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How AI for Industrial Process Control is Transforming Operational Strategies and Performance?

Once the AI system learns the behavior, they can autonomously make adjustments to optimize performances. 

Traditionally, optimization relies on human experience and problems are often addressed after they happen. AI for industrial process control, on the other hand, enables proactive and real-time decision-making that often outperforms human intervention as per its ability to analyze vast amounts of data. 

In this blog, you will explore how AI has brought changes into regular industrial processes and how it has enhanced for better functionality.

Artificial Intelligence's Effect on Industrial Procedures

According to a report by McKinsey, AI could boost the global economy by about $13 trillion by 2030. 

Additionally, around $1 trillion in value is still to be gained from the industrial sector. 

Although the adoption of AI in the industrial sector is quite low, the existing infrastructure statistics show that operators that have applied AI for industrial process control reported a 10 to 15% increase in production and a 4 to 5% increase in EBITA. (source)

In a process plant, the system uses sensors to collect thousands of process measurements, which provide information to the control logic for the various controls. 

The data recovered from these sources is then processed by three types of sources:

Basic Single Variable Control Loop

This is the simplest type of a control loop where only one process variable (like temperature, pressure, or flow) is monitored and actively controlled to maintain a desired set point. It typically consists of 3 main components: a sensor, a controller, and an actuator.

2. Advanced Regulatory Controls

ARC (Advanced Regulatory Control) is a set of advanced techniques used in industrial processes to improve stability and performance. 

It builds on basic control systems (like PID controllers) and uses strategies such as cascade, split range, and adaptive gain. These techniques are usually applied within existing control systems (DCS) without needing extra hardware or software, helping to optimize the process with tools already available.

3. Advanced Process Controls (APC)

This comes of great use when there is a need to control multiple variables and interactions between them. 

Unlike a single variable, which is used for controlling and monitoring a single variable. APC can be used for controlling multiple interacting variables simultaneously.

For example, an APC in a chemical plant can optimize the flow rates of raw materials, temperature, pressure, and other variables to ensure the process operates at maximum efficiency while adhering to safety and quality requirements.

Most plants have years of detailed historical data on process conditions and performance. 

This data can be used to train AI for industrial process control models that can streamline processes and minimize human efforts. Some APC vendors have started integrating elements of advanced analytics (AA) and AI to improve the accuracy of their process models.

Operators rely on experience and intuition to run plant processes, but the element of human bias and tendency to maintain the status quo have an impact on the potential for continuous improvement.

Brief Overview on AI for Industrial Process Control

At an open-pit core mine, the operators believed that there were only 3 types of ore to process and as a result, they rarely changed the processing set points. However, ML programs discovered that there are, in fact, 7 distinct ore types.

The types of material going into the mill were changing more often than the operators noticed. This created an opportunity to use sensors, data analysis, and process controls to detect these changes in real-time and adjust the processing recipe accordingly. As a result, production increased by over 10% in less than six months. (source)

How Does AI Enhance Industrial Process Control?

Machine learning, deep learning, and neural networks all work together to analyze large amounts of data generated by industrial systems in real-time. 

Predictive Maintance

Traditional maintenance schedules are often based on predefined time intervals, regardless of the actual condition of the equipment. 

AI for industrial process control algorithms that are continuously digesting the historical data, sensor readings, and operational parameters allow the system to predict when machinery is likely to fail.

According to some reports, predictive maintenance can yield cost savings of up to 8 to 12% over preventive maintenance and up to 40% over reactive maintenance. (source)

2. Process Optimization

AI for industrial process control can continuously monitor system performance and can make adjustments in real time. Things can be automated, such as automatically turning off a machine when it reaches a certain temperature. 

Studies show that AI can handle up to 80% of repetitive tasks, saving people about 20% of their time. By using AI to improve workflows, companies can get more done in less time, allowing teams to focus on important and creative tasks. (source)

3. Quality Control

Automated inspections eliminate the requirement for subject matter experts and even in the absence of manual monitoring, the system keeps on analyzing and reporting the overall performance. 

By utilizing this technology, industries are minimizing the potential errors that can somehow be possible during manual interventions. The major advantage is that these systems can work without fatigue and can analyze a vast amount of data, which is manually very time-consuming or may not be possible.

4. Energy Management

In an industrial setting, energy consumption is a significant operational cost, and AI for industrial process control can help optimize energy use by identifying inefficiencies and suggesting adjustments. 

Google's DeepMind AI system, for instance, was able to reduce the energy used for cooling Google's data centers by a remarkable 40%. It achieved this by predicting the data center's future cooling requirements and adjusting systems accordingly. (source)

Conclusion

With the AI’s ability to do much of the work on its own, it presents a great opportunity for industries to tap into substantial cost-saving opportunities. 

Also, the ongoing advancements in AI, such as edge computing and 5G connectivity, will further enhance real-time data processing capabilities.

Businesses that invest in building their AI for industrial process control will be best positioned to take advantage of these advancements. For more such informative articles, follow WinterGreen Research. Visit the website to explore more topics!

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