Patrimonio Histórico Minero-Metalúrgico, Patrimonio Cultural/ Mining-Metallurgical Historical Heritage, Cultural Heritage

El panel se divide en tres secciones : Minería histórica , Patrimonio Minero y Museos.

Improved ore processing assessed by digital image análisis of the ores: a key to the sustainable development of the Iberian Pyrite Belt

Mining in the Iberian Pyrite Belt (IPB), the biggest VMS metallogenetic province known in the world to date, has to face a deep crisis in spite of the huge reserves still known after ≈5 000 years of production. This is due to several factors, as the difficult processing of complex Cu-Pb-Zn-Ag- Au ores, the exhaustion of the oxidation zone orebodies (the richest for gold, in gossan), the scarce demand for sulphuric acid in the world market, and harder environmental regulations. Of these factors, only the first and the last mentioned can be addressed by local ore geologists. A reactivation of mining can therefore only be achieved by an improved and more efficient ore processing, under the constraint of strict environmental controls. Digital image analysis of the ores, coupled to reflected light microscopy, provides a quantified and reliable mineralogical and textural characterization of the ores. The automation of the procedure for the first time furnishes the process engineers with real-time information, to improve the process and to preclude or control pollution; it can be applied to metallurgical tailings as well. This is shown by some examples of the IPB.

A Methodological model to assist the optimization and risk management of mining investment decisions

Identifying, quantifying, and minimizing technical risks associated with investment decisions is a key challenge for mineral industry decision makers and investors. However, risk analysis in most bankable mine feasibility studies are based on the stochastic modelling of project “Net Present Value” (NPV)which, in most cases, fails to provide decision makers with a truly comprehensive analysis of risks associated with technical and management uncertainty and, as a result, are of little use for risk management and project optimization. This paper presents a value-chain risk management approach where project risk is evaluated for each step of the project lifecycle, from exploration to mine closure, and risk management is performed as a part of a stepwise value-added optimization process.

Is it appropriate protective figure "Plant Micro-Reserves" to protect tree species? The example of Betula pendula subsp. fontqueri in "La Garganta de los Caballeros" (Ávila)

The figure of protection "micro-reserves" was created in the Region of Valencia (ANONYMOUS, 1994) with the aim of protecting endangered plant species. This is one of the areas of greatest floristic richness and uniqueness of the western Mediterranean. In this area rare, endemic or threatened vascular flora has a peculiar distribution: they appear to form small fragments spread over the entire region (LAGUNA, 1994; LAGUNA, 2001) The protection of every these small populations of great scientific value has significant challenges. It doesn´t try to protect every species that set out in Annex IV of the by then existing Law 4 / 1989 (repealed in 2007), or to protect to the most ecological level with the creation of Natural Protected Area but an intermediate level: the plant community of small size. According to the decree: “as Micro-Reserve will be declared the natural parcels of land under 20 hectares that contain a high concentration of rare plants, endemic, threatened or of high scientific interest” (ANONYMOUS, 1994) . Of course, the statement of an area as micro-reserve carries certain prohibitions that are harmful to the vegetal community

Dynamic response of hydro power plants for providing secondary regulation reserves considering elastic water column effects

In this paper, the dynamic response of a hydro power plant for providing secondary regulation reserve is studied in detail. Special emphasis is given to the elastic water column effects both in the penstock and the tailrace tunnel. For this purpose, a nonlinear model based on the analogy between mass and momentum conservation equations of a water conduit and those of wave propagation in transmission lines is used. The influence of the plant configuration and design parameters on the fulfilment of the Spanish Electrical System Operator requirements is analysed

On loader productivity, rock strength and explosive energy in metal mining

Twenty production blasts in two open pit mines were monitored, in rocks with medium to very high strength. Three different blasting agents (ANFO, watergel and emulsion blend) were used, with powder factors ranging between 0.88 and 1.45 kg/m3. Excavators were front loaders and rope shovels. Mechanical properties of the rock, blasting characteristics and mucking rates were carefully measured. A model for the calculation of the productivity of excavators is developed thereof, in which the production rate results as a product of an ideal, maximum, productivity rate times an operating efficiency. The maximum rate is a function of the dipper capacity and the efficiency is a function of rock density, strength, and explosive energy concentration in the rock. The model is statistically significant and explains up to 92 % of the variance of the production rate measurements.

Adapting autonomously classification data mining algorithms for ubiquitous devices

Abstract Due to recent scientific and technological advances in information sys¬tems, it is now possible to perform almost every application on a mobile device. The need to make sense of such devices more intelligent opens an opportunity to design data mining algorithm that are able to autonomous execute in local devices to provide the device with knowledge. The problem behind autonomous mining deals with the proper configuration of the algorithm to produce the most appropriate results. Contextual information together with resource information of the device have a strong impact on both the feasibility of a particu¬lar execution and on the production of the proper patterns. On the other hand, performance of the algorithm expressed in terms of efficacy and efficiency highly depends on the features of the dataset to be analyzed together with values of the parameters of a particular implementation of an algorithm. However, few existing approaches deal with autonomous configuration of data mining algorithms and in any case they do not deal with contextual or resources information. Both issues are of particular significance, in particular for social net¬works application. In fact, the widespread use of social networks and consequently the amount of information shared have made the need of modeling context in social application a priority. Also the resource consumption has a crucial role in such platforms as the users are using social networks mainly on their mobile devices. This PhD thesis addresses the aforementioned open issues, focusing on i) Analyzing the behavior of algorithms, ii) mapping contextual and resources information to find the most appropriate configuration iii) applying the model for the case of a social recommender. Four main contributions are presented: - The EE-Model: is able to predict the behavior of a data mining algorithm in terms of resource consumed and accuracy of the mining model it will obtain. - The SC-Mapper: maps a situation defined by the context and resource state to a data mining configuration. - SOMAR: is a social activity (event and informal ongoings) recommender for mobile devices. - D-SOMAR: is an evolution of SOMAR which incorporates the configurator in order to provide updated recommendations. Finally, the experimental validation of the proposed contributions using synthetic and real datasets allows us to achieve the objectives and answer the research questions proposed for this dissertation.

