Remote Sensing Detection of atmospheric pollutants using Lidar, Sodar and correlation with air quality data in an industrial area

Optical remote sensing techniques have obvious advantages for monitoring gas and aerosol emissions, since they enable the operation over large distances, far from hostile environments, and fast processing of the measured signal. In this study two remote sensing devices, namely a Lidar (Light Detection and Ranging) for monitoring the vertical profile of backscattered light intensity, and a Sodar (Acoustic Radar, Sound Detection and Ranging) for monitoring the vertical profile of the wind vector were operated during specific periods. The acquired data were processed and compared with data of air quality obtained from ground level monitoring stations, in order to verify the possibility of using the remote sensing techniques to monitor industrial emissions. The campaigns were carried out in the area of the Environmental Research Center (Cepema) of the University of São Paulo, in the city of Cubatão, Brazil, a large industrial site, where numerous different industries are located, including an oil refinery, a steel plant, as well as fertilizer, cement and chemical/petrochemical plants. The local environmental problems caused by the industrial activities are aggravated by the climate and topography of the site, unfavorable to pollutant dispersion. Results of a campaign are presented for a 24- hour period, showing data of a Lidar, an air quality monitoring station and a Sodar. © 2011 SPIE.

Los compromisos adquiridos en acuerdos de libre comercio bilaterales y multilaterales y los espacios para una política industrial en México y Centroamérica

Incluye Bibliografía

Avaliação do potencial de compostos naturais (argila, turfa e carvão) na remoção do Chumbo (Pb) e da toxicidade de um efluente industrial

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A gestão de estoques em sistemas produtivos Engineering-To-Order e Make-To-Stock: estudo de casos em empresas do setor gráfico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Híbridos de tomate industrial cultivados em ambiente protegido e campo, visando produção de frutos para mesa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Process for genetically engineering acidophilic bacteria and constructing a transformation vector

The present invention describes a method for transforming chemolithotrophic acidophilic bacteria using electroporation technology. The proposed method allows transforming a bacterial line using a transformation vector, the pAF vector, which contains an origin of vegetative replication that allows the vector to replicate inside the bacteria without altering the natural physiological functions of the latter. Also disclosed is the use of the bacteria modified according to the invention in bioleaching processes of sulphated copper, gold, uranium, nickel, zinc and cobalt ore, inter alia.

O design industrial e as engenharias: uma possibilidade de integração por intermédio da engenharia reversa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Numerical modeling of flow through an industrial burner orifice

This paper presents numerical modeling of a turbulent natural gas flow through a non-premixed industrial burner of a slab reheating furnace. The furnace is equipped with diffusion side swirl burners capable of utilizing natural gas or coke oven gas alternatively through the same nozzles. The study is focused on one of the burners of the preheating zone. Computational Fluid Dynamics simulation has been used to predict the burner orifice turbulent flow. Flow rate and pressure at burner upstream were validated by experimental measurements. The outcomes of the numerical modeling are analyzed for the different turbulence models in terms of pressure drop, velocity profiles, and orifice discharge coefficient. The standard, RNG, and Realizable k-epsilon models and Reynolds Stress Model (RSM) have been used. The main purpose of the numerical investigation is to determine the turbulence model that more consistently reproduces the experimental results of the flow through an industrial non-premixed burner orifice. The comparisons between simulations indicate that all the models tested satisfactorily and represent the experimental conditions. However, the Realizable k-epsilon model seems to be the most appropriate turbulence model, since it provides results that are quite similar to the RSM and RNG k-epsilon models, requiring only slightly more computational power than the standard k-epsilon model. (C) 2014 Elsevier Ltd. All rights reserved.

Structural properties of beds packed with agro-industrial solid by-products applicable for solid-state fermentation: Experimental data and effects on process performance

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Rhamnolipids know-how: Looking for strategies for its industrial dissemination

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Challenges of Students’ Industrial Work Experience Scheme (SIWES) in Library and Information Science in the ICT Environment

The ICT revolution has permeated every profession and all areas of human endeavour. Professions such as law, medicine , engineering, and library and information science are adjusting to the ICT environment through re-tooling, retraining, and curriculum revision. The purpose of this study is to explore the impact of ICT on the student industrial work experience scheme (SIWES) of library and information science students. It traces the historical development of the scheme, the ICT development trends in LIS, and the challenges this development brings to SIWES. Strategies to absorb this shock created by ICT are offered.

