(359e) Evaluation of Smart Manufacturing (SM) Benefits in Industrial Steam-Methane Reformers (SMR)

Steam methane reformers (SMRs) provide the bulk of the hydrogen consumed in refineries and for production of important chemicals such as ammonia and methanol. Consequently, these are large scale systems, with modern hydrogen plants producing above 100 million standard cubic feet hydrogen per day (MMSCFD)¹. In this context, energy efficient operation is an imperative necessity for long-term economic viability.

Mathematical modeling of SMRs becomes a crucial tool for achieving high energy efficiency. The models can be either high-fidelity (e.g., using computational fluid dynamics), data-driven (and typically low-order) empirical models, or a combination of both.^2,3 Furthermore, the development of mathematical models should be accompanied by online deployment. The latter must be supported by a smart manufacturing framework, that includes, i) capabilities for acquiring appropriate process data via systematically placed sensors, ii) adequate high-performance computational resources for just-in-time computations, and, iii) a user-friendly visualization interface for operator use. The model-based computations are themselves a sequence/workflow of calculations being executed in series or parallel. Seamless interactions between these different layers require an appropriate IT infrastructure that allows the intensive computations to be performed on cloud and intercommunications between various components of the workflow, and provides appropriate database management capabilities. Moreover, rapid adoption of such practices demand that such an IT infrastructure should be easy to use and deploy. These requirements constitute the natural and essential form of â??Smart manufacturingâ??⁴ (SM). In this work, we present an example application of SM concepts to an industrial scale steam methane reformer along with the benefits obtained.

The reforming process studied in this work takes place in a large scale, high-temperature SMR, where the endothermic reforming reactions are carried out in hundreds of catalyst-filled tubes placed in a gas-fired furnace. The overall productivity (energy consumed per unit H₂ produced) of the plant is strongly dependent on how efficiently the SMR is operated, which further depends on the spatial temperature distribution inside the furnace, where a more uniform distribution paves the way for reduced plant-wide energy use. An integrated framework comprising of state-of-the-art infrared cameras, kepler-based workflow for Matlab and Ansys Fluent-based calculations, PI OSI-based database system, and Tableau-based visualization interface assisted by a novel open-source IT platform/wireframe was deployed for the furnace temperature homogenization leading to ~ 44% improvement in the furnace operation.⁵ The cloud-based SM Platform provides on demand scalability and reusability of resources which opens the possibility of scaling IT infrastructure and deploying similar systems much faster with significantly lower costs and reduced staff resources at remote plants and facilities around the globe. Specifically, we demonstrate how the SM approach rendered itself further useful by allowing reusability of the integrated framework for quick deployment at other SMR facilities as well as other similar furnace-based manufacturing processes leading to reduction in deployment costs.

References:

¹Latham, 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 Process. Technol., 92 , 1574-1586. 

²Kumar, A., Baldea, M., Edgar, T. F., & Ezekoye, O. A. (2015). Smart Manufacturing Approach for Efficient Operation of Industrial Steam-Methane Reformers. Industrial & Engineering Chemistry Research, 54 (16), 4360-4370.

³Kumar, A., Baldea, M., Edgar, T. F. Real-time optimization of an industrial steam-methane reformer under distributed sensing. Control Engineering Practice [Submitted, Jan 2016].

⁴Korambath, P., Wang, J., Kumar, A., Hochstein, L., Schott, B., Graybill, R., Baldea, M., & Davis, J. (2014). Deploying Kepler Workflows as Services on a Cloud Infrastructure for Smart Manufacturing. Procedia Computer Science, 29 , 2254-2259.

⁵Korambath, P., Wang, J., Kumar, A., Davis, J., Graybill, R., Schott, B., & Baldea, M. (2016). A smart manufacturing use case: Furnace temperature balancing in steam methane reforming process via Kepler workflows. Procedia Computer Science [Accepted, April 2016].