Course Details

Subject {L-T-P / C} : MN5023 : Computer Application in Mining Laboratory { 0-0-3 / 2}

Subject Nature : Practical

Coordinator : Tushar Gupta

Syllabus

List of Experiments:

1 MATLAB: Basic programming exercises, Flowcharts and Algo
2 LEM: Design a Slope with given dimensions. Put material properties. Find the FOS and depth of Critical failure circle
3 LEM: Support the slope and find optimum reinforcement by varying bolt properties. Do basic cost analysis sensitivity.
4 FEM: Simple bar stretch simulation. Max deformation analysis against a given load.
5 FEM: See change in deformation. Sensitivity Analysis. See effect of change in load, change in length, change in EM, change in meshing
6 FEM: Compression of ground soil while dumping operation. Find maximum deformation on ground: Stages
7 FEM: Dump design based on ground soil condition. Find maximum height.
8 FEM: Slope design and calculating critical SRF. Supporting and checking difference.
9 FEM: 3D analysis of loaded bar on ANSYS
10 FVM: Fluid flow through varying cross section pipe : ANSYS
11 Ventilation Simulation Using VENTSIM Basic flow exercise
12 MATLAB: Curve fitting linear regression exercise. Custom equation, best fit, R2 and Adjusted R2
13 MATLAB: MVRA: polynomial fitting. Compare with Excel
14 MATLAB: Artificial Neural Networks(ANN) and application
15 MATLAB: Adaptive Neuro Fuzzy Inference System (ANFIS) exercise

Course Objectives

Course Outcomes

CO 1. Understand the fundamental concepts of computational programming, flowchart development, and algorithm design using MATLAB for solving mining-related problems.
CO 2. Apply numerical methods such as Limit Equilibrium Method (LEM), Finite Element Method (FEM), and Finite Volume Method (FVM) to analyze slope stability, soil deformation, and fluid flow in mining applications.
CO 3. Analyze the effects of different material properties, boundary conditions, and external forces on slope stability, ground deformation, and ventilation performance using computational tools like ANSYS and VENTSIM.
CO 4. Evaluate the accuracy and efficiency of predictive modeling techniques such as regression analysis, artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS) for mining applications.
CO 5. Design optimized engineering solutions for slope reinforcement, dump design, and ventilation management by integrating numerical simulations and data-driven approaches.

Essential Reading

Supplementary Reading