Just-in-time learning for the prediction of oil sands ore characteristics using GPS data in mining applications

For the mining based oilsands industry, it is desirable to determine the quality of the ore delivered to the extraction processes in real-time to make optimal operational decisions such as optimal ore blending to achieve maximal bitumen recovery. Currently, the industry determines the real-time ore characteristics for any given shovel Global Positioning System (GPS) location by first determining the shovel elevation from the topological mine map and then using the determined geological coordinates in the 3D geological block model. It should be noted that the block model is built based on the widely spaced core hole samples, and it is updated only on a yearly basis due to high cost of narrower core hole sampling. Thus, the block model predictions are often inaccurate in between the core hole spacing. On the other hand, mining operations data are available that contain accurate ore characteristics information in the already mined area. Therefore, in this work, we present a just-in-time based data-driven modelling strategy that utilizes the recently available mining operations data to obtain reliable ore characteristics given the GPS data. The prediction capability of ore characteristics using the proposed modelling strategy is validated at core hole locations. Further, the prediction of ore characteristics at non-core hole points demonstrate promising results.     

An artificial immune network for multiobjective optimization problems

Multiobjective optimization is an important problem of great complexity and evolutionary algorithms have been established as a dominant approach in the field. This article suggests a method for approximating the Pareto front of a given function based on artificial immune networks. The proposed method uses cloning and mutation on a population of antibodies to create local subsets of the Pareto front. Elements of these local fronts are combined, in a way that maximizes diversity, to form the complete Pareto front of the function. The method is tested on a number of well-known benchmark problems, as well as an engineering problem. Its performance is compared against state-of-the-art algorithms, yielding promising results.     

Contribution for optimal sizing of grid-connected PV-systems using PSO

Particle Swarm Optimization (PSO) is an optimization algorithm considered to be highly efficient for the solution of complicated problems. This paper presents the application of this method for the design optimization of photovoltaic grid-connected systems (PVGCSs). The purpose of the proposed methodology is to locate the optimal number of system devices and the optimal values of the PV module installation details, such that the total net economic benefit achieved during the system operational lifetime period is maximized. The optimization's decision variables are the optimal number of the PV modules, the PV modules optimal tilt angle, the optimal placement of the PV modules within the available installation area and the optimal distribution of the PV modules among the DC/AC converters. The objective function of the proposed optimization process is the lifetime system's total net profit which is calculated according to the method of the Net Present Value (NPV). The methodology's resulting system structures are economically evaluated through the methods of the discounted payback time and the Internal Rate of Return (IRR). The PSO algorithm is compared to the application of Genetic Algorithms (GAs) in terms of efficiency for the current problem.     

Reconciling continuum and non-continuum data with industrial application

In order to perform data reconciliation, it is important that noises in the data have well-defined distributions. The motivation behind this study was to enable the comparison between a discrete and continuous data set so that means can be compared for gross error over the short term; this required that local variables exhibit similar distributions.A case study was done on a system where non-continuum loads from a dump truck were to be reconciled with two downstream continuum weightometers. An algorithm was developed using the binomial distribution and time delay in order to simulate the effect of the dump pocket.Regression analysis based on principal components was used to evaluate the performance of the smoothing algorithm and to determine the most likely maximum hopper capacity that locates between the two weightometers.     

Total Syntheses of Kapakahines E and F

This article describes the synthesis of kapakahines E and F, along with what we believe to be a general method for the total synthesis of the kapakahine family of marine natural products. The method utilizes a unique heterodimerization reaction between bromopyrroloindolines and indole nucleophiles, a novel pyrroloindoline-to-α-carboline rearrangement, and a Negishi coupling reaction of C(3)-iodoindoles.     

Relating indices of knowledge structure coherence and accuracy to skill-based performance: Is there utility in using a combination of indices?

The authors examined the relative criterion-related validity of knowledge structure coherence and two accuracy-based indices (closeness and correlation) as well as the utility of using a combination of knowledge structure indices in the prediction of skill acquisition and transfer. Findings from an aggregation of 5 independent samples (N = 958) whose participants underwent training on a complex computer simulation indicated that coherence and the accuracy-based indices yielded comparable zero-order predictive validities. Support for the incremental validity of using a combination of indices was mixed; the most, albeit small, gain came in pairing coherence and closeness when predicting transfer. After controlling for baseline skill, general mental ability, and declarative knowledge, only coherence explained a statistically significant amount of unique variance in transfer. Overall, the results suggested that the different indices largely overlap in their representation of knowledge organization, but that coherence better reflects adaptable aspects of knowledge organization important to skill transfer. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Distribution of Active ingredients in Supported Catalysts Prepared by Impregnation

Supported catalysts, one of the commonest forms of heterogeneous catalysts in practical use, consist of small crystallites of a catalytically active component dispersed in a porous support of high surface area. Impregnation of the support with an aqueous solution of a compound containing the appropriate catalytic component is an important and frequently used method of preparing this type of catalyst. A nonaqueous solution should be used if the sup port surface is hydrophobic or if hydrolysis of the support surface is to be avoided. In its simplest form, this impregnation method involves three steps: (1) contacting the support with impregnating solution for a certain period of time, (2) drying the support to remove the imbibed liquid, and (3) activating the catalyst by calcination, reduction, or other appropriate treatment.     

Characterization of the surface functionality on precision machined engineering surfaces

The surfaces characterization of a component generated from the machining process is important as the surfaces have significant effects on the performance of the component and associated product. The work presented aims to investigate the surfaces functionality characterization of a component by using 3D surface parameters. The paper also investigates the surface generation in relation with the machining process and the associated functionality formation. The corresponding surface characterization is investigated with two case studies on typical engineering components. The generation of the sample surfaces and associated surface functionalities are also studied by MATLAB-based modelling and simulation. The relationship between surface characterization and surface functionality are further studied by the simulations. The paper concludes with discussions on the applications and potential of this approach for the achievement of high quality surfaces, optimization and control of their functional performance at the machining stage.