No abstract

No abstract

Steam methane reforming is a mature and complex process extensively used worldwide for hydrogen production from methane. The process takes place in a steam methane reformer (SMR), with the endothermic reforming reactions being carried out in catalyst-filled tubes placed in a gas-fired furnace. The SMR is an energy-intensive process unit, and maximizing energy efficiency is of primary interest. However, the high-temperature conditions and large physical scale of the process (hundreds of tubes and burners) pose several operational challenges related to distributed sensing, actuation, and feedback control. Various efforts have been reported on optimization of furnace operation using rigorous computational fluid dynamics (CFD)-based models but, being computationally intensive, these models are unsuitable for real-time optimization. In this paper, we present an integrated framework that relies on the use of advanced temperature sensors, soft sensors, and reduced-order and rigorous SMR CFD models for distributed-parameter control of a hydrogen production test bed. We show a validation of our strategy through a case study on a representative SMR model. Furthermore, we describe the implementation of these methodologies in a readily deployable smart-manufacturing computational infrastructure.

No abstract

No abstract

No abstract

No abstract

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.