Location
Rolla, Missouri
Session Dates
11 Jun 1999 - 17 Jun 1999
Keywords and Phrases
Genetic Algorithms; Ventilation Network; Operating Costs; Energy Efficiency; Booster Fan
Abstract
This paper reports on results of an application of a genetic algorithm to the optimisation of a large UK Coal Mine Ventilation Network. The genetic algorithm technique has been developed into a computer program for minimising the total network operating fan power costs. The application of booster fans may become an attractive alternative for ventilation engineers to provide an adequate supply of fresh air around the working areas in some deep and/or extensive mines. The objective of this research is to minimize the total power consumption of a ventilation system by determining the optimum combinations of (1) main fan and booster fans ratings and (2) booster fan position(s). A modular computer program, which combines the application of the genetic algorithm optimisation technique together with a ventilation network simulator, has been developed using the C++ language. The ventilation network simulator uses the standard hardy-cross iterative scheme implicit within the VNET mine ventilation software that was developed at the University of Nottingham (McPherson, 1966). This paper presents detail of a study on an extensive UK coal mine ventilation network. The ventilation of this network is investigated using various configurations - a single main surface fan, or a main surface fan with either a single, two or three underground booster fans. The paper highlights the major genetic operators that are used to evolve the optimum solution. It is concluded that the genetic algorithm approach is an efficient and flexible solution method.
Department(s)
Mining Engineering
Meeting Name
8th U.S. Mine Ventilation Symposium
Publisher
University of Missouri--Rolla
Document Version
Final Version
Document Type
Article - Conference proceedings
File Type
text
Language
English
Recommended Citation
Yang, Zhi-yuan; Lowndes, I. S.; and Denby, B., "Genetic Algorithm Optimization of a Large U.K. Coal Mine Ventilation Network" (1999). U.S. Mine Ventilation Symposium. 5.
https://scholarsmine.mst.edu/usmvs/8usmvs/8usmvs-theme16/5
Genetic Algorithm Optimization of a Large U.K. Coal Mine Ventilation Network
Rolla, Missouri
This paper reports on results of an application of a genetic algorithm to the optimisation of a large UK Coal Mine Ventilation Network. The genetic algorithm technique has been developed into a computer program for minimising the total network operating fan power costs. The application of booster fans may become an attractive alternative for ventilation engineers to provide an adequate supply of fresh air around the working areas in some deep and/or extensive mines. The objective of this research is to minimize the total power consumption of a ventilation system by determining the optimum combinations of (1) main fan and booster fans ratings and (2) booster fan position(s). A modular computer program, which combines the application of the genetic algorithm optimisation technique together with a ventilation network simulator, has been developed using the C++ language. The ventilation network simulator uses the standard hardy-cross iterative scheme implicit within the VNET mine ventilation software that was developed at the University of Nottingham (McPherson, 1966). This paper presents detail of a study on an extensive UK coal mine ventilation network. The ventilation of this network is investigated using various configurations - a single main surface fan, or a main surface fan with either a single, two or three underground booster fans. The paper highlights the major genetic operators that are used to evolve the optimum solution. It is concluded that the genetic algorithm approach is an efficient and flexible solution method.
