Ángeles Saavedra

Application of geostatistical techniques to exploitation planning in slate quarries

When planning workings of slate extraction by mechanical means, it is important to know the quality of the rock mass to optimize the results of the cuttings in the extraction bank, because techniques to be used depend on relative percent of first quality or second quality slate. In this paper, a methodology for quality evaluation applied to slate extraction is developed in three phases, as a tool of support to the planning. The first phase consists of defining the geotechnical parameters that affect the bank and to appraise them in the visible faces. The second phase consists of applying multivariate statistical techniques related to discriminant analysis data to evaluate a quality function. This function can be seen as a recovery index of the slate mass, being its variance intervals established in the analyzed faces. The third phase consists of a forecast of the values of the index from visible faces to the inside of the extraction bank, using geostatistics to evaluate the quality of the mass and to plan the mechanical cutting works, in order to get the best results.

Rate of convergence of a convolution-type estimator of the marginal density of a MA(1) process

In this paper moving-average processes with no parametric assumption on the error distribution are considered. A new convolution-type estimator of the marginal density of a MA(1) is presented. This estimator is closely related to some previous ones used to estimate the integrated squared density and has a structure similar to the ordinary kernel density estimator. For second-order kernels, the rate of convergence of this new estimator is investigated and the rate of the optimal bandwidth obtained. Under limit conditions on the smoothing parameter the convolution-type estimator is proved to be -consistent, which contrasts with the asymptotic behavior of the ordinary kernel density estimator, that is only -consistent.

Evaluation of the quality of a granite quarry

Abstract The determination of the quality of a mass of granite with a view to its exploitation for ornamental purposes is carried out by means of the identification of the geological, geotechnical and aesthetic factors that characterize the granite. In order to draw up an optimal exploitation method for any given granite deposit, an assessment of the relationship existing between these factors and the quality of the block is of the utmost importance. Using as a starting point, data based on the observation of these factors at an extraction bank, a simple linear index was elaborated using multivariant techniques, permitting the classification of each block in terms of quality. Subsequently, using geostatistical techniques, the index is applied to areas of the extraction bank for which there are no a priori data, with a view to predicting possible areas of maximum quality. Included is a description of an application whereby the quality of a granite quarry was evaluated on the basis of 569 observations at an extraction bank. The methodology developed may be considered an objective quality evaluation method applicable to ornamental rock quarries.

Quality index for ornamental slate deposits

A methodology for exploitation of ornamental slate deposits was developed from data obtained from borehole exploration. The method begins with the definition of geological, geotechnical and aesthetic parameters that determine the quality of the slate deposit. Then a collection of data from continuous sampling exploration was carried out in order to propose a quality index through multivariate statistical techniques that integrate all variables. This quality index is considered as a regional variable and, by geostatistical methods, its value was extrapolated for each sample from surface exposures and boreholes. This allows quantification of the quality of the deposit.

Nonparametric maximum likelihood estimators for ar and ma time series

The problem of estimating the parameters of moving average or autoregressive time series is studied when the error distribution is completely unknown. Four nonparametric maximum likelihood estimators (NPMLE) are presented for this purpose. These estimators are compared with the classical moment and least squares estimators in a simulation study. The behavior of these NPMLEs is much better than the classical ones, suggesting that they should be used extensively when no parametric information is known in advance about the error distribution. An application of these estimators to coal mining accidents data is also included.

Using model-based geostatistics to predict lightning-caused wildfires

The probability of fire in a particular area depends on a range of environmental and geographic variables. Fire prevention planning can be assisted by the construction of models to identify the variables that have a significant influence on the occurrence of fires and by building maps showing the spatial probability distribution for fires occurring in specific geographic areas. We used generalized spatial linear models to predict spatially distributed probabilities for fire occurrence in locations where storms featuring lightning occurred, on the basis of a set of variables related to climatology, orography, vegetation and lightning characteristics, and to assess the relative importance of these variables. A comparison of this model with simple logistic regression models used by other researchers to resolve similar problems demonstrates the importance of bearing in mind spatial correlation between variables.

