• Energy Research

Indagation in the sphere of nanoparticle utilisation has provided commendatory upshots in discrete areas of application varying from medicinal use to environmental degradation alleviation. This study incorporates alumina nanoparticles as additives to diesel and biodiesel blends. The prime objective of the present study was the scrutinisation of the denouement of Al2O3 nanoparticle incorporation in diesel–biodiesel blends on a diesel engine’s performance and emission characteristics. Test fuel samples were prepared by blending different proportions of biodiesel and dispersing two concentrations of alumina nanoparticles (25 and 50 ppm) in the diesel. Dispersion was made without the use of a nanoparticle stabiliser to meet real-world feasibility. High-speed shearing was employed to blend the biodiesel and diesel, while nanoparticles were dispersed in the blends by ultrasonication. The blends so devised were tested using a single-cylinder diesel engine at fixed RPM and applied load for three compression ratios. Upshots of brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE) for fuel samples were measured with LabView-based software, whereas CO emissions and unburnt hydrocarbon (UBHC) emissions were computed using an external gas analyser attached to the exhaust vent of the engine. Investigation revealed that the inclusion of Al2O3 nanoparticles culminates in the amelioration of engine performance along with the alleviation of deleterious exhaust from engine. Furthermore, the incorporation of alumina nanoparticles assisted in the amelioration of dwindled performance attributed to biodiesel blending. More favourable results of nanoparticle inclusion were obtained at higher compression ratios compared to lower ones. Reckoning evinced that the Al2O3 nanoparticle is a lucrative introduction for fuels to boost the performance and dwindle the deleterious exhaust of diesel engines.

This paper details the selection of machine learning models for predicting the effectiveness of a heat pipe system in a concentric tube exchanger. Heat exchanger experiments with methanol as the working fluid were conducted. The value of the angle varied from 0° to 90°, values of temperature varied from 50 °C to 70 °C, and the flow rate varied from 40 to 120 litres per min. Multiple experiments were conducted at different combinations of the input parameters and the effectiveness was measured for each trial. Multiple machine learning algorithms were taken into consideration for prediction. Experimental data were divided into subsets and the performance of the machine learning model was analysed for each of the subsets. For the overall analysis, which included all the three parameters, the random forest algorithm returned the best results with a mean average error of 1.176 and root-mean-square-error of 1.542.

Sustainable Development Goals have become a key factor in the design in the twenty-first century. The relationship between the architectural and structural systems is becoming a matter of relevance for sustainable design. The search for minimum material consumption can be seen by drawing inspiration from the solutions found in Nature. The high efficiency of natural forms, has become a contribution to research on tree-like structures. The purpose of the research was to identify the main aspects of arboreal supporting structures shaping and optimization at the early state of design. The methodology is to optimize the geometry of dendriforms, based on optimizing the shape of the bending moment diagram and adjusting it to the shape of the final bar structure. The primary conclusion of the studies indicates that the structural and architectural optimization, implemented in an early stage of designing might significantly improve material consumption without substantial changes in architectural appearance.

Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

This research investigates a paradigm shift in heat treatment practises that is characterised by transformational changes. Despite their widespread use, traditional technologies are often linked to issues such as energy inefficiency, pollution, and material waste. In order to tackle these aforementioned issues, this study explores novel methodologies like high-pressure gas quenching, laser-assisted heat treatment, additive manufacturing for customised microstructures, and ultra-fast induction heating. These methodologies provide not only enhanced material functionality but also environmentally friendly outcomes by means of energy conservation and waste minimization. This study highlights the crucial significance of these breakthroughs in defining a future where improved material qualities align with environmentally responsible practises. It does this by examining their advantages, environmental consequences, and problems in implementation. The use of heat treatment techniques has been shown to significantly boost the performance of materials. This academic study aims to explore the sustainability aspects of heat treatment methods, particularly in comparison to conventional approaches. The focus will be on evaluating the energy efficiency and reduction of material waste associated with a specific heat treatment technique known as high-pressure gas quenching.

The practical effects of incorporating artificial intelligence (AI) into Industry 5.0 smart city services are made evident by this empirical research. The use of AI-powered smart traffic management yields a noteworthy 32.94% rise in traffic volume, signifying a noteworthy progression towards improved urban mobility. AI waste management optimization results in a 5.71% increase in collection efficiency, highlighting the importance of operational effectiveness and resource conservation. The control of energy use shows an 8.57% decrease, confirming AI's importance in sustainable energy practices. AI-enhanced public safety offers dependable event prediction, indicating safer cityscapes. These results highlight AI's revolutionary potential and establish smart cities as safe, secure, and sustainable urban environments.

This bibliometric analysis delves into the intricate realm of sustainability within the life cycle assessments (LCAs) of electric vehicles (EVs). Our study navigates the vast body of literature to uncover the evolving landscape, emerging trends, and critical research areas concerning the environmental impact of EVs throughout their entire life cycle. By placing a strong emphasis on sustainability, this analysis sheds light on how EVs can significantly contribute to eco-friendly transportation solutions. A systematic search was conducted using the Web of Science database, covering publications from 2001 to 2023. The search strategy used a combination of keywords related to LCA and EVs. The study found a total of 161 publications that met the inclusion criteria. The analysis revealed that the number of publications on LCA of EVs has been increasing steadily over the years, with a sharp rise in the last decade. The study identified the main research themes in the field, including LCA methodology, environmental impacts, energy use, and policy analysis. The analysis also highlighted the research gaps, such as the lack of studies on social impacts and the need for more comprehensive and comparative assessments. The findings of this study provide insights into the current state of research on LCA of EVs and can guide future research in this field.

The global energy situation requires the efficient use of resources and the development of new materials and processes for meeting current energy demand. Traditional materials have been explored to large extent for use in energy saving and storage devices. Graphene, being a path-breaking discovery of the present era, has become one of the most-researched materials due to its fascinating properties, such as high tensile strength, half-integer quantum Hall effect and excellent electrical/thermal conductivity. This paper presents an in-depth review on the exploration of deploying diverse derivatives and morphologies of graphene in various energy-saving and environmentally friendly applications. Use of graphene in lubricants has resulted in improvements to anti-wear characteristics and reduced frictional losses. This comprehensive survey facilitates the researchers in selecting the appropriate graphene derivative(s) and their compatibility with various materials to fabricate high-performance composites for usage in solar cells, fuel cells, supercapacitor applications, rechargeable batteries and automotive sectors.