(756a) Enhanced Furnace Balancing Scheme Via an Integrated Computational Fluid Dynamics/Data-Based Optimization Approach

The steam methane reforming (SMR) process is highly endothermic and is widely used for commercial-scale production of hydrogen from superheated steam and methane in nickel-based catalyst packed bed reactors [1]. It is typically carried out in a top-fired steam methane reformer that has two closed domains (i.e., the furnace side in which fuel is burned in excess air to generate thermal energy to drive the SMR process, and the tube side in which the SMR process takes place to produce hydrogen). Reformers are often energy-intensive such that the efficiency of hydrogen plants depends on the reformer efficiency, but the plant efficiency is often decreased by nonuniformity in the outer reforming tube wall temperature (OTWT) distributions (i.e., the distributions of the radially-averaged tube outer wall temperatures at a fixed height within the reformer) [2,3,4]. Specifically, when there is a large discrepancy between the maximum and minimum temperature for the distribution at a given reformer height but the average temperature of the distribution is close to the design value of the reforming tube wall material to maximize production of hydrogen from the endothermic SMR process, the outer wall temperature values of some reforming tubes would likely be higher than the design value. This would shorten the reformer service life and put the hydrogen plant at risk of having substantial capital and production losses due to creep rupture of the reforming tubes [3,4,5]. An intuitive strategy to resolve this problem is to reduce the total mass flow rate through the burners to reduce the heat generated in the furnace side and thus to reduce the maximum outer reforming tube wall temperature below the design value. However, this also decreases the methane conversion and hydrogen production rate, and thus should not be performed in an ad hoc fashion, but should instead be part of an optimization-based approach for furnace balancing (reducing the nonuniformity in the OTWT distribution at a given height within the reformer by adjusting the distribution of flow rates through the burners).

Motivated by this, the present work focuses on developing an advanced furnace balancing scheme via an integrated computational fluid dynamics/data-based optimization approach that optimizes not only the distribution of burner mass flow rates but also the total mass flow rate through the burners such that the degree of temperature nonuniformity inside the reformer is minimized, while the maximum outer reforming tube wall temperature is kept strictly below the design temperature of the wall material, and the hydrogen production rate is maximized without reducing the reformer service life. The furnace balancing scheme employs computational fluid dynamics modeling to generate data that is used in identifying a data-driven model for the optimization-based furnace balancing scheme and for validating the data-driven model. The furnace balancing optimization problem simultaneously solves for the optimal total mass flow rate through the burners and the individual mass flow rates through each burner. The robustness of the furnace balancing scheme to disturbances is compared with the robustness of a furnace balancing scheme developed in [4] that assumes a constant total mass flow rate. The data-driven model development follows statistical principles to allow it to make reasonable out-of-sample predictions of the OTWT distribution for various mass flow rate distributions and also for various total mass flow rates. The ability to adjust the total mass flow rate for the advanced furnace balancing scheme is of special interest for the hydrogen manufacturing industry as it can potentially lead to substantial savings in the re-tubing cost of the reformer.

[1] Ewan BCR, Allen RWK. A figure of merit assessment of the routes to hydrogen. International Journal of Hydrogen Energy. 2005;30:809–819.

[2] Dunn AJ, Yustos J, Mujtaba IM. Modelling and simulation of a top-fired primary steam reformer using gPROMS. Developments in Chemical Engineering and Mineral Processing. 2002;10:77–87.

[3] Kumar A, Baldea M, Edgar TF, Ezekoye OA. Smart manufacturing approach for efficient operation of industrial steam-methane reformers. Industrial & Engineering Chemistry Research. 2015;54:4360–4370.

[4] Tran A, Aguirre A, Crose M, Durand H, Christofides PD. Temperature balancing in steam methane reforming furnace via an integrated CFD/data-based optimization approach. Computers & Chemical Engineering. in press.

[5] Pantoleontos G, Kikkinides ES, Georgiadis MC. A heterogeneous dynamic model for the simulation and optimisation of the steam methane reforming reactor. International Journal of Hydrogen Energy. 2012;37:16346-16358.