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We're seeing a clear shift:

🛠️️ Customers are now applying TrendMiner not just for production, but increasingly for utilities like steam, cooling water, HVAC, and especially cleanrooms.

 

Why Cleanroom Monitoring?

💡 Cleanrooms can account for up to 50–75% of total facility energy demand

💨 Inefficient operations & redundancies: air systems often run at full power - even when not needed

⚠️ Energy peaks are detected too late

📋 Compliance pressure (GMP, ISO, FDA)

 

Key Challenges: Tons of data, but too few insights

🐌 Root-cause analyses remain time-consuming

🚫 Limited live monitoring & alerting

• 🧱 Data silos block collaboration

 

Smarter Monitoring with TrendMiner

💡 No-code time-series analytics: data-driven decision-making cuts analysis time from days to minutes

📈 Analyze years of historical data in seconds to spot trends and inefficiencies

🔍 Real-time insights into asset performance and critical parameters like airflow, temperature, pressure, and particle count

⚠️ Detect anomalies & drifts before failure

🔄 Correlate energy consumption with process behavior to uncover savings

✅ Optimize air-system settings without compromising cleanroom standards

 

Real Results from Our Customer Base

⏱️ Analysis time reduced from days to minutes

🚀 20% energy savings in ultra-pure air systems

🔁 Predictive & prescriptive maintenance for critical assets

🔍 Early detection of system drift

 

Beyond Air Systems

Cleanroom monitoring is more than air quality. With TrendMiner, teams can optimize multiple systems to drive efficiency, reliability, and cost savings:

🌡️️ Temperature & humidity control in high-precision production

♨️ Heat exchanger fouling detection

💧 Ultra-pure water supply optimization

⚙️ OEE enhancements for vacuum chambers

🗂️️ Easy, traceable reporting for regulatory compliance

 

➡️ With TrendMiner, cleanroom and facility teams turn data into decisions - boosting efficiency, uptime, and sustainability.

Very interesting post ​@Charlotte! We are currently adding and retrofitting energy-meters via LoRa-Wan.  


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