Applying discrete element modelling to vertical and horizontal shaft impact crushers

The PFC3D (particle flow code) that models the movement and interaction of particles by the DEM techniques was employed to simulate the particle movement and to calculate the velocity and energy distribution of collision in two types of impact crusher: the Canica vertical shaft crusher and the BJD horizontal shaft swing hammer mill. The distribution of collision energies was then converted into a product size distribution for a particular ore type using JKMRC impact breakage test data. Experimental data of the Canica VSI crusher treating quarry and the BJD hammer mill treating coal were used to verify the DEM simulation results. Upon the DEM procedures being validated, a detailed simulation study was conducted to investigate the effects of the machine design and operational conditions on velocity and energy distributions of collision inside the milling chamber and on the particle breakage behaviour. (C) 2003 Elsevier Ltd. All rights reserved.

Development of a more efficient classifying cyclone

A more efficient classifying cyclone (CC) for fine particle classification has been developed in recent years at the JKMRC. The novel CC, known as the JKCC, has modified profiles of the cyclone body, vortex finder, and spigot when compared to conventional hydrocyclones. The novel design increases the centrifugal force inside the cyclone and mitigates the short circuiting flow that exists in all current cyclones. It also decreases the probability of particle contamination in the place near the cyclone spigot. Consequently the cyclone efficiency is improved while the unit maintains a simple structure. An international patent has been granted for this novel cyclone design. In the first development stage-a feasibility study-a 100 mm JKCC was tested and compared with two 100 min commercial units. Very encouraging results were achieved, indicating good potential for the novel design. In the second development stage-a scale-up stage-the JKCC was scaled up to 200 mm in diameter, and its geometry was optimized through numerous tests. The performance of the JKCC was compared with a 150 nun commercial unit and exhibited sharper separation, finer separation size, and lower flow ratios. The JKCC is now being scaled up into a fill-size (480 mm) hydrocyclone in the third development stage-an industrial study. The 480 mm diameter unit will be tested in an Australian coal preparation plant, and directly compared with a commercial CC operating under the same conditions. Classifying cyclone performance for fine coal could be further improved if the unit is installed in an inclined position. The study using the 200 mm JKCC has revealed that sharpness of separation improved and the flow ratio to underflow was decreased by 43% as the cyclone inclination was varied from the vertical position (0degrees) to the horizontal position (90degrees). The separation size was not affected, although the feed rate was slightly decreased. To ensure self-emptying upon shutdown, it is recommended that the JKCC be installed at an inclination of 75-80degrees. At this angle the cyclone performance is very similar to that at a horizontal position. Similar findings have been derived from the testing of a conventional hydrocyclone. This may be of benefit to operations that require improved performance from their classifying cyclones in terms of sharpness of separation and flow ratio, while tolerating slightly reduced feed rate.

An energy-based model for swing hammer mills

An energy-based swing hammer mill model has been developed for coke oven feed preparation. it comprises a mechanistic power model to determine the dynamic internal recirculation and a perfect mixing mill model with a dual-classification function to mimic the operations of crusher and screen. The model parameters were calibrated using a pilot-scale swing hammer mill at various operating conditions. The effects of the underscreen configurations and the feed sizes on hammer mill operations were demonstrated through the fitted model parameters. Relationships between the model parameters and the machine configurations were established. The model was validated using the independent experimental data of single lithotype coal tests with the same BJD pilot-scale hammer mill and full operation audit data of an industrial hammer mill. The outcome of the energy-based swing hammer mill model is the capability to simulate the impact of changing blends of coal or mill configurations and operating conditions on product size distribution. Alternatively, the model can be used to select the machine settings required to achieve a desired product. (C) 2003 Elsevier Science B.V. All rights reserved.

Kinetic parameters associated with self-heating of New Zealand coals under adiabatic conditions

Adiabatic self-heating tests were carried out on five New Zealand coal samples ranging in rank from lignite to high-volatile bituminous. Kinetic parameters of oxidation were obtained front the self-heating curves assuming Arrhenius behaviour. The activation energy E (kJ mol(-1)) and the pre-exponential factor A (s(-1)) were determined in the temperature range of 70-140 degreesC. The activation energy exhibited a definite rank relationship with a minimum E of 55 kJ mol(-1) occurring at a Suggate rank of similar to6.2 corresponding to subbituminous C. Either side of this rank there was a noticeable increase in the activation energy indicating lower reactivity of the coal. A similar rank trend was also observed in the R-70 self-heating rate index values that were taken from the initial portion of the self-heating curve front 40 to 70 degreesC. From these results it is clear that the adiabatic method is capable of providing reliable kinetic parameters of coal oxidation.

