387165 Distributed Control of Steam Methane Reformer Temperatures in a Smart Manufacturing Framework
Steam methane reforming is a key process in the formation of syngas for hydrogen, methanol and ammonia production. 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. Typical furnaces can comprise hundreds of tubes and burners, and their dimensions are comparable to those of a four story building. The SMR is an energy-intensive process unit, and maximizing energy efficiency is of primary interest. SMR operation faces several significant disturbances, including weather (which affects heat losses to the environment) and potentially large changes in product demand.
Together with natural degradation of reforming catalyst activity, these disturbances have a strong effect on the spatial temperature distribution in the furnace. Such temperature imbalances have a negative impact on operating efficiency. Moreover, excessive temperatures can jeopardize the longevity of the tubes, and strict upper bounds must be enforced on the reformer temperature during operation. This is, however, a challenging task due to the distributed nature of the system and the difficulty of obtaining distributed temperature measurements (the latter associated with the extreme operating conditions and the complex geometry of the furnace).
In this paper, we present our results concerning the monitoring of temperature distribution in an industrial SMR furnace using a large array of infrared camera sensors, which produce a significant stream of data regarding the furnace temperature distribution. We reconstruct the furnace temperature distribution from infrared image data, and use this map as a basis for formulating a model predictive control problem for the distributed-parameter system, centered on minimizing the temperature imbalance in the furnace based on modulating the flow rate of fuel to the array of burners. We show an initial validation of this strategy using a detailed computational fluid dynamics model of the furnace.
To our knowledge, this work comprises of multiple firsts, including the only open-literature discussion of using massive scale image information for the monitoring of an industrial SMR, as well as the first application of distributed-parameter control of steam methane reforming. Furthermore, we report on the implementation of our monitoring and control algorithms in a readily-deployable smart-manufacturing computational infrastructure.