Skip to main content
ARS Home » Research » Publications at this Location » Publication #240872

Title: Simulation tools for design of the next gseneration of milk processing plants

item Tomasula, Peggy
item NUTTER, DARIN - University Of Arkansas
item Yee, Winnie
item McAloon, Andrew

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 6/10/2009
Publication Date: N/A
Citation: N/A

Interpretive Summary: N/A

Technical Abstract: A growing number of food retailers have incorporated environmental sustainability into their business plans and are concerned about the greenhouse gas emissions (GHG) and the carbon footprint associated with the fluid milk value chain. The fluid milk value chain begins on the farm with crop production and then milk production; the raw milk is then transported to the processing plant for processing, packaging, and distribution to retail, where the milk is refrigerated until sold. Estimated sources of GHG for the fluid milk value chain total 28 million metric tons of carbon dioxide/year with approximately 80% of the emissions generated on the farm. A Dairy Sustainability Summit, held in 2008, brought together representatives of the entire fluid milk value chain with the goal of creating initiatives for each sector of the value chain to reduce GHG by 25% by the year 2020. One of the initiatives created at that meeting was the D-CREE Initiative (Dairy Processor Carbon Reduction Through Energy Efficiency). In one of its objectives, a fluid milk process simulation tool is to be developed to allow processors to assess individual plants and their next practice opportunities. Tools are available for estimating overall GHG production but do not provide quantitative information on mass and energy flows or water usage at each unit operation in a processing plant. We have used a commercial process simulator program to generate a flow sheet and calculate the mass and energy usage of each unit operation for small, medium, and large milk processing plants. The associated GHG were also calculated. Our long-term goal is to compare the energy and GHG data with data obtained from processing plants to refine the simulation so that processors can assess their own plants and use sensitivity analysis to test next practice opportunities, such as alternative unit operations and alternative energy technologies that reduce GHG. The application of the simulator to development of sustainable milk processes will be discussed.