Application of genetic algorithms to determine the best combination of main and booster fans

Update Item Information
Publication Type dissertation
School or College College of Mines and Earth Sciences
Department Mining Engineering
Author Shriwas, Mahesh Kumar
Title Application of genetic algorithms to determine the best combination of main and booster fans
Date 2014-12
Description One major challenge in mine ventilation is to determine the best combination of main and booster fan pressures to satisfy the airflow requirements and minimize the overall power consumption. This study presents a genetic algorithm-based program to solve mine ventilation network problems. The program, written in C++ language, combines genetic algorithms (GAs, developed by MIT) and a ventilation simulator (xyz.c, developed by MVS Engineering). The program also known as GVENT is used to determine the best combination of main and booster fan pressures for a sample and a coal mine ventilation network. For a sample network, the program generated the power requirement for two alternatives: 1. Single-fan system, 2. Two-fan systems. A comparison of the results shows that the second alternative reduces main fan pressure and leakages and consequently results in net savings of 487 kW (19%). For the coal mine ventilation network, the program generated the power requirements for two alternatives: 1. three surface fan system and 2. three surface and two booster fan system. A comparison of the results shows that the second alternative (three surface and two booster fans) results in a net savings of 209 kW. The results generated by this program were compared with those generated by a ventilation simulator, VnetPC, and found that these were within the 0.5% accuracy. Using this new approach, the results were generated faster and with less human intervention than those generated by the simulator. In addition to the GA-based program, two separate programs were developed to evaluate the network results for flow recirculation. These programs, based on search routines, were used to test the results of the sample network problem described previously. In each case, the outcomes were positive-no recirculation paths were found. The program identified the recirculation paths successfully. In summary, this research study presents a GA-based fan selection program that can be used by mine operators to determine the best combination of fan pressures (surface and underground booster fans) that satisfies the mine flow requirements, reduces leakage, and minimizes the total power consumption.
Type Text
Publisher University of Utah
Subject Fan; Genetic alogrithms; Mine; Recirculation; Risk; Ventilation
Dissertation Name Doctor of Philosophy
Language eng
Rights Management Copyright © Mahesh Kumar Shriwas 2014
Format Medium application/pdf
Format Extent 2,807,778 bytes
Identifier etd3/id/3309
ARK ark:/87278/s65t6trd
Setname ir_etd
ID 196874
Reference URL https://collections.lib.utah.edu/ark:/87278/s65t6trd
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