Air quality parameters outliers detection using functional data analysis in the Langreo urban area (Northern Spain)

Polluted air of cities is a harmful factor to health that may eventually cause respiratory problems and cardiovascular disease. The monitoring and control of pollutants is an essential activity in order to protect the environment and the health by minimizing pollution levels through the detection of contaminants. Contaminants are emissions of substances to the atmosphere (mainly gases and particulate matter) whose values are greater than the limits allowed by the environmental legislation (they are anomalous values). Thus they are considered as vector samples where each component represents the gas concentration value in the air. In this sense, a model based on functional analysis has been implemented for the outliers detection in air quality samples in this research work. This model transforms the vectorial sample by creating a new functional sample in order to determine functional outliers by adjusting the concept of depth to the functional event. This method has been compared to classical outliers analysis from a vectorial point of view, emphasizing the power of use of such functional techniques over the traditional ones. The main aim of this research work is to compare the results corresponding to the classical and the functional methods and to obtain the most appropriate methodology to analyze this type of dataset in order to reach a better solution for the air quality control.

Geostatistical study of the feldspar content and quality of a granite deposit

Abstract In order to characterise the saleable feldspar in a granite deposit, a methodology was developed in accordance with the exploitation process. This consisted of mechanically extracting the surface layer of the batholith and separating the feldspar from the quartz using the granulometric separation method, given that the size of the grains of the feldspar is greater than that of quartz. Following washing, grinding and magnetic separation of the feldspar in order to eliminate the ferromagnesium minerals, the saleable feldspar was characterised in terms of the factors that determine its market value, namely, its content in Al 2 O 3 , SiO 2 , Na 2 O and K 2 O. Following the opening of prospecting pits in the granite massif, samples were analysed in the laboratory using three different granulometric cuts and by reproducing the treatment process. The values for the quality variables of saleable feldspar were obtained, and the results were interpolated to the entire deposit using the kriging method. In order to summarise the information from the above-mentioned variables, a quality index was constructed using multivariate statistics and by employing market criteria, and subsequently, the values of the index were interpolated to the entire deposit using bidimensional kriging. The map of saleable quality feldspar from the deposit permits both affirmation of the treatment process yield for each granulometric cut and the planning of extraction from the deposit to obtain a homogeneous quality in the saleable feldspar.

Biomass Thermogravimetric Analysis: Uncertainty Determination Methodology and Sampling Maps Generation

The objective of this study was to develop a methodology for the determination of the maximum sampling error and confidence intervals of thermal properties obtained from thermogravimetric analysis (TG), including moisture, volatile matter, fixed carbon and ash content. The sampling procedure of the TG analysis was of particular interest and was conducted with care. The results of the present study were compared to those of a prompt analysis, and a correlation between the mean values and maximum sampling errors of the methods were not observed. In general, low and acceptable levels of uncertainty and error were obtained, demonstrating that the properties evaluated by TG analysis were representative of the overall fuel composition. The accurate determination of the thermal properties of biomass with precise confidence intervals is of particular interest in energetic biomass applications.

Heterogenic Solid Biofuel Sampling Methodology and Uncertainty Associated with Prompt Analysis

Accurate determination of the properties of biomass is of particular interest in studies on biomass combustion or cofiring. The aim of this paper is to develop a methodology for prompt analysis of heterogeneous solid fuels with an acceptable degree of accuracy. Special care must be taken with the sampling procedure to achieve an acceptable degree of error and low statistical uncertainty. A sampling and error determination methodology for prompt analysis is presented and validated. Two approaches for the propagation of errors are also given and some comparisons are made in order to determine which may be better in this context. Results show in general low, acceptable levels of uncertainty, demonstrating that the samples obtained in the process are representative of the overall fuel composition.