Harnessing the Potential of AI in Industrial Refrigeration Systems
The Regents of the University of California, Santa Barbara
Recipient
Santa Barbara, CA
Recipient Location
21st
Senate District
37th
Assembly District
Active
Project Status
Project Update
The Issue
California plays a critical role in the cold storage market, with nearly 400 million cubic feet of storage space supporting national and international food supply chains. Growing demand for cold storage—driven by the rise of online grocery sales (projected to grow 11.7% annually) and the need for temperature-controlled pharmaceutical storage—has increased the urgency to improve energy efficiency in these facilities.
While energy reduction and thermal load shifting have shown great potential, their widespread adoption remains limited due to several key challenges:
1) Outdated Infrastructure: The average U.S. cold storage facility is 34 years old, relying on antiquated systems to manage billions of dollars in products.
2) Lack of Scalable Algorithmic Solutions: Load shifting is critical for state-of-the-art operations but is largely confined to facilities with the expertise and resources to implement it. The operational burden of maintaining safe, efficient, and scalable load-shifting capabilities hinders broader adoption.
These challenges are compounded by highly variable operational requirements across facilities, aging infrastructure, complex rate structures, limited visibility of new solutions, compliance variations, cybersecurity risks, and a lack of skilled labor. Addressing these barriers is essential for achieving scalable, energy-efficient operations in California’s cold storage sector.
Project Innovation
Over the past five years, our partner CrossnoKaye has developed the ATLAS Platform (TRL 8), an intelligent cloud control software that transforms outdated industrial facilities into smart ones by moving control architecture from the physical plant to the cloud. ATLAS enables remote control, external data integration, 24/7 monitoring, and offers economic benefits such as energy visualization, rate optimization, and load management.
While ATLAS has proven effective for energy reduction and load shifting, significant hurdles remain for scaling these technologies across cold storage facilities. These challenges include adapting control algorithms to varying operational conditions, forecasts, rate structures, and storage availability—complex tasks that are impractical for manual implementation or traditional decision-making software, often increasing costs and errors.
Fortunately, recent advances in AI, reinforcement learning, and optimal control can address these issues. While such technologies have transformed many sectors, they have yet to be fully applied to refrigeration and heavy industry processes. This project, led by experts from UCSB and CrossnoKaye, aims to fill these gaps by deploying scalable AI-driven energy efficiency and load shifting solutions for industrial refrigeration, with the support of Lineage Logistics, a leader in cold storage.
The project will focus on two main technology areas:
1) Energy Reduction: AI-based control algorithms that optimize facility operations to minimize total energy expenditure.
2) Load Shifting: Scalable AI control solutions that safely shift refrigeration load and leverage thermal energy storage to align energy use with lower-cost periods or high renewable energy availability, reducing costs and carbon footprints.
Project Goals
AI-Based Algorithms for Scalable and Robust Load Shifting
AI-Based Algorithms for Scalable Dynamic Adjustment of Suction and Discharge Pressure in Conjunction with Load Shifting
AI-Based Algorithms for Scalable and Robust Dynamic Compressor Sequencing in Conjunction with Load Shifting
AI-guided Recommendation Systems for Integration of Thermal Energy Storage and Behind-the-Meter Generation Technologies
Project Benefits
The goals of this project are to deploy new AI-based algorithms for load shifting in industrial refrigeration facilities that can reduce energy expenditures and greenhouse gas emissions by over 20%.
Key Project Members
Jason R Marden
Professor, Department of Electrical and Computer Engineering
University of California, Santa Barbara
Mahnoosh Alizadeh
Professor, Department of Electrical and Computer Engineering
University of California, Santa Barbara
Corrin Terrones
Administrator
University of California, Santa Barbara
Jesse Crossno
Chief Product Officer
CrossnoKaye
Alex Woolf
Vice President, Research and Development
Lineage Logistics
Subrecipients
Crossno & Kaye Inc.
Match Partners
Tech Partner
Lineage Logistics, LLC
Tech Partner
Crossno &
Kaye Inc.
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