Planning RES Projects in Exhausted Surface Lignite Mines—Challenges and Solutions

In the context of the complete phase-out of lignite-fired power plants and the corresponding surface mines, the central priority is to ensure a fair development transition for the lignite mining areas. In the context of the installation of renewable energy system projects in the surface lignite mines of Western Macedonia, this paper aims to analyze the challenges for developing photovoltaic projects in areas with different characteristics and to propose solutions for selecting suitable areas, based on corresponding analysis. The investigated parameters cover a wide range of spatial criteria. The results contribute to a pragmatic transition to green energy generation involving a circular economy and sustainable development.

Improve Energy Efficiency in Surface Mines Using Artificial Intelligence

This chapter demonstrates the practical application of artificial intelligence (AI) to improve energy efficiency in surface mines. The suggested AI approach has been applied in two different mine sites in Australia and Iran, and the achieved results have been promising. Mobile equipment in mine sites consumes a massive amount of energy, and the main part of this energy is provided by diesel. The critical diesel consumers in surface mines are haul trucks, the huge machines that move mine materials in the mine sites. There are many effective parameters on haul trucks’ fuel consumption. AI models can help mine managers to predict and minimize haul truck energy consumption and consequently reduce the greenhouse gas emission generated by these trucks. This chapter presents a practical and validated AI approach to optimize three key parameters, including truck speed and payload and the total haul road resistance to minimize haul truck fuel consumption in surface mines. The results of the developed AI model for two mine sites have been presented in this chapter. The model increased the energy efficiency of mostly used trucks in surface mining, Caterpillar 793D and Komatsu HD785. The results show the trucks’ fuel consumption reduction between 9 and 12%.

Introducing the IDEF0 Methodology in the Strategic Planning of Projects for Reclamation and Repurposing of Surface Mines

The closure of surface mines is a complex framework characterized by extended reclamations of post-mining sites towards an environmentally friendly and sustainable land-use system development with beneficial returns to society and the economy. The paper demonstrates the critical mine closure problem, draws research questions, and introduces the IDEF0 (Integrated DEFinition Function) process modelling method as a low-cost and easy development tool for use by mining experts to perform strategic planning of sustainable mine reclamation and repurposing projects. A case study for the method applied in a Greek lignite mine is presented.

Development of Emission Factor Equations for Surface Mining Activities: The Case of the Stacker

In surface mines, various activities (e.g., excavations, loading and unloading of material, moving vehicles on unpaved haul roads, etc.) represent significant sources of fugitive dust. The estimation of dust generation from each individual source is a basic step in planning and implementation decision-making systems regarding the air quality of the surrounding area. Typically, this can be obtained by using emission factor or prediction-type equations. A detailed study was carried out at four surface lignite mines to determine PM emission factors and to develop the prediction-type equations of various surface mining activities. In this work, the data, method and results referring to the stacker, one of and the significant fugitive dust emissions source in mining operations are presented and analyzed.

Truck dispatching in surface mines -Application of fuzzy linear programming

Material handling in surface mines accounts for around 50% of the operational cost. Optimum truck dispatching plays a critical role in the reduction of this operational cost in truck and shovel surface mines. Researchers in this field have presented several mathematical models to solve the truck dispatching problem optimally. However, a critical survey of the literature has shown that three significant drawbacks exist in the available truck dispatching models. The published models underestimate the importance of the interaction between truck fleet, shovel fleet, and the processing plants. They also disregard goals set by strategic-level plans. Moreover, none of the available models account for the uncertainty associated with the input parameters. In this paper we present a new truck dispatching model that covers all of these drawbacks, using a fuzzy linear programming method. The performance of the developed model was evaluated through implementatin in an active surface mining operation. The results show a significant improvement in production and fleet utilization.