Annual Meeting Preliminary Program - Full Brochure (PDF) - SME
Annual Meeting Preliminary Program - Full Brochure (PDF) - SME
Annual Meeting Preliminary Program - Full Brochure (PDF) - SME
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wednesday, february 27<br />
morning<br />
Coal & Energy: Coal Preparation<br />
Bulk Material Handling by Conveyor Belt 7:<br />
Solving Real Problems Using Numerical<br />
Analysis and Simulation<br />
9:00 AM • Wednesday, February 27<br />
Chair: T. Hollingsworth, Conveyor Engineering, Meridian, ID<br />
9:05 AM<br />
Simulation for Equipment Sizing – Longwall to Stockpile<br />
E. O’Donovan; E.J. O’Donovan & Associates, Spring Hill, QLD, Australia<br />
When longwall equipment is being specified, a nominal capacity is generally<br />
given on which to base the design of the various face and out-bye systems and<br />
components. This capacity generally comes from a nominal annual capacity<br />
requirement based on realistic utilization rates. From illustrative examples of<br />
simple system simulation, this paper sets out to show that a single nominal<br />
capacity is not sufficient to size the different systems. The manner in which<br />
the Longwall is operated and the overall system configuration can have a significant<br />
impact on overall productivity. The importance of component availability<br />
on overall output is also addressed.<br />
9:35 AM<br />
Simulation as a Tool to Determine Stockyard Handling Capacity<br />
E. Monrad and H. King; Simulation Modelling Group, Sandwell<br />
Engineering, Vancouver, BC, Canada<br />
Discrete event simulation modeling provides a tool to quantify the throughput<br />
capacity of stockyards. Interactions between the main equipment types<br />
(trains, dumpers, conveyors, stackers, reclaimers, shiploaders) can be captured,<br />
including route blockages, breakdowns, and planned maintenance.<br />
This tool has been successfully used at world-class stockyard facilities to determine<br />
current capacity and to identify gains resulting from improvement<br />
projects. Recent projects have focused on iron-ore, coal, and soya facilities,<br />
but the technology is suitable to be used on any bulk material type.<br />
10:05 AM<br />
Interfacing Belt Feeders and Hoppers to Achieve Reliable Operation<br />
J. Carson 1 , F. Cabrejos 3 and M. Rulff 2 ; 1 Jenike & Johanson, Inc., Tyngsboro,<br />
MA; 2 Jenike & Johanson, Ltd, Toronto, ON, Canada and 3 Jenike & Johanson<br />
Chile S.A., Vina del Mar, Chile<br />
Belt feeders are commonly used to meter the flow of bulk solids from bins<br />
and hoppers. If the interface between the hopper outlet and belt feeder is not<br />
designed correctly, flow from the bin may be severely compromised, resulting<br />
in problems of no-flow, segregation, flooding, etc. By knowing the flow<br />
characteristics of the bulk solid being handled and applying proven design<br />
techniques, such problems can be avoided. Relevant bulk solids flow characteristics<br />
will be identified, along with measurement techniques. Design procedures<br />
for correcting problems with existing feeders as well as proper design<br />
of new feeders will be presented, along with case histories of successful<br />
application of these procedures.<br />
10:35 AM<br />
Predicting Material Behavior on Conveyors using DEM<br />
B. Ren and G. Mustoe; Colorado School of Mines, Golden, CO<br />
Energy losses are important factors in a conveyor’s performance and design.<br />
A significant energy loss due to belt sag within a conveyor system occurs<br />
when shearing motion between the bulk material particles occurs as the material<br />
moves along the belt. This sag energy loss has been studied previously<br />
using analytical mechanical methods that employ several simplifying approximations<br />
such as: a) dry bulk material, b) assumed pressure distributions between<br />
the bulk material and belt, and c) inertial effects within the bulk material<br />
and belt, etc. The current work employs a discrete element modeling<br />
(DEM) approach eliminating the need for the simplifying assumptions required<br />
in previous analyses. The DEM method models: a) the bulk material as<br />
a discrete system of particles with a specified size distribution, and interaction<br />
laws for dry and wet materials, and b) the belt geometry shape with a detailed<br />
3D CAD description. In this paper the effects of: a) idler spacing, b)<br />
