Case-based reasoning enabling database mining for cryo-preserving algae applications

Case-based Reasoning's (CBR) origins were stimulated by a desire to understand how people remember information and are in turn reminded of information, and that subsequently it was recognized that people commonly solve problems by remembering how they solved similar problems in the past. Thus CBR became an appropriate way to find out the most suitable solution method for a new problem based on the old methods for the same or even similar problems. The research highlights how to use CBR to aid biologists in finding the best method to cryo preserve algae. The study found CBR could be used successfully to find the similarity percentage between the new algae and old cases in the case base. The prediction result showed approximately 93.75% accuracy, which proves the CBR system can offer appropriate recommendations for most situations. © 2011 IEEE.

Online mining abnormal period patterns from multiple medical sensor data streams

With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient's status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients' streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.

An adaptive neighborhood search algorithm for optimizing stochastic mining complexes

Les métaheuristiques sont très utilisées dans le domaine de l'optimisation discrète. Elles permettent d’obtenir une solution de bonne qualité en un temps raisonnable, pour des problèmes qui sont de grande taille, complexes, et difficiles à résoudre. Souvent, les métaheuristiques ont beaucoup de paramètres que l’utilisateur doit ajuster manuellement pour un problème donné. L'objectif d'une métaheuristique adaptative est de permettre l'ajustement automatique de certains paramètres par la méthode, en se basant sur l’instance à résoudre. La métaheuristique adaptative, en utilisant les connaissances préalables dans la compréhension du problème, des notions de l'apprentissage machine et des domaines associés, crée une méthode plus générale et automatique pour résoudre des problèmes. L’optimisation globale des complexes miniers vise à établir les mouvements des matériaux dans les mines et les flux de traitement afin de maximiser la valeur économique du système. Souvent, en raison du grand nombre de variables entières dans le modèle, de la présence de contraintes complexes et de contraintes non-linéaires, il devient prohibitif de résoudre ces modèles en utilisant les optimiseurs disponibles dans l’industrie. Par conséquent, les métaheuristiques sont souvent utilisées pour l’optimisation de complexes miniers. Ce mémoire améliore un procédé de recuit simulé développé par Goodfellow & Dimitrakopoulos (2016) pour l’optimisation stochastique des complexes miniers stochastiques. La méthode développée par les auteurs nécessite beaucoup de paramètres pour fonctionner. Un de ceux-ci est de savoir comment la méthode de recuit simulé cherche dans le voisinage local de solutions. Ce mémoire implémente une méthode adaptative de recherche dans le voisinage pour améliorer la qualité d'une solution. Les résultats numériques montrent une augmentation jusqu'à 10% de la valeur de la fonction économique.

