Steam methane reforming furnace temperature balancing via CFD model-based optimization

In this work, we introduce a furnace balancing algorithm that generates an optimized furnace-side feed distribution that has the potential to improve the thermal efficiency of reformers. The furnace balancing algorithm is composed of three major components: data generation, model identification and a model-based balancing scheme. Initially, we adopt a computational fluid dynamics (CFD) model of an industrial-scale reformer developed in our previous work for the data generation, as this model has been confirmed to simulate the typical transport and chemical reaction phenomena observed during reformer operation, and the CFD simulation data is in good agreement with various sources in literature. Then, we propose a model identification scheme in which the algorithm is formulated based on the least squares regression method and basic knowledge of radiative heat transfer. Subsequently, we create a model-based balancing scheme that is formulated as an optimization problem within which the furnace-side feed distribution is the decision variable, and minimizing the sum of the weighted squared deviations of outer reforming tube wall temperatures from a set-point value for all reforming tubes is used as the objective function. CFD simulation results provide evidence that the optimized furnace-side feed distribution created by the furnace balancing algorithm can reduce the temperature nonuniformity inside the combustion chamber, and therefore, allow the reformer thermal efficiency to be increased without shortening the unit's service life.