Dairy processing plants could be losing millions of dollars annually through milk and dairy products discharged into wastewater during production start-ups, shutdowns, interruptions and cleaning operations. New approaches combining continuous sensing, process data and artificial intelligence (AI) could help processors identify these losses earlier and improve operational efficiency.
According to Dairy Foods, peer-reviewed research published in Desalination has reported non-accidental losses of milk and dairy products to wastewater equivalent to 1%–3% of milk processed. Using the USDA’s 2026 all-milk price forecast of $19.90 per hundredweight, the article estimates that this range could represent approximately $2.9 million to $8.7 million in annual raw-milk value at a facility processing four million pounds of milk daily. This is an illustrative calculation, not a measurement of actual losses at the facility mentioned.
Production Transitions Are a Major Source of Loss
Some of the most significant losses occur when a dairy plant switches between production and cleaning. During start-ups, shutdowns, interruptions and rinsing, product mixes with water, creating uncertainty about when recoverable milk ends and the wastewater stream begins.
Conventional instruments provide only part of the picture. Flowmeters measure volume, conductivity probes detect changes in liquid properties, and laboratory tests provide detailed composition data, often after the event. Plant monitoring systems record equipment activity but may not establish exactly what material is moving through the pipes.
The article argues that connecting these measurements with production states can help operators understand when product loss occurs and what operational event triggered it.
AI and Continuous Sensing Offer a Potential Solution
Optical spectroscopy can identify changes in liquid composition by measuring responses across multiple wavelengths. However, its performance can be affected by temperature, air bubbles, suspended solids, changing product formulations and residue on optical surfaces.
The article highlights a more integrated approach: combine continuous sensor readings with existing plant data, including production status, valve positions, flow rates, cleaning-in-place (CIP) sequences, laboratory results and maintenance records.
This context can help distinguish normal changes during cleaning from potential product-loss events. Instead of generating a generic anomaly alert, an integrated system could help operators identify when a change began, which process events preceded it and what corrective action may be appropriate.
The article describes this approach through AquaMesh, a company developing systems that connect spectral water sensing with plant operating data.
Better Cleaning Control Can Reduce Water Use
The article cites an industrial dairy study in which improved process measurement reduced a UHT cleaning sequence from 142 minutes to 66 minutes. Effluent volume also declined from approximately 8,300 litres to 4,400 litres per cleaning cycle.
The improvement came from using physical measurements to determine when individual cleaning phases had reached their endpoints, rather than relying solely on fixed time settings.
Such findings demonstrate the potential to reduce cleaning time, water consumption and wastewater generation. Actual results will depend on plant design, equipment, product type and validated sanitation requirements.
Food Safety Must Remain the Priority
AI-based monitoring is intended to support process decisions, not replace established food-safety controls. Cleaning validation, verification and appropriate microbial testing remain essential to ensuring hygienic processing.
The article recommends beginning with read-only monitoring, allowing the system to analyse conditions and provide recommendations while existing programmable logic controllers, safety interlocks and sanitation procedures remain authoritative.
This approach enables dairy processors to assess the reliability of new technology before considering greater automation.
A Practical Business Case for Dairy Processors
The article recommends measuring savings through specific operational improvements rather than assuming a universal reduction in losses. Processors can establish a baseline for a costly event, detect it earlier, adjust the operating decision and verify the outcome.
At high processing volumes, even a partial reduction in avoidable product loss could deliver meaningful savings in raw-material costs. More efficient cleaning can also reduce water use and wastewater loads.
For dairy businesses, the key opportunity lies in combining reliable physical measurements with production context. Identifying what changed, why it changed and how quickly operators can respond could help plants protect product value while improving resource efficiency.
Source: Dairynews7x7 14 Oct, 2026 Read full article here
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