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AbstractThis work examines the impact of fuel injection pressure (FIP) on engine's behavior of a multi cylinder gasoline direct injection (GDI) engine using gasohol (85% gasoline+15% ethanol by mass) as fuel. The FIP was varied from 90r to 120 bar at 10 bar intervals with the fuel injection timing 320° before top dead center (BTDC). experiments were performed at variable power at a constant speed of 2500 rpm. Computational fluid dynamics (CFD) simulations were carried out for the above said conditions to understand engine's behavior. Considering the conventional FIP (i.e.90 bar), increasing the FIP showed improvement in engine's brake thermal efficiency (BTE) up to110 bar. The maximum BTE was observed as 33.5% at the engine power output of 21 kW (where as it was 27.2% with the FIP of 90 bar). CFD results confirmed the improvement in the air‐fuel mixing rate and swirl motion due to the increased FIP. The hydrocarbon (HC) and carbon monoxide (CO) emission were reduced with increased FIPs. CFD results on the HC and CO emissions indicated well agreement with the experiments. With the higher injection pressures, oxides of nitrogen (NOx) emissions showed an increase at all loads. The in cylinder pressure was observed to be higher with higher FIPs. It is concluded that increasing the FIP might improve performance and lower HC and CO emissions of a Gasohol based fuel in GDI engine. The optimized FIP of 110 bar could be recommended for the aforementioned engine's greatest performance without any modifications in the engine design while injecting the fuel at 320° BTDC. Increase in NOx emissions at higher injection pressure needs attention. The present study restricted the amount of ethanol as 15% by mass. Higher proportions of ethanol admissions require major modifications in the engine design.

Herein, a novel approach for H2 production is presented by exploiting the synergistic potential of digestate derived from household waste (D) in the co-digestion process with oily sludge (OS), rich in mono-ethylene glycol (MEG). Our approach significantly mitigates the environmental impact of OS, a hazardous byproduct of petroleum industry, while addressing the challenge of MEG inhibition in H2 production, along with biochar recovery. Remarkably, anaerobic co-digestion of OS:D (60:40) yielded a remarkable 23.1-fold increase in hydrogen potential (436.3 ± 34.8 mL) compared to 18.7 ± 0.9 mL in OS:D (100:0). This enhanced hydrogen productivity was complemented by a notable MEG biodegradation efficiency of 86.2 ± 7.8%. Furthermore, the microbial community played a crucial role in metabolizing MEG into ethanol, acetaldehyde, and acetate through the enzymatic activities of aldehyde dehydrogenase and alcohol dehydrogenase. Dominant strains such as Clostridium (3.9%), Acinetobacter (12.7%), and Bacillus (2.1%) were identified for MEG degradation, contributing significantly to high H2-productivity.

AbstractKelp forests serve as the foundation for shallow marine ecosystems in many temperate areas of the world but are under threat from various stressors, including climate change. To better manage these ecosystems now and into the future, understanding the impacts of climate change and identifying potential refuges will help to prioritize management actions. In this study, we use a long‐term dataset of observations of kelp percentage cover for two dominant canopy‐forming species off the coast of Victoria, Australia: Ecklonia radiata and Phyllospora comosa. These observations were collected across three scuba sampling programs that extend from 1998 to 2019. We then associated those observations with habitat and environmental variables including depth, seafloor structure, wave climate, currents, temperature, and population connectivity in generalized additive mixed‐effects models and used these models to develop predictive maps of kelp cover across the Victorian marine protected areas (MPAs). These models were also used to project kelp coverage into the future by replacing wave climate and temperature with future projections (2090, Representative Concentration Pathways [RCPs] 4.5 and 8.5). Once the spatial predictions were compiled, we calculated percent cover change from 1998 to 2019, stability over the same period, and future predicted change in percent cover (2019–2090) to understand the dynamics for each species across the MPAs. We also used the current percentage cover, stability, and future percentage cover to develop a ranking system for classifying the maps into very unlikely refugia, unlikely refugia, neutral, potential refugia, and likely refugia. A management framework was then developed to use those refugia ranking values to inform management actions, and we applied this framework across three case studies: one at the scale of the MPA network and two at the scale of individual MPAs, one where management decisions were the same for both species, and one where the actions were species‐specific. This study shows how species distribution models, both contemporary and with future projections, can help to identify potential refugia areas that can be used to prioritize management decisions and future‐proof restoration actions.