Researchers at Perm National Research Polytechnic University (PNIPU) have unveiled an intelligent control algorithm designed for industrial gas burners. The university’s press service shared with socialbites.ca that the system can automatically select the most efficient operating mode, adapting in real time to changing conditions on the shop floor [PNIPU].
Gas burner systems are common in production settings, where rapid heating of materials or drying of bulk goods is required. These operations rely on sophisticated control schemes that depend on precise data about device parameters. Because such parameters can be hard to measure and update during continuous production, scientists looked to neural networks for a solution that learns from ongoing data and improves accuracy over time [PNIPU].
During experiments, researchers found that adding historical measurement data and standardized reference values to the live model introduced sizable errors. In one case, a stove’s heat-loss estimation exhibited an error near 6%, equivalent to roughly 33 °C. The finding highlighted the risk of relying on static data without adaptive learning in dynamic industrial environments [PNIPU].
To overcome this, the team trained a neural network with a data sample that reflected real operating conditions. The result was a refined computational model capable of reducing error dramatically and in a fraction of a second. Specifically, the error dropped from 5.9% to 0.35%, with a processing cycle time around 0.03 seconds. This accelerated feedback loop enables faster, more accurate adjustments to combustion control and fuel efficiency in production lines [PNIPU].
According to PNIPU, implementing a neural network in these systems makes it possible to rapidly identify changes in the calorific value of the fuel gas, detect fouling and carbon deposits on chamber walls, and monitor shifts in heat output during combustion. Such insights help maintain stable operation, optimize energy use, and extend equipment life in industrial settings [PNIPU].