Chapters authored
The Application of Artificial Intelligence to Reduce Greenhouse Gas Emissions in the Mining Industry By Ali Soofastaei
Mining industry consumes a significant amount of energy and makes greenhouse gas emissions in various operations such as exploration, extraction, transportation and processing. A considerable amount of this energy and gas emissions can be reduced by better managing the operations. The mining method and equipment used mainly determine the type of energy source in any mining operation. In surface mining operations, mobile machines use diesel as a source of energy. These machines are haul trucks excavators, diggers and loaders, according to the production capacity and site layout and they use a considerable amount of fuel in surface mining operation; hence, the mining industry is encouraged to conduct some research projects on the energy efficiency of mobile equipment. Classical analytics methods that commonly used to improve energy efficiency and reduce gas emissions are not sufficient enough. The application of artificial intelligence and deep learning models are growing fast in different industries, and this is a new revolution in the mining industry. In this chapter, the application of artificial intelligence methods to reduce the gas emission in surface mines with some case studies will be explained.
Part of the book: Green Technologies to Improve the Environment on Earth
Improve Energy Efficiency in Surface Mines Using Artificial Intelligence By Ali Soofastaei and Milad Fouladgar
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%.
Part of the book: Alternative Energies and Efficiency Evaluation
Energy Efficiency Improvement in Surface Mining By Ali Soofastaei and Milad Fouladgar
This chapter aims to provide an overview of energy efficiency in the mining industry with a particular focus on the role of fuel consumption in hauling operations in mining. Moreover, as the most costly aspect of surface mining with a significant environmental impact, diesel consumption will be investigated in this chapter. This research seeks to develop an advanced data analytics model to estimate the energy efficiency of haul trucks used in surface mines, with the ultimate goal of lowering operating costs. Predicting truck fuel consumption can be accomplished by first identifying the significant factors affecting fuel consumption: total resistance, truck payload, and truck speed. Second, developing a comprehensive analysis framework. This framework involves generating a fitness function from a model of the relationship between fuel consumption and its affecting factors. Third, the model is trained and tested using actual data from large surface mines in Australia, obtained through field research. Finally, an artificial neural network is selected to predict haul truck fuel consumption. The visualized results also clarify the general minimum areas in the plotted fuel consumption graphs. These areas potentially open a new window for researchers to develop optimization models to minimize haul truck fuel consumption in surface mines.
Part of the book: Latest Research on Energy Recovery
Mines and Machines: Virtual Assistants in Modern Mining By Ali Soofastaei
Virtual Assistants in Modern Mining delves into the transformative power of technology, specifically focusing on the role of virtual assistants in revolutionizing the mining industry. This chapter looks at how Artificial Intelligence, Machine Learning, and virtual assistants alter the traditional landscapes of mining operations. From improving safety protocols to optimizing resource extraction, these technologies are at the forefront of innovation, efficiency, and sustainability. The chapter begins by providing historical context, underscoring the mining industry’s rich tapestry of evolution and its enduring significance in human civilization. It then transitions into the modern era, explaining today’s mining industry’s challenges, including environmental considerations and ethical concerns. Central to this discourse is exploring how virtual assistants are poised to address these challenges. By presenting case studies, empirical data, and expert interviews, the chapter’s content makes a compelling argument for the positive impacts of implementing virtual assistants in mining operations. Topics include real-time data analysis, predictive maintenance, safety monitoring, and environmental conservation. Aimed at industry professionals, researchers, and policy-makers, “Mines & Machines” serves as both a resourceful guide and a catalyst for discussing the future of mining in the age of AI.
Part of the book: Advanced Virtual Assistants - A Window to the Virtual Future
Intelligent Scheduling: How AI and Advanced Analytics Are Revolutionizing Time Optimization By Ali Soofastaei
Scheduling is critical in optimizing time and resources across industries, yet traditional scheduling methods struggle to manage the complexity and dynamic nature of modern operational environments. This chapter explores how artificial intelligence (AI) and advanced analytics transform scheduling by providing innovative solutions to age-old challenges. Through machine learning, predictive analytics, and optimization algorithms, AI-driven systems are enabling real-time decision-making and demonstrating adaptability by adjusting schedules to evolving conditions, thereby delivering higher efficiency. The chapter delves into key AI techniques such as genetic algorithms, dynamic scheduling, and multi-objective optimization, showcasing how these tools are applied in manufacturing and logistics industries. Real-world examples illustrate the tangible benefits AI offers, from reducing production downtime to improving patient care and optimizing delivery routes. This emphasis on practical value is intended to convince the audience of AI’s significant impact on scheduling. Looking forward, the chapter explores emerging trends such as autonomous scheduling, the integration of AI with the Internet of Things (IoT), and the potential of quantum computing to revolutionize scheduling further. Ultimately, this chapter demonstrates that AI and advanced analytics are solving today’s complex scheduling problems and reshaping the future of time optimization across industries.
Part of the book: Mastering Time
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