Chemical structure, surface properties and biological activities of the biosurfactant produced by Pseudomonas aeruginosa LBI from soapstock

Pseudomonas aeruginosa LBI isolated from petroleum-contaminated soil produced rhamnolipids (RLLBI) when cultivated on soapstock as the sole carbon source. HPLC-MS analysis of the purified culture supernatant identified 6 RL homologues (%): R-2 C-10 C-10 28.9; R-2 C-10 C-12:1 23.0; R-1 C-10 C-10 23.4; R-2 C-10 C-12 11.3; R-2 C-10 C-12 7.9; R-2 C-10 C-12 C-12 5.5. To assess the potential antimicrobial activity of the new rhamnolipid product, RLLBI, its physicochemical properties were studied. RLLBI had a surface tension of 24 mN m(-1) and an interfacial tension 1.31 mN m(-1); the cmc was 120 mg l(-1). RLLBI produced stable emulsions with hydrocarbons and vegetable oils. This product showed good antimicrobial behaviour against bacteria: MIC for Bacillus subtilis, Staphylococcus aureus and Proteus vulgaris was 8 mg l(-1), for Streptococcus faecalis 4 mg l(-1), and for Pseudomonas aeruginosa 32 mg l(-1). RLLBI was active against phytopathogenic fungal species, MIC values of 32 mg l(-1) being found against Penicillium, Alternaria, Gliocadium virens and Chaetonium globosum. Due to its physicochemical properties and antimicrobial behaviour, RLLBI could be used in bioremediation treatment and in the food, cosmetic and pharmaceutical industries.

Development of intelligent systems for well drilling and petroleum production

Domains where knowledge representation is too complex to be described analytically and in a deterministic way is very common in the petroleum industry, particularly in the field of exploration and production. In these domains, applications of artificial intelligence techniques are very suitable, especially in cases where the preservation of corporate and technical knowledge is important. The Laboratory for Research on Artificial Intelligence Applied to Petroleum Engineering (LIAP) at Unicamp, has, during the last 10 years, dedicated research efforts to build intelligent systems in well drilling and petroleum production fields. In the following sections, recent advances in intelligent systems, under development in the research laboratory, are described. (C) 2001 Published by Elsevier B.V. B.V.

Improving the Fluorescence Detectability of Polycyclic Aromatic Hydrocarbons for Evaluation of Workplace Environments of Cement Industries Processing Organic Residues

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

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Digital radiography for the inspection of weld seams of pipelines - better sensitivity

Conventional radiography, using industrial radiographic films, has its days numbered. Digital radiography, recently, has taken its place in various segments of products and services, such as medicine, aerospace, security, automotive, etc. As well as the technological trend, the digital technique has brought proven benefits in terms of productivity, sensitivity, the environment, tools for image treatment, cost reductions, etc. If the weld to be inspected is on a serried product, such as, for example, a pipe, the best option for the use of digital radiography is the plane detector, since its use can reduce the length of the inspection cycle due to its high degree of automation. This work tested welded joints produced with the submerged arc process, which were specially prepared in such a way that it shows small artificial cracks, which served as the basis forcomparing the sensitivity levels of the techniques involved. After carrying out the various experiments, the digital meth odshowed the highest sensitivity for the image quality indicator (IQI) of the wire and also in terms of detecting small discontinuities, indicating that the use of digital radiography using the plane detector had advantages over the conventional technique (Moreira et al. Digital radiography, the use of plane detectors for the inspection of welds in oil pipes and gas pipes.9th COTEQ and XXV National Testing Congress for Non Destructive Testing and Inspection; Salvador, Bahia, Brazil and Bavendiek et al. New digital radiography procedure exceeds film sensitivity considerably in aerospace applications. ECNDT; 2006; Berlin). The works were carried out on the basis of the specifications for oil and gas pipelines, API 5L 2004 edition (American Petroleum Institute. API 5L: specification for line pipe. 4th ed. p. 155; 2004) and ISO 3183 2007 edition (International Organization for Standardization, ISO 3183. Petroleum and gas industries - steel pipes for pi pelines transportation systems. p. 143; 2007). © 2010 Taylor & Francis.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

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.

Design of biogas pipeline - Energy and sanitation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Biomass-to-bio-products application of feruloyl esterase from Aspergillus clavatus

The structural polysaccharides contained in plant cell walls have been pointed to as a promising renewable alternative to petroleum and natural gas. Ferulic acid is a ubiquitous component of plant polysaccharides, which is found in either monomeric or dimeric forms and is covalently linked to arabinosyl residues. Ferulic acid has several commercial applications in food and pharmaceutical industries. The study herein introduces a novel feruloyl esterase from Aspergillus clavatus (AcFAE). Along with a comprehensive functional and biophysical characterization, the low-resolution structure of this enzyme was also determined by small-angle X-ray scattering. In addition, we described the production of phenolic compounds with antioxidant capacity from wheat arabinoxylan and sugarcane bagasse using AcFAE. The ability to specifically cleave ester linkages in hemicellulose is useful in several biotechnological applications, including improved accessibility to lignocellulosic enzymes for biofuel production. © 2012 Springer-Verlag Berlin Heidelberg.

Evidence for euphotic zone anoxia during the deposition of Aptian source rocks based on aryl isoprenoids in petroleum, Sergipe-Alagoas Basin, northeastern Brazil

Four crude oil samples from the Sergipe-Alagoas Basin, northeastern Brazil, were analyzed using full scan gas chromatography-quadrupole mass spectrometry (GC-qMS) for biomarkers, in order to correlate them using aromatic carotenoids thereby enhancing knowledge about the depositional environment of their source rocks. The geochemical parameters derived from saturated fractions of the oils show evidence of little or no biodegradation and similar thermal maturation (Ts/(Ts+Tm) for terpanes, C29 αββ/(αββ+ααα), C27, and C29 20S/(20S+20R) for steranes). Low pristane/phytane ratios and the abundance of gammacerane and β-carotane are indicative of an anoxic and saline depositional environment for the source rocks. Moreover, we identified a large range of diagenetic and catagenetic products of the aromatic carotenoid isorenieratene, including C40, C33, and C32 diaryl isoprenoids and aryl isoprenoid derivatives with short side chains and/or additional rings. These results indicate anoxia in the photic zone during the deposition of the source rocks. © 2013 The Authors.

Avaliação da genotoxicidade e mutagenicidade das águas dos rios Jaguari, Atibaia e Piracicaba, na região de influência da refinaria de Paulínia - SP

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

Avaliação da gestão de áreas contaminadas de uma unidade industrial, localizada no município de Cubatão, São Paulo

Pós-graduação em Geociências e Meio Ambiente - IGCE