ABSTRACT
Studies have been initiated to investigate the potential impact of connected and automated vehicles (CAVs) on transportation infrastructure. However, most existing research only focuses on the wandering patterns of CAVs. To bridge this gap, an apple-to-apple comparison is first performed to systematically reveal the behavioural differences between the human-driven vehicle (HDV) and CAV trajectory patterns for the first time, with the data collected from the camera-based next generation simulation dataset and autonomous driving co-simulation platform, CARLA and SUMO, respectively. A gradient boosting-based ensemble learning model for pavement performance (i.e. international roughness index) prediction is then developed with the input features including three driving pattern features, namely, lateral wandering deviation, longitudinal car-following distance and driving speed, plus 20 other context variables. A total of 1707 observations is extracted from the long-term pavement performance database for model training purposes. The result indicates that the trained model can accurately predict pavement deterioration and that CAV deteriorates pavement faster than HDV by 8.1% on average. According to the sensitivity analysis, CAV deployment will create a greater impact on the younger pavements, and the rate of pavement deterioration is found to be stable under light traffic, whereas it will increase under congested traffic. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
ABSTRACT
Environmentally hazardous P(V), Mo(VI) and W(VI) generated oxyanions exist widely in the waste liquid of nonferrous hydrometallurgy. In this work, Fe-MOF material was simply prepared via solvothermal synthesis and then used as an adsorbent to remove P(V), Mo(VI) and W(VI) oxyanions from hydrometallurgical waste liquid. Several important parameters, including solution pH, oxyanion concentration, contact time, adsorbent amount, temperature and coexistent heavy metal ions, were systematically investigated. The results demonstrate that adsorption process was almost pH-independent over a broad range of pH 3.0-10.0. The adsorption efficiency was strongly associated with the chemical species of oxyanions. The higher polymerisation degree of oxyanions was more favourable for removal efficiency. Additionally, the maximum removal efficiencies for P(V), Mo(VI) and W(VI) oxyanions under optimum conditions were approximately 100%. Furthermore, the adsorption kinetics and isotherms of oxyanions on the adsorbent separately belonged to the pseudo-second-order and Langmuir isotherm models. XPS analysis revealed that inner-sphere complexation played a dominant role in the adsorption removal process. Fe-MOFs with pH-independent properties, abundant binding sites and high stability are prospective adsorbents for treating waste liquids in the hydrometallurgical industry.
Subject(s)
Water Pollutants, Chemical , Adsorption , Chromium/analysis , Hydrogen-Ion Concentration , Ions , Kinetics , Polymerization , Prospective Studies , Water Pollutants, Chemical/analysisABSTRACT
Oily ferroalloy scraps generated from machinery honing enterprises are typical hazardous municipal materials that release benzene-series volatile organic compounds (VOC), which endanger human physical and mental health. Therefore, harmless treatment and resource reuse for these hazardous materials is urgent. In this study, the VOC emission, and pyrolysis and de-oiling behaviors of oily honing scrap was first characterized to evaluate the environmental risks. Smelting separation was then proposed to economically and eco-friendly recover valuable metals from the de-oiled ferroalloy scraps. The thermodynamics of Al2O3-SiO2 and CaO-Al2O3-SiO2 systems was calculated to optimize the slag formation. Results showed that after de-oiling and smelting with CaO addition, the hazardous VOC are removed, and the valuable metals are recovered in ferroalloy state. Under optimum conditions, a crude Fe-Mo-Cu alloy with Fe, Mo and Cu recoveries of 98.5 wt%, 97.9 wt%, and 98.4 wt% were obtained. In addition, the slag containing few toxic elements and VOC can be used for silicate cement production. Pyrolysis, de-oiling behaviors and mechanism for slagging and growth of Fe-Mo-Cu alloy during smelting were discussed via various testing techniques, and leaching toxicity of the cleaned slag was also characterized in this work. This process is also applicative to recover metals from various honing ferroalloy scraps.
ABSTRACT
In the present work, a method was developed for the determination of ultra-trace levels of Cd in sediment samples by atomic fluorescence spectrometry (AFS). A flow injection on-line separation and preconcentration technique coupled with an intermittent injection vapor generation technique was employed in the study. The instrument operating parameters and chemical conditions were optimized. In a 2.0 mol x L(-1) HCl solution, Cd (II) was adsorbed on 717-strong alkaline anion exchange resin, while Cu (II) and Pb (II) passed throngh. Then Cd (II) was eluted by 0.5 mol x L(-1) HNO3. The eluting solution was determined directly by intermittent injection vapor generation atomic fluorescence spectrometry. It was showed that Cd (II) can be preconcentrated effectively and the interference can be completely eliminated by this improved method. The Cd atomic vapor generation efficiency could be greatly enhanced in the presence of Co (II) and 1, 10-phenanthroline. The linear range of the determination was 0-12 microg x L(-1) with a detection limit of 0.058 microg x L(-1). The RSD (5 microg x L(-1), n=7) was 1.09%. The method was convenient, rapid and successfully validated by using national water sediment standard reference materials.