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Mine ventilation, though costly, has to be maintained for any underground coal mine for the sake of miners' safety and health. Higher productivity and more stringent environmental standards require greater quantities of air at the underground working faces. These conditions have brought a great increase in the costs of mining. To survive in the current state of market competition for the mining industry, more concerns have to be addressed on developing a ventilation system which is functionally effective and reliable, and yet economically efficient. Mine ventilation network optimization is one of the approaches to reach that objective. This dissertation is intended to introduce nonlinear programming techniques into mine ventilation network optimization. In the research, network optimization was modeled and formulated as a standard nonlinear programming problem. Then, nonlinear programming techniques for solving the network optimization problem were investigated. The research successfully introduced MINOS, one of the available nonlinear programming systems, for mine ventilation network optimization. Network optimization was also solved using the generalized reduced gradient (GRG) method. In general, the GRG method is one of the most effective and efficient methods for solving a large-scale nonlinear programming problem. Throughout the dissertation research, a computer program, MVENTOPT-GRG, based on the GRG method was developed for mine ventilation network optimization. The solution of the network optimization using MINOS and the GRG method are demonstrated with examples. The introduction of the nonlinear programming techniques provides new tools for ventilation engineers or analysts to handle ventilation problems.