clear effect of temperature. This modeling offers valuable data about the feasibility studies and economic evaluation of a gas separation unit operation as a helpful unit in the industry.