The identified measures
AI training for employees
Linde's first challenge was to train experts in the domains and operational employees on the topic of artificial intelligence. Previously, there was a lack of experts who were knowledgeable in both AI technology and plant operations. Therefore, there was a need for domain experts who could pinpoint where AI could be used to improve plant efficiency. Raising awareness of AI activities within the company was important to build understanding for AI and accelerate its implementation.
Optimizing plant operations and automating control systems
Linde was already using advanced technologies for plant operations, with a large portion of its production plants remotely controlled by Remote Operating Centers (ROCs). A joint team of Linde domain experts and appliedAI engineers identified further opportunities for improving plant control to make them more efficient. In response to requests from the business community, Linde decided to use artificial intelligence to push the boundaries of optimized plants even further.
The plant control system consists of low-level and high-level controls. The task of the former is to ensure the correct settings for individual valves as well as actuators. This is managed by a Distributed Control System (DCS). The mechanisms of high-level control, on the other hand, have the task of controlling the plant as a whole. The controllers must be tuned regularly, and from time to time the plant load must be changed. This is a task that still had to be done manually.
Setting up new parts of the plant manually, as well as maintaining and adjusting the plant control algorithms on a regular basis, was particularly time-consuming. Although high-level control tasks still need to be performed manually by employees to ensure that equipment is operating in unison and optimally, these tasks could be handled by AI. It would work more efficiently and accurately.
Because the control algorithms in ROCs are not self-learning, they require a variety of data to predict the behavior of a plant. This data is also critical to apply a specific strategy to control the valves. This is the only way to ensure that process values are close to target values and achieve optimal results.
Although this process works well, it can be further improved to make it more efficient and sustainable. AI has the potential to provide an adaptive system that learns the plant's behavior and responds even better in dynamic situations.
AI plant control can predict real-world plant behavior and respond to it directly. This would allow Linde to save significant amounts of energy and would have a reduced workload. At the same time, better and more energy-efficient results would be achieved.