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In this work we have made use of a geometric approach which quantifies robustness and performance and we finally combine them using a cost function. In particular, we calculate the robustness of the estimate of standard deviation of nominally Laplacian distribution. As this distribution is imperfectly known, we employ a more general family, the generalized Gaussian; Laplacian distribution, is one of the members of this family. We compute parameter estimates and present a classical algorithm which is then analyzed for distribution from the generalized Gaussian family. We calculate the mean squared error according to the censoring height k. We measure performance as a function of (1/MSE) and combine it with robustness using a cost criterion and design a robust estimator which optimizes a mix of performance and robustness specified by the user.

Predicting well inflow performance relationship accurately is very important for production engineers. From these predictions, future plans for handling and improving well performance can be established. One method of predicting well inflow performance is to use artificial neural networks. Vogel's reference curve, which is produced from a series of simulation runs for a reservoir model proposed by Weller, is typically used to predict inflow performance relationship for solution-gas-drive reservoirs. In this study, I reproduced Vogel's work, but instead of producing one curve by conventional regression, I built three neural network models. Two models predict the IPR efficiently with higher overall accuracy than Vogel's reference curve.

The main purpose of this study was to determine the effect of contour height on the passage ratio of pulp through screen apertures, and determine which operating variable has the greatest affect on screen performance. In addition, a freeness model was to be developed, in hopes of helping to predict the freeness drop between feed and accepts. The study was conducted at The University of British Columbia (UBC) using a laboratory scale pressure screen. Slot velocity, feed consistency and contour height were the changing variables. Samples were collected from which passage ratio, freeness, fibre length and coarseness were determined. From the studies conducted it was found that slot velocity had the greatest influence on the screen operation. As the slot velocity increased a greater force was applied to the fibre to help push it through the screen aperture. However, this increase in slot velocity decreases the fractionation ability (separation of fibres into different lengths) of the screen. The second most important variable was the contour height. The main function of the contour height is to disrupt the flow of thick stock at the wall of the screen and allow for unhindered movement of fibre to the screen wall. The greater the contour height is, the greater the passage ratio (pulp fibre passing through screen). However, there is a decrease in fractionation. The third most important factor was the feed consistency. At low feed consistencies there is less crowding in the screen. Less crowding leads to more loosely-formed flocs, which are easier for the contour height and the rotor to dissipate and thus leads to unhindered movement. Thicker feed stock has a negative effect on passage A Freeness model was developed that showed that freeness had a power law relationship to passage ratio. The passage ratio was raised to a constant B, which is a function of the contour height and the feed consistency.

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