Aerosols monitoring in Rio Claro, Brazil: Using lidar and air pollution analyzers

Brazil has an important role in the biomass burning aerosol activity. During the Dry Season (June-September) of 2009 an aerosol profiling campaign was carried out using a backscattering and Raman lidar system in Rio Claro-SP, Brazil (22°23'S and 47°32'W). The main goal of this campaign was to observe the biomass burning aerosol load due to sugarcane crops and also study the air dispersion conditions, planetary boundary and mixed layer daily evolution. In this paper we aim to present the preliminary results of the influence of this type of aerosol over the city of Rio Claro-SP, Brazil and one case study to evaluate the aerosol profile in a biomass burning episode that occurred in July, 2009. On July 15 an intense burning was observed about 300 m away from the lidar location. Throughout the measurements it was observed that the plumes reached up to 900 m, and that there was a time gap between the plumes. The gas analyzers showed a strong influence of this burning as it was noticed in the measurements of CO, NO x and nephelometer, whereas the PM10 did not have due to this burning, possibly because the particulate was deposited further from the emission source, not being detected by the equipment. © Sociedad Española de Óptica.

Specialist tool for monitoring the measurement degradation process of induction active energy meters

This paper presents a methodology and a specialist tool for failure probability analysis of induction type watt-hour meters, considering the main variables related to their measurement degradation processes. The database of the metering park of a distribution company, named Elektro Electricity and Services Co., was used for determining the most relevant variables and to feed the data in the software. The modeling developed to calculate the watt-hour meters probability of failure was implemented in a tool through a user friendly platform, written in Delphi language. Among the main features of this tool are: analysis of probability of failure by risk range; geographical localization of the meters in the metering park, and automatic sampling of induction type watt-hour meters, based on a risk classification expert system, in order to obtain information to aid the management of these meters. The main goals of the specialist tool are following and managing the measurement degradation, maintenance and replacement processes for induction watt-hour meters. © 2011 IEEE.

Development of an instrumentation system embedded on FPGA for real time measurement of mechanical vibrations in rotating machinery

The real-time monitoring of events in an industrial plant is vital, to monitor the actual conditions of operation of the machinery responsible for the manufacturing process. A predictive maintenance program includes condition monitoring of the rotating machinery, to anticipate possible conditions of failure. To increase the operational reliability it is thus necessary an efficient tool to analyze and monitor the equipments, in real-time, and enabling the detection of e.g. incipient faults in bearings. To fulfill these requirements some innovations have become frequent, namely the inclusion of vibration sensors or stator current sensors. These innovations enable the development of new design methodologies that take into account the ease of future modifications, upgrades, and replacement of the monitored machine, as well as expansion of the monitoring system. This paper presents the development, implementation and testing of an instrument for vibration monitoring, as a possible solution to embed in industrial environment. The digital control system is based on an FPGA, and its configuration with an open hardware design tool is described. Special focus is given to the area of fault detection in rolling bearings. © 2012 IEEE.

Design of a labVIEW system applied to predictive maintenance

The implementation of vibration analysis techniques based on virtual instrumentation has spread increasingly in the academic and industrial branch, since the use of any software for this type of analysis brings good results at low cost. Among the existing software for programming and creation of virtual instruments, the LabVIEW was chosen for this project. This software has good interface with the method of graphical programming. In this project, it was developed a system of rotating machine condition monitoring. This monitoring system is applied in a test stand, simulating large scale applications, such as in hydroelectric, nuclear and oil exploration companies. It was initially used a test stand, where an instrumentation for data acquisition was inserted, composed of accelerometers and inductive proximity sensors. The data collection system was structured on the basis of an NI 6008 A/D converter of National Instruments. An electronic circuit command was developed through the A/D converter for a remote firing of the test stand. The equipment monitoring is performed through the data collected from the sensors. The vibration signals collected by accelerometers are processed in the time domain and frequency. Also, proximity probes were used for the axis orbit evaluation and an inductive sensor for the rotation and trigger measurement. © (2013) Trans Tech Publications, Switzerland.

Monitoring in precision metal drilling process using multi-sensors and neural network

This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes. © 2012 Springer-Verlag London Limited.

Development and application of an electronic tongue for detection and monitoring of nitrate, nitrite and ammonium levels in waters

This work presents the first study and development of an electronic tongue analysis system for the monitoring of nitrogen stable species: nitrate, nitrite and ammonium in water. The electronic tongue was composed of an array of 15 potentiometric poly(vinyl chloride) membrane sensors sensitive to cations and anions plus an artificial neural network (ANN) response model. The building of the ANN model was performed in a medium containing sodium, potassium, and chloride as interfering ions, thus simulating real environmental samples. The correlation coefficient in the cross-validation of nitrate, nitrite and ammonium was satisfactory in the three cases with values higher than 0.92. Finally, the utility of the proposed system is shown in the monitoring of the photoelectrocatalytic treatment of nitrate. © 2013 Elsevier B.V.

Rn-222, Ra-226 and hydrochemistry in the Bauru Aquifer System, Sao Jose do Rio Preto (SP), Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Real-time monitoring of ozone in air using substrate-integrated hollow waveguide mid-infrared sensors

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Artificial Immune Systems with Negative Selection Applied to Health Monitoring of Aeronautical Structures

In this paper we present a system for aircraft structural health monitoring based on artificial immune systems with negative selection. Inspired by a biological process, the principle of discrimination proper/non-proper, identifies and characterizes the signs of structural failure. The main application of this method is to assist in the inspection of aircraft structures, to detect and characterize flaws and decision making in order to avoid disasters. We proposed a model of an aluminum beam to perform the tests of the method. The results obtained by this method are excellent, showing robustness and accuracy.

Monitoring of diclofenac with biomimetic sensor in batch and FIA systems

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

Construction of a chamber for in situ monitoring of the drying process of gels and porous solids

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

On-line monitoring of electrochemical degradation of paracetamol through a biomimetic sensor

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

Grinding process monitoring based on electromechanical impedance measurements

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

A sewage sludge co-composting process using respirometric monitoring method in hermetic rotary reactor

This paper has the objective of monitoring the biological activity of composting process of sewage sludge, sugarcane bagasse and ground coffee in a hermetic rotary reactor using the respirometric method in laboratory scale, in order to obtain parameters and system design for large scale projects. Another particularity of this study is the use of a hermetic reactor with gas purging cycles. Purging was performed when the percentage of oxygen reached less than 5%, thus eliminating the gaseous mixture (with elevated CO2 ratio) and the introduction of environmental air with around 21% of O2, successively until the compost was stabilized. The average purge intervals obtained were 29 h and 2 min with reactor rotation frequency of 15 min. The time of the compost stabilization was optimized in 60% if compared to the 90 days in the traditional method. The results obtained can be used to design the process in industrial scale using a simple O2 gas analyzer.