(558b) Kepler Workflow in a Cloud Infrastructure for Temperature Balancing in a Steam Methane Reformer Furnace Using a Computational Fluid Dynamics and a Data-Driven Optimization Approach | AIChE

(558b) Kepler Workflow in a Cloud Infrastructure for Temperature Balancing in a Steam Methane Reformer Furnace Using a Computational Fluid Dynamics and a Data-Driven Optimization Approach


Aguirre, A. - Presenter, University of California, Los Angeles
Tran, A., University of California, Los Angeles
Ding, Y., University of California, Los Angeles
Christofides, P., University of California, Los Angeles
Steam methane reforming (SMR) is widely used in industrial-scale hydrogen production process because of its relatively low cost compared to other methods such as electrochemical process. Even though the industrial SMR process is relatively well optimized, it still has room for improvement in the energy efficiency where industry can save hundreds of thousands of dollars in operation cost. SMR is a net endothermic process which requires heat to be supplied by the furnace. This, in turn, drives the reaction of methane and super-heated steam at approximately 1100 K to produce hydrogen. Hot spots can cause critical failure, and dramatically reduce the lifespan of the tubes. Prolonged increase of temperature even by 20 K can decrease the lifespan of the tubes by half [1], [2]. Therefore, balancing the temperature gradients inside the furnace and across all tube walls is important.

This was the motivation for our previous work to develop a computational fluid dynamics (CFD) model [3], [4] and a balancing algorithm [5] for a top-fired furnace with 336 tubes and 96 burners. This procedure requires an expert knowledge in CFD and mathematical models to implement, but we want this to be used by non-experts such as plant operators. This is also the vision of the Department of Energy Smart Manufacturing Project where studies such as this one, needs to be implemented in a reusable fashion across the industry.

In this study, we are using ANSYS Fluent to create an accurate CFD model of the reformer and a reduced order model (ROM) of temperature distribution and burner rate distribution is used to balance the furnace. To create the ROM, we are using IPOPT and CPLEX software packages. To build a converged ROM, we composed a mini workflow within Ansys Fluent itself using the user defined function (UDF) feature of Fluent and integrated the code for ROM as a shared library. In this way Ansys Fluent will be called in a loop as many times as required until the results from ROM calculation and CFD calculation are within acceptable tolerance. A typical CFD calculation takes 1 to 3 days on 80 cores of distributed computing power on a high-performance computing cluster and a ROM calculation can be carried out within minutes on a single core. So, in a real-time furnace temperature balancing process, a ROM calculation is quicker and practical.

Since we are designing this procedure for the automatic operation by plant operators. We also created a scientific workflow using Kepler software [6] to integrate and run this model in a cloud computing environment. We tested this procedure on a computing platform where the input data from a plant is being received through a Historian [7]. We used this input to run CFD and ROM calculations. Outputs of the calculations containing updated optimized valve positions to adjust the fuel flow rate through the burners and predicted temperatures are sent back to the plant operator. In a typical scenario, a plant operator needs to just visit the user interface and start the workflow. The workflow will then automatically perform a series of file transfer and computation processes. The entire process appears as a black box to the operator as all steps inside the workflow are already composed by an expert in the field.

[1] Latham D. Master’s Thesis: Mathematical Modeling of an Industrial Steam Methane Reformer. Queen’s University, 2008.

[2] 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.

[3] Lao L, Aguirre A, Tran A, Wu Z, Durand H, Christofides PD. CFD modeling and control of a steam methane reforming reactor. Chemical Engineering Science. 2016;148:78-92.

[4] Aguirre A, Tran A, Lao L, Durand H, Crose M, Christofides PD. CFD modeling of a pilot-scale steam methane reforming furnace. In: Kopanos G, Liu P, Georgiadis M (Eds.). Advances in Energy Systems Engineering. Springer, Switzerland, 2017, pp. 75-117.

[5] 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.

[6] Korambath P, Wang J, Kumar A, Hochstein L, Schott B, Graybill R, Baldea M, Davis J. Deploying Kepler workflows as services on a cloud infrastructure for smart manufacturing. Procedia Computer Science. 2014;29:2254-2259.

[7] Korambath P, Wang J, Kumar A, Davis J, Graybill R, Schott B, Baldea M. A smart manufacturing use case: Furnace temperature balancing in steam methane reforming process via Kepler workflows. Procedia Computer Science. 2016;80:680-689.