Lineage Logistics Implements Facility Improvements to Optimize Refrigeration Compressor Operation


Lineage Logistics, a warehousing and logistics company, sought to reduce the cost of refrigeration per pallet at their facility in Mira Loma, California, where electricity costs totaled $2.2 million a year in 2016. On average, each of the company’s buildings store roughly the same amount of food as 770,000 home freezers. This requires an array of energy-intensive equipment, including refrigeration units and blast freezers that brings interior temperatures down to minus 20 degrees. In fact, energy consumption is the company’s second highest cost after labor.

The Better Plants partner posited that by scheduling the refrigeration system to over-cool during periods of low energy cost – such as nighttime – overall energy consumption and costs could be reduced. After analyzing the site’s load profile and control logic of the refrigeration controls, they developed and implemented a “flywheeling” algorithm; their term for AI software that identifies periods where energy consumption can be reduced by pre-cooling and can be applied to individual freezer spaces and different facilities.

In thinking through how to lower facility energy costs, Lineage Logistics workers came to the conclusion that the thermal load on the refrigeration system could be reduced without adverse impact to the product. The partner recognized that the facility product could be cooled to a lower temperature than needed (about five degrees) during off peak rates, without damage. During times of increased thermal loading, the mass of the product would help maintain freezer temperature and delay compressor operation during times of peak rates. Lineage Logistics ultimately followed this approach:

  1. Identify and correct facility issues contributing to the thermal load of the freezers;
    • Poorly sealing doors were replaced
    • Automatic door controllers were installed
    • Variable frequency drives (VFDs) were installed or upgraded on compressors and evaporators; VFD’s enabled these systems to run slower for a lower kilowatt hour (kWh) consumption during periods of lower demand
    • Replaced evaporators with more thermally efficient models
    • Installed solenoid liquid feed valves to increase control over the liquid levels in low-pressure vessels
    • Upgraded controller proportional-integral-derivative (PID) logic
    • Modified the rate structure to take advantage of the lower demand that resulted from the infrastructure modifications (specifically, the facility won a lottery bid that enabled it to switch to a rate structure with variable rates that correspond to market prices; Lineage Logistics workers than optimized power usage according to the day ahead prices)
  2. Develop an algorithm model to predict times of increased thermal loading resulting from environmental changes and reduce energy consumption accordingly;
  3. Implement the algorithm at a facility;
  4. Validate the algorithm model;
  5. Leverage the effort and implement the algorithm at other facilities.

The algorithm correlates data from warehouse sensors with other information like the price of electricity, the existing cooling schedule, and unpredictable variables like the weather. It then adapts the system’s operation based on predefined rules. For example, it can shift the load based on peak utility-pricing changes or keep power off longer due to colder-than-expected outside temperatures.

Implementing the algorithm required coordination among a variety of Lineage Logistics departments: data scientists, engineering, production, safety, and quality control. However, a strong corporate culture of integration and cooperation helped minimize difficulty.

Table 1 below summarizes capital expenditures, annual savings, and payback period.

Table 1 – Expenditures and savings


$ Spent

$ Saved/year

Payback (years)





Compressor VFDs




Motorized valves












Controls & Scheduling








If the 2016 refrigeration system had been run in 2018, energy costs would have totaled an estimated $3.09 million based on utility rates and energy consumption per pallet. After implementing the various aspects of the project, the actual 2018 cost was $1.90 million – a reduction of almost 39%.

The optimization process was implanted with scaling in mind; two other Lineage Logistics sites have made site improvements and are using the algorithm for energy use planning and two more are in the implementation stage. An additional 32 facilities are moving forward with plans to implement the process. The partner aims to finish the process at a minimum of 8 sites in 2019 and another 12 in 2020.

Although a control system is in place that flags disruptions or changes in the system that will interrupt the load schedule and increase energy consumption, a more automated system is being developed to allow sites to measure and report on demand use as part of their daily operational metrics.

Graph 1 below displays electrical energy consumed per pallet at the Mira Loma facility from January 2016 through July 2018.







Graph 2 below plots the energy cost savings associated with the different technologies involved in the Mira Loma optimization project



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Identifying or evaluating energy-saving technologies

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Case Study