Experimental Modeling of NOx and PM Generation from Combustion of Various Biodiesel Blends for Urban Transport Buses

Biodiesel has diverse sources of feedstock and the amount and composition of its emissions vary significantly depending on combustion conditions. Results of laboratory and field tests reveal that nitrogen oxides (NOx) and particulate matter (PM) emissions from biodiesel are influenced more by combustion conditions than emissions from regular diesel. Therefore, NOx and PM emissions documented through experiments and modeling studies are the primary focus of this investigation. In addition, a comprehensive analysis of the feedstock-related combustion characteristics and pollutants are investigated. Research findings verify that the oxygen contents, the degree of unsaturation, and the size of the fatty acids in biodiesel are the most important factors that determine the amounts and compositions of NOx and PM emissions

Topic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling

Topic modeling algorithms are statistical methods that aim to discover the topics running through the text documents. Using topic models in machine learning and text mining is popular due to its applicability in inferring the latent topic structure of a corpus. In this paper, we represent an enriching document approach, using state-of-the-art topic models and data fusion methods, to enrich documents of a collection with the aim of improving the quality of text clustering and cluster labeling. We propose a bi-vector space model in which every document of the corpus is represented by two vectors: one is generated based on the fusion-based topic modeling approach, and one simply is the traditional vector model. Our experiments on various datasets show that using a combination of topic modeling and fusion methods to create documents’ vectors can significantly improve the quality of the results in clustering the documents

A parent-school initiative to assess and predict air quality around a heavily trafficked school

Many primary schools in the UK are situated in close proximity to heavily-trafficked roads, yet long-term air pollution monitoring around such schools to establish factors affecting the variability of exposure is limited. We co-designed a study to monitor particulate matter in different size fractions (PM , PM , PM ), gaseous pollutants (NO , O and CO) and meteorological parameters (ambient temperature, relative humidity) over a period of one year. The period included phases of national COVID-19 lockdown and its subsequent easing and removal. Statistical analysis was used to assess the diurnal patterns, pollution hotspots and underlying factors driving changes. A pollution episode was observed early in January 2021, owing to new year celebration fireworks, when the daily average PM was around three-times the World Health Organisation limit. PM and NO exceeded the threshold limits on 15 and 10 days, respectively, as the lockdown eased and the school reopened, despite the predominant wind direction often being away from the school towards the roads. The peak concentration levels for all pollutants occurred during morning drop-off hours; however, some weekends showed higher or comparable concentrations to those during weekdays. The strong disproportional Pearson correlation between CO and temperature demonstrated the possible contribution of local sources through biomass burning. The impact of lifting restrictions after removing the weather impact showed that the average pollution levels were low in the beginning and increased by up to 22.7 % and 4.2 % for PM and NO , respectively, with complete easing of lockdown. The Prophet algorithm was implemented to develop a prediction model using an NO dataset that performed moderately (R , 0.48) for a new monthly dataset. This study was able to build a local air pollution database at a school gate, which enabled an understanding of the air pollution variability across the year and allowed evidence-based mitigation strategies to be devised

Techno-Economic Assessment of the AHP Based Selected Method for Separating Formic Acid from an Aqueous Effluent

Formic acid (FA) is used across the world for a wide variety of applications spanning from chemical production to textile and pharmaceutical industries. FA can be synthesized efficiently from the lignocellulosic biomass constituent carbohydrates by acid hydrolysis in a dilute aqueous reaction media. Since FA forms an azeotrope with water, its purification, water recycle and reuse are vital to establishing a cost-competitive process. In this study, the analytic hierarchy process (AHP) was implemented to determine the desired separation method for isolating FA from a 3 wt.% aqueous solution by considering the advantages and disadvantages of each process. Four parameters named as scalable, quality of the final product, repeatable, and energy consumption were defined as criteria to perform AHP analysis. Furthermore, six alternative approaches namely (i) azeotropic distillation, (ii) extractive distillation with a liquid solvent and (iii) solid salt, (iv) the combination of liquid solvent and solid salt, (v) pressure-swing distillation, and (vi) liquid-liquid extraction (LLE) were examined to decide the most preferred separation method with respect to the goal, which is the desired separation method. The AHP results indicated that the alternative approach, the LLE and the scalable criteria have the highest preference with 39.4% and 54% priority, respectively. The proposed process based on the alternative approach could extract 99% of FA by using diethyl ether. Moreover, an estimated minimum selling price (MSP) of 2.48 $/kg FA with 97.4% purity was achieved by using techno-economic assessment for a typical plant with 1715 ton/day capacity

