(734f) Real-Time Control and Balancing of a Reformer Furnace

In a vision of carbon-emission-free and sustainable energy systems, the hydrogen economy, in which hydrogen is the major energy carrier of the renewable energy sources and is the main transportation fuel, is a viable option and has the potential to reduce greenhouse gas emissions and to resolve issues associated with climate change without halting economic growth. Therefore, solving challenges encountered in commercial-scale production of hydrogen and optimizing these production lines has become an issue of great interest to both academia and industry. Among a variety of commercial hydrogen production processes, steam methane reforming (SMR) is by far the most common. For instance, SMR was responsible for 80% to 85% of the world-wide hydrogen production in 2007 [1]. SMR is typically carried out in top-fired steam methane reforming furnaces which are the most expensive equipment in terms of the maintenance and operating costs compared to other major equipment such as the hydrotreating, prereforming, water-shift and purification units at centralized SMR-based facilities. For instance, the re-tubing cost of a furnace is ∼10% of the total capital investment, and the annual operating cost to procure fresh natural gas for a SMR-based hydrogen plant with a production rate of 2.7 million Nm³ per day is ∼$62 million [2]. In addition, the conversion of methane via SMR in the furnace can only be increased by increasing the total energy input, which is constrained by the extreme sensitivity of the reforming tube service life to its operating temperature [3]; consequently, the plant throughput is compromised to avoid premature failure of the reformer and to reduce the plant risk of suffering substantial capital and production losses. Hence, the need for a robust tool that identifies the optimized total fuel flow rate and the corresponding valve distribution such that the conversion of methane via SMR is maximized, and the maximum OTWT along the reforming tube length among all reforming tubes is strictly less than the design temperature of the reforming tube wall material becomes apparent.

Motivated by the above considerations, the present work utilizes the framework for the furnace-balancing scheme [3], the valve-to-flow-rate converter [3], the statistical-based model identification [4] and a heuristic search algorithm to create a real-time balancing procedure, which recursively tests different total fuel flow rates of which the respective spatial distribution to burners is optimized in real-time until key operational specifications are satisfied. The ability to adjust the total fuel flow rate of the balancing procedure 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. Subsequently, a computational fluid dynamic (CFD) model of the furnace developed in [5] 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.

[1] Simpson, A.P., Lutz, A.E., 2007. Exergy analysis of hydrogen production via steam methane reforming. International Journal of Hydrogen Energy 32, 4811–4820.

[2] Latham, D.A., McAuley, K.B., Peppley, B.A., Raybold, T.M., 2011. Mathematical modeling of an industrial steam-methane reformer for on-line deployment. Fuel Processing Technology 92, 1574–1586.

[3] Tran, A., Aguirre, A., Crose, M., Durand, H., Christofides, P.D., 2017a. Temperature balancing in steam methane reforming furnace via an integrated CFD/databased optimization approach. Computers & Chemical Engineering 104, 185–200.

[4] Tran, A., Pont, M., Aguirre, A., Durand, H., Crose, M., Christofides, P.D., in press, 2018. Bayesian model averaging for estimating the spatial temperature distribution in a steam methane reforming furnace. Chemical Engineering Researchand Design.

[5] Tran, A., Aguirre, A., Durand, H., Crose, M., Christofides, P.D., 2017b. CFD modeling of a industrial-scale steam methane reforming furnace. Chemical Engineering Science 171, 576–598.