Thesis (MEng)--University of Stellenbosch, 2000. / ENGLISH ABSTRACT: The design of an effective layout for a facility plays an important roll in its successful operation. The centralisation of certain activities has advantages in that it can reduce inventory levels, simplify material management, ease standardisation and provide better control over the quality of the final product. This thesis identifies the factors that must be taken into account in order to obtain a layout that functions effectively. Different layout types, material handling concepts and flow patterns are investigated. A variety of tools to analyse and evaluate these factors are discussed. A logical, practical and simple process is discussed which addresses the planning of the facility layout in a systematic manner. The volume of components that have to be rebuilt by the facility is obtained by making use of existing maintenance plans. The labour requirement is determined based on this volume. Activities and support services within the facility are compared to each other in order to determine the importance of the relationship between each. This relationship plays an important role in the placement of cells relative to each other. The layout is adjusted by taking the practical limitations of the current facility into account. A few alternative layouts are developed and they are rated against a list of parameters in order to obtain the most suitable layout. The processes and floor layout of the current facility are investigated and discussed. Aspects such as material, equipment, cleaning of components and the flow of documentation and information are discussed. Finally the way of operating in the future is discussed. This is obtained by looking at the theoretically determined layout and adapting it by taking both the good and bad points of the current layout into consideration. / AFRIKAANSE OPSOMMING: Die ontwerp van 'n effektiewe uitleg vir 'n fasiliteit speel 'n belangrike rol in die suksesvolle werking daarvan. Die sentralisering van sekere aktiwiteite het voordele deurdat dit voorraadvlakke kan verlaag, beheer van voorraad vereenvoudig, standaardisering vergemaklik en beter beheer oor die gehalte van die finale produk bied. Hierdie tesis identifiseer die faktore wat in berekening gebring moet word ten einde 'n uitleg te verkry wat effektief funksioneer. Verskillende tipes uitlegte, materiaal hanterings konsepte en vloei patrone word ondersoek. 'n Verskeidenheid hulpmiddels om hierdie faktore te ontleed en te evalueer word bespreek. 'n Logiese, praktiese en eenvoudige proses word bespreek wat die beplanning van die fasiliteits uitleg sistematies aanpak. Deur van beskikbare instandhoudings planne gebruik te maak word die volume van komponente wat deur die fasiliteit herbou moet word bepaal. Die mannekrag behoeftes word bepaal gebaseer op hierdie volume. Aktiwiteite en ondersteunings funksies binne die fasiliteit word vergelyk ten einde die belangrikheid van die verhoudings tussen elkeen te bepaal. Hierdie verhoudings speel 'n belangrike rol in die plasing van selle relatief tot mekaar. Deur die praktiese beperkings van die huidige fasiliteit in ag te neem word die uitleg aangepas. 'n Paar alternatiewe uitlegte word ontwikkel en evalueer teen 'n lys parameters om die mees geskikte uitleg te verkry. Die prosesse en vloeruitleg van die huidige uitleg word ondersoek en bespreek. Hieronder word aspekte soos toerusting, materiaal, skoonmaak van komponente en die vloei van dokumentasie en inligting gedek. Ten slotte word gekyk na die toekomstige werkswyse wat gevolg gaan word. Dit word bereik deur die teoreties bepaalde uitleg te neem en aan te pas deur sommige van die goeie en slegte punte van die huidige uitleg in ag te neem.

Thesis (D.Tech.: Mechanical Engineering)-Dept. of Mechanical Engineering, Durban Institute of Technology, 2005 xxi, 265 leaves / The aim of this study is to develop, using neural networks, a model to aid the performance monitoring of operational diesel engines in industrial settings. Feed-forward and modular neural network-based models are created for the prediction of the specific fuel consumption on any normally aspirated direct injection four-stroke diesel engine. The predictive capability of each model is compared to that of a published quadratic method. Since engine performance maps are difficult and time consuming to develop, there is a general scarcity of these maps, thereby limiting the effectiveness of any engine monitoring program that aims to manage the fuel consumption of an operational engine. Current methods applied for engine consumption prediction are either too complex or fail to account for specific engine characteristics that could make engine fuel consumption monitoring simple and general in application. This study addresses these issues by providing a neural network-based predictive model that requires two measured operational parameters: the engine speed and torque, and five known engine parameters. The five parameters are: rated power, rated and minimum specific fuel consumption bore and stroke. The neural networks are trained using the performance maps of eight commercially available diesel engines, with one entire map being held out of sample for assessment of model generalisation performance and application validation. The model inputs are defined using the domain expertise approach to neural network input specification. This approach requires a thorough review of the operational and design parameters affecting engine fuel consumption performance and the development of specific parameters that both scale and normalize engine performance for comparative purposes. Network architecture and learning rate parameters are optimized using a genetic algorithm-based global search method together with a locally adaptive learning algorithm for weight optimization. Network training errors are statistically verified and the neural network test responses are validation tested using both white and black box validation principles. The validation tests are constructed to enable assessment of the confidence that can be associated with the model for its intended purpose. Comparison of the modular network with the feed-forward network indicates that they learn the underlying function differently, with the modular network displaying improved generalisation on the test data set. Both networks demonstrate improved predictive performance over the published quadratic method. The modular network is the only model accepted as verified and validated for application implementation. The significance of this work is that fuel consumption monitoring can be effectively applied to operational diesel engines using a neural network-based model, the consequence of which is improved long term energy efficiency. Further, a methodology is demonstrated for the development and validation testing of modular neural networks for diesel engine performance prediction.

