This experimental investigation examines the influence of surface treatment on the strength of low-fired tempered ceramic bodies. Specimens prepared with commercial raw materials are employed, either finger-smoothed, burnished, textured, slipped/burnished, or resin coated; two building techniques, coiling and paddle-and-anvil, are also included since these forming processes affect the vessel surface. The modulus of rupture or flexural strength is determined by means of a four-point bending test, which is sensitive to critical surface flaws. By means of a univariate analysis of variance, small differences in strength are found between ceramic briquettes with different surface treatments. Although the differences in means for the moduli of rupture values are statistically significant, the differences are of such a low magnitude that they cannot be considered behaviorally relevant. These results are only valid for the materials, mode of preparation, and test procedures employed.

Thesis (M.S.)--Auburn University, 2007. / Abstract. Vita. Includes bibliographic references (ℓ. 67-71)

Thesis (Ph. D.)--University of Rhode Island, 2003. / Typescript. Includes bibliographical references (leaves 228-239).

Thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Technology: Department of Chemical Engineering, Durban University of Technology, 2007. / The metal finishing industry has been rated among the most polluting industries worldwide. This industry has traditionally been responsible for the release of heavy metals such as chrome, nickel, tin, copper etc into the environment. The application of cleaner production systems to a range of industries, including the metal finishing industry has provided significant financial and environmental benefits. An example of a successful application cleaner production in the metal finishing industry is the reduction in the typical water consumption from 400 1/m² to less than 10 1/m² of plated product. The successful application of cleaner production to the mental finishing industry has encountered many barriers. These barriers include the need for a highly skilled cleaner production auditor and the need for rigorous plant data to effectively quantify the cleaner production potential of the company under consideration. This study focuses on providing an alternate user-friendly audit system for the implementation of cleaner production in the mental finishing industry. The audit system proposed eliminates the need for the need for both a technical auditor and rigid plant data. The proposed system functions solely on plant operator inputs. The operator’s knowledge is harnessed and used to conduct an efficient and effective cleaner production audit. The research is based on expert knowledge, which was gained by conducting audits on some 25 companies using traditional auditing tools. This company audits were used to construct a database of data that was used in the verification of the models developed in this study. The audit is separated into different focus components. The first system developed was based on fuzzy logic multi variable decision-making. For this system the plant was categorized into different sections and appropriate fuzzy ratings were allocated based on experience. Once the allocations were completed multi variable decision analysis was used to determine the individual variable impact. The output was compared and regressed to the database equivalent. Operator inputs can then be used to determine the individual category outputs for the cleaner for the production rating for the company under consideration. The second part of this study entails the development of mathematical models for the quantification of chemical and water consumptions. This was based on the present and ideal (cleaner production) plant configuration. Cleaner production operations are compared to present operations and potential savings quantified. Mathematical models were developed based on pilot scale experiments for the acid, degreaser and zinc plating process. The pilot experiments were carried out on a PLC controlled pilot plant. These models were developed form factorial experimentation on the variables of each of the plating processes. The models developed aid in the prediction of the relevant optimum consumptions. The key challenge in traditional evaluation systems has been the quantification of the plant production. The most effective measure of production is by means of the surface area plated. In this study a novel approach using the modeled acid consumption is proposed. It was assumed that the operator inputs for the above models would not be precise. The models developed allowed for input variations. These variations were incorporated into the model using the Monte Carlo technique. The entire cleaner production evaluation system proposed is based on an operator questionnaire, which is completed in visual basic. The mathematical model was incorporated into the visual basic model. For the purpose of model verification the mathematical models were programmed and tested using the engineering mathematical software, Mat Lab. The combined fuzzy logic and mathematical models prove to be a highly effective means of completing the cleaner production evaluation in minimal time and with minimal resources. A comparative case study was conducted at a local metal finishing company. The case study compares the input requirements and outputs from the traditional systems with the system proposed in this study. The traditional model requires 245 inputs whilst the model proposed in this study is based on 56 inputs. The data requirements for the model proposed in this study is obtained from a plant operator in less than one hour whilst previous models required high level expertise over a period of up to two weeks. The quality of outputs from the model proposed is found to be very comparable to previous models. The model is actually found to be superior to previous models with regards predicting operational variations, water usages, chemical usages and bath chemical evolution. The research has highlighted the potential to apply fuzzy-mathematical hybrid systems for cleaner production evaluation. The two limitations of the research were found to be the usage of a linear experimental design for model development and the availability of Mat Lab software for future application. These issues can be addressed as future work. It is recommended that a non-linear model be developed for the individual processes so as to obtain more detailed process models. / National Research Foundation, Water Research Commission and Durban University of Technology

published_or_final_version / Business Administration / Master / Master of Business Administration

