Electrochemical Determination of Inorganic Contaminants in Automotive Fuels

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Chemometrics in fuel science: demonstration of the feasibility of chemometrics analyses applied to physicochemical parameters to screen solvent tracers in Brazilian commercial gasoline

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Increase on incineration capacity and NOx control for air enrichment in the experimental simulation of aqueous residue incineration

An experimental investigation of air enrichment in a combustion chamber designed to incinerate aqueous residues is presented. Diesel fuel and liquefied petroleum gas (LPG) were used independently as fuels. An increase of 85% in the incineration capacity was obtained with nearly 50% O-2 in the oxidant gas, in comparison to incineration with air only. The incineration capacity continues increasing for enrichment levels above 50% O-2 , although at a lower pace. For complete oxy-flame combustion (100% O-2 ), the increase of the incineration capacity was about 110% relative to the starting conditions and about 13.5% relative to the condition with 50% O-2 . The CO concentration measured near the flame front decreases drastically with the increase of O-2 content in the oxidant gas. At the chamber exit, the CO concentration was always near zero, indicating that the chamber residence time was sufficient to complete fuel oxidation in any test setting. For diesel fuel, the NOx was entirely formed in the first region of the combustion chamber. For diesel fuel, there was some increase in the NOx concentration up to 35% of O-2 ; this increase became very sharp after that. From 60 ppm, at operation with air only, the NOx concentration raises to 200 ppm at 35% O-2 , and then to 2900 ppm at 74% O-2 . The latter corresponds to six times more NOx in terms of the ratio of mass of NO to mass of residue, compared to the situation of combustion with air only. For LPG, the NOx concentrations reached 4200 ppm at 80% O-2 , corresponding to nine times more, also in terms of the ratio of mass of NO to mass of residue, in comparison with combustion with air only. Results of different techniques used to control the NOx emission during air enrichment are discussed: (a) variation of the recirculated zone intensity, (b) increase of the spray Sauter mean diameter, (c) fuel staging, (d) oxidizer staging, and (e) ammonia injection. The present paper shows that NOx emission may be controlled without damage of the increase of incineration capacity by the enrichment and with low emission of partial oxidation pollutants such as CO.

A square-wave voltammetric method for analysing the colour marker quinizarine in petrol and diesel fuels

This work presents an electroanalytical method based on square-wave voltammetry (SWV) for the determination of quinizarine (QNZ) in a mixture of Britton-Robinson buffer 0.08 mol L-1 with 30% of acetonitrile. The QNZ was oxidized at glassy carbon electrode in and the well-defined peak at +0.45 V vs. Ag/AgCl can be used for its determination as colour marker in fuel samples. All parameters were optimized and analytical curves can be constructed for QNZ concentrations ranging from 2.0 x 10(-6) mol L-1 to 1.4 x 10(-5) mol L-1, using f = 60 Hz and E-sw = 25 mV. The method offers a limit detection of 4.12 x 10(-7) mol L-1 and a standard deviation of 4.5% when six measurements of 1.25 x 10(-5) mol L-1 are compared. The method was successfully applied for determining QNZ in gasoline and diesel oil and the obtained results showed good agreement with those reported previously. (c) 2006 Elsevier Ltd. All rights reserved.

Leak detection in petroleum pipelines using a fuzzy system

A methodology for pipeline leakage detection using a combination of clustering and classification tools for fault detection is presented here. A fuzzy system is used to classify the running mode and identify the operational and process transients. The relationship between these transients and the mass balance deviation are discussed. This strategy allows for better identification of the leakage because the thresholds are adjusted by the fuzzy system as a function of the running mode and the classified transient level. The fuzzy system is initially off-line trained with a modified data set including simulated leakages. The methodology is applied to a small-scale LPG pipeline monitoring case where portability, robustness and reliability are amongst the most important criteria for the detection system. The results are very encouraging with relatively low levels of false alarms, obtaining increased leakage detection with low computational costs. (c) 2005 Elsevier B.V. All rights reserved.

A case study of compact cogeneration using various fuels

A method for studying the technical and economic feasibility of absorption refrigeration systems in compact cogenerators is presented. The system studied consists of an internal combustion engine, an electric generator and a heat exchanger to recover residual heat from the refrigeration water and exhaust gases. As an application, a computer program simulates the cogeneration system in a building which already has 75 kW of installed electric power. The maximum electric and refrigeration demands are 45 kW and 76 kW respectively. This study simulates the system performance, utilizing diesel oil, sugar cane alcohol and natural gas as possible fuels. (C) 1997 Elsevier B.V. Ltd.

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.

Cogasification of solid fuels

High efficiency gas turbine based systems, utility deregulation and more stringent environmental regulations strongly favor the use of natural gas over coal and other solid fuels in new electricity generators. Solid fuels could continue to compete, however, if a low cost gasifier fed by low cost feedstocks can be coupled with a gas turbine system. We examine on-site gasification of coal with other domestic fuels in an indirectly heated gasifier as a strategy to lower the costs of solid fuel systems. The systematics of gaseous pyrolysis yields assembled with the help of thermal measurement data and molecular models suggests blending carbonaceous fuels such as coal, coke or char with oxygenated fuels such as biomass, RDF, MSW, or dried sewage sludge. Such solid fuel blending can, with the help of inexpensive catalysts, achieve an optimum balance of volatiles, heating values and residual char thus reducing the technical demands upon the gasifier. Such simplifications should lower capital and operating costs of the gasifier to the mutual benefit of both solid fuel communities.

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.

Desempenho dinâmico de um trator agrícola utilizando biodiesel destilado de óleo residual

The objective of this study was to evaluate the dynamic performance of an agricultural tractor utilizing distilled biodiesel (50% ethylic + 50% methylic) as a function of the proportion of biodiesel and diesel of petroleum (0 and 100%, 5 and 95%, 15 and 85%, 25 and 75%, 50 and 50%, 75 and 25% and 100 and 0%), respectively. This research was done in the area of the Department of Rural Engineering of the Paulista State University (UNESP), Jaboticabal Campus, SP, located in the latitude 21° 14′ 28″ S and longitude 48° 17′12″ W. A tractor 4 x 2 FWA was used, with a 73.6 kW (100 HP) motor and a ballast tractor. The biodiesel used was produced from spent oil from food frying. The experimental design was entirely randomized, with 7 treatments and 5 repetitions, totaling 35 observations. The results showed that the biodiesel and diesel blend significantly influenced the hourly volumetric consumption, hourly mass consumption, fuel consumption per worked area and specific fuel consumption variables. When the tractor operated with 100% of biodiesel (B100) the specific fuel consumption increased 18% on average in relation to diesel (B0).

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.

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.