Thesis (D.Tech.: Mechanical Engineering)-Dept. of Mechanical Engineering, Durban Institute of Technology, 2005 xxi, 265 leaves / The aim of this study is to develop, using neural networks, a model to aid the performance monitoring of operational diesel engines in industrial settings. Feed-forward and modular neural network-based models are created for the prediction of the specific fuel consumption on any normally aspirated direct injection four-stroke diesel engine. The predictive capability of each model is compared to that of a published quadratic method. Since engine performance maps are difficult and time consuming to develop, there is a general scarcity of these maps, thereby limiting the effectiveness of any engine monitoring program that aims to manage the fuel consumption of an operational engine. Current methods applied for engine consumption prediction are either too complex or fail to account for specific engine characteristics that could make engine fuel consumption monitoring simple and general in application. This study addresses these issues by providing a neural network-based predictive model that requires two measured operational parameters: the engine speed and torque, and five known engine parameters. The five parameters are: rated power, rated and minimum specific fuel consumption bore and stroke. The neural networks are trained using the performance maps of eight commercially available diesel engines, with one entire map being held out of sample for assessment of model generalisation performance and application validation. The model inputs are defined using the domain expertise approach to neural network input specification. This approach requires a thorough review of the operational and design parameters affecting engine fuel consumption performance and the development of specific parameters that both scale and normalize engine performance for comparative purposes. Network architecture and learning rate parameters are optimized using a genetic algorithm-based global search method together with a locally adaptive learning algorithm for weight optimization. Network training errors are statistically verified and the neural network test responses are validation tested using both white and black box validation principles. The validation tests are constructed to enable assessment of the confidence that can be associated with the model for its intended purpose. Comparison of the modular network with the feed-forward network indicates that they learn the underlying function differently, with the modular network displaying improved generalisation on the test data set. Both networks demonstrate improved predictive performance over the published quadratic method. The modular network is the only model accepted as verified and validated for application implementation. The significance of this work is that fuel consumption monitoring can be effectively applied to operational diesel engines using a neural network-based model, the consequence of which is improved long term energy efficiency. Further, a methodology is demonstrated for the development and validation testing of modular neural networks for diesel engine performance prediction.

Indiana University-Purdue University Indianapolis (IUPUI) / This paper presents a novel approach of particulate material (soot) measurement in a Diesel particulate filter using Electrical Capacitance Tomography. Modern Diesel Engines are equipped with Diesel Particulate Filters (DPF), as well as on-board technologies to evaluate the status of DPF because complete knowledge of DPF soot loading is very critical for robust efficient operation of the engine exhaust after treatment system. Emission regulations imposed upon all internal combustion engines including Diesel engines on gaseous as well as particulates (soot) emissions by Environment Regulatory Agencies. In course of time, soot will be deposited inside the DPFs which tend to clog the filter and hence generate a back pressure in the exhaust system, negatively impacting the fuel efficiency. To remove the soot build-up, regeneration of the DPF must be done as an engine exhaust after treatment process at pre-determined time intervals. Passive regeneration use exhaust heat and catalyst to burn the deposited soot but active regeneration use external energy in such as injection of diesel into an upstream DOC to burn the soot. Since the regeneration process consume fuel, a robust and efficient operation based on accurate knowledge of the particulate matter deposit (or soot load)becomes essential in order to keep the fuel consumption at a minimum. In this paper, we propose a sensing method for a DPF that can accurately measure in-situ soot load using Electrical Capacitance Tomography (ECT). Simulation results show that the proposed method offers an effective way to accurately estimate the soot load in DPF. The proposed method is expected to have a profound impact in improving overall PM filtering efficiency (and thereby fuel efficiency), and durability of a Diesel Particulate Filter (DPF) through appropriate closed loop regeneration operation.