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AI-Driven Refrigeration Control Creates Smarter Cold Storage Operations


Applied Thermal Engineering / Elsevier

AI-Driven Refrigeration Optimization Links Warehouse Operations with Cold Chain Energy Efficiency

Rantai Dingin

Apa yang telah terjadi

Researchers have proposed a new approach for industrial cold storage optimization by connecting warehouse logistics activities with refrigeration control strategies.

The study introduces a logistics-aware supervisory temperature scheduling framework designed to improve energy efficiency while maintaining required storage conditions.

Traditional cold warehouses typically operate refrigeration systems using fixed temperature strategies.

Namun, actual thermal demand changes throughout the day depending on:

  • pergerakan palet
  • bukaan pintu
  • loading activity
  • unloading schedules
  • operasi gudang

The research explores whether refrigeration can become more intelligent by responding to logistics activity rather than operating independently.

Cara Kerjanya

Industrial cold storage systems normally maintain temperature continuously.

Namun, not every moment requires the same cooling intensity.

Misalnya:

During periods of high warehouse activity:

  • doors open more frequently
  • warm air enters storage areas
  • product movement increases

During inactive periods:

  • thermal load decreases
  • cooling demand becomes lower

The proposed system combines:

Logistics Information

Including:

  • warehouse schedules
  • material movement
  • operational timing

Refrigeration Control

Adjusting:

  • setpoint suhu
  • cooling demand
  • konsumsi energi

Optimization Algorithms

Balancing:

  • penghematan energi
  • stabilitas suhu
  • operational requirements

Simulation results showed:

  • energy reduction of approximately 5.78%–6.23%
  • peak power reduction of approximately 8.75%–9.45%
  • no temperature violations

Mengapa itu penting

Cold storage is one of the largest energy consumers in food and pharmaceutical logistics.

Operators face increasing pressure from:

  • electricity costs
  • target keberlanjutan
  • carbon reduction requirements

Traditional efficiency improvements focus on:

  • isolasi
  • kompresor
  • peralatan pendingin

AI optimization introduces another pathway:

using operational intelligence to reduce unnecessary cooling demand.

This is important because cold warehouses are dynamic environments.

A facility handling hundreds of pallet movements per day has very different thermal behavior from a low-activity storage warehouse.

Dampak B2B

Untuk operator penyimpanan dingin:

Future facilities may integrate:

  • WMS data
  • refrigeration controls
  • sistem manajemen energi

Untuk pemasok pendingin:

Growth opportunities include:

  • smart controllers
  • AI optimization modules
  • predictive maintenance systems

For warehouse automation providers:

Integration between:

  • robot
  • inventory movement
  • pendinginan

may become a new competitive advantage.

For sustainability teams:

AI refrigeration optimization can support:

  • pengurangan energi
  • pelaporan karbon
  • efisiensi operasional

For cold chain technology companies:

Future intelligent warehouses may operate as connected systems where:

  • logistics data controls refrigeration
  • refrigeration data improves logistics decisions

Wawasan Akhir

The future cold warehouse will not simply maintain temperature.

It will dynamically coordinate:

  • pergerakan produk
  • cooling demand
  • konsumsi energi
  • operational schedules

AI-driven refrigeration control represents a shift from passive temperature maintenance toward intelligent cold chain management.

 

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