Company Profile: Pivovarna Laško is the leading Slovenian brewery.
Project: A preinvestment study and information system for energy management. During active monitoring and implementing the appropriate measures, Pivovarna Laško was able to decrease their water use by 14%, their electric energy use by 11%, their heat consumption by 10%, and compressed air by 10%.
Increased energy costs became a kind of breaking point for Pivovarna Laško, as they felt an urgency to install an energy overview system in order to further improve their competitiveness in the market.
The implementation process took two and a half years and started by calling for offers by energy management system providers. After a year of collecting offers, they decided on the solution by Solvera Lynx: the GemaLogic energy management system.
– Pre-investment study
A reinvestment study was done on implementing an energy management information system in Pivovarna Laško; The pre-investment study encompassed a description and documentation of the condition and the proposed solution about how to set up the energy management information system for the electric energy and all other energy sources (gas, steam, compressed air, technological water, CO2, cooling). The study also included the financial aspects of introducing the system and the expected results.
RESULTS AND BENEFITS:
The precise data on energy, consumption displays, and data analysis (“benchmarking,” contour diagrams, KPI, M&T, etc.) enabled by the GemaLogic system show trends and areas where a change is required. Better insight into their energy use and processes. By regularly monitoring their energy use, the company was able to reach a faster reaction time in their machine and equipment maintenance. During active monitoring and implementing the appropriate measures, Laško Brewery was able to decrease the water use by 14%, electric energy use by 11%, heat consumption by 10%, and compressed air by 10%. The precise data supplied by GemaLogic are also used to assess and optimize all the organizational and investment measures, which was a problem before due to the lack of data. Based on the system data, they also optimized their co-generation of heat and energy working time (cogeneration) to match their needs for heat in the manufacturing process.