Use machine learning to find biases

In Figure 3, the ML model identifies the key factors that affect the target outcome in order to perform a root cause analysis of the bias. Provide insight into emission control decisions by constantly updating and ranking important features in real time. This number indicates the importance of a feature, and the greater the value, the greater the impact.

The figure also shows the mean, minimum, maximum, and trend of feature importance over time. With this data, we can intervene in a timely manner and seize opportunities to improve process control, performance and emissions reduction.

Figure 3: Deviation analysis between prediction model and measured results of key operating parameters

CX5130-0125/408 Integrating advanced machine learning models with the AVEVA PI System operational Big Data management platform enables organizations to maximize the potential of their operational data. As shown in Figure 4, this integration provides actionable insights to optimize device performance and enable data-driven decision making. By using models analyzed from historical data, businesses can make real-time predictions, detect biases and potential root causes, and thereby improve performance, reduce costs, and gain a competitive advantage.

The integration process is simple and easy to operate, and can be completed in the following few steps:

1. Configure a VM or cloud environment.

2. Configure PI system to realize real-time data storage and notification management;

3. Configure the Python environment and create the necessary files.

4. Set up PI connectors for common file and stream loaders to import external source data directly into the AVEVA PI System operational Big Data management platform.

All of this ensures seamless and efficient integration.

Figure 4: AVEVA PI System operates a big data management platform

Optimized emission monitoring

This use case demonstrates an innovative ML approach that reduces the environmental impact of the energy and chemical industries. By integrating complex models with the AVEVA PI System operational big data management platform, the project was able to:

• Develop powerful ML prediction models to accurately predict emissions, allowing timely decisions to be made to avoid overshoot of greenhouse gas emissions.

• Generate process-related predictors for different chemical process plants to gain a comprehensive understanding of the performance of specific process plants so that timely adjustments can be made.

CX5130-0125/408 The solution integrates seamlessly with AVEVA PI Vision, improving visibility and accessibility of critical data. Reports on PI Vision also help with things like maintenance planning and make it easy for management to understand greenhouse gas emissions issues.

The integration of the emissions monitoring tool with the AVEVA PI System operational Big Data management platform highlights the potential of advanced technology to address complex challenges and drive continuous improvement, while marking a solid step towards data-driven operations.

At the upcoming Schneider Electric 2024 Innovation Summit on June 6, with the theme of “Double Progress, New Wisdom”, Schneider Electric will showcase more innovative technologies and successful practices for industry and energy, helping industry accelerate towards an efficient and sustainable future! Stay tuned.