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Abstract The catalytic hydrothermal liquefaction of biomass under a hydrogen atmosphere is a promising technology to produce stable biocrude oil as a sustainable alternative to petroleum crude. A series of iron‐based non‐noble mix metal‐oxide‐on‐silica catalysts were evaluated to mimic the natural transformation that may have led to the conversion of terrestrial biomass to fossilized fuels. Switchgrass powder was liquefied to a stable bio‐oil with a 71.2% yield by using FeO_x/SiO₂catalyst in ethanol under a 5.5 MPa hydrogen atmosphere at 210 °C. The use of Fe‐MO_x/SiO₂(M = V, Mn, Co, Ni, Cu and Mo) type bimetallic oxide catalysts instead of FeO_x/SiO₂can produce improvements in liquefaction yields by using Mn, Co, Ni, and Cu as the second metal. The highest liquefaction yield of 78.8% was observed with the Fe‐CuO_x/SiO₂catalyst. Liquefaction oils were formed that were composed of complex mixtures of C6‐C12 alcohols, esters, aldehydes, and phenols. The lignin products:holocellulose products ratio changed in the range 0.35 to 0.15 and the composition of oils changed significantly with the use of mixed metal oxides in place of single metal FeO_x/SiO₂The most effective catalyst, Fe‐CuO_x/SiO₂could be reused in five cycles with a small loss in liquefaction yield from 78.8% to 70.0% after four reuse cycles and after regeneration of the catalyst at 500 °C for 3 h in air. 

Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size. 

This study examines biodegradability (BD) and optimum conditions for the solid-state anaerobic digestion (SS-AD) of organic solid poultry waste (organs, intestines, offal, and unprocessed meat) to maximize biomethane production. Three main parameters, substrate-to-inoculum (S/I) ratio, pH, and temperature, were evaluated for the SS-AD of organic solid poultry waste. pH was evaluated at non-adjusted pH, initially adjusted pH, and controlled pH conditions at a constant S/I ratio of 0.5 and temperature of 35 ± 1 °C. The S/I ratios were examined at (0.3, 0.5, 1, and 2) at a controlled pH of ≈7.9 and temperature of 35 ± 1 °C. The temperature was assessed at mesophilic (35 ± 1 °C) and thermophilic (55 ± 1 °C) conditions with a constant S/I ratio of 0.5 and controlled pH of ≈7.9. The results demonstrate that the highest biomethane production and BD were achieved with a controlled pH of ≈7.9 (689 ± 10 mg/L, 97.5 ± 1.4%). The initially adjusted pH (688 ± 14 mg/L, 97.3 ± 1.9%) and an S/I ratio of 0.3 (685 ± 8 mg/L, 96.8 ± 1.2%) had approximately equivalent outcomes. The thermophilic conditions yielded 78% lower biomethane yield than mesophilic conditions. The challenge of lower biomethane yield under thermophilic conditions will be resolved in future studies by determining the rate-limiting step. These observations highlight that SS-AD is a promising technology for biomethane production from solid organic poultry waste. 

The abundant availability of crop waste and forestry residues in Texas provides great potential for producing renewable diesel in the local towns of Texas. This study aims to evaluate the environmental impacts of renewable diesel use in Texas transportation and the potential of renewable diesel production in Texas. The GREET model was used to customize the life cycle pathway of renewable diesel and evaluate its environmental impacts. The models of renewable diesel produced from forestry residue and corn stover were built to calculate life cycle gas emissions of combination short-haul heavy-duty trucks fueled with renewable diesel. Life cycle GHG emissions of renewable diesel are much lower than those of low-sulfur diesel. However, with respect to renewable diesel derived from corn stover, life cycle PM10 and PM2.5 emissions were almost double those of low-sulfur diesel in 2024, and both emissions will be reduced by 37–38% in 2035. The life cycle emission trends of SOx, black carbon, and primary organic carbon are very similar to those of PM10 and PM2.5. The total cost of ownership (TCO) of heavy-duty trucks using renewable diesel produced from forestry residues or corn stover would be 10.3–14.8% higher than those consuming regular low-sulfur diesel in Texas. 

The primary goal in designing wind turbine blades is to maximize aerodynamic efficiency. One promising approach to achieve this is by modifying the blade geometry, with winglets to the tip. Winglets are intended to reduce the strength of the tip vortices, thereby reducing induced drag, increasing torque, and, ultimately, improving the power output of the wind turbines. In this study, computational fluid dynamics (CFD) simulations were utilized to assess the aerodynamic performance of wind turbine blades with and without winglets at various wind speeds (5, 7, 10, 13, 15, 20, and 25 m/s). The results indicate that winglets have a limited effect at low (5 and 7 m/s) and high (20 and 25 m/s) wind speeds due to fully attached and separated flows over the blade surface. However, within the 10–15 m/s range, winglets significantly enhance torque and power output. While this increased power generation is beneficial, it is essential to consider the potential impact of the associated increase in thrust force on turbine stability. 

