CORTEX aiPROCESS provides a suite of end-to-end integrated AI-based solutions that provides process and equipment troubleshooting, monitoring and optimization using all available structured and unstructured da
Rapid process or equipment troubleshooting involves identifying and understanding causes of variation in key performance indicators (such as throughput, quality, yield) or key production, process or equipment related variables. The causal and sensitivity analysis is typically done on machine learning based AI models that are trained to be representative of specific process or equipment behaviour. Further insights can be extracted by using these AI models in what if scenario analysis and benefit estimation simulations. These models can then also be used in real-time or batch fashion in monitoring and diagnostic solutions that feed off real-time or batch data. Other real-time applications include advanced process control and set-point optimization.
Figure 26 shows some use cases for process and equipment performance enhancement. By stabilizing the feed rate in a grinding process, a 5% increase in throughput was achieved. In the equipment performance enhancement use case, the availability of haul trucks was increased from 70% to 85% using predictive analytics that focused on the engine, transmission and breaking characteristics of the haul trucks.
- Process optimization
- Maximize throughput
- Grind quality
- Lower energy
- Predictive analytics
- Engine, transmission and breaking
- Centrally hosted
- Remotely maintained