Treatment of paper and pulp wastewater and removal of odorous compounds by a Fenton‐like process at the pilot scale

A Fenton‐like process, involving oxidation and coagulation, was evaluated for the removal of odorous compounds and treatment of a pulp and paper wastewater. The main parameters that govern the complex reactive system [pH and Fe(III) and hydrogen peroxide concentrations] were studied. Concentrations of Fe(III) between 100 and 1000 mg L^?1 and of H₂O₂ between 0 and 2000 mg L^?1 were chosen. The main mechanism for color removal was coagulation. The maximum COD, color and aromatic compound removals were 75, 98 and 95%, respectively, under optimal operating conditions ([Fe(III)] = 400 mg L^?1; [H₂O₂] = 500–1000 mg L^?1; pH = 2.5; followed by coagulation at pH 5.0). The biodegradability of the wastewater treated increased from 0.4 to 0.7 under optimal conditions and no residual hydrogen peroxide was found after treatment. However, partially or non‐oxidized compounds present in the treated wastewater presented higher acute toxicity to Artemia salina than the untreated wastewater. Based on the optimum conditions, pilot‐scale experiments were conducted and revealed a high efficiency in relation to the mineralization of organic compounds. Terpenes [(1S)‐α‐pinene, β‐pinene, (1R)‐α‐pinene and limonene] were identified in the wastewater and were completely eliminated by the Fenton‐like treatment. Copyright © 2006 Society of Chemical Industry     

Performance analysis of radial-inflow turbine of ORC: New combined approach of preliminary design and 3D CFD study

Journal of Mechanical Science and Technology - A new methodology for the aerothermodynamic design of radial-inflow turbines used in organic Rankine cycles is presented. Initially, a fully detailed...     

Fukunaga–Koontz Convolutional Network with Applications on Character Classification

Neural Processing Letters - Several convolutional neural network architectures have been proposed for handwritten character recognition. However, most of the conventional architectures demand large...     

Storage as a tool to improve wood fuel quality

This work analysed the influence of storage in the quality of forest biomass for energy generation in the region of Lages, Brazil. Logs of Pinus taeda L. and Eucalyptus dunnii Maiden were harvested and piled during the four different seasons: spring, summer, fall and winter. The analyses were performed immediately after harvesting (without being stored), after two, four and six months of storage. The evaluated properties were: moisture content, gross and net calorific value, ash content and solubility in cold water, hot water and sodium hydroxide. The species composition, storage span, harvesting season and storage season influenced the forest biomass characteristics. In general, eucalyptus presented better results than pine, losing moisture faster, having less alteration in the chemical composition and producing greater energetic gain over storage time. For both species, the ideal storage time was four months. Furthermore, spring and summer were the best harvesting seasons. Thus, if the forest biomass is harvested at the end of winter or beginning of spring with subsequent storage during the summer, this biomass will have the best performance for energy production.     

Efficient generic face model fitting to images and videos

In this paper we present a robust and lightweight method for the automatic fitting of deformable 3D face models on facial images. Popular fitting techniques such as those based on statistical models of shape and appearance require a training stage based on a set of facial images and their corresponding facial landmarks, which have to be manually labeled. Therefore, new images in which to fit the model cannot differ too much in shape and appearance (including illumination variation, facial hair, wrinkles, etc.) from those used for training. By contrast, our approach can fit a generic face model in two steps: (1) the detection of facial features based on local image gradient analysis and (2) the backprojection of a deformable 3D face model through the optimization of its deformation parameters. The proposed approach can retain the advantages of both learning-free and learning-based approaches. Thus, we can estimate the position, orientation, shape and actions of faces, and initialize user-specific face tracking approaches, such as Online Appearance Models (OAMs), which have shown to be more robust than generic user tracking approaches. Experimental results show that our method outperforms other fitting alternatives under challenging illumination conditions and with a computational cost that allows its implementation in devices with low hardware specifications, such as smartphones and tablets. Our proposed approach lends itself nicely to many frameworks addressing semantic inference in face images and videos.     

Mass Transfer and Liquid‐Film Formation in a Spray Tower for SO2 Removal in Sodium Hydroxide Solution

Spray towers allow for controlling air pollution in which a liquid is sprayed in small droplets to produce a large interfacial area for mass transfer between a gas and a liquid phase. An experimental study of a spray tower for removing SO₂ is described. The experiments were carried out under different operating conditions by varying the gas velocity, liquid flow rate, and SO₂ concentration. SO₂ removal efficiency, volumetric mass transfer coefficient, and liquid‐film formation as a result of the collision of droplets against the tower wall are investigated. Removal efficiency and volumetric mass transfer coefficient are analyzed as a function of gas velocity, liquid flow rate, and SO₂ concentration, while liquid‐film formation is evaluated as a function of tower height. The results indicate high removal efficiency. Correlations to predict the volumetric mass transfer coefficient are also proposed.     

