Assessment of water and energy use at process level in the U.S. beef packing industry: Case study in a typical U.S. large‐size plant

Abstract

Food processing industries consume intensive water and energy to produce food products. However, their water and energy data are scarce and require for good measurement approaches. This study presents data collection and analysis of process‐level water and energy use in a large‐size U.S. beef packing plant through a combined use of portable and in‐line meters and theoretical calculations. The kill floor and plant cleaning are the primary water users, accounting for 28.7 and 24.0%, respectively. The refrigeration compressor system is the largest user of electricity, consuming 24.5% of plant‐wide electricity. Heating of water for plant cleaning and food safety purposes is the largest thermal energy use in summer (81%) and second‐largest in winter (49.7%), with unit heating values of 625 and 666 MJ/ton live cattle weight in the summer and winter, respectively. Twice as much thermal energy is used in the winter than summer due to space heating requirements. A regression analysis found that as outdoor temperatures increased, a slight water use increase and larger energy use decrease were observed. Practical applications Currently available data on water and energy use in the U.S. beef packing industry are scarce, thus limiting the ability to develop new water and energy efficient technologies and policies based on an understanding of the current baseline operations. The current study conducts a detailed assessment of water and energy use at process level to enhance the understanding of the food‐energy‐water nexus in the beef processing industry, where limited data are available. Engineers can apply this case study as an example to share with their clients seeking to collect and analyze data with the goal of identifying water and energy conservation approaches. These results will inform beef processing researchers as they design novel technologies with that may result in improved water and energy efficiencies.

Where Will ‘Water-Energy-Food’ Research Go Next?—Visualisation Review and Prospect

“…An American beef packaging plant has taken as an example to estimate the amount of water and energy consumed during its beef processing. The results showed that the slaughtering and cleaning processes consumed the most amount of water, and the refrigeration compressor system consumed the most energy [17].…”

National Alliance for Water Innovation (NAWI) Agriculture Sector Technology Roadmap 2021

“…The high rates of electrical energy and natural gas consumption are associated with the refrigeration and freezing of produced meat and with the heating of water for meat processing. 125 A combination of waste heat from plant operations and biofuels produced in the treatment of wastewater could improve the economic viability of different treatment options.…”

Life cycle assessment of the U.S. beef processing through integrated hybrid approach

“…The process-specific data (e.g., resource inputs and waste outputs in Fig. 1) are collected from the two commercial US beef processing plants located in the Midwest (Li et al, 2019a(Li et al, , 2018bZiara et al, 2018). Those data are further normalized based on the functional unit (1000 kg of live-cattle weight) and processed as the technology matrix coefficient for the process-based system.…”

“…Therefore, assumptions are made to describe parametric probability distributions of those four uncertainty sources based on available data and literature. For AP (technology coefficient), triangular distribution with 70%, 130% of process-specific data (e.g., m 3 /1000 kg LCW, kWh/1000 kg LCW) was assigned as the lower and upper limits, respectively, based on the coefficients of variation in onsite data via one-year data collection (Li et al, 2018b). For uncertainty in EP (direct environmental emissions per physical unit), it is assumed to follow a lognormal distribution with 10% of mean values as one standard deviation based on precalculations of environmental impacts per unit under the default uncertainty information within the ecoinvent 3.3 (Ciroth et al, 2016).…”