belt speed, b) belt sag, and c) different bulk materials are studied.<br />
9:00 AM • Wednesday, February 27<br />
Chairs: R. Honaker, University of Kentucky, Lexington, KY<br />
M. Mohanty, Southern Illinois University Carbondale,<br />
Carbondale, IL<br />
9:05 AM<br />
Capital and Operating Cost Models for Coal Preparation Plants<br />
Z. Huang 1 , M. Mohanty 2 , H. Sevim 3 , B. Arnold 4 and S. Bhagwat 5 ; 1 Mintec<br />
Inc., Tucson, AZ; 2 Department of Mining and Mineral Resources<br />
Engineering, Southern Illinois University Carbondale, Carbondale, IL;<br />
3<br />
School of Engineering, Southern Illinois University Edwardsville,<br />
Edwardsville, IL; 4 PrepTech, Inc., Apollo, PA and 5 Illinois State Geological<br />
Survey, Champaign, IL<br />
Circuit-wise capital and operating cost models are developed for those cleaning<br />
and dewatering circuits that are widely used in current coal preparation<br />
plants. For each circuit, capital and operating costs are calculated for at least<br />
five feed capacities. For each feed capacity, the sizes of the major and ancillary<br />
equipment in the circuit are determined. The purchasing cost and operating<br />
cost of each piece of equipment are calculated based on the cost data collected<br />
from a variety of sources. The circuit capital cost is calculated as the<br />
sum of the capital cost of each piece of equipment in the circuit. Similarly,<br />
circuit operating cost is the sum of the operating cost of individual equipment<br />
in the circuit. Capital and operating cost estimating equations are then obtained<br />
by best-fitting the cost data as a function of feed capacities. The capital<br />
cost also includes the installation cost. Capital and operating cost tables,<br />
equations and figures are given for each cleaning and dewatering circuit. The<br />
utilization of these equations is also introduced by applying them in a new<br />
developed coal preparation plant simulator for conducting cost and<br />
economic analysis.<br />
9:25 AM<br />
The Design, Construction, Commissioning and Operation of the Arch<br />
Castle Valley Plant in Wellington, Utah<br />
P. Bethell 1 and M. Kelley 2 ; 1 Arch Coal Inc., Charleston, WV and 2 Castle<br />
Valley Plant, AWBG, Wellington, UT<br />
This paper will describe the design philosophy behind the 400 t.p.h. “Coarse<br />
Only Washing” preparation plant. The study of the potential feed coals lead to<br />
the design incorporating the latest technology of fine dry coal screening<br />
(Roxon Roller Screen). The plant also incorporates a Peters Dense Media<br />
Vessel treating the plus 1 ⁄4” material. Once the circuit was designed, the plant<br />
was bid and CEnty of Salt Lake City, Utah was awarded the contract to build<br />
the facility. The new Roxon/Dense Media Vessel plant was retrofitted into an<br />
old McNally jig plant, which posed obvious demolition/construction issues.<br />
The plant was successfully commissioned in October 2006 and has subsequently<br />
run highly efficiently at in excess of 20% above design capacity.<br />
Plant efficiency data will be reviewed, as will availability and quality data.<br />
9:45 AM<br />
Development of a Transponder-Based Tracer System for Evaluating<br />
Dense Medium Separator Performance<br />
C. Barbee 1 , C. Wood 2 and G. Luttrell 3 ; 1 Arch Coal, Inc., Charleston, WV;<br />
2<br />
Partition Enterprises Pty Ltd., Indooroopilly, QLD, Australia and 3 Mining<br />
& Minerals Engineering, Virginia Tech, Blacksburg, VA<br />
Density tracers are a useful diagnostic tool for evaluating the separation efficiency<br />
of dense medium circuits. Unfortunately, the full capabilities of density<br />
tracers are often not realized in practice due to problems that occur during<br />
the retrieval step. To overcome this problem, an electronic monitoring<br />
system has been developed to automatically identify and count the tracers as<br />
they pass through a coal cleaning circuit. This technique, which relies on recent<br />
technological breakthroughs in transponder technology, improves the reliability<br />
of tracer data by eliminating statistical errors associated with lost<br />
tracers. Field tests showed that very accurate counts (>98%) of the transponder-based<br />
tracers could be achieved in full-scale trials. In addition, this simple<br />
system makes it possible for efficiency tests to be performed very rapidly<br />
by a single person in an extremely cost-efficient manner.<br />
50