Proyecto de viabilidad de una planta de tratamiento para mineral de estaño y tántalo

El proyecto tiene como objeto definir la viabilidad de explotación de un importante yacimiento de Estaño y Tántalo que se encuentra en una formación geológica de base pegmatítica situada en el norte de España. En base a las reservas calculadas, se define la capacidad de tratamiento de la planta de procesamiento del mineral para un periodo de explotación de 10 años. Como primer paso se estudian los ensayos de caracterización y concentración realizados en laboratorio a partir de muestras de mano representativas del mineral así como otros en planta piloto llevados a cabo anteriormente. Una vez definida la recuperación del Estaño y Tántalo se procede al diseño conceptual del proceso. Posteriormente se desarrolla un diseño e ingeniería preliminar más aproximados, a partir de los cuales se evalúan los costes de equipos y operacionales que, en base a los retornos por la venta de los concentrados, permitirán calcular la rentabilidad del proyecto y riesgos de la inversión. ABSTRACT The purpose of this project is to define the feasibility of mining a major deposit of tin and tantalum found in a pegmatite formation in northern Spain. Based on the estimated reserves, the operating capacity for mining and mineral processing plant was defined for a period of 10 years. As a first step for the development, a research program for characterization and concentration of the ore, were performed in the laboratory based on representative samples from the deposit. In addition, previous pilot plant results were also taken into account. Once determined the recovery of tin and tantalum, the conceptual design process was defined. As a second step, it was developed a preliminary design and engineering, from which the capital and operating costs were estimated .By means of the calculated returns from the sale of concentrates, the profitability of the project and investment risks were finally assessed

A method to assess annual average renewable groundwater reserves for large regions in Spain

This paper proposes a method for assessing the groundwater renewable reserves of large regions for an average year, based on the integration of the recession curves for their basins springs or the natural base flow of their rivers. In this method, the hydrodynamic volume (or renewable reserves), were estimated from the baseflow equation. It was assumed that the flow was the same as the natural recharge, and that the recession coefficients were derived by the hydrogeological parameters and geometrical characteristics of aquifers, and adjusted to fit the recession curves at gauging stations. The method was applied to all the aquifers of Spain, which have a total groundwater renewable reserve of 86,895 hm3 four times the mean annual recharge. However, the distribution of these reserves is very variable; 18.6% of the country aquifers contain 94.7% of the entire reserve.

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Dynamic response of hydro power plants to load variations for providing secondary regulation reserves considering elastic water column effects

In this paper, the dynamic response of a hydro power plant for providing secondary regulation reserve is studied in detail. S pecial emphasis is given to the elastic water column effects both in the penstock and the tailrace tunnel. For this purpose, a nonline ar model based on the analogy between mass and momentum conservation equations of a water conduit and those of wave propagation in transmission lines is used. The influence of the plant configuration and design parameters on the fulfilment of the Spanish Electrical System Operator requirem ents is analysed.

Equity pricing in the mining sector: evidence from NYSE and LSE

The study examines the Capital Asset Pricing Model (CAPM) for the mining sector using weekly stock returns from 27 companies traded on the New York Stock Exchange (NYSE) or on the London Stock Exchange (LSE) for the period of December 2008 to December 2010. The results support the use of the CAPM for the allocation of risk to companies. Most companies involved in precious metals (particularly gold), which have a beta value less than unity (Table 1), have been actuated as shelter values during the financial crisis. Values of R2 do not shown very explanatory power of fitted models (R2 < 70 %). Estimated coefficients beta are not sufficient to determine the expected returns on securities but the results of the tests conducted on sample data for the period analysed do not appear to clearly reject the CAPM

Self-configuring data mining for ubiquitous computing

Ubiquitous computing software needs to be autonomous so that essential decisions such as how to configure its particular execution are self-determined. Moreover, data mining serves an important role for ubiquitous computing by providing intelligence to several types of ubiquitous computing applications. Thus, automating ubiquitous data mining is also crucial. We focus on the problem of automatically configuring the execution of a ubiquitous data mining algorithm. In our solution, we generate configuration decisions in a resource aware and context aware manner since the algorithm executes in an environment in which the context often changes and computing resources are often severely limited. We propose to analyze the execution behavior of the data mining algorithm by mining its past executions. By doing so, we discover the effects of resource and context states as well as parameter settings on the data mining quality. We argue that a classification model is appropriate for predicting the behavior of an algorithm?s execution and we concentrate on decision tree classifier. We also define taxonomy on data mining quality so that tradeoff between prediction accuracy and classification specificity of each behavior model that classifies by a different abstraction of quality, is scored for model selection. Behavior model constituents and class label transformations are formally defined and experimental validation of the proposed approach is also performed.

Collaborative data stream mining in ubiquitous environments using dynamic classifier selection

In ubiquitous data stream mining applications, different devices often aim to learn concepts that are similar to some extent. In these applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real world datasets.