Real-time optimization of an industrial steam-methane reformer under distributed sensing

•Control-relevant empirical low-order model for steam methane reformers (SMR).•Model calibrated using distributed measurements from array of infrared camera.•Optimization of SMR temperature distribution is carried out based on the model.•Industrial results confirm excellent performance of proposed framework.

Industrial hydrogen production takes place in large-scale steam methane reformer (SMR) units, whose energy efficiency depends on the interior spatial temperature distribution. In this paper, a control-relevant empirical reduced-order SMR model is presented that predicts the furnace temperature distribution based on fuel input to a group of burners. The model is calibrated using distributed temperature measurements from an array of infrared cameras. The model is employed to optimize in real-time the temperature distribution and increase the energy efficiency in an industrial furnace. Experimental results confirm that the proposed framework has excellent performance.