Power tower concentrated solar power systems integrated with thermal energy storage systems offer promising solutions for reliable and cost-effective energy production. This research applies Artificial Intelligence techniques to enhance the operational efficiency, reliability, and economic performance of a power tower system. A comprehensive real-time data-driven optimization model was developed incorporating an AI-based machine learning technique - Random Forest Regressor combined with grid search cross-validation to accurately predict output power. Furthermore, an interdependent dual-parameter optimization was conducted to optimize critical system parameters, including mirror angles and heat transfer fluid flow rates. The proposed model facilitates energy forecasting, performance optimization, and operational decision-making, as well as economic, weather impact, and sensitivity analysis. Economic feasibility was evaluated using Net Present Value and Levelized Cost of Energy calculations, while sensitivity analysis highlighted the system's resilience to variations in fuel prices, discount rates, and technology cost. The results indicate a highly accurate prediction, with a Mean Squared Error of 0.0676 and an R2 score of 0.9999, featuring the model's robustness. Additionally, a weather impact and correlation analysis was conducted to analyze the system's operational capabilities under varying weather conditions. Moreover an environmental impact assessment illustrated the sustainability advantages of integrating thermal energy storage (TES) with the concentrated solar power (CSP) system, particularly in improving energy dispatch and reducing emissions. Overall, integrating the TES significantly enhanced dispatch capabilities, particularly under varying weather scenarios.

Poultry feathers represent a substantial keratin-rich waste stream with potential for valorisation into bio-based materials. This study evaluates the environmental performance of three novel feather treatment processes ((Steam Explosion (SE), Microbial Fermentation (MF) and Mechanical Grinding (MG)) intended for producing sustainable bioplastic feedstock, using Life Cycle Assessment. A gate-to-gate analysis compared the processes per 1000 kg feather input across multiple impact categories, including GWP, Acidification Potential (AP), Eutrophication Potential (EP), Respiratory Inorganics, and Water Scarcity. The scope was expanded to cradle-to-gate to include upstream farming impacts and compare results with conventional plastics. Gate-to-gate results showed MG had the lowest impacts for GWP (475 kg CO<inf>2</inf> eq.), AP (0.65 kg SO<inf>2</inf> eq.), EP (0.08 kg Phosphate eq.), and Respiratory Inorganics, driven by lower energy use. However, MG showed the highest Water Scarcity (7787 m3 world eq.) due to feather washing. MF exhibited the highest GWP (2035 kg CO<inf>2</inf> eq.) and Respiratory Inorganics, while SE showed the highest AP (1.25 kg SO<inf>2</inf> eq.). Cradle-to-gate, MG and SE offered significant GWP advantages over conventional plastics like PP, LDPE, and HDPE (up to 59 % and 27 % lower GWP, respectively). Similarly, MG and SE demonstrated lower AP (up to 56 % and 48 % lower, respectively) compared to these plastics. However, feather routes showed higher EP when upstream farming impacts were included. In conclusion, MG is the most favourable process regarding climate impact, though its water use is significant. SE provides a balanced alternative. Valorising feather waste offers environmental benefits over conventional plastics, but optimising energy efficiency and water consumption is crucial for enhancing the sustainability of these technologies

Lightweight materials with high strength are desirable for advanced applications in transportation, sports equipment, construction, automotive, and aerospace. Aspen is fast growing, has low flammability, and is renewable and readily available. In this study, we present a continuous, high-yielding, efficient, scalable, and sustainable approach for the fabrication of strong materials from aspen by synergistic selective chemical modification and continuous hot pressing. FTIR analysis revealed changes in the chemical composition of the wood polymers, including the introduction of anionic groups, while SEM images showed morphological and structural transformations such as smoother surfaces and a more compact wood structure. The proposed strategy achieved up to 258 MPa (530% increase) in tensile strength by combining enhanced ion-bonding and hydrogen-bonding with the alignment of cellulose nanofibrils and the solidification of softened, depolymerized lignin through cross-linking reactions. This work demonstrates the continuous large-scale production of lightweight, strong structural materials under energy-efficient and mild modification conditions, suitable for the green fabrication of next-generation advanced materials from wood. 

