Gday All!
My name is Gary Eggleston and i am wondering on the community's thoughts of AI in Blast design assistance?
I am currently looking into the subject as a potential business in hard rock lithium blast design. Having been a shotfirer within the spodumene deposits,
I have seen many issues with ore recovery, especially in trying to minimize dilution. I have had success in improving the recovery rate of the ore through tie ins, but i am thinking that it can be made more streamlined and efficient .
My thought processes are as follows:
- AI information scraping for Best Practices
- Continuous updating of information (techniques and formulas)
- A library for members to access - summarized
- Information sharing so the information can be analyzedby AI and sent to Members as updates
- A potential plug in to the major design programs (like surpac and Vulcan)
- The plug in to be able to receive information from the website to ensure onsite live updates
- the live updates to help designers to streamline blast designs and time required to do so
- The potential for International information sharing.
Please note, I am aware that not all Pegmatitic Spodumene are the same, but through constant analysis of reports and information, I am certain that AI can assist in a general "better practice" for a majority of the basic deposits.
The plan is not to replace jobs, but to assist the overburdened technical services in getting out blast designs more efficiently while still keeping up other tasks.
Your thoughts are most appreciated
Many thanks
Gary Eggleston
Comments
Hi Gary,
Really interesting concept—thanks for sharing. It’s clear you’ve been hands-on and seen firsthand the challenges around dilution and ore recovery in spodumene blasting. The idea of integrating AI as an assistance tool rather than a job replacer is definitely the right framing, especially with many tech services teams stretched thin these days.
A few thoughts:
Your focus on continuous information updating and best practice extraction could be game-changing—especially if the system accounts for geotechnical variability between deposits.
A summarized library sounds particularly valuable for newer engineers or teams ramping up in unfamiliar ground conditions.
The plug-in integration with Vulcan or Surpac is ambitious but smart—designers would benefit from live insights without needing to leave their environment.
Have you considered partnering with a university or tech company with AI/ML capability for a prototype? Also, crowdsourcing design data while ensuring confidentiality could be a key challenge—but if cracked, it’d add huge value.
Keen to see how this develops. Definitely a niche with room for innovation.
Cheers
Informative!