Steam Methane Reforming Furnace Temperature Balancing Using Bayesian Model Identification

The present work extends our recent efforts on designing a systematic operating method for reformers at commercial-scale hydrogen plants that adjusts the furnace-side feed (FSF) distribution to reduce the nonuniformity in the outer tube wall temperature (OTWT) distributions along the reforming tube length, which allows the the reformer efficiency to be subsequently increased. To this end, the computational fluid dynamic (CFD) model of the reformer, the furnace-balancing scheme framework and the statistical-based model identification procedure developed in our previous work are integrated with the balancing procedure to create a more versatile scheme that simultaneously optimizes the total FSF mass flow rate and its respective distribution to maximize the reformer efficiency without damaging the reforming tubes. The balancing procedure is designed to use information about the current OTWT distribution to guide the search for the optimized total FSF flow rate in a heuristic fashion. For instance, it iteratively proposes different total FSF flow rates based on the previous guess or the current value (in the case of the first iteration of the balancing procedure) using the algorithm developed in this work and, then finds the corresponding FSF distributions until the maximum value of the OTWT distribution estimated by the empirical model for the OTWT distribution approximates the design temperature of the reforming tube wall from the left. Finally, the reformer CFD model is used to validate the optimized total FSF flow rate and its distribution generated from the balancing procedure to demonstrate the effectiveness of the approach.