Biomass-derived porous activated carbons hold great promise for applications in supercapacitors due to their high surface area, low cost, self-heteroatom doping, and stable physicochemical features. However, their electrochemical performance, particularly in aqueous environments, is largely influenced by the nature of the pore structure, which significantly relies on the activation process and the nature of biomass precursor. Here, we present an effective and environmentally friendly strategy for synthesizing nitrogen-doped hierarchical porous carbon materials (NRCs) for supercapacitor applications. In this method, algae-derived bio-oil distillation residue is used as a carbon source in the presence of nano-CaCO3 and K2CO3 as a templating agent and activator, respectively. The effect of carbonization temperature and the stoichiometric ratio between CaCO3 and K2CO3 have been widely investigated. The NRC1:2-800 sample, with a 1:2 ratio of CaCO3 to K2CO3 at a carbonization temperature of 800 °C, shows the highest surface area (2376.95 m2 g−1) among other samples, reflecting the highest specific capacitance either (352.5 F g−1). The material further shows a maximum energy density of 15.9 Wh kg−1 at 400 W kg−1 for the symmetric device measured in 1 M Na2SO4. Finally, the effect of pore size on the electrochemical performance of the prepared activated carbons was investigated via theoretical calculations, indicating that a pore size of 2–5 nm is more favorable for ion transport and thus better improves the electrochemical performance of activated carbons. This study thus lays a solid foundation for creating highly efficient and porous activated carbon for energy storage devices.

Nearly 40 % current global annual energy-related CO2 emissions come from the fossil fuel-dominated power sector. Accurately accounting for carbon emissions in power systems from the consumption-based perspective is crucial for achieving the low-carbon power transition. Consumption-based carbon accounting has emerged as a major research focus, which aids in the implementation of targeted measures such as low-carbon demand response and dispatch. Choosing an appropriate method to account carbon emission needs thorough consideration of characteristics of various methods. There still lacks a systematic review that concludes the essence and application status of these methods, as well as comparing their advantages and disadvantages. To address this gap, a consumption-based carbon accounting framework for power systems is proposed. This framework groups four typical methods into two perspectives: Attributional methods and consequential methods. The principles, calculation approaches, and research application status of these methods are comprehensively summarized in a transparent, integrated and comparative manner, which makes progress in two critical limitations: (i) temporal and spatial granularity, and (ii) consideration of the actual topology and operational constraints of the power grid. As improvements in the transparency and quality of electricity data and expansion of application scenarios, the flexibility and applicability of the framework will continue to improve to achieve the unity of efficiency and fairness. The proposed framework can serve as a valuable guide to conducting research and exploration on low-carbon energy management, policy and regulatory decisions and to inform the development of effective strategies for the low-carbon transition of power systems. 

Power grids play a crucial role in connecting electricity suppliers and consumers. They facilitate efficient power transmission and energy management, significantly contributing to the transition toward low-carbon practices across both upstream and downstream sectors. Effectively managing carbon reduction in the power industry is essential for enhancing carbon reduction efficiency and achieving dual-carbon goals. Recent studies have focused on the outcomes of carbon reduction efforts rather than the management process. However, when power grids prioritize the process of carbon reduction in their management, they are more likely to achieve better results. To address this gap, we propose an evaluation model for managing carbon reduction activities in power grids, comprising the carbon management efficiency (CME) module based on the maturity model and the carbon reduction efficiency (CRE) module based on the entropy method. The CME module provides a scorecard corresponding to a detailed and continuous evaluation model for carbon management processes to calculate its performance. Simultaneously, the CRE module relates carbon reduction results to the development direction of the government and power grid, allowing for effective adjustments and updates based on actual situations. The evaluation model was applied to provincial power grids within the China Central Power Grid. The results reveal that despite some fluctuations in carbon reduction performance, provincial power grids within the China Central Power Grid have made continuous progress in carbon reduction efforts. According to the synergy model, there is evidence suggesting that power grids are steadily improving their carbon reduction performance, and a more organized approach would lead to a greater degree of synergy. The evaluation model applies to power grids, and its framework can be extended to other industries, providing a theoretical reference for evaluating their carbon reduction efforts. 

