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Phone: +86 16626708626Description
The Yokogawa CP451 functions as a next-generation central processing hub in distributed control systems (DCS), building on the reliability of units like the CP401-10 while adding machine learning (ML) capabilities. It acts as both a controller and an analytics platform, processing real-time data from field devices—such as analog input modules, servo drives, and sensors—through advanced algorithms that identify patterns and predict process deviations. Positioned at the intersection of operational technology (OT) and information technology (IT), it integrates with cloud platforms and edge computing devices, enabling seamless data flow between shop floors and enterprise systems. The unit supports redundant configurations with hot-swap capabilities, ensuring uninterrupted operation in critical applications. It also features enhanced cybersecurity protocols, including secure boot and encrypted communication, to protect against evolving digital threats in connected industrial environments.
CP451
Technical specifications:
Model NumberCP451
BrandYokogawa
TypeIntelligent Process Control Unit
Input Voltage24V DC
Operating Temp Range-10 to 60°C
Mounting StyleRack mount (19-inch standard)
Dimensions482mm x 210mm x 320mm (WxHxD)
WeightApprox. 6.8 kg
Interface/BusVnet/IP, 5G, Wi-Fi 6
ComplianceIEC 61508 (SIL 3), CE, UL, RoHS
Supported ProtocolsOPC UA over TSN, MQTT Sparkplug, Ethernet/IP
Typical Power Draw55W (continuous)
contact us:
Phone:+86 16626708626
WeChat/WhatsApp:+86 16626708626
Email: 3772179818@qq.com
CP451
Main features and advantages:
Engineered for the smart factories of today and tomorrow, the Yokogawa CP451 delivers transformative benefits to industrial operations. Its embedded machine learning models analyze historical and real-time data to predict equipment degradation, process inefficiencies, or quality deviations—often hours or days before they occur. This predictive capability reduces unplanned downtime by up to 30% in field studies, as maintenance teams can address issues during scheduled shutdowns. The unit’s adaptive control algorithms automatically adjust parameters based on changing conditions, such as raw material variations or environmental factors, ensuring consistent product quality even in dynamic processes. Compared to the CP401-10, the Yokogawa CP451 offers 50% faster processing speeds and 3x the data storage capacity, enabling it to handle the massive data flows of modern industrial IoT (IIoT) systems without performance lags.
CP451
Application areas:
The Yokogawa CP451 excels in industries where precision, efficiency, and adaptability are critical. In pharmaceutical manufacturing, it ensures compliance with strict regulatory standards by using ML to monitor batch consistency and predict deviations from quality parameters, reducing the risk of costly product recalls. In semiconductor fabrication, where even nanoscale variations can ruin production runs, the unit’s predictive analytics optimize cleanroom conditions and equipment performance, increasing yield rates. In renewable energy plants, such as solar farms or wind parks, it analyzes weather patterns and equipment data to predict energy output, enabling better grid integration and maximizing revenue from power sales. In all these applications, the Yokogawa CP451’s ability to turn data into actionable insights makes it an indispensable tool for operational excellence.
CP451
Related products:
CP451
Installation and maintenance:
Before installing the Yokogawa CP451, ensure that the rack mount system can support its increased weight compared to the CP401 series and that the 24V DC power supply can deliver stable current to handle the higher power draw. Verify compatibility with existing DCS components—while it integrates with most Yokogawa modules, firmware updates for older I/O devices may be necessary. Check that network infrastructure supports the unit’s 5G and Wi-Fi 6 capabilities if wireless connectivity is required, and ensure firewalls are configured to allow secure communication with cloud platforms. During maintenance, schedule quarterly reviews of ML model performance to ensure accuracy, as process drifts over time can affect prediction reliability. Inspect cooling systems monthly, as the advanced processing components generate more heat than traditional units, and update both control firmware and ML algorithms regularly to leverage the latest improvements.
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