Date of Award
8-1-2012
Degree Name
Doctor of Philosophy
Department
Engineering Science
First Advisor
Chugh, Yoginder
Abstract
Underground mines are the source of 33% of US coal production and 60% of worldwide coal production. Room-and-pillar mining with continuous miners has been the most common production system used in these mines since the 1960s. The introduction of continuous miners mechanized the underground coal mining industry triggering a period of sustained growth in mine productivity; however, productivity peaked at the turn of the century and has been in decline for a decade. Research on productivity in underground coal mines began at Southern Illinois University Carbondale in 2000 and led to development of a deterministic spreadsheet model for evaluating continuous miner production systems. As with other production models, this model uses a heuristic approach to define the fundamental input parameter known as a cut sequence. This dissertation presents a dynamic programming algorithm to supplant that trial-and-error practice of determining and evaluating room-and-pillar mining sequences. Dynamic programming has been used in mining to optimize multi-stage processes where production parameters are stage-specific; however, this is the inaugural attempt at considering parameters that are specific to paths between stages. The objective of the algorithm is to maximize continuous miner utilization for true production when coal is actually being loaded into haulage units. This is accomplished with an optimal value function designed to minimize cut-cycle time. In addition to loading time, cut-cycle time also includes change-out and place change times. The reasonableness of the methodology was validated by modeling an actual mining sequence and comparing results with time study and production report data collected from a cooperating mine over a two-week time period in which more than 300 cuts were mined. The validation effort also inspired some fine-tuning adjustments to the algorithm. In a case study application of the dynamic programming algorithm, a seven-day "optimal mining sequence" was identified for three crosscuts of advance on an eleven-entry super-section developing a main entry system for a new mine in southern Illinois. Productivity improvements attributable to the optimal sequence were marginal but the case study application reconfirmed the reasonableness of the methodology.
Access
This dissertation is Open Access and may be downloaded by anyone.