Our XpertFactory platform evolved as a result our extensive experience over two decades in applying advanced AI technologies to improve manufacturing and process plants.
XpertFactory combines our methodology and track record with our Viabl.ai technology to provide a high-performance compact AI engine for native deployment on industrial edge or cloud.
A platform for all industrial sectors
We work with our partners in a collaborative way in many sectors to help them develop/deploy vertical solutions (that capture their sector Domain Expertise) thereby allowing them to gain competitive advantage in their industrial sector. The XpertRule Intelligent Manufacturing Team and our partners have experience across multiple sectors – pharmaceutical, chemical, food & beverage, minerals, additive manufacturing, subtractive manufacturing and hybrid manufacturing.
XpertFactory technology has been successfully deployed world-wide in many industrial sectors. In almost all cases such deployments started with a process understanding analysis using our unique explainable AI machine learning tools, then progressed to process monitoring and process optimisation advisory (open loop control advisory). Below is a summary of some of these success stories.
Powder processing plants uptime increased
Hosokawa Micron uses XpertFactory to develop its Gen4 remote monitoring solution. The solution supports condition monitoring for preventative maintenance, on-line diagnostics for improved plant and product quality and trouble-shooting bots for minimising downtime on its milling equipment. This has resulted in 15 percent improvement in uptime; 20% reduction in energy usage and capacity gains of about 10% being recorded by Hosokawa customers.
Reduction in energy use
Reducing CO2 emissions at an oil company
Machine learning was used to understand how energy costs and emissions were dependent on external factors, control settings and operating practices. We identified opportunities for reducing energy costs by 3% and reducing CO2 emissions by 7%. Models of critical performance factors were also developed for use as benchmark for intelligent monitoring.
Reduction in CO2 emissions
Reducing energy costs at a UK brewery
Machine learning was used on data from a brewery central refrigeration system to understand the variations in the efficiency of the plant. The machine learning models identified opportunities to reduce energy costs by changing the control strategy and operating practices with minimal capital cost.
Annual reduction in energy costs
Want to find out more?