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Around 600 million m3 of wastewater and 6 million tonnes of leather solid wastes, are generated annually worldwide, with a chromium content of 1 to 4 %. In this context, the thermochemical valorisation of tannery sludge (TS) by hydrothermal liquefaction (HTL) process represents a promising route both for the reduction of the material to dispose in landfill and for the production of an energy carrier. HTL process produces bio-crude from wet biomasses in a hot pressurised water environment, thus avoiding the energy-intensive drying step commonly associated to other thermochemical processes. Moreover, HTL, not aiming at the complete oxidation of the organic component, potentially avoids the oxidation of Cr in its harmful hexavalent form. In this study, a TS was investigated as solid waste for HTL carried out in a 500 mL batch reactor to obtain a bio-crude for energy purposes. Results show that, under the best operating HTL condition (350 °C and 10 min), the H/C ratio of bio-crude was similar to that of starting biomass while the O/C ratio was about three times smaller than in the parent TS. The bio-crude yield was about 25–30 % on dry and ash-free basis, with an associated energy recovery of about 40–45 %. NMR analysis of bio-crude revealed that it is a complex mixture mainly constituted by aliphatic units. Moreover, ICP-MS, atomic absorption and UV–visible spectroscopy analyses proved that inorganic elements are mainly retrieved in the solid residue, and that Cr was present in its starting trivalent form.

Aqueous zinc batteries are emerging technologies for energy storage, owing to their high safety, high energy, and low cost. Among them, the development of low-cost and long-cycling cathode materials is of crucial importance. Currently, Zn-ion cathodes are heavily centered on metal-based inorganic materials and carbon-based organic materials; however, the metal–organic compounds remain largely overlooked. Herein, we report the electrochemical performance of metal phthalocyanines, a large group of underexplored compounds, as alternative cathode materials for aqueous zinc batteries. We discover that the selection of transition metal plays a vital role in affecting the electrochemical properties. Among them, iron phthalocyanine exhibits the most promising performance, with a reasonable capacity (~60 mAh g−1), a feasible voltage (~1.1 V), and the longest cycling (550 cycles). The optimal performance partly results from the utilization of zinc chloride “water-in-salt” electrolyte, which effectively mitigates material dissolution and enhances battery performance. Consequently, iron phthalocyanine holds promise as an inexpensive and cycle-stable cathode for aqueous zinc batteries.

Energy forecasting is an essential task in power system operations. Operators usually issue forecasts and leverage them to schedule energy dispatch ahead of time. However, forecast models are typically developed in a way that overlooks the operational value of the forecasts. To bridge the gap, we design a value-oriented point forecasting approach for sequential energy dispatch problems with renewable energy sources. At the training phase, we align the loss function with the overall operation cost function, thereby achieving reduced operation costs. The forecast model parameter estimation is formulated as a bilevel program. Under mild assumptions, we convert the upper-level objective into an equivalent form using the dual solutions obtained from the lower-level operation problems. Additionally, a novel iterative solution strategy is proposed for the newly formulated bilevel program. Under such an iterative scheme, we show that the upper-level objective is locally linear regarding the forecast model output, and can act as the loss function. Numerical experiments demonstrate that, compared to commonly used statistical quality-oriented point forecasting methods, forecasts obtained by the proposed approach result in lower operation costs. Meanwhile, the proposed approach is more computationally efficient than traditional two-stage stochastic programs. Accepted in IEEE Transactions on Smart Grid