Ensuring clean water sources is pivotal for sustainable development and the well-being of communities worldwide. This study represents a pioneering effort in water purification, exploring an innovative approach utilizing modified reduced graphene oxide (rGO) aerogels. These advanced materials promise to revolutionize environmental remediation efforts, specifically removing organic contaminants from aqueous solutions. The study investigates the exceptional adsorption properties of rGO-aerogel, enhanced with cysteamine, to understand its efficacy in addressing water pollution challenges. The characterization methods utilized encompass various analytical techniques, including FE-SEM, BET, FTIR, TGA, DSC, XPS, NMR, and elemental analysis. These analyses provide valuable insights into the material’s structural modifications and surface chemistry. The research comprehensively explores the intricacies of adsorption kinetics, equilibrium, and isothermal study to unravel the underlying mechanisms governing contaminant removal. MO and Ni2+ exhibited adsorption of 542.6 and 150.6 mg g−1, respectively, at 25 °C. Ni2+ has unveiled the highest removal at pH 5, and MO has shown high removal in a wide pH range (pH 4–7). Both contaminants have shown fast adsorption kinetic performance on an rGO-aerogel surface. This study aims to identify the synergistic effect of cysteamine and rGO in aerogel formation to remove heavy metals and organic contaminants. These findings mark a significant stride in advancing sustainable water-treatment methods and pioneering in synthesizing innovative materials with versatile applications in environmental contexts, offering a potential solution to the global water pollution crisis. 

Photocatalysis, mainly using TiO2 as a catalyst, has emerged as a promising method to address the issue of wastewater treatment. This study explores the enhanced photocatalytic activity of TiO2 through the introduction of reduced graphene oxide (rGO) and cadmium sulfide (CdS) as selective metal dopants. The incorporation of rGO and CdS into the TiO2 lattice aims to optimize its photocatalytic properties, including bandgap engineering, charge carrier separation, and surface reactivity. The unique combination of CdS and rGO with TiO2 is expected to boost degradation efficiency and reduce the reliance on expensive and potentially harmful sensitizers. This experimental investigation involves the synthesis and characterization of TiO2-based photocatalysts. The photocatalytic degradation of methyl orange (MO) and methylene blue (MB) was assessed under controlled laboratory conditions, studying the influence of metal dopants on degradation kinetics and degradation efficiency. Furthermore, the synthesized photocatalyst is characterized by advanced techniques, including BET, SEM, TEM, XRD, and XPS analyses. The degraded samples were analyzed by UV-Vis spectroscopy. Insights into the photoexcitation and charge transfer processes shed light on the role of metal dopants in enhancing photocatalytic performance. The results demonstrate the potential of a TiO2-rGO-CdS-based photocatalyst in which 100% degradation was achieved within four hours for MO and six hours for MB, confirming efficient azo dye degradation. The findings contribute to understanding the fundamental principles underlying the photocatalytic process and provide valuable guidance for designing and optimizing advanced photocatalytic systems. Ultimately, this research contributes to the development of sustainable and effective technologies for removing azo dyes from various wastewaters, promoting environmental preservation and human well-being. 

To implement sustainable water resource management, the industries that produce a huge amount of wastewater are aiming to recycle wastewater. Reverse osmosis (RO) is an advanced membrane process that can produce potable water from wastewater. However, the presence of diverse pollutants in the wastewater necessitates effective pretreatment to ensure successful RO implementation. This study evaluated the efficiency of microfiltration (MF) and ultrafiltration (UF) as two pretreatment methods prior to RO, i.e., MF-RO and UF-RO, for recycling poultry slaughterhouse wastewater (PSWW). The direct treatment of PSWW with RO (direct RO) was also considered for comparison. In this study, membrane technology serves as a post treatment for PSWW, which was conventionally treated at Sanderson Farm. The results demonstrated that all of the processes, including MF-RO, UF-RO, and direct RO treatment of PSWW, rejected 100% of total phosphorus (TP), over 91.2% of chemical oxygen demand (COD), and 87% of total solids (TSs). Total nitrogen (TN) levels were reduced to 5 mg/L for MF-RO, 4 mg/L for UF-RO, and 9 mg/L for direct RO. In addition, the pretreatment of PSWW with MF and UF increased RO flux from 46.8 L/m2 h to 51 L/m2 h, an increase of approximately 9%. The product water obtained after MF-RO, UF-RO, and direct RO meets the required potable water quality standards for recycling PSWW in the poultry industry. A cost analysis demonstrated that MF-RO was the most economical option among membrane processes, primarily due to MF operating at a lower pressure and having a high water recovery ratio. In contrast, the cost of using RO without MF and UF pretreatments was approximately 2.6 times higher because of cleaning and maintenance expenses related to fouling. This study concluded that MF-RO is a preferable option for recycling PSWW. This pretreatment method would significantly contribute to environmental sustainability by reusing well-treated PSWW for industrial poultry purposes while maintaining cost efficiency.