Variation of the crystalline structure of coal char during gasification

The variation of the crystallite structure of several coal chars during gasification in air and carbon dioxide was studied by high-resolution transmission electron microscopy (HRTEM) and X-ray diffraction (XRD) techniques. The XRD analysis of the partially gasified coal chars, based on two approaches, Scherrer's equation and Alexander and Sommer's method, shows a contradictory trend of the variation of the crystallite height with carbon conversion, despite giving a similar trend for the crystallite width change. The HRTEM fringe images of the partially gasified coal chars indicate that large and highly ordered crystallites exist at conversion levels as high as 86%. It is also demonstrated that the crystalline structure of chars can be very different although their pore structures are similar, suggesting a combination of crystalline structure analysis with pore structure analysis in studies of carbon gasification.

Activation energy distribution of thermal annealing of a bituminous coal

A bituminous coal was pyrolyzed in a nitrogen stream in an entrained flow reactor at various temperatures from 700 to 1475 degreesC. Char samples were collected at different positions along the reactor. Each collected sample was oxidized nonisothermally in a TGA for reactivity determination. The reactivity of the coal char was found to decrease rapidly with residence time until 0.5 s, after which it decreased only slightly. On the bases of the reactivity data at various temperatures, a new approach was utilized to obtaining the true activation energy distribution function for thermal annealing without the assumption of any distribution function form or a constant preexponential factor. It appears that the true activation energy distribution function consists of two separate parts corresponding to different temperature ranges, suggesting different mechanisms in different temperature ranges. Partially burnt coal chars were also collected along the reactor when the coal was oxidized in air at various temperatures from 700 to 1475 degreesC. The collected samples were analyzed for the residual carbon content and the specific reaction rate was estimated. The characteristic time of thermal deactivation was compared with that of oxidation under realistic conditions. The characteristic times were found to be close to each other, indicating the importance of thermal deactivation during combustion of the coal studied.

Variation of the pore structure of coal chars during gasification

The variation of the pore structure of several coal chars during gasification in air and carbon dioxide was studied by argon adsorption at 87 K and CO2 adsorption at 273 K. It is found that the surface area and volume of the small pores (10 Å for air gasification is constant over a wide range of conversion (>20%), while for CO2 gasification similar results are obtained using the total surface area. However, in the early stages of gasification (

Gestão do conhecimento usando data mining: estudo de caso na Universidade Federal de Lavras

A gestão do conhecimento abrange toda a forma de gerar, armazenar, distribuir e utilizar o conhecimento, tornando necessária a utilização de tecnologias de informação para facilitar esse processo, devido ao grande aumento no volume de dados. A descoberta de conhecimento em banco de dados é uma metodologia que tenta solucionar esse problema e o data mining é uma técnica que faz parte dessa metodologia. Este artigo desenvolve, aplica e analisa uma ferramenta de data mining, para extrair conhecimento referente à produção científica das pessoas envolvidas com a pesquisa na Universidade Federal de Lavras. A metodologia utilizada envolveu a pesquisa bibliográfica, a pesquisa documental e o método do estudo de caso. As limitações encontradas na análise dos resultados indicam que ainda é preciso padronizar o modo do preenchimento dos currículos Lattes para refinar as análises e, com isso, estabelecer indicadores. A contribuição foi gerar um banco de dados estruturado, que faz parte de um processo maior de desenvolvimento de indicadores de ciência e tecnologia, para auxiliar na elaboração de novas políticas de gestão científica e tecnológica e aperfeiçoamento do sistema de ensino superior brasileiro.

Ash deposition during the co-firing of bituminous coal with pine sawdust and olive stones in a laboratory furnace

This article describes an experimental study on ash deposition during the co-firing of bituminous coal with pine sawdust and olive stones in a laboratory furnace. The main objective of this study was to relate the ash deposit rates with the type of biomass burned and its thermal percentage in the blend. The thermal percentage of biomass in the blend was varied between 10% and 50% for both sawdust and olive stones. For comparison purposes, tests have also been performed using only coal or only biomass. During the tests, deposits were collected with the aid of an air-cooled deposition probe placed far from the flame region, where the mean gas temperature was around 640 degrees C. A number of deposit samples were subsequently analyzed on a scanning electron microscope equipped with an energy dispersive X-ray detector. Results indicate that blending sawdust with coal decreases the deposition rate as compared with the firing of unblended coal due to both the sawdust low ash content and its low alkalis content. The co-firing of coal and sawdust yields deposits with high levels of silicon and aluminium which indicates the presence of ashes with high fusion temperature and, thus, with less capacity to adhere to the surfaces. In contrast, in the co-firing of coal with olive stones the deposition rate increases as compared with the firing of unblended coal and the deposits produced present high levels of potassium, which tend to increase their stickiness.

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.

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.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.