Doctor of Philosophy / Department of Animal Sciences and Industry / James S. Drouillard / Four studies evaluated effects of mineral supplementation on feedlot performance, carcass characteristics and ruminal fermentation of finishing cattle. Study 1 supplemented 0 or 3.3 g/d yeast combined with Cr propionate to steers separated into light and heavy groups. No treatment x weight group interactions were observed for ADG, DMI, final BW, carcass traits, or plasma glucose of lactate concentrations (P ≥ 0.06). A treatment x weight group interaction was observed for G:F (P = 0.03). In study 2, steers were supplemented 60 or 300 mg Zn/kg DM with or without zilpaterol hydrochloride (ZH). No interactions or effects of Zn or ZH were observed for IGF-1, plasma glucose, or lactate concentrations (P > 0.05). Plasma urea nitrogen (PUN) concentration decreased with ZH (P < 0.01). No interactions or effects of Zn or ZH were detected for ADG, DMI, final BW, G:F, and carcass traits were minimally affected (P ≥ 0.05). Study 3 evaluated effects of supplementing 30 or 100 mg Zn/kg DM (30 or 100Zn) with and without ractopamine hydrochloride (RH; 200 mg/d). No interactions or effects of Zn were observed for feedlot performance or PUN (P ≥ 0.07). Final BW, ADG, and HCW increased when heifers were fed RH (P ≤ 0.02). Zinc x RH interactions were observed for LM area and yield grade (P ≤ 0.01), but other carcass traits were not affected (P ≥ 0.08). In study 4, heifers were supplemented 0, 30, 60, or 90 mg Zn/kg DM. Zinc supplementation did not affect final BW, ADG, or DMI (P ≥ 0.07), but G:F increased linearly (P = 0.02). Carcass traits were not affected by Zn supplementation (P ≥ 0.07). Effects of in vitro Zn titration (0, 30, 60, 60, 90, 120, or 150 mg/kg Zn) were evaluated using ground corn and soybean meal as substrate. In vitro fermentation was not affected by added Zn (P ≥ 0.05). These studies suggest Cr and Zn supplementation minimally affected carcass traits, but Zn supplementation up to 60 mg/kg improved feed efficiency with minimal impact on ruminal fermentation. Supplementing increased Zn concentrations may alter fat and muscle deposition when fed with RH.

The Cattle Value Discovery System (CVDS) was developed to predict growth and feed requirements of individual cattle fed in groups based on animal, diet, and environment information (Tedeschi et al., 2006). Evaluations of the CVDS using several databases of finishing cattle were conducted to determine the accuracy and precision of the model in predicted dry matter required (DMR) of pen-fed cattle. As well, the sensitivity of the model's predictions to deviations from actual ration metabolizable energy (ME) value was conducted. A meta-analysis of growing and finishing steers evaluated to model's accuracy in predicting DMR of individually fed steers, and the relationships between several model-predicted variables and actual performance and efficiency measures. Results for the first CVDS model evaluation involving pen-fed Santa Gertrudis cattle fed finishing diets revealed that accurate predictions of DMR are possible. The average mean bias for both steers and heifers was 2.43%. The sensitivity analysis of dietary ME values revealed that the model tends to consistently over- and under-predict DMR when the ME values are under- and over-estimated, respectively. However the ranking of pens was not affected by this mis-estimation of diet ME. In the second evaluations, both methods (mean body weight; MBW, dynamic iterative model; DIM) of CVDS were highly accurate and precise in allocating feed to pens of steers fed diverse types of diets and environmental conditions, with both models having a mean bias under 4%. The DIM model was slightly more accurate than the MBW model in predicting DMR. An evaluation of sources of variation revealed that for both models a large portion of the error was random, indicating that further work is needed to account for this variation. The meta-analysis study revealed that the model was able to account for 64% and 67% of the variation in observed dry matter intake (DMI) for growing and finishing steers, respectively. The two model-predicted efficiency measures, the ratio of DMR to average daily gain (ADG) and predicted intake difference (PID), were strongly to moderately correlated with their observed efficiency counterparts. In growing and finishing steers, DMR: ADG was able to account for 76% and 64% of the variation in observed feed conversion ratio (FCR) in growing and finishing studies, respectively. Strong correlations were also found between residual feed intake (RFI) and PID, suggesting that there may also be some similarity on these two measurements.

The Cattle Value Discovery System (CVDS) was developed to predict growth and feed requirements of individual cattle fed in groups based on animal, diet, and environment information (Tedeschi et al., 2006). Evaluations of the CVDS using several databases of finishing cattle were conducted to determine the accuracy and precision of the model in predicted dry matter required (DMR) of pen-fed cattle. As well, the sensitivity of the model’s predictions to deviations from actual ration metabolizable energy (ME) value was conducted. A meta-analysis of growing and finishing steers evaluated to model’s accuracy in predicting DMR of individually fed steers, and the relationships between several model-predicted variables and actual performance and efficiency measures. Results for the first CVDS model evaluation involving pen-fed Santa Gertrudis cattle fed finishing diets revealed that accurate predictions of DMR are possible. The average mean bias for both steers and heifers was 2.43%. The sensitivity analysis of dietary ME values revealed that the model tends to consistently over- and under-predict DMR when the ME values are under- and over-estimated, respectively. However the ranking of pens was not affected by this mis-estimation of diet ME. In the second evaluations, both methods (mean body weight; MBW, dynamic iterative model; DIM) of CVDS were highly accurate and precise in allocating feed to pens of steers fed diverse types of diets and environmental conditions, with both models having a mean bias under 4%. The DIM model was slightly more accurate than the MBW model in predicting DMR. An evaluation of sources of variation revealed that for both models a large portion of the error was random, indicating that further work is needed to account for this variation. The meta-analysis study revealed that the model was able to account for 64% and 67% of the variation in observed dry matter intake (DMI) for growing and finishing steers, respectively. The two model-predicted efficiency measures, the ratio of DMR to average daily gain (ADG) and predicted intake difference (PID), were strongly to moderately correlated with their observed efficiency counterparts. In growing and finishing steers, DMR: ADG was able to account for 76% and 64% of the variation in observed feed conversion ratio (FCR) in growing and finishing studies, respectively. Strong correlations were also found between residual feed intake (RFI) and PID, suggesting that there may also be some similarity on these two measurements.

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