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The problem of utilizing blending techniques to control coal quality at the production-consumption phase is considered. Three blending models were developed to provide coal of high thermal content and low pollutants. With the aid of operational mine planning, coal is blended at the coal producing mines such that the best quality of coal is mined during a planning period, while meeting the management production objectives. The first model developed uses 0-1 programming formulation to select potential working areas of a mine on the basis of predicted grade values obtainable from geostatistics. A second model developed combines economically coals produced by different suppliers to meet the specification of a power plant. The second model uses a linear programming formulation to develop coal purchasing strategy. Finally, a multiobjective programming technique is used to determine the tonnages of coal which must be cleaned from various sources (e.g. stockpiles) in order to result in clean coal of high thermal content and low sulfur content. The two objectives used are minimization of total sulfur and maximization of total Btu. Both the operational mine planning and coal purchasing models were tested on actual mine data. The study demonstrated the capability of controlling coal quality by blending technique with the aid of the three models. This can be translated into dollar savings to both the coal producer and the coal consumer.

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This study utilizes a Monte Carlo simulation to examine the performance of multivariate geometric moving average control chart schemes for controlling the mean of a multivariate normal process. The study compares the performance of the proposed method with a multivariate Shewhart chart, a multiple univariate cumulative sum (CUSUM) control chart, a multivariate CUSUM control chart and a multiple univariate geometric moving average control chart.

Digitized by Kansas Correctional Industries

Pacific whiting, with a maximum sustainable yield between 150,000 and 250,000 metric tons, is the largest stock of fish found off Oregon. The majority of the fish are processed into surimi. Hundreds of variables could potentially affect surimi quality (gel strength). Alternative harvesting and processing input combinations, as well as product quality attributes and their influences, were collected for the 1992-94 Pacific whiting seasons. This data was combined with other research on Pacific whiting quality to develop a comprehensive model of the Pacific whiting fishery. Neural network and induction modeling methods were used to isolate the importance of each input variable and document its interactive effects on other variables. Neural network modeling does not have the limitations of standard modeling techniques. A neural network model can "learn" and adjust weights among inputs and interactions as situations change. This allows for development of models which assign weights to all inputs, yet is easily maintained and updated. Another modeling method, known as induction, divides the information into smaller, more defined, subgroups which are analyzed separately using regression. This strategy reduces complications due to discontinuities in the data. A hybrid model was developed by combining results of the two modeling methods. These methods were compared to multiple regression for their effectiveness in prediction. The hybrid model provided the most accurate predictions (96% of predictions within 10% of actual value), followed by neural networks (92%), induction (84%), and regression (74%). Of the 88 variables examined, only ten and their interactions were significantly related to final product quality. These variables include the time it takes to process the fish from capture, the temperature the fish are stored until processing, the salinity, moisture content, and pH of the fish, the length and weight of the fish, the date and place where the fish were captured, and the water:meat wash ratio of the various surimi washes during processing. Most of the variables were highly interactive and nonlinear. The information derived from these models can be used to optimize production decisions and maximize profit. Quality influences of Pacific whiting are crucial for long term production and can be used to benefit the entire industry. / Graduation date: 1996

This research investigates the conceptual relationships among quality costs, productivity, and quality proposed by Garvin, Juran, and Feigenbaum. It provides an empirical evaluation of these relationships by applying a specific linear programming approach, namely, Data Envelopment Analysis (DEA), to various production specifications developed for a linerboard manufacturing case study. The research investigates the changes in the efficiency values when various models derived from these qualities, productivity, and cost specifications are evaluated. The use of DEA as a method to support the modeling of these relationships is also discussed. The I.D.E.A.S. software program developed by Ali is used to implement DEA in the research. / Master of Science

Conselho Nacional de Desenvolvimento Científico e Tecnológico / Nesta dissertação é apresentada a avaliação da dispersão de contaminantes no Rio São Pedro, pequeno curso situado na Bacia Hidrográfica do Rio Macaé, centro-norte do Estado do Rio de Janeiro. Para tanto, utilizou-se uma abordagem que conjugou simulação e observação. A simulação compreendeu a modelagem do transporte de substância conservativa em um dado trecho desse rio. O modelo adotado utilizou a formulação unidimensional da equação de advecção-dispersão, que tem como parâmetro crítico o coeficiente de dispersão longitudinal. Paralelamente foi conduzido um experimento de campo, que constou da injeção instantânea de massa conhecida de sal nesse trecho do rio, tendo sido monitorada a concentração resultante em uma seção situada a jusante do ponto de lançamento. Códigos numéricos foram implementados para obter-se o melhor ajuste entre simulação e observação, ajuste esse obtido através da estimativa do coeficiente de dispersão. / In this dissertation we performed an assessment of contaminat dispersion in a small river located at Macaé Watershed Basin. We adopted an approach that made use both of simulation and experiments. The simulation solved the one dimensional transport model for a conservative substance, on which the dispersion coefficient plays a critical role. The observation data was obtained in an experiment especially designed for this purpose. An instantaneous injection of a known quantity of mass of salt in a given position of the river was discharged and the resulting concentration was monitored in two downstream stations. Numerical codes were built in order to obtain a better fitting between simulation and observation.