IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN SHORT-TERM MINE PLANNING OF OPEN PIT MINE. A CASE STUDY OF BOZSHAKOL MINE.

Abstract

Using Bozshakol Mine as a case study, this thesis investigates how Artificial Intelligence (AI) is applied in short-term mine planning. Open-pit mining operations, like those at Bozshakol, struggle to develop flexible, responsive mine plans that fit with the changing dynamics of the mining environment. Often relying on static, rule-based systems, traditional approaches find it difficult to handle real-time data and unanticipated operational interruptions. This study offers an AI-driven solution to mine planning that combines machine learning and optimization techniques to increase planning accuracy, lower human error, and optimize Net Present Value (NPV) in order to meet these difficulties. The project creates a web-based artificial intelligence model that analyzes data from the Bozshakol Mine, including block models, mining face polygons, and operational factors such as ore grade, mining costs, and haulage distances. The AI system aims to produce best mining scenarios, hence maximizing NPV and reducing running costs. The performance of the AI model is compared with conventional human-generated plans, showing notable gains in NPV, time efficiency, and operational flexibility. By means of dynamic, data-driven decision-making, this thesis emphasizes how artificial intelligence might transform short-term mining planning. The results imply that artificial intelligence can simplify planning procedures, cut scenario evaluation time, and boost general profitability. The study also points out important issues including data quality and system integration and offers advice for more progress in AI-augmented mine planning.