Influence of swirl number and fuel equivalence ratio on NO emission in an experimental LPG-fired chamber

The well-known kinetic mechanism of nitrogen oxides formation by thermal fixation of atmospheric nitrogen suggests the control of these pollutants by modifying the equivalence ratio and/or the temperature in the combustion region. In this experimental investigation a burner was specifically designed to take advantages of these chemical kinetic aspects. In particular, it was used to investigate the influence of swirl number and fuel equivalence ratio on nitric oxide concentration formed in a cylindrical combustion chamber. A set of 14 experimental runs was carried out to examine the influence of these factors, which were varied at seven and two levels (7¹ × 2¹) in the range of 0.36–1.32 and between 0.61 and 0.84, respectively. Liquefied petroleum gas (LPG) was always fired in the experiments. Temperature, O₂ and NO concentration were monitored in 42 different radial and axial positions along the chamber at four different operating conditions in terms of NO formation. The effect of increasing the swirl number and reducing the fuel equivalence ratio was to reduce approximately 31% and 33% the nitric oxide emission, respectively.     

Lipase-Catalyzed Production of Biodiesel by Hydrolysis of Waste Cooking Oil Followed by Esterification of Free Fatty Acids

Biodiesel is conventionally produced by alkaline‐catalyzed transesterification, which requires high‐purity oils. However, low‐quality oils can be used as feedstocks for the production of biodiesel by enzyme‐catalyzed reactions. The use of enzymes has several advantages, such as the absence of saponification side reactions, production of high‐purity glycerol co‐product, and low‐cost downstream processing. In this work, biodiesel was produced from lipase‐catalyzed hydrolysis of waste cooking oil (WCO) followed by esterification of the hydrolyzed WCO (HWCO). The hydrolysis of acylglycerols was carried out at 30 °C in salt‐free water (WCO/water ratio of 1:4, v/v) and the esterification of HWCO was carried out at 40 °C with ethanol in a solvent‐free medium (HWCO/ethanol molar ratio of 1:7). The hydrolysis and esterification steps were carried out using immobilized Thermomyces lanuginosus lipase (TLL/WCO ratio of 1:5.6, w/w) and immobilized Candida antarctica lipase B (10 wt%, CALB/HWCO) as biocatalysts, respectively. The hydrolysis of acylglycerols was almost complete after 12 h (ca. 94 %), and in the esterification step, the conversion was around 90 % after 6 h. The purified biodiesel had 91.8 wt% of fatty acid ethyl esters, 0.53 wt% of acylglycerols, 0.003 wt% of free glycerol, viscosity of 4.59 cP, and acid value of 10.88 mg KOH/g. Reuse hydrolysis and esterification assays showed that the immobilized enzymes could be recycled five times in 10‐h batches, under the conditions described above. TLL was greatly inactivated under the assay conditions, whereas CALB remained fully active. The results showed that WCO is a promising feedstock for use in the production of biodiesel.     

Methane oxidation biosystem in landfill fugitive emissions using conventional cover soil and compost as alternative substrate—a field study

Clean Technologies and Environmental Policy - Landfill is an important anthropogenic source of greenhouse gases (GHG). Aiming at methane mitigation through the use of a cover layer in the form of...     

USING A NEURAL NETWORK FOR SUPPORTING RADIOGRAPHIC DIAGNOSIS OF DENTAL CARIES

This study uses an artificial intelligent model, a radial basis function neural network (RBF), to support radiography diagnosis of dental caries. One hundred and sixty radiography images of proximal faces of extracted human teeth were analyzed by 25 examiners, which diagnosed the presence or absence of dental caries. The same teeth were then subjected to optical microscope analysis, which allowed the verification of their actual conditions. Such information was classified as gold standards, and was employed to training a neural network to diagnose caries by means of radiography images. In order to verify the network's ability to diagnose new cases, data were organized in two subgroups: a training subgroup and a test subgroup. Receiver operating characteristics (ROC) curves allowed the comparison between diagnosis efficacy with or without the use of a neural network, showing that the adopted artificial intelligent model significantly improved diagnosis qualities.