Food & Beverage
Equipment efficiency that holds
quality and cost at once
Data scattered across heating and cooling equipment, power, LNG and steam is connected through an ontology, and an AI agent catches efficiency loss first and explains the cause. From chiller operation tuning to per-batch energy intensity and replacement simulation for ageing equipment.
These are the problems on
food and beverage sites today
- Thermal and refrigeration losses become cost
- Compressors, chillers and boilers carry the load
- Efficiency loss turns into quality risk
- A stoppage costs a great deal
- Hygiene and quality rules are strict
- One interruption is a large loss
- Energy patterns shift batch to batch
- A shift in output moves the energy profile
- Cost, carbon and aging arrive together
- Cost pressure does not let up
- Carbon compliance is pressing
- Equipment keeps aging
This is how
Refinery solves it
Chiller and compressor power and operating pattern analysis
- Problem
- Equipment efficiency slipping without ever surfacing
- Approach
- Power consumption and operating patterns are overlaid to locate where efficiency falls off.
- Outcome
- Better equipment efficiency and lower energy cost
Energy simulation before and after replacing aging equipment
- Problem
- Replacement benefit that cannot be shown in numbers
- Approach
- Post-replacement energy use is estimated from current operating data and placed side by side with current use.
- Outcome
- Investment case verified and replacement benefit quantified
Unified electricity, LNG, steam and water monitoring
- Problem
- Multiple energy sources, each aggregated separately
- Approach
- Electricity, LNG, steam, water and consumption by asset are connected through an ontology into one structure, so the whole plant energy flow sits on one screen.
- Outcome
- Usage patterns made visible by energy source
Energy intensity tied to output
- Problem
- Batch-to-batch variance with no baseline to compare it against
- Approach
- Production records and energy use are linked to derive energy intensity by batch and product automatically.
- Outcome
- Better cost structure and comparable process efficiency
Chiller efficiency analysis and operating optimization
- Problem
- Chillers that keep running with no reference point
- Approach
- Chiller COP is computed continuously so the most efficient units are run first.
- Outcome
- Lower thermal and refrigeration energy, and steadier quality
Vibration-based equipment monitoring
- Problem
- Rotating-equipment anomalies that surface only after a stop
- Approach
- An AI agent reads anomalies in vibration trends to catch early signs, and proposes both the likely cause and the next action with the history behind it.
- Outcome
- Failures prevented and downtime reduced
How it fits together
How field data gains meaning and turns into a decision.
- Chillers
- air compressors
- boilers
- temperature sensors
- MES
- SCADA
- ERP
- SAP
- Modbus TCP/IP
- LoRaWAN
- 4–20 mA
- Ontology
- AI agent
- rules and automation
- Equipment dashboard
- temperature excursion alerts
- batch reports
Where PLC or MES already exists, a layer sits on top of it and integrates bidirectionally rather than replacing it. Where none exists, collection is built from the ground up.
Systems we connect to
The systems and protocols commonly used on food manufacturing sites.
Systems not listed here can still be connected over standard protocols and APIs. Get in touch and we will walk through it.
What you gain
Benefits across operations, engineering and management.
Replacement justified with evidence
Before and after are set out in numbers to justify the spend.
Chiller and compressor tuning
Operating patterns are refined to save thermal and refrigeration energy.
Efficiency compared against output
Energy is compared batch to batch to improve the cost structure.
Retracing equipment on a quality issue
Equipment state at the moment of the issue is retraced on one timeline.
Less downtime
Vibration catches anomalies early, preventing failures.
