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Clean-in-place systems
Focusing on the processes and equipment that consume the most energy helps to make the biggest improvements. Within food and beverage and life sciences, clean-in-place( CIP) and steam-in-place( SIP) systems can use nearly 30 percent of a plant’ s utilities, but manufacturers often do not know exactly how much energy and media each cleaning cycle consumes. Also, cycles typically run for a preset amount of time that may be longer than required for proper cleaning. This results in wasted time, water, steam, chemicals and energy. Again, greater visibility into the systems and control of the processes is key to optimizing their efficiency. This is provided by CIP / SIP utility solutions that combine sensorequipped devices, such as pressure regulators and valves, with integrated software to capture and aggregate critical process data for analysis. These solutions automate the necessary reporting process, while also providing an in-depth view into the process to help manufacturers improve efficiency, reliability and sustainability. By tracking utilities and comparing each cycle against an ideal CIP / SIP cycle, manufacturers can reduce water consumption by 20-to-35 percent.
Focusing on the processes and equipment that consume the most energy helps to make the biggest improvements. Within food and beverage and life sciences, cleanin-place( CIP) and steamin-place( SIP) systems can use nearly 30 percent of a plant’ s utilities...
Optimizing compressed air use
Pneumatic systems are essential to power many processes and machines, but compressed air generation makes up 20-to- 30 percent of electricity consumption at a typical manufacturing facility. There is often a lack of visibility to the amount of compressed air used by each line or machine and the subsequent energy consumption. Without monitoring, up to 30 percent of compressed air generated can be lost due to leaks and sub-optimization of machines and devices. Compressed air monitoring solutions can continuously measure air flow in real time, identify anomalies early, and send personnel notifications to address them. Taking a floorto-cloud approach, these solutions utilize air flow sensors to collect compressed air data, with edge gateways transmitting data to cloud-hosted software that provides easy access to the data from any location. Statistics and trends help to determine leakage, energy consumption and the amount of carbon dioxide produced, enabling operators to make quick, informed decisions about the most appropriate course of action.
Machine learning
Taking this one step further, machine learning models are now available that enhance pneumatic system applications by learning ideal system behavior and accurately forecasting anomalies. When minor air flow deviations occur, AI can pinpoint leaks before they escalate, allowing personnel to address them proactively. The models can even detect creeping leakage – a subtle but impactful issue that can reduce overall equipment effectiveness( OEE). AIdriven condition monitoring and prediction helps facilities to optimize energy use and improve sustainability. Moreover, by reducing energy costs and preventing unplanned shutdowns, these solutions deliver a rapid return on investment.
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