Drill-and-Blast Feedback Loop Optimizer
Connects blast design, fragmentation outcomes, and daily mine planning to improve drilling and blasting decisions, increase productivity, and reduce downstream impacts on hauling and processing.
The Problem
“Drilling and Blasting Optimization Feedback Loop for Daily Mine Planning”
Organizations face these key challenges:
Blast design decisions are weakly linked to measured fragmentation outcomes
Daily mine planning does not consistently incorporate blast performance feedback
Fragmentation data is delayed, sparse, or manually collected
Geology, drill logs, explosive parameters, and equipment productivity data are siloed
Impact When Solved
The Shift
Human Does
- •Review blast plans, drill execution records, and geology notes across separate reports
- •Compare fragmentation observations with loader, haulage, and crusher performance after blasting
- •Adjust burden, spacing, powder factor, timing, and stemming based on experience and delayed feedback
- •Update daily mine plans using planner judgment and limited blast performance history
Automation
Human Does
- •Approve recommended blast parameter changes and daily plan adjustments
- •Set operating priorities and constraints for fragmentation, productivity, and downstream stability
- •Investigate exceptions such as unusual geology, safety concerns, or conflicting operational targets
AI Handles
- •Combine blast design, geology, execution, fragmentation, fleet, and plant data into a continuous performance view
- •Predict fragmentation, oversize risk, diggability, haul productivity, and crusher feed impacts for planned blasts
- •Recommend next-best blast design and planning adjustments within operational constraints
- •Monitor actual post-blast outcomes versus predictions and flag root-cause deviations for follow-up
Operating Intelligence
How it works
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not change blast parameters or daily mine plans without approval from the responsible engineer or planner [S1].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth