The EDAM Project: Mining Atmospheric Aerosol Datasets

Data mining has been a very active area of research in the database, machine learning, and mathematical programming communities in recent years. EDAM (Exploratory Data Analysis and Management) is a joint project between researchers in Atmospheric Chemistry and Computer Science at Carleton College and the University of Wisconsin-Madison that aims to develop data mining techniques for advancing the state of the art in analyzing atmospheric aerosol datasets. There is a great need to better understand the sources, dynamics, and compositions of atmospheric aerosols. The traditional approach for particle measurement, which is the collection of bulk samples of particulates on filters, is not adequate for studying particle dynamics and real-time correlations. This has led to the development of a new generation of real-time instruments that provide continuous or semi-continuous streams of data about certain aerosol properties. However, these instruments have added a significant level of complexity to atmospheric aerosol data, and dramatically increased the amounts of data to be collected, managed, and analyzed. Our abilit y to integrate the data from all of these new and complex instruments now lags far behind our data-collection capabilities, and severely limits our ability to understand the data and act upon it in a timely manner. In this paper, we present an overview of the EDAM project. The goal of the project, which is in its early stages, is to develop novel data mining algorithms and approaches to managing and monitoring multiple complex data streams. An important objective is data quality assurance, and real-time data mining offers great potential. The approach that we take should also provide good techniques to deal with gas-phase and semi-volatile data. While atmospheric aerosol analysis is an important and challenging domain that motivates us with real problems and serves as a concrete test of our results, our objective is to develop techniques that have broader applicability, and to explore some fundamental challenges in data mining that are not specific to any given application domain

Closure estimations from underground observations and their comparison to closure from elastic numerical modelling

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of Science in Engineering. Johannesburg, October 2017The gold reserves in South Africa have been mined for decades, depleting all the easily accessible reserves. In pursuing the deeper reserves South African mining industry has for many years led the development of mining and particularly rock engineering. Various design criteria and tools have been developed and used by South African rock engineers in different mining environments. It must also be understood that these criteria were developed decades ago in different mining environments compared to where mining is currently taking place. In using these design criteria one needs to look at the relevance of such criteria and question if they are still applicable or if new criteria are required. Scheepers et.al, (2012) reviewed the design criteria used in designing ultra-deep narrow reef stopes in the West Wits and identified that there was no clear correlation between the design criteria used and the seismicity which is the highest FOG risk in ultra-deep mines. They then decided to use modelled elastic closure as design parameter which can be correlated to seismicity. This report details an investigation into the correlation of the modelled elastic closure to the estimated closure from underground and how modelled closure can be adjusted to better reflect the anticipated closure underground. The investigation was conducted using underground observations and stoping width estimations using installed timber support and numerical modelling results (MAP3D). Before correlating the modelled closure and the estimated closure, it was critical to understand the basis of the work done by (Scheepers et.al, 2012) in correlating the modelled closure to seismic hazard. McGarr, (1976) introduced the concept of correlating seismic energy to volume changes in stope. However this correlation was on the basis that the closure in the stope is only as a result of seismic failure. This was the basis of work done by (Scheepers et.al, 2012) in correlating volume change due to seismicity (seismic potency) to modelled closure. It must be understood that (Scheepers et.al, 2012) aim was not for the modelled closure to reflect underground closure, however was to give an indication of the anticipated seismic activity relative to closure. This report further looks at what would the underground closure be relative to the modelled closure which has been used as a design parameter against seismicity. This report showed no correlation between the 0.27m modelled closure determined by Scheepers et.al, (2012) for Mponeng mine to the estimated closure. Through (Scheepers et.al, 2012) work, it was also shown that the correlation of potency to modelled closure was only in the first 10000m2 of mining a new raise line. Seismic potency is highly dependent on the seismic moment of a seismic event and the larger the event, the larger the seismic potency without any consideration to the mining layout. The elastic modelled closure was found to be on average only 55.3% of the estimated closure. The MAP3D model only considered the elastic properties of the rock and did not take into account any discontinuities or non-homogeneity in the rock mass, hence the large difference to the measured closure. It is important to note that seismic potency and elastic closure modelled do not take into account critical factors that contribute to both rock mass deformation and seismicity in deep mines. More work is required to gain a better understanding on the correlation of rock mass deformation in ultra-deep mines to seismicity. Of importance from the research is to acknowledge that the use of modelled elastic closure should always be supported with a good understanding of the actual rock mass behaviour. The elastic properties used in numerical modelling programs could be varied in such a way that the elastic modelling results can closely depict the actual rock mass behaviour in terms of closure. Accurate estimation of closure would be useful in the design of support systems and mining layouts in ensuring the stability of excavations for the required periods. Closure can be estimated by conducting underground measurements and calculated by running numerical modelling programs. Better correlations between the two results would be possible once the elastic properties used in a model are varied until the results obtained from the model are similar to the underground measurements. The inclusion of the backfill material into the elastic model has significant influence on the resultant closure. This was shown by varying the stoping width used in the model. In a pure elastic model without backfill the stoping width has no influence on the resultant modelled closure as it is evident in the elastic closure formula by (Malan, 2003) which does not take into account stoping width. Varying the poison’s ration has very little influence of the modelled closure while the adjustments to the young’s modulus has a significant influence to the modelled closure.MT 201

