Study of the voltammetric behaviour of metam and its application to an amperometric flow system

The electrochemical behaviour of the pesticide metam (MT) at a glassy carbon working electrode (GCE) and at a hanging mercury drop electrode (HMDE) was investigated. Different voltammetric techniques, including cyclic voltammetry (CV) and square wave voltammetry (SWV), were used. An anodic peak (independent of pH) at +1.46 V vs AgCl/Ag was observed in MTaqueous solution using the GCE. SWV calibration curves were plotted under optimized conditions (pH 2.5 and frequency 50 Hz), which showed a linear response for 17–29 mg L−1. Electrochemical reduction was also explored, using the HMDE. A well defined cathodic peak was recorded at −0.72 V vs AgCl/ Ag, dependent on pH. After optimizing the operating conditions (pH 10.1, frequency 150 Hz, potential deposition −0.20 V for 10 s), calibration curves was measured in the concentration range 2.5×10−1 to 1.0 mg L−1 using SWV. The electrochemical behaviour of this compound facilitated the development of a flow injection analysis (FIA) system with amperometric detection for the quantification of MT in commercial formulations and spiked water samples. An assessment of the optimal FIA conditions indicated that the best analytical results were obtained at a potential of +1.30 V, an injection volume of 207 μL and an overall flow rate of 2.4 ml min−1. Real samples were analysed via calibration curves over the concentration range 1.3×10−2 to 1.3 mg L−1. Recoveries from the real samples (spiked waters and commercial formulations) were between 97.4 and 105.5%. The precision of the proposed method was evaluated by assessing the relative standard deviation (RSD %) of ten consecutive determinations of one sample (1.0 mg L−1), and the value obtained was 1.5%.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

New molecularly-imprinted polymer for carnitine and its application as ionophore in potentiometric selective membranes

Carnitine (CRT) is a biological metabolite found in urine that contributes in assessingseveral disease conditions, including cancer. Novel quick screening procedures for CRT are therefore fundamental. This work proposes a novel potentiometric device where molecularly imprinted polymers (MIPs) were used as ionophores. The host-tailored sites were imprinted on a polymeric network assembled by radical polymerization of methacrylic acid (MAA) and trimethylpropane trimethacrylate (TRIM). Non-imprinted polymers (NIPs) were produced as control by removing the template from the reaction media. The selective membrane was prepared by dispersing MIP or NIP particles in plasticizer and poly(vinyl chloride), PVC, and casting this mixture over a solid contact support made of graphite. The composition of the selective membrane was investigated with regard to kind/amount of sensory material (MIP or NIP), and the need for a lipophilic additive. Overall, MIP sensors with additive exhibited the best performance, with near-Nernstian response down to ~ 1 × 10− 4 mol L− 1, at pH 5, and a detection limitof ~ 8 × 10− 5 mol L− 1. Suitable selectivity was found for all membranes, assessed by the matched potential method against some of the most common species in urine (urea, sodium, creatinine, sulfate, fructose and hemoglobin). CRT selective membranes including MIP materials were applied successfully to the potentiometric determination of CRT in urine samples.

Data mining applications in power systems - case-studies and future trends

Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.

Exploração de dados de uma solução mobile wallet usando as técnicas de data mining

A descoberta de conhecimento em dados hoje em dia é um ponto forte para as empresas. Atualmente a CardMobili não dispõe de qualquer sistema de mineração de dados, sendo a existência deste uma mais-valia para as suas operações de marketing diárias, nomeadamente no lançamento de cupões a um grupo restrito de clientes com uma elevada probabilidade que os mesmos os utilizem. Para isso foi analisada a base de dados da aplicação tentando extrair o maior número de dados e aplicadas as transformações necessárias para posteriormente serem processados pelos algoritmos de mineração de dados. Durante a etapa de mineração de dados foram aplicadas as técnicas de associação e classificação, sendo que os melhores resultados foram obtidos com técnicas de associação. Desta maneira pretende-se que os resultados obtidos auxiliem o decisor na sua tomada de decisões.

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Previsão de vento baseado em técnicas de Data Mining

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Data mining languages for business intelligence

Doctoral Thesis in Information Systems and Technologies Area of Engineering and Manag ement Information Systems

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.

Towards a unified European electricity market: The contribution of data-mining to support realistic simulation studies

Worldwide electricity markets have been evolving into regional and even continental scales. The aim at an efficient use of renewable based generation in places where it exceeds the local needs is one of the main reasons. A reference case of this evolution is the European Electricity Market, where countries are connected, and several regional markets were created, each one grouping several countries, and supporting transactions of huge amounts of electrical energy. The continuous transformations electricity markets have been experiencing over the years create the need to use simulation platforms to support operators, regulators, and involved players for understanding and dealing with this complex environment. This paper focuses on demonstrating the advantage that real electricity markets data has for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations will bring to the participant countries. A case study using MASCEM (Multi-Agent System for Competitive Electricity Markets) is presented, with a scenario based on real data, simulating the European Electricity Market environment, and comparing its performance when using several different market mechanisms.

Using data mining with time series data in short-term stocks prediction

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on short- time stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.

Electrochemical sensor for simultaneous determination of herbicide MCPA and its metabolite 4-chloro-2-methylphenol. Application to photodegradation environmental monitoring

The development and application of a polyaniline/carbon nanotube (CNT) cyclodextrin matrix (PANI-β-CD/MWCNT)-based electrochemical sensor for the quantitative determination of the herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA) and its main transformation product 4-chloro-2-methylphenol in natural waters are described. A simple cyclic voltammetry-based electrochemical methodology, in phosphate buffer solution at pH 6.0, was used to develop a method to determine both MCPA and 4-chloro-2-methylphenol, without any previous extraction or derivatization steps. A linear concentration range (10 to 50 μmol L−1) and detection limits of 1.1 and 1.9 μmol L−1, respectively, were achieved using optimized cyclic voltammetric parameters. The proposed method was successfully applied to the determination of MCPA and 4-chloro-2-methylphenol in natural water samples with satisfactory recoveries (94 to 107 %) and in good agreement with the results obtained by an established high-performance liquid chromatography technique, no significant differences being found between the methods. Interferences from ionic species and other herbicides used for broad-leaf weed control were shown to be small. The newly developed methodology was also successfully applied to MCPA photodegradation environmental studies.

An architecture for an effective usage of data mining in business intelligence systems

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the last years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. Therefore, a lack of an effective usage of DM in BI can be found in some BI systems. An architecture that pretends to conduct to an effective usage of DM in BI is presented.