To listen to the podcast interview, click the play button in the player below, or download the Control Amplified podcast on your favorite podcasting platform.

To read a transcript of the conversation click here.

The post Podcast: No-code approach lowers AI roadblocks appeared first on Griffin Open Systems.

The highly complex and sensitive nature of combustion processes makes it one of the most challenging systems to control in a coal-fired power plant. Behaving almost as its own living entity with each process having its own individual characteristics and behavior, managing, and controlling such systems is a difficult task.

Griffin’s Combustion Optimization System (COS) uses many advanced AI methods available within the Griffin AI Toolkit to develop highly specialized and accurate models of each unique system to reduce emissions, improve heat rate, and achieve other process objectives.

Custom Combustion Optimization

There is no “one-size-fits-all” method to gaining real and repeatable improvements from combustion optimization systems. Griffin’s COS application approach has been developed and refined over more than 50 years of experience providing combustion optimization solutions. Building upon the proven Griffin COS template of reliable and robust control, each COS application is tailor-made to address the unique characteristics of the subject system.

Advanced AI modeling and optimization methods are used to represent process complexities and take advantage of each and every existing opportunity; from subtle damper adjustments to shifting whole operating conditions within acceptable ranges, all prospective improvements are leveraged to provide maximum performance benefits.

Agile No-Code Implementation

Existing site-knowledge from those who know your system best – your engineers and your operators, often spanning decades of operation – is then formalized and rapidly implemented within the Griffin no-code platform to further complement the advanced AI components in place within the COS.

This process results in a highly customized and fully relevant optimization system capable of providing control benefits to the system in as little as 1 – 2 weeks. As the AI components continue to learn and adjust from observing real-time operation, observed benefits continue to improve automatically over time.

What to Expect From The Griffin Combustion Optimization System

The Griffin COS applications are flexible and purpose-built so that they can be configured to realize any number of performance objectives. Common benefits include:

• Reduction of NOx emissions
• Heat rate improvements
• Mitigation of unplanned outages
• Enhanced alarm condition management & resolution
• Consistent unit controls across all operating crews and conditions

A Griffin COS is a valuable addition to any combustion process with a proven record of improving multiple aspects of process performance. Regardless of system complexity or existing limitations, benefits can be obtained through this powerful application. Please contact us today to learn more about your options!

The post Combustion Optimization with the Griffin AI Toolkit appeared first on Griffin Open Systems.

At Griffin Open Systems, our AI Toolkit is the backbone of what we call Adivarent Control®, a concept that allows process industries to leverage artificial intelligence (AI) in manufcaturing to streamline the data flow between the plant supervisory (level 2) and production control (level 3) levels. Because of this, Adivarent Control® enables both control operators & control systems to operate with increased efficiency – and these efficiencies can increase throughput, decrease unplanned downtime, and ultimately optimize processes.

So what exactly is Adivarent Control®? We like to say it serves as a real-time assistant to control operators and control systems. But, it’s perhaps easier to dive into what exactly it is and what it isn’t …

Adivarent Control® is built on a reliable, open, and customizable AI platform and features a no-code graphical programming interface to enable control operators and engineers to swiftly test, iterate upon, and deploy new process models – all without having to dig into the code & logic in the control system. Because of this no-code platform approach, it allows tacit knowledge from control operators to be institutionalized and scaled in a much more agile manner. This allows manufacturing process improvements & optimizations to be realized rapidly, leading to quicker results on the bottom-line.

Further, it is a closed-loop system that integrates AI at the juncture of the plant supervisory and production control levels. Unlike other systems that implement AI at the production control level as advisor tools for operators, or embed lower AI capabilities into the control layer, Adivarent Control® bridges that gap and allows AI to work with greater complexity, often integrating discrete control elements.

More important than what Adivarent Control® is, might be what it isn’t.

Adivarent Control® is not meant to be a large CAPEX investment in hardware replacing your existing control system or your control operators. Because of its open architecture and vendor agnosticism, it is also not a complex “black box” system that beholds users to specific hardware, I/Os, or control systems. Lastly, it is not a monolithic, pre-packaged solution – industrial processes vary, and as such systems to control & optimize them should be flexible to account for that.

Want to learn more about how to leverage AI in manufacturing to break-down and improve the flow of data & control between levels in the Purdue Model? Contact us today to learn the roadmap to implementing Adivarent Control.

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