UZWIL, SWITZERLAND — Bühler Group is kicking off an Internet of Things (IoT) Solutions Exhibition in Uzwil, Switzerland, on Nov. 20 with 48 hours of live tweeting from the company’s IoT edge gateway, installed on an in-process Aeroglide dryer.

Tweets will showcase AeroPro Moisture Control (MC) and AeroPro Process Monitoring (PM) IoT products. The @buhlerIoT handle will tweet diagnostics every 10 minutes to demonstrate the enormous flexibility, possibilities and transparency gained from IoT data. The same Twitter handle also will answer questions and interact with users at the exhibition and abroad. Bühler started taking user questions at 8 a.m.

“Connected machines leveraging IoT sensors, cloud managed big data and analytics are disrupting the norm in the drying machinery sector,” said Doug Beloskur (@beloskur), Bühler Aeroglide Product Manager for Automation and IoT. “This technology is paving the way for advanced machine intelligence and machine / process AI formation.”

AeroPro Moisture Control gives operators access to full time, real-time monitoring with hardware, software, and ongoing process expertise, combined with technical support, to ensure the most efficient dryer operations ever. Controlled drying parameters ensure that a food or feed product is shelf-stable, removing the precise amount of moisture.

With AeroPro MC, the user can view production data in real time via computer, phone, tablet or other web-connected device. Custom dashboards can provide the most critical data. Data storage is cloud-based.

Real-time management that can achieve a net increase of just 1% in finished moisture can result in a return-on-investment increase of $300,000. This translates into an energy savings valued at $20,000 per year, Bühler said.

AeroPro Process Control ensures consistent processing using microwave sensors, coupled with algorithms built on decades of drying experience, to deliver a comprehensive closed loop system for automated dryer control.

With AeroPro PC, the user can receive real-time log reduction data based on parameter sensors that send information to a program modeled to a particular process. Should a process change occur, the user is alerted so that the process can be stopped or diverted to ensure food safety.