AI’s potential role in the coal industry

My name is Adam Kokorkhoev, and I’m a co-founder of FTOREX, a company specializing in commercial brokerage and commodity supply. The company supplies several grades and specifications of high-caloric value coal to customers worldwide, focusing on Southeast Asia and the Middle East. We aim to provide direct contracts and competitive pricing straight from the manufacturers. 

Over the past few years, I have been deeply involved in the coal industry, witnessing a transformative shift driven by the integration of new technologies in our sector. I believe the industry’s evolution will be driven by artificial intelligence technology.

How do you envision AI’s potential role in the coal industry and its impact on overall efficiency and productivity?

AI can automate and optimize various processes in the coal industry, making them more efficient and accurate. It can determine optimal parameters, analyze coal demand to predict future trends and prices, optimize energy consumption in mining and processing — and more. The big news for the whole industry is the joint launch of the Pangu Mine Model by Huawei and Shandong Energy Group, the world’s first commercial AI model for the energy sector and the coal industry in particular. 

The model identified 21 application scenarios related to coal mining, tunneling, primary transportation, auxiliary transportation, lifting, safety monitoring, rock burst prevention, coal preparation, and coking. It’s been tested in several coal mines in Shandong, and the whole industry eagerly awaits the results.

In what specific areas of the coal industry do you believe AI technologies could be implemented to optimize operations and processes?

AI technologies can be used to explore deposits, drilling, mining, processing, and equipment maintenance. It can analyze geological data to find promising coal deposits, optimize quality control and accident prevention, monitor coal quality, determine optimal cleaning and sorting parameters, and predict possible failures or breakdowns, increasing overall efficiency.

How can AI be used to improve safety conditions for coal miners and reduce workplace accidents in the coal mining sector?

Introducing this tech can greatly improve miner safety and reduce workplace accidents. Its applications range from accident and working conditions monitoring to security data analysis, virtual training, and automation. Also, AI could eventually replace some underground workers, which would significantly lower risks. 

Given the current environmental concerns related to coal usage, how can AI be leveraged to develop cleaner and more sustainable practices within the coal industry?

Artificial intelligence can be used to develop cleaner and more sustainable practices to reduce the coal industry’s environmental impact. It can create algorithms to develop emergency energy sources, alternative energy power plants, and energy storage systems and analyze the environment around coal mines and power plants to monitor pollution levels.

What challenges do you foresee in integrating AI technologies into the coal industry, and how do you propose overcoming these obstacles?

One of the biggest problems is the lack of available data for routine AI training. We can help solve that problem through collaboration with universities, research centers, and independent study groups to gather data or create the necessary databases. This poses another problem: some of this data is confidential. Insufficient data protection could lead to an abuse of this information, so a high level of data security, encryption, access control, and monitoring must be put in place.

I also think that there is a shortage of qualified specialists with enough field knowledge in machine learning, data analysis, and algorithm development. Here again, the solution would be to invest in education, personnel training, and experience from other organizations. 

And lastly, introducing AI may raise concerns about the loss of jobs, so it would be mandatory to engage in public dialogue. Overcoming these challenges requires collaboration from government agencies, academics, and quality professionals to push the necessary research, training, and development forward. Only then can we begin to approach the ethical and scientific aspects of AI implementation in the coal industry.

How can AI and machine learning algorithms assist in predicting and mitigating potential geological risks in coal mining, such as collapses or gas emissions?

AI can process large amounts of data on organic environments, accumulation history, seismic activity, increased danger, and stress in the ground to predict possible collapses. 

Once the AI has had time to learn what a normal environment looks like, it could help develop predictive models using data on previous occurrences of gas buildup or rock bursts. The Pangu Mine Model I mentioned earlier can analyze the quality of stress relief drilling and assist rockburst prevention personnel in quality verification, reducing their review workload by over 80%. These applications may even roll over to the development of security systems and personnel training. 

However, it is important to understand that AI is not a panacea and should be evaluated against other security methods.

Considering the trend towards renewable energy sources, what role can AI play in helping the coal industry adapt and diversify its operations to remain relevant in the changing energy landscape?

In a nutshell, AI can help the coal industry to adapt and diversify its operations, keeping up with the pace of modern tech. This could be achieved by optimizing performance to increase output while reducing harmful effects or forecasting market demand to automatically change output levels to match. Whatever the future holds for renewables, the coal industry needs to adapt to new interests, industries, and stakeholders.

How might AI-driven automation impact job roles within the coal industry, and what measures can be taken to support the workforce through this transition?

AI-drivel automation could replace some jobs in the coal industry. For example, if the coal is detected, AI-powered machines may do the job that was previously done manually, such as drilling or transporting coal. 

Artificial intelligence can be used to continuously monitor the personnel’s health or the status of equipment to prevent accidents and address issues timely. It can optimize logistics, including route calculation and inventory management, reducing transportation costs and improving the efficiency of coal delivery. Besides that, it can analyze large amounts of data collected in the coal industry, help identify patterns, and make more informed decisions.

To support the workforce during this transition period, we can include retraining and training, creation of new jobs, introducing social programs and support, and cooperation with trade unions and public organizations.

Data security and privacy are crucial in any AI application. How can the coal industry ensure that sensitive data, such as geological surveys or employee information, is protected when using AI technologies?

Ensuring data security and privacy is a very high priority when building AI technologies, more so in an industry like coal. All sensitive data, like geological surveys, must be encrypted, access to control systems must be limited to authorized personnel, and the physical security of servers needs a great deal of attention, too.

Once you have these security measures in place, employee training on the importance of data security and information leaks becomes paramount. Regular security audits to look for vulnerabilities, security regulation and compliance, and network security maintenance are all important and necessary steps when working with this kind of data. 

I believe that data security and privacy must be built into all aspects of AI technologies in the coal industry.

AI systems are only as good as the data they are trained on. How can the coal industry ensure that the data used for AI implementation is accurate, reliable, and representative of the industry’s diversity?

The data used to study AI systems should reflect different aspects of the industry, including data from different deposits, used equipment, and types of mining. It’s necessary to implement data quality control processes to correct errors and distortions and analyze it to detect inconsistencies. Data checks from coal industry experts and regular updates would help make sure the data is relevant.

It is important to note that data quality assurance is an ongoing process, and the coal industry must constantly work to improve it to ensure the accuracy and reliability of AI systems.

(The views and opinions expressed herein are the views and opinions of the author, Anthony Clarke.)