Characterisation of flame-generated soot and soot-in-oil using electron tomography volume reconstructions and comparison with traditional 2D-TEM measurements

This work characterises soot nanoparticles by electron tomography using Weighted Back Projection algorithm and appraises the uncertainties in two-dimensional calculations by comparison with 3D parameters for flame-generated soot and diesel soot-in-oil. Bright field TEM was used to capture 2D images of soot. Large uncertainties exist in 2D-measured morphological parameters. The flame-generated particle showed an extensive 3D structure while the soot-in-oil was notably two-dimensional. Morphological parameters of flame-generated soot and diesel soot-in-oil were different; primary particles, volume, and surface area varied significantly over the range of viewing angle, with differences as large as 60%. 2D flame-generated soot volume underestimated 3D measurements by 38%; soot-in-oil 2D and 3D-derived volumes were within 4%. 2D calculations of fractal dimension generally underestimate the 3D value

A review of electrostatic monitoring technology: The state of the art and future research directions

Electrostatic monitoring technology is a useful tool for monitoring and detecting component faults and degradation, which is necessary for system health management. It encompasses three key research areas: sensor technology; signal detection, processing and feature extraction; and verification experimentation. It has received considerable recent attention for condition monitoring due to its ability to provide warning information and non-obstructive measurements on-line. A number of papers in recent years have covered specific aspects of the technology, including sensor design optimization, sensor characteristic analysis, signal de-noising and practical applications of the technology. This paper provides a review of the recent research and of the development of electrostatic monitoring technology, with a primary emphasis on its application for the aero-engine gas path. The paper also presents a summary of some of the current applications of electrostatic monitoring technology in other industries, before concluding with a brief discussion of the current research situation and possible future challenges and research gaps in this field. The aim of this paper is to promote further research into this promising technology by increasing awareness of both the potential benefits of the technology and the current research gaps

How low-cost air pollution sensors could make homes smarter?

The majority of people spend most of their time indoors, where they are exposed to indoor air pollutants. Indoor air pollution is ranked among the top ten largest global burden of a disease risk factor as well as the top five environmental public health risks, which could result in mortality and morbidity worldwide. The spent time in indoor environments has been recently elevated due to coronavirus disease 2019 (COVID-19) outbreak when the public are advised to stay in their place for longer hours per day to protect lives. This opens an opportunity to low-cost air pollution sensors in the real-time Spatio-temporal mapping of IAQ and monitors their concentration/exposure levels indoors. However, the optimum selection of low-cost sensors (LCSs) for certain indoor application is challenging due to diversity in the air pollution sensing device technologies. Making affordable sensing units composed of individual sensors capable of measuring indoor environmental parameters and pollutant concentration for indoor applications requires a diverse scientific and engineering knowledge, which is not yet established. The study aims to gather all these methodologies and technologies in one place, where it allows transforming typical homes into smart homes by specifically focusing on IAQ. This approach addresses the following questions: 1) which and what sensors are suitable for indoor networked application by considering their specifications and limitation, 2) where to deploy sensors to better capture Spatio-temporal mapping of indoor air pollutants, while the operation is optimum, 3) how to treat the collected data from the sensor network and make them ready for the subsequent analysis and 4) how to feed data to prediction models, and which models are best suited for indoors