Thesis (Ph. D.)--West Virginia University, 1998. / Title from document title page. "December, 1998." Document formatted into pages; contains xiv, 171 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 130-132).

No description available.

Thesis (M.S.)--Ohio University, June, 1991. / Title from PDF t.p.

Thesis (Professional Degree)--University of Missouri, School of Mines and Metallurgy, 1938. / The entire thesis text is included in file. Typescript. Title from title screen of thesis/dissertation PDF file (viewed April 20, 2010)

Thesis (M. Eng.)(Industrial)--University of Pretoria, 2004. / Includes summary. Includes bibliographical references.

No description available.

The operational scheduling at coal handling facilities is of the utmost importance to ensure that the coal consuming processes are supplied with a constant feed of good quality coal. Although the Sasol Coal Handling Facility (CHF) were not designed to perform coal blending during the coal handling process, CHF has to blend the different sources to ensure that the quality of the feed supplied is of a stable nature. As a result, the operation of the plant has become an extremely complex process. Consequently, human intelligence is no longer sufficient to perform coal handling scheduling and therefore a scheduling model is required to ensure optimal plant operation and optimal downstream process performance. After various attempts to solve the scheduling model optimally, i.e. with exact solution methods, it was found that it is not possible to accurately model the complexities of CHF in such a way that the currently available exact solvers can solve it in an acceptable operational time. Various alternative solution approaches are compared, in terms of solution quality and execution speed, using a simplified version of the CHF scheduling problem. This investigation indicates that the Simulated Annealing (SA) metaheuristic is the most efficient solution method to provide approximate solutions. The metaheuristic solution approach allows one to model the typical sequential thoughts of a control room operator and sequential operating procedures. Thus far, these sequential rules could not be modelled in the simultaneous equation environment required for exact solution methods. An SA metaheuristic is developed to solve the practical scheduling model. A novel SA approach is applied where, instead of the actual solution being used for neighbourhood solution representation, the neighbours are indirectly represented by the rules used to generate neighbourhood solutions. It is also found that the initial temperature should not be a fixed value, but should be a multiple of the objective function value of the initial solution. An inverse arctan-based cooling schedule function outperforms traditional cooling schedules as it provides the required diversification and intensification behaviour of the SA. The scheduling model solves within 45 seconds and provides good, practically executable results. The metaheuristic approach to scheduling is therefore successful as the plant complexities and intricate operational philosophies can be accurately modelled using the sequential nature of programming languages and provides good approximate optimal solutions in a short solution time. Tests done with live CHF data indicate that the metaheuristic solution outperforms the current scheduling methodologies applied in the business. The implementation of the scheduler will lead to a more stable factory feed, which will increase production yields and therefore increase company profits. By reducing the amount of coal re-handling (in terms of throw-outs and load-backs at mine bunkers), the scheduler will reduce the coal handling facility’s annual operating cost by approximately R4.6 million (ZAR). Furthermore, the approaches discussed in this document can be applied to any continuous product scheduling environment. Additional information available on a CD stored at Level 3 of the Merensky Library. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2011. / Industrial and Systems Engineering / unrestricted

The objective of this project is to develop an operational scheduling model for Sasol Mining’s coal handling facility, Sasol Coal Supply (referred to as SCS), to optimise daily operations. In this document, the specific scheduling problem at SCS is presented and solved using Mixed Integer Non-Linear Programming (MINLP) continuous time representation techniques. The most recent MINLP scheduling techniques are presented and applied to an example problem. The assumption is made that the results from the example problem will display trends which will apply to the SCS scheduling problem as well. Based on this assumption, the unit-specific event based continuous time formulation is chosen to apply to the SCS scheduling problem. The detail mathematical formulation of the SCS scheduling problem, based on the chosen technique, is discussed and the necessary changes presented to customise the formulation for the SCS situation. The results presented show that the first phase model does not solve within 72 hours. A solution time of more than three days is not acceptable for an operational scheduling model in a dynamic system like SCS. Various improvement approaches are applied during the second phase of the model development. Special Ordered Sets of Type 1 (SOS1) variables are successfully applied in the model to reduce the amount of binary variables. The time and duration constraints are restructured to simplify the structure of the model. A specific linearization and solution technique is applied to the non-linear equations to ensure reduced model solution times and reliable results. The improved model for one period solves to optimality within two minutes. This dramatic improvement ensures that the model will be used operationally at SCS to optimise daily operations. The scheduling model is currently being implemented at SCS. Examples of the input variables and output results are presented. It is concluded that the unit-specific event based MINLP continuous time formulation method, as presented in the literature, is not robust enough to be applied to an operational industrial-sized scheduling problem such as the SCS problem. Customised modifications to the formulation are necessary to ensure that the model solves in a time acceptable for operational use. However, it is proved that Mixed Integer Non-linear Programming (MINLP) can successfully be applied to optimise the scheduling of an industrial-sized plant such as SCS. Although more research is required to derive robust formulation techniques, the principle of using mathematical methods to optimise operational scheduling in industry can dramatically impact the way plants are operated. The optimisation of daily schedules at SCS by applying the MINLP continuous time scheduling technique, has made a significant contribution to the coal handling industry. Finally, it can be concluded that the SCS scheduling problem was successfully modelled and the operational scheduling model will add significant value to the Sasol Group. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2006. / Industrial and Systems Engineering / unrestricted