More than 200 cement professionals from around the world joined PROMECON´s Fireside Chat to get experts view about a question that is becoming increasingly important: Can Smart APCs or any Artificial Intelligence System improve the pyro process if the underlying data cannot be trusted?

The discussion highlighted a clear consensus: Before implementing Expert Systems or AI, plants must establish reliable, drift-free process measurements. High-quality process data remains the foundation for better decisions, stable operation, higher alternative fuel rates and improved energy efficiency.

Our panel shared practical studies demonstrating how real-time gas flow measurements reveal process conditions that would otherwise remain hidden: from tertiary air ducts refractory wear and material buildups to damper aging, fan power overdrafting and combustion control.

Here are the Key Takeaways

1) Unbalanced tertiary air flow leads to improper air–fuel ratios in both the kiln and the calciner. This can be monitored and optimized by real-time tertiary air flow data instead of calculated soft sensors.

2) Alternative Fuels are integral parts of decarbonation and often cost reduction, but they bring a lot of new variables. To control those variations and then optimize the various process control loops air/flue gas continuous and reliable flow measurement is essential. Plants can’t just rely on pressures and temperatures.

3) Unknown split between secondary and tertiary air is one of the hidden trouble-makers in a huge number of plants. While operators put some effort into the measurement and the control of primary air flow rates at burners, there is often no real online information about the actual air supply to both, kiln and calciner available. Oxygen measurements in the preheater exhaust gas and in the kiln inlet chamber are used as tools to get an idea about it, but those are quite unreliable. Here McON Air set the measure as reliable TA flow data source.

Messages from the Panel:

Alexandra Graf: “Before we discuss AI, let us first slow down to speed up. Let me ask you three simple questions:

• Do you know your real tertiary air flow?
• Do you know your actual secondary air to tertiary air split?
• Do you know how much fan energy you could save by knowing your realtime fuel:air ratio? If not... why are we already talking about Artificial Intelligence?”

John Kline: “If you have, for instance, a kiln inlet buildup due to the alkali condensation, that will block a bit the draft at the kiln inlet. So that will reduce the flow. And if you are able to measure the flow at the tertiary air duct, you will see an increase of the speed or the flow at the duct. A reliable measurement is much more helpful than just relying on pressure of the draft to know that a buildup is coming. You could even monitor the buildup at the kiln inlet.”

Jean-Philippe Gravel: “When gas flows are not well understood or controlled, first, we have to realize that an unbalanced tertiary airflow leads to improper air-fuel ratio in both: the kiln and the calciner. That creates problems of energy management and, of course, creating gases like CO and nitrogen oxide. That's the big main issue.”

Xavier d'Hubert: “Many plants don't really have a reliable clean kiln inlet gas analyzer, nor do they have any frequent free lime measurements. And this would be the two absolutely critical data to optimize and control the pyro processing.”

Matthias Schumacher: “Generally said yes, AI can compensate for some weak measurements or some interrupted measurements. If this AI is able to reconstruct missing information in general or missing information for a while if it is interrupted. (…) and if you're applying alternative fuels then an AI based prediction of flow rates of air flow rates which is purely based on AI sensors will most probably not work from my point of view. Some measurements are essential anchors for an AI based control. And to my opinion, the gas flow measurement is one of those essential anchors to provide a stable and reliable AI control system.”

The discussion reinforced one central message: Artificial Intelligence is only as powerful as the process data behind it. Reliable measurements remain the essential first step toward successful digitalization and smarter cement production.

To watch the full session, click here: Watch the Video