Automated Face Mining Control With Real-Time Simulation
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Solution Overview
Problem
Current material extraction systems in mining lack automation, relying heavily on operator intervention and manual control, which can lead to inefficiencies and safety risks due to the inability to accurately detect and respond to real-time changes in mining geometry, obstacles, and material properties.
Innovation Solution
A method utilizing real-time sensor data and model-based simulations to automate the operation of material extraction systems, enabling the detection of mining geometry, obstacles, and material properties, and adjusting operating parameters to optimize the extraction process, including path planning and collision avoidance, through a combination of deterministic and self-learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual control and operator intervention are used for material extraction systems, then operational flexibility and adaptability are maintained, but safety risks increase and operational efficiency decreases due to inability to accurately detect and respond to real-time changes
Solution Approach 1:
The system enables automated operation of material extraction systems through self-learning algorithms that continuously learn from sensor data and operational feedback. The control unit automatically adjusts operating parameters, plans paths, and detects obstacles without human intervention, allowing the system to serve itself while improving safety and efficiency
Solution Approach 2:
Manual mechanical control by operators is replaced with an automated control system that uses sensor data, model-based simulations, and self-learning algorithms. The control unit processes information and actuates components electronically, substituting human mechanical operations with automated computational control to eliminate safety risks associated with manual operation
2Productivity
If manual control is used for material extraction systems, then device complexity remains lower, but productivity and operational efficiency decrease due to slower response to real-time changes
Solution Approach 1:
The control unit serves multiple functions: it processes sensor data, performs model-based simulations, executes self-learning algorithms, plans paths, detects obstacles, and adjusts operating parameters. This multi-functional integration improves productivity by consolidating various control tasks into a single automated system rather than requiring separate manual operations for each function
Solution Approach 2:
The system continuously operates sensors, simulations, and self-learning algorithms to maintain real-time awareness of the extraction environment. This continuous automated operation eliminates delays associated with manual monitoring and decision-making, maintaining optimal productivity through uninterrupted adaptive control of extraction parameters
3Reliability
If automated operation with real-time sensor data and model-based simulations is implemented, then safety and efficiency improve through continuous optimization, but device complexity increases
Solution Approach 1:
A control unit acts as an intermediary between sensor data acquisition and execution of automated control actions. This intermediary component processes sensor inputs, runs model-based simulations, applies self-learning algorithms, and generates control outputs, thereby managing system complexity by centralizing the computational mediation between sensing and actuation
Solution Approach 2:
Model-based simulations are performed in advance to predict system behavior and optimize extraction parameters before actual operations. This preliminary computational action allows the system to prepare optimal control strategies ahead of time, reducing the complexity of real-time decision-making while maintaining high safety standards through pre-computed optimization
4Productivity
If automated path planning and collision avoidance are implemented, then operational efficiency and safety improve, but device complexity and computational requirements increase
Solution Approach 1:
The system continuously receives feedback from sensors monitoring the extraction environment, material properties, and equipment status. This feedback loop enables real-time path planning and collision avoidance by constantly updating the model-based simulations and self-learning algorithms with current conditions, improving operational efficiency through adaptive response while managing complexity through iterative optimization
Solution Approach 2:
Path planning and collision avoidance strategies are computed in advance using model-based simulations that predict potential hazards and optimal routes. This preliminary computational preparation reduces the complexity of real-time collision avoidance by pre-calculating safe paths and avoidance maneuvers before operations begin or when changes are anticipated
Data Source
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AI summary
The present invention relates to a method and a device for the automatable operation of a material extraction plant at a face (130) of a material extraction site, wherein the material extraction plant comprises at least one mobile and movable mining machine (100) for producing bulk material and at least one first conveying machine (105) for transporting away the mined bulk material, and wherein it is provided in particular that, on the basis of sensor data (203) obtained by means of a sensor system (200, 300 - 315), a model-based real-time simulation of the operation of the material extraction plant and of the corresponding process of mining material at the face (130) of the material extraction site is carried out and that the material extraction site is operated in an automated manner on the basis of the results of the real-time simulation.