Selective Mercury Sequestration from a Silver/Mercury Cyanide Solution

Silver and mercury are both dissolved in cyanide leaching and the mercury co-precipitates with silver during metal recovery. Mercury must then be removed from the silver/mercury amalgam by vaporizing the mercury in a retort, leading to environmental and health hazards. The need for retorting silver can be greatly reduced if mercury is selectively removed from leaching solutions. Theoretical calculations were carried out based on the thermodynamics of the Ag/Hg/CN- system in order to determine possible approaches to either preventing mercury dissolution, or selectively precipitating it without silver loss. Preliminary experiments were then carried out based on these calculations to determine if the reaction would be spontaneous with reasonably fast kinetics. In an attempt to stop mercury from dissolving and leaching the heap leach, the first set of experiments were to determine if selenium and mercury would form a mercury selenide under leaching conditions, lowering the amount of mercury in solution while forming a stable compound. From the results of the synthetic ore experiments with selenium, it was determined that another effect was already suppressing mercury dissolution and the effect of the selenium could not be well analyzed on the small amount of change. The effect dominating the reactions led to the second set of experiments in using silver sulfide as a selective precipitant of mercury. The next experiments were to determine if adding solutions containing mercury cyanide to un-leached silver sulfide would facilitate a precipitation reaction, putting silver in solution and precipitating mercury as mercury sulfide. Counter current flow experiments using the high selenium ore showed a 99.8% removal of mercury from solution. As compared to leaching with only cyanide, about 60% of the silver was removed per pass for the high selenium ore, and around 90% for the high mercury ore. Since silver sulfide is rather expensive to use solely as a mercury precipitant, another compound was sought which could selectively precipitate mercury and leave silver in solution. In looking for a more inexpensive selective precipitant, zinc sulfide was tested. The third set of experiments did show that zinc sulfide (as sphalerite) could be used to selectively precipitate mercury while leaving silver cyanide in solution. Parameters such as particle size, reduction potential, and amount of oxidation of the sphalerite were tested. Batch experiments worked well, showing 99.8% mercury removal with only ≈1% silver loss (starting with 930 ppb mercury, 300 ppb silver) at one hour. A continual flow process would work better for industrial applications, which was demonstrated with the filter funnel set up. Funnels with filter paper and sphalerite tested showed good mercury removal (from 31 ppb mercury and 333 ppb silver with a 87% mercury removal and 7% silver loss through one funnel). A counter current flow set up showed 100% mercury removal and under 0.1% silver loss starting with 704 ppb silver and 922 ppb mercury. The resulting sphalerite coated with mercury sulfide was also shown to be stable (not releasing mercury) under leaching tests. Use of sphalerite could be easily implemented through such means as sphalerite impregnated filter paper placed in currently existing processes. In summary, this work focuses on preventing mercury from following silver through the leaching circuit. Currently the only possible means of removing mercury is by retort, creating possible health hazards in the distillation process and in transportation and storage of the final mercury waste product. Preventing mercury from following silver in the earlier stages of the leaching process will greatly reduce the risk of mercury spills, human exposure to mercury, and possible environmental disasters. This will save mining companies millions of dollars from mercury handling and storage, projects to clean up spilled mercury, and will result in better health for those living near and working in the mines.

An adaptive neighborhood search algorithm for optimizing stochastic mining complexes

Les métaheuristiques sont très utilisées dans le domaine de l'optimisation discrète. Elles permettent d’obtenir une solution de bonne qualité en un temps raisonnable, pour des problèmes qui sont de grande taille, complexes, et difficiles à résoudre. Souvent, les métaheuristiques ont beaucoup de paramètres que l’utilisateur doit ajuster manuellement pour un problème donné. L'objectif d'une métaheuristique adaptative est de permettre l'ajustement automatique de certains paramètres par la méthode, en se basant sur l’instance à résoudre. La métaheuristique adaptative, en utilisant les connaissances préalables dans la compréhension du problème, des notions de l'apprentissage machine et des domaines associés, crée une méthode plus générale et automatique pour résoudre des problèmes. L’optimisation globale des complexes miniers vise à établir les mouvements des matériaux dans les mines et les flux de traitement afin de maximiser la valeur économique du système. Souvent, en raison du grand nombre de variables entières dans le modèle, de la présence de contraintes complexes et de contraintes non-linéaires, il devient prohibitif de résoudre ces modèles en utilisant les optimiseurs disponibles dans l’industrie. Par conséquent, les métaheuristiques sont souvent utilisées pour l’optimisation de complexes miniers. Ce mémoire améliore un procédé de recuit simulé développé par Goodfellow & Dimitrakopoulos (2016) pour l’optimisation stochastique des complexes miniers stochastiques. La méthode développée par les auteurs nécessite beaucoup de paramètres pour fonctionner. Un de ceux-ci est de savoir comment la méthode de recuit simulé cherche dans le voisinage local de solutions. Ce mémoire implémente une méthode adaptative de recherche dans le voisinage pour améliorer la qualité d'une solution. Les résultats numériques montrent une augmentation jusqu'à 10% de la valeur de la fonction économique.

An approximate method for the solution of an influence function foil rolling model

Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.