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

Evaluation of long-hole mine design influences on unplanned ore dilution

Unplanned ore dilution or stope overbreak, which has a direct and large influence on the cost of a stope, and ultimately on the profitability of a mining operation, can be attributed to both the mining process and to geologic setting. The research undertaken in this document, applicable to a wide range of underground mines employing the blasthole mining method to extract tabular orebodies, focuses on examining factors attributable to the generation of unstable stope hanging-walls.The primary objective of the research undertaken is to establish new models for stope and orezone design, with respect to anticipated stope overbreak, focusing on the position and type of stope within the orezone extraction sequence. Identified factors influencing unplanned dilution, such as: induced stress environment, stope geometry, and the setting of individual stopes are considered.The research undertaken incorporates a variety of components, including (i) parametric 3-D numerical modelling to examine influences of individual factors on hanging-wall overbreak, (ii) case example analysis, and (iii) orezone extraction sequence simulation, using 3-D elastic numerical modelling. Design criteria, developed from the parametric modelling, was applied to the orezone sequence modelling to develop trends for stope dilution, as functions of stope design and construction.It was found that hanging-wall overbreak is not significantly influenced by depth alone, and that stopes with large vertical and short horizontal dimensions or stopes having long horizontal and short vertical dimensions are more stable than large square-like stopes. Also, through parametric and case studies, it was demonstrated that, in addition to stope dimension, the amount of unplanned dilution differed according to stope type. Five stope types were identified, based on their position within a tabular blasthole mining sequence. Measured overbreak varies with stope type, with secondary stopes generating a greater volume of hanging-wall dilution than do primary stopes. A pillarless mining sequence will generate less overall dilution than a primary stope: secondary pillar mining sequence

Actionable Knowledge Discovery using Multi-Step Mining

Data mining at enterprise level operates on huge amount of data such as government transactions, banks, insurance companies and so on. Inevitably, these businesses produce complex data that might be distributed in nature. When mining is made on such data with a single-step, it produces business intelligence as a particular aspect. However, this is not sufficient in enterprise where different aspects and standpoints are to be considered before taking business decisions. It is required that the enterprises perform mining based on multiple features, data sources and methods. This is known as combined mining. The combined mining can produce patterns that reflect all aspects of the enterprise. Thus the derived intelligence can be used to take business decisions that lead to profits. This kind of knowledge is known as actionable knowledge.Data mining is a process of obtaining trends or patterns in historical data. Such trends form business intelligence that in turn leads to taking well informed decisions. However, data mining with a single technique does not yield actionable knowledge. This is because enterprises have huge databases and heterogeneous in nature. They also have complex data and mining such data needs multi-step mining instead of single step mining. When multiple approaches are involved, they provide business intelligence in all aspects. That kind of information can lead to actionable knowledge. Recently data mining has got tremendous usage in the real world. The drawback of existing approaches is that insufficient business intelligence in case of huge enterprises. This paper presents the combination of existing works and algorithms. We work on multiple data sources, multiple methods and multiple features. The combined patterns thus obtained from complex business data provide actionable knowledge. A prototype application has been built to test the efficiency of the proposed framework which combines multiple data sources, multiple methods and multiple features in mining process. The empirical results revealed that the proposed approach is effective and can be used in the real world