The Adrar region (Algeria) has a total of 397,800 date palm trees (Phoenix dactylifera L.). Due to annual palm cleaning, large quantities of lignocellulosic biomass are produced. Depending on the variety, an average of 65 kg of biowaste is obtained per palm tree. Since the value of this biowaste is underrated, most of the palms are burned outdoors, causing air and visual pollution. This work explores the gasification potential of lignocellulosic waste from date palms (Phoenix dactylifera L. Takarbouche variety) into useful energy. The technology investigated is air updraft fixed-bed gasification, thanks to an originally designed and built reactor, with the capability to process 1 kg of feedstock. Four types of palm waste—namely, palms, petioles, bunch, and bunch peduncles—are first characterized (bulk density, proximate analysis, fixed carbon, elemental composition, and calorific value) and then used as feedstock for two gasification tests each. The syngas produced for the four date palm wastes is combustible, with an outlet temperature between 200 and 400 °C. The operating temperature inside the gasifier varies according to the feature of the biomass cuts (from 174 °C for the peduncles to 557 °C for palms). The experimental setup is also equipped with a cyclone, allowing for the recovery of some of the tar produced during the tests. Finally, the results show that the residence time has a positive effect on the conversion rate of date palm waste, which can significantly increase it to values of around 95%. 

Since 2000, global plastic waste production and consumption have doubled, escalating from 250 to 500 million tonnes. Merely 9 % of plastic waste undergoes global recycling, leaving the majority either in landfills or poorly managed. This research introduces a new catalyst, GMOF, created by growing Metal-Organic Framework (MOFs) rods on the flaked, carpet-like structure of Graphene Oxide (GO) nanosheets. The aim is to enhance the quality of pyrolysis products derived from high-density polyethylene (HDPE) and low-density polyethylene (LDPE) waste using this GMOF catalyst. HDPE and LDPE, sourced from post-consumer plastic packaging, underwent specific treatment involving cleaning, drying, and shredding. Morphological and property evaluations of GO nanosheets before and after MOF decoration employed techniques including Field-Emission Scanning Electron Microscopy (FE-SEM), Energy-Dispersive X-ray Spectroscopy (EDS), and Fourier Transform Infrared Spectroscopy (FTIR). Flash pyrolysis at 500 °C for 1 min using a sample-to-catalyst ratio of 4:1 in a Quartz Wool Matrix (QWM) reactor was conducted via a Thermogravimetric Analyzer (TGA) and Frontier LAB pyrolizers. Thermal stability and characteristics of feedstocks and catalysts were assessed using TGA. Gas Chromatography-Mass Spectrometry (GC–MS) analyzed and quantified pyrolysis product compounds, while a Micro GC Fusion system determined non-condensable pyrolyzate permanent gas distribution. Results showcased that the GMOF catalyst’s unique morphology efficiently captured smaller radicals on its surface, providing increased surface area for effective radical–radical interactions during pyrolysis. In HDPE pyrolysis, the GMOF catalyst notably decreased selectivity of C21-C40 and C40 + wax fractions to 49.07 % and 7.73 %, respectively, while boosting C1-C20 olefin production by 2.54 %. Conversely, LDPE pyrolysis with the GMOF catalyst notably amplified the CO2 peak intensity by 3.17 %, signifying a gasification phase. Primary gases produced were C3 aliphatic hydrocarbons, propane, and propylene, yielding 79.46 % collectively.