Metabolic engineering of beta-oxidation in Penicillium chrysogenum for improved semi-synthetic cephalosporin biosynthesis

Industrial production of semi-synthetic cephalosporins by Penicillium chrysogenum requires supplementation of the growth media with the side-chain precursor adipic acid. In glucose-limited chemostat cultures of P. chrysogenum, up to 88% of the consumed adipic acid was not recovered in cephalosporinrelated products, but used as an additional carbon and energy source for growth. This low efficiency of side-chain precursor incorporation provides an economic incentive for studying and engineering the metabolism of adipic acid in P. cluysogenum. Chemostat-based transcriptome analysis in the presence and absence of adipic acid confirmed that adipic acid metabolism in this fungus occurs via beta-oxidation. A set of 52 adipate-responsive genes included six putative genes for acyl-CoA oxidases and dehydrogenases, enzymes responsible for the first step of beta-oxidation. Subcellular localization of the differentially expressed acyl-CoA oxidases and dehydrogenases revealed that the oxidases were exclusively targeted to peroxisomes, while the dehydrogenases were found either in peroxisomes or in mitochondria. Deletion of the genes encoding the peroxisomal acyl-CoA oxidase Pc20g01800 and the mitochondrial acyl-CoA dehydrogenase Pc20g07920 resulted in a 1.6- and 3.7-fold increase in the production of the semi-synthetic cephalosporin intermediate adipoyl-6-APA, respectively. The deletion strains also showed reduced adipate consumption compared to the reference strain, indicating that engineering of the first step of beta-oxidation successfully redirected a larger fraction of adipic acid towards cephalosporin biosynthesis. (C) 2012 Elsevier Inc. All rights reserved.

Bootstrap confidence intervals for industrial recurrent event data

Industrial recurrent event data where an event of interest can be observed more than once in a single sample unit are presented in several areas, such as engineering, manufacturing and industrial reliability. Such type of data provide information about the number of events, time to their occurrence and also their costs. Nelson (1995) presents a methodology to obtain asymptotic confidence intervals for the cost and the number of cumulative recurrent events. Although this is a standard procedure, it can not perform well in some situations, in particular when the sample size available is small. In this context, computer-intensive methods such as bootstrap can be used to construct confidence intervals. In this paper, we propose a technique based on the bootstrap method to have interval estimates for the cost and the number of cumulative events. One of the advantages of the proposed methodology is the possibility for its application in several areas and its easy computational implementation. In addition, it can be a better alternative than asymptotic-based methods to calculate confidence intervals, according to some Monte Carlo simulations. An example from the engineering area illustrates the methodology.

Synthetic biology: a novel approach for the construction of industrial microorganisms

The recent achievement of synthesising a functioning bacterial chromosome marks a coming of age for engineering living organisms. In the future this should allow the construction of novel organisms to help solve the problems facing the human race, including health care, food, energy and environmental protection. In this minireview, the current state of the field is described and the role of synthetic biology in biotechnology in the short and medium term is discussed. It is particularly aimed at the needs of food technologists, nutritionists and other biotechnologists, who might not be aware of the potential significance of synthetic biology to the research and development in their fields. The potential of synthetic biology to produce interesting new polyketide compounds is discussed in detail.

A system for classification of time-series data from industrial non-destructive device

This work proposes a system for classification of industrial steel pieces by means of magnetic nondestructive device. The proposed classification system presents two main stages, online system stage and off-line system stage. In online stage, the system classifies inputs and saves misclassification information in order to perform posterior analyses. In the off-line optimization stage, the topology of a Probabilistic Neural Network is optimized by a Feature Selection algorithm combined with the Probabilistic Neural Network to increase the classification rate. The proposed Feature Selection algorithm searches for the signal spectrogram by combining three basic elements: a Sequential Forward Selection algorithm, a Feature Cluster Grow algorithm with classification rate gradient analysis and a Sequential Backward Selection. Also, a trash-data recycling algorithm is proposed to obtain the optimal feedback samples selected from the misclassified ones.