Minealytics offers a comprehensive, easy to use data analytical platform that gives valuable insight to plant operators in real-time. Data tells if a motor is undersized, a conveyor speed-up curve is inefficient, a bin is under utilised or if a PID loop is sub-optimal. The data analytical platform feeds into our AI enhanced RPAs.Learn More
Our mining industry specific control modules enable mine operators to integrate with their control systems remotely or on premise, quickly by connecting their process data, running analytical, training and optimisation algorithms and finally selecting a suitable control algorithm.Learn More
The Minealytics Simulation framework provides an API for a virtual mining environment for algorithm development and automated machine learning training. There are a large selection of modelled mining processes as well as state of the art control algorithms all in one framework.Learn More
Minealytics algorithms have specifically been designed for mining application to maximise productivity.
Through advanced control and data analytics instant plant availability improvements can be achieved.
The implementation of Minealytics solutions can be the matter of hours. Laborious data collection and plant analytical tasks can be done through our web based platform to kick start bottleneck studies and investigations.
Minealytics aims to automate some of the most complex aspects of a mining operation. A successful application means that you remain either with excess capacity or increased productivity.
Through automation, Minealytics reduces the workload of mine operators so they can focus on higher level tasks. Alarm system performance increase, control loop performance improvement, high level control automation are a few examples, where Minealytics can be useful.
Often, there are hidden inefficiencies, dormant or secondary failures that obscure real problems and often, there are just too many problems within a mining operation and it is hard to see where to begin.
Minealytics guides you through a sequence of analytical modules and presents insight in a visual, interactive form. It combines your process data with your expert knowledge to formulate insight. Minealytics speeds up the process of collecting site specific operations and design data of your mine, reducing costs by eliminating expensive site visits and endless iterations of emails requesting more and more information.
Plant operators deal with 2 domains of operations. One is process control, tuning and optimisation, which can be fully automated or augmented by the Minealytics Plant Operator Digital Twin.
The second domain of operator tasks is the coordination of failure recovery and trouble shooting, which is more intuitive.
The Minealytics Robotic Process Operator (RPO) uses Reinforcement Learning to learn the optimal recovery sequences used by the most experienced operators. The Minealytics RPO is able to recreate complex failure scenarios and quickly accumulate vast experience by training in simulated environment.
Minealytics offers on premise or in the cloud solutions for reliability metrics derivation
By coupling vision data with process measurement like bucket-wheel current and boom throughput, the reliability function of the bucker-wheel, boom conveyor belt and chutes can be modeled and their life-time optimised.
Minealytics uses AI sequence models to detect anomalies in process variables and predict plant upsets before they can occur.