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.

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Real-time artificial intelligence optimization systems have been shown to consistently reduce emission rates of fossil-fuel power plants. In this presentation, Comparison of State-of-the-Art Dynamic Machine Learning Methods for MPC of Coal-Fired Utility Generator Performance, ten different dynamic machine learning methods are analyzed and applied to a case study to determine the optimal model for enhanced coal-fired utility generator performance. Each method was compared on their ability to accurately and reliably predict NOx emission rates from a coal-fired power plant 60 timesteps into the future.

The most accurate and stable model for this application was the gated recurrent unit network (GRU), displaying an average RMSE of less than 5% across the complete time horizon, and a standard deviation of time horizon RMSE of less than 1%. 

See how the other models stacked up and watch a full presentation on how accurate each method was at predicting the NOx emission rate.

While operational support costs of using predictive machine learning models can be costly, the Griffin AI toolkit can be paired with the Power Plant Initiative, a maintenance program that reduces labor and maintenance costs. For a limited time save 80% on the Power Plant Initiative program with only a one year commitment or replace your existing system at a discounted installation rate. Try the Griffin AI Toolkit today or contact us to take advantage of the cost savings.

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Numerous approaches to modeling and system configurations are discussed in the presentation, Long-Term Hybrid AI Expert Combustion Optimization System for Coal-Fired Electricity Generation NOx Reduction. Many of the approaches represent offline or only short-duration online performance studies and not integrated into a layer for real-time interaction with the control system. The hybrid AI-expert system aims to satisfy the need for a long-term evaluation and characterization of the performance and effects of a hybrid AI-expert combustion optimization system (COS) on NOx emission rates, as well as the secondary effects on the remainder of the system. The COS uses artificial neural networks to model the combustion process, particle swarm optimization (PSO) to optimize the 80+ manipulated variables, and expert logic to address adverse conditions in real-time using this adivarent control layer to leverage the existing digital control system assets for improved performance. 

Over a two-year operational period, the hybrid AI-expert system was able to aid in the reduction of the NOx emission rate by more than 22.5% compared to baseline, while improving unit temperature management and realizing high operator acceptance leading to a system service factor greater than 86%. 

Read more and watch a full presentation on the hybrid AI-expert system. 

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Reheat Furnace Optimization with AI

Reheat furnaces are widely used in the steel and metallurgical industries to heat metal slabs to the proper temperatures for rolling, forging, or extruding. Despite there being many different types and designs of reheat furnaces they all face common challenges such as:

• Inefficient energy usage
• Low granularity/difficult to use control systems
• High reject rates due to unacceptable slab temperatures after extraction

Leveraging an AI control system optimizer to ensure optimal temperature setpoints can help address all three of those common challenges. To learn more about temperature setpoint optimization in reheat furnaces, download our whitepaper, “Temperature Setpoint Optimization in Steel Reheat Furnaces Using Open Architecture Neural Network and Modeling Software”.

How to Select AI for the Steel Industry

One of the most common challenges steel manufacturers face when optimizing their processes is dealing with complex and difficult to use control systems. Often times these systems don’t offer high enough granularity of data or are simply not designed with the operator in mind. As such, when selecting an AI solution to supplement the control system, ensuring it can integrate data flows easily in a way that is easy for operators and engineers is key.

Many AI tools are now built with a no-code UI allowing operators and engineers to quickly and easily model, test, and implement optimizations for processes. Other important features to look for are systems that can leverage multiple data flows. The promise of AI is that it can bring many disparate data flows together to help reach optimal setpoints, and ensuring that the AI solution you are working with isn’t a “black box” or has limited integrations is of the utmost importance.

To learn about our solutions for the steel industry click here: Griffin AI Toolkit in Steel and Reheat Furnaces.

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Powdered activated carbon (PAC) injections for the removal of mercury (Hg) from combustion emissions can be costly and have adverse effects on other aspects of the combustion system – primarily exit gas stream opacity out of the stack.

It is preferable to inject only the minimum necessary amount of PAC to lower mercury below limits while not wasting PAC material and inflating opacity measurements. Common PAC injection control systems operate very slowly due to the nature of the adsorption process and the extremely low concentrations of mercury; however, the effects of injection can rapidly raise opacity, often beyond acceptable limits, requiring manual corrective actions. This often creates a cycle of control movements that culminate in the entire process performing inefficiently.

However, there exists an opportunity to intelligently balance priorities and optimize PAC injection rates to remove mercury while controlling opacity and minimizing PAC usage.

Advanced AI & No-Code Implementation for Rapid Results

The Griffin Open Systems’ Hg PAC Optimization application prioritizes multiple objectives in real-time to achieve optimal performance of your system while staying within all process limits. Through an advanced method of prioritization, long- and short-term objectives of multiple parameters are considered and respected, leading to overall improved performance.

The Griffin Open Systems’ Hg PAC Optimization application can be implemented in an agile manner. Requiring minimal I/O and leveraging its user-friendly no-code interface, operators and engineers don’t need to change code in any systems allowing the application to be implemented in as quickly as one week.

What to Expect from the Griffin Hg PAC Optimization Application

As the application can be implemented in an agile manner, engineers and operators can expect to see as high as a 15% – 20% reduction in PAC injection while lowering the amount of high opacity events and maintaining or improving mercury emission rates.

We at Griffin Open Systems look forward to aiding you in achieving optimal performance of your cooling towers as well as all systems within your process. Please contact us to learn more about our many solutions today!

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