Brau Union

Assembly Line

General Mills and Brau Union Take Aim at Factory Electricity Bills

đź“… Date:

✍️ Author: Grant Gerke

đź”– Topics: Sustainability

🏭 Vertical: Food

🏢 Organizations: General Mills, Brau Union, Cybertrol Engineering, Endress Hauser, Vanmark, Siemens, ThinkIQ


The average factory electricity bill varies across the manufacturing industry. The dairy industry hovers around 5% to 8%, and breweries cite 5% to 10% of their operating costs on energy. Factory electricity bills for meat processors can reach 15%, and the sugar industry touches 30%.

Operators have been adding equipment sensors and “quick-win” automation tools to produce more actionable data, while management is going big with evaluations of energy management systems. “Advances in instrumentation by various manufacturers have significantly enhanced data collection and analysis,” says Tim Barthel, executive vice president at Cybertrol Engineering. “Modern systems now offer far more data than what was realized from an analog signal just four years ago.”

Freshwater consumption per peeler is reduced to 0.5 to 2 gal./thousand (GPM) during regular operation. The recycled water is drained and flushed periodically. Moreover, the OEM also offers an option via its system starch separator for its line of Lamina Hydrocutting equipment. According to Vanmark, traditional potato processing includes 2% of water being bled out and is continuously replaced with clean water. The supplier’s system starch separator creates a cyclone in the line that pushes the starchiest water to the pipe’s edge and removes the water. This new feature reduces water consumption for the “bleeding process” while providing the right level of cleaning.

Recently, General Mills worked with ThinkIQ and used its machine learning algorithms to forecast a savings of $480,000 annually with the food and beverage giant’s energy bills. ThinkIQ’s software as a service (SaaS) platform identifies and forecasts “blind spots” within manufacturing sites by implementing an informational model to capture data, visualize plant applications and promote machine learning.

Read more at Food Engineering