Abstract:

The demand for minerals will increase in the future to fuel a low carbon emission future.
2023 was the year in which Artificial Intelligence (AI) entered the consciousness of the mainstream, and also the mining sector saw accelerated movement to leverage the power of Machine Learning and AI.
To successfully embrace the technological advances we must directly face the challenges of high variability and difficult operating conditions in processing plants in addition to challenges with legacy technology infrastructure at operation sites.
We will present our approach to applying AI to processing problem settings to run our operations optimally and stable.
We offer a suite of integrated AI solutions that revolutionize mineral processing including enhanced feed comprehension, live plant monitoring, and sophisticated online measurements. Our recommendation system holistically optimizes operations from start to finish, dynamically balancing constraints, safety parameters, and product specifications with unparalleled precision.
Our approach solves the following challenges:
1. Leverage AI to provide high-quality, near-real-time information unattainable through physical instruments, thereby stabilizing and optimizing operations.
2. AI-driven fine-tuning of operating conditions for maximum output and grade in near real-time, tailored to dynamic feed changes and plant conditions.
3. AI-driven recommendation engine for global optimization allows decision makers to respond to ore variability and operational changes impacting multiple circuits in parallel. We leverage state-of-the-art AI technologies, blending data-driven analytics with fundamental first-principle knowledge, to deliver unparalleled insights and real-time operational excellence.
  
  Our solutions enhance on-premise control systems, elevating decision-making through advanced HMI, and streamlining user adoption and operational efficiency.
We will share lessons learned and a future outlook of AI-based advanced in the Plant Optimization space that includes more automated and adaptive operational strategies.

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Dr. Jens Adamczak

Biography