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Second-generation bioethanol can be produced from various lignocellulosic biomasses such as wood, agricultural or forest residues. Lignocellulosic biomass is inexpensive, renewable and abundant source for bioethanol production. The conversion of lignocellulosic biomass to bioethanol could be a promising technology though the process has several challenges and limitations such as biomass transport and handling, and efficient pretreatment methods for total delignification of lignocellulosics. Proper pretreatment methods can increase concentrations of fermentable sugars after enzymatic saccharification, thereby improving the efficiency of the whole process. Conversion of glucose as well as xylose to bioethanol needs some new fermentation technologies to make the whole process inexpensive. The main goal of pretreatment is to increase the digestibility of maximum available sugars. Each pretreatment process has a specific effect on the cellulose, hemicellulose and lignin fraction; thus, different pretreatment methods and conditions should be chosen according to the process configuration selected for the subsequent hydrolysis and fermentation steps. The cost of ethanol production from lignocellulosic biomass in current technologies is relatively high. Additionally, low yield still remains as one of the main challenges. This paper reviews the various technologies for maximum conversion of cellulose and hemicelluloses fraction to ethanol, and it point outs several key properties that should be targeted for low cost and maximum yield.

SF 6 is widely used in high-voltage equipment due to its excellent insulation ability. However, when the moisture content in SF 6 exceeds a certain value, its insulation could be damaged, threatening the normal operation of high-voltage equipment. Therefore it's of great importance to establish proper control standards on moisture content in SF 6 . In this paper, the influence of moisture content on flashover voltages of cylindrical insulator models in SF 6 is studied by controlling the moisture content in a specially designed organic glass chamber, and then the feasibility of three common moisture controlling methods are discussed. The results show that controlling moisture content in the form of volume fraction can directly limit the absolute content of water vapor and prevent the formation of harmful decomposition products. However, the measured values of volume fraction at different temperatures should be rectified to a common temperature, making it inconvenient to use; the form of relative humidity can reflect the condensation margin of water vapor and could be used without consideration of temperature, but it cannot directly show the absolute content of water vapor, making it difficult to control the production of discharge products; the dew point temperature cannot reflect the real condensation temperature of water vapor, so the method of controlling dew point temperature below zero degree is not scientific. In conclusion, 200 uL/L and 15% are suggested as the limiting values in the form of volume fraction and relative humidity according to the experimental results, which can serve as reference for the revision of the existing moisture control standards.

Abstract Previous studies have shown that titanium-based glazes containing silicon and calcium tend to crystallize into titanite. In this work, ZnO was found to be an effective additive to produce rutile but not titanite. Images of the micromorphology showed that the glaze doped with 8 wt% ZnO was UV–VIS–NIR of acicular rutile and granular silica. The refractive index of crystalline rutile is higher than that of crystalline titanite, which leads to higher reflectance in the near-infrared range. However, excessive rutile counteracted this improvement in the UV–VIS–NIR spectrum reflectance of solar energy because it decreased the visible spectrum reflectance. As a result, the best ratio of added ZnO was confirmed to be 2 wt%.

The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids.

<abstract> <p>The rising proportion of inverter-based renewable energy sources in current power systems has reduced the rotational inertia of overall microgrid systems. This may cause high-frequency fluctuations in the system leading to system instability. Several initiatives have been suggested concerning inertia emulation based on other integrated external energy sources, such as energy storage systems, to combat the ever-declining issue of inertia. Hence, to deal with the aforementioned issue, we suggest the development of an optimal fractional sliding mode control (FSMC)-based frequency stabilization strategy for an industrial hybrid microgrid. An explicit state-space industrial microgrids model comprised of several coordinated energy sources along with loads, storage systems, photovoltaic and wind farms, is considered. In addition to this, the impact of electric vehicles and batteries with adequate control of the state of charge was investigated due to their short regulation times and this helps to balance the power supply and demand that in turn brings the minimization of the frequency deviations. The performance of the FSMC controller is enhanced by setting optimal parameters by employing the tuning strategy based on an iterative teaching-learning-based optimizer (ITLBO). To justify the efficacy of the proposed controller, the simulated results were obtained under several system conditions by using a vehicle simulator in a MATLAB/Simulink environment. The results reveal the enhanced performance of the ITLBO optimized fractional sliding mode control to effectively damp the frequency oscillations and retain the frequency stability with robustness, quick damping, and reliability under different system conditions.</p> </abstract>

Smart grid (SG) is an innovative technology which aims to make the conventional power grids capable enough to handle the ever increasing demands of power in an efficient manner. SG technology renders the electric distribution system with the capability of accumulating energy from various sources like wind, solar etc. But these sources have intermittency issues which can be handled in an effective manner with the coupling of electric vehicles (EVs) into the SGs. Thus, this paper presents a novel concept in the vehicle-to-grid (V2G) configuration. The primary objective of this paper to provide frequency support to grid by regulating the charging and discharging rates of EVs. These EVs are made to charge and discharge their respective energies at the charging stations (CSs) based on grid's overall requirements. Aggregators (AGs) at the CS level have been specially deployed to regulate EVs activities and maintain grid's stability. It has been verified through extensive simulations that EVs in V2G environment can stabilize the grid in terms of frequency if the coordination amongst the EVs is achieved through aggregators. The results obtained clearly depict that the controlled charging and discharging of EVs' battery can stabilize the grid in terms of frequency.

Acidogenic anaerobic fermentation route was explored for the production of bioethanol and volatile fatty acids (VFA) from the press mud (PM) obtained from sugar mill. Slurry was prepared from PM having 10% of total solids and the same was hydrolyzed under acidic thermal conditions. Both press mud slurry (PMS) and pre-treated press mud slurry (PTPMS) was used as feedstock with mixed microbial consortia (MMC) and enriched mixed microbial consortia (EMMC). Mix of bioethanol and VFA were obtained in all the four cases (PMS-MMC, PMS-EMMC, PTPMS-EMC and PTPMS-EMMC), but, bioethanol and VFA yield of 0.04 g/g and 0.27 g/g, respectively obtained from PTPMS with EMMC was found to be comparatively higher. Control experiments carried out with glucose yielded bioethanol and VFA of 0.042 g/g and 0.28 g/g, respectively demonstrating that the organism was using reducible sugars in the feedstock for the generation of bioethanol by simultaneously producing the VFA from COD.