Chemical Engineering Research & Design, Vol.134, 238-256, 2018
Real-time furnace balancing of steam methane reforming furnaces
This paper focuses on the development of a real-time furnace-balancing scheme for a reformer at a centralized hydrogen facility using steam methane reforming (SMR) technology so that the reformer fuel input can be optimized in real-time to increase the plant throughput and to reject operational disturbances associated with flow control valves. Initially, the framework for the furnace-balancing scheme, the statistical-based model identification and the valve-to-flow-rate converter developed in Tran et al. (2017a, 2018) are integrated with a heuristic search algorithm to create a real-time balancing procedure, which recursively calculates different total fuel flow rates of which the respective spatial distribution to burners is optimized until key operational specifications, e.g., the reformer throughput is maximized, and the outer tube wall temperature (OTWT) along the reforming tube length of all reforming tubes must not exceed the design temperature of the reforming tube wall, are satisfied. Subsequently, a com;1;putational fluid dynamic (CFD) model of the reformer developed in Tran et al. (2017b) is used to represent the on-line unit at the SMR-based hydrogen facility and is used to characterize the previously unstudied dynamic behavior of the reformer, based on which we develop an optimal strategy to implement the optimized total fuel flow rate to maximize the reformer throughput. Finally, a case study in which the balancing procedure is implemented on the reformer initially operated under the nominal reformer input is proposed, and the results are used to demonstrate that the furnace-balancing scheme successfully determines the optimized reformer fuel input to increase the reformer throughput while meeting the OTWT limits. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Keywords:Steam methane reforming;CFD modeling;Data-based modeling;Process optimization;Furnace balancing