Plantweb is an analytics and asset alert predictive intelligence tool designed to provide maintenance staff real time access to critical heat exchanger diagnostics in chemical, oil and gas and refining operations.

 

Emerson reliability and wireless sales manager for Southeast Australia Craig Abbot told Energy News the new technology was applicable to LNG plants and especially to refineries and other petrochemical plants. 

 

"If you want to be more efficient, you need to have measurable KPIs to track. We like to paraphrase Peter Drucker by saying, ‘you can't manage what you don't measure," he said.

 

He said that though sensor pricing was coming down they were still not cheap, and there would always be a demand to be economical, so the company prefers a "surgical" approach to the industrial Internet of Things. 

 

"We know what information can be measured in an asset, and how that data can be used to create useful information and from the information." 

 

Plantweb joins a suite of offerings including apps for pumps, steam traps and pressure gauges and it uses pre-built algorithms that have been built from Emerson's decades of process experience and industry-vetted analytics to deliver predictive diagnostics that enable maintenance prioritisation. 

 

The interface allows maintenance and operations instantly from within a browser from any laptop, tablet, smartphone or other device connected to the network. 

 

Plantweb Insight monitors shell and tube heat exchangers to provide real-time status and alerts including fouling, heat duty and heat transfer coefficient and doesn't require integration with the plant's existing monitoring and control system; it can operate completely independent of existing or legacy control systems.

 

The app can also leverage existing data points when needed using OPC Unified Architecture - a machine-to-machine communication protocol - to collect data stored in the control system, historian or data base if access is available.

 

"If we are specific about what we measure, and we apply the right algorithms to the data, then we can derive useful information to support actions that drive plant efficiency, reliability and safety improvement projects," Abbott said.