The automotive industry is under increasing pressure to develop cleaner and more efficient technologies in response to stringent emission regulations. Hydrogen-powered internal combustion engines represent a promising alternative, offering the potential to reduce carbon-based emissions while improving efficiency. However, the accurate estimation of in-cylinder pressure is crucial for optimizing the performance and emissions of these engines. While traditional simulation tools such as GT-POWER are widely utilized for these purposes, recent advancements in artificial intelligence provide new opportunities for achieving faster and more accurate predictions. This study presents a comparative evaluation of the predictive capabilities of GT-POWER and an artificial neural network model in estimating in-cylinder pressure, with a particular focus on improvements in computational efficiency. Additionally, the artificial neural network is employed to predict the equivalent flame radius, thereby obviating the need for repeated tests using dedicated high-speed cameras in optical access research engines, due to the resource-intensive nature of data acquisition and post-processing. Experiments were conducted on a single-cylinder research engine operating at low-speed and low-load conditions, across three distinct relative air–fuel ratio values with a range of ignition timing settings applied for each air excess coefficient. The findings demonstrate that the artificial neural network model surpasses GT-POWER in predicting in-cylinder pressure with higher accuracy, achieving an RMSE consistently below 0.44% across various conditions. In comparison, GT-POWER exhibits an RMSE ranging from 0.92% to 1.57%. Additionally, the neural network effectively estimates the equivalent flame radius, maintaining an RMSE of less than 3%, ranging from 2.21% to 2.90%. This underscores the potential of artificial neural network-based approaches to not only significantly reduce computational time but also enhance predictive precision. Furthermore, this methodology could subsequently be applied to conventional road engines exhibiting characteristics and performance similar to those of a specific optical engine used as the basis for the machine learning analysis, offering a practical advantage in real-time diagnostics.

The northern star coral, Astrangia poculata , is a temperate, facultatively symbiotic, scleractinian coral spanning the coastal western Atlantic. This calcifying species is mixotrophic with a broad geographical range, and therefore has high utility in addressing questions related to community ecology, symbiosis, population genetics, biomineralization and resilience to environmental perturbations. Here, we review the current A. poculata peer-reviewed literature, which is primarily found in six focal areas: geographic range, habitat and ecology, symbiosis, life history, microbiome and genomics and transcriptomics. A cross-cutting theme of these studies emerges as the value of an experimental system that is facultatively symbiotic. Yet, the historic overgeneralization of symbiotic versus ‘aposymbiotic’ A. poculata has constrained the interpretation of the basic biology and generalizability of conclusions. Emergent from our review, and timely with respect to climate change, is the value that A. poculata brings as an experimental system with the potential to test questions on range adaptability and environmental resilience. We identify future avenues of research for A. poculata studies that include integration of population genetics with organismal–molecular–cellular biology across the geographical range, while leveraging the power of the facultative symbiosis context.

The construction industry consumes natural resources rapidly due to the increased population which requires the development of modern buildings. Therefore, several researchers pay attention to promoting sustainable construction. Among different types of waste, ceramic waste (CW) gained attention in concrete production which reduced the waste dumps from the ceramic industry and improved concrete sustainability. Although several researchers recommend the suitability of CW in concrete production. However, a detailed review is required which summarizes all the relevant information and provides compressive information on its impact on concrete performance. Recently, different researchers reviewed the suitability of CW in concrete. However, most researchers focus on strength properties while limited researchers focus on the durability and microstructure properties of CM concrete. Therefore, this review summarized the concrete durability and microstructure aspects with the substitution of CW. The durability performance of concrete was evaluated through percentages of voids, chloride penetration, water absorption, sulfuric acid resistance, shrinkage, freeze and thaw effect, corrosion resistance, and sulfate resistance. Furthermore, microstructure was reviewed through x ray diffraction, thermal stability, pozzolanic activity and scanning electronic microstructure. Also, the review evaluates the environmental and cost-benefits analysis of CW concrete through embodied energy (EE), carbon emissions (ECO2e), and costs. The findings indicate that CW can effectively replace 10%–15% of conventional materials in concrete, offering both environmental and economic advantages.

Oyster reef loss represents one of the most dramatic declines of a foundation species worldwide. Oysters provide valuable ecosystem services (ES), including habitat provisioning, water filtration, and shoreline protection. Since the 1990s, a global community of science and practice has organized around oyster restoration with the goal of restoring these valuable services. We highlight ES-based approaches throughout the restoration process, consider applications of emerging technologies, and review knowledge gaps about the life histories and ES provisioning of underrepresented species. Climate change will increasingly affect oyster populations, and we assess how restoration practices can adapt to these changes. Considering ES throughout the restoration process supports adaptive management. For a rapidly growing restoration practice, we highlight the importance of early community engagement, long-term monitoring, and adapting actions to local conditions to achieve desired outcomes.

This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system's stability. The paper considers a space of control actions that involve both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black box counterparts. One important observation is that strategically~\emph{removing} transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts.