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 distribute...
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