Process development and deployment for the direct reforming of crude oil to hydrogen and carbon materials

Problem statement

Hydrogen is a clean energy source and carrier because of its non−polluting combustion, making it an excellent alternative to the current fossil fuel-dominated energy scenario. Nonetheless, there are several critical challenges to implementing a broad sustainable use of hydrogen. In this project, we develop a laboratory−scale setup with stable operation and high hydrogen production.

We aim at assessing (i) different hydrocarbon feedstock (from n-heptane to crude oil) fed to the reactor with water as emulsions, carried by steam or vaporized; (ii) steam reforming (SR) and auto thermal reforming (ATR); and (iii) stable and energy efficient catalysts for the efficient production of hydrogen inside packed, fluidized, and multifunctional reactors. These, coupled with carbon capture technologies, minimize the carbon footprint of the overall process.

We support our research with simulations and techno−economic analysis to assess the approach's feasibility. C2H can use the current refinery infrastructure to reduce costs and the impact of market volatility on refinery operations.


  • Develop and scale up advanced catalysts and reactors for converting crude to hydrogen
  • Model process simulations to analyze the viability of the process 
  • Scaling the technical catalysts for their demanding application: endothermic process, poisoning, massive coke deposition, and fluidized-bed reactors
  • Analyze different process conditions to optimize hydrogen production and stability in the process

Related People

Related Publications

Improving robustness of kinetic models for steam reforming based on artificial neural networks and ab initio calculations

by Morlanes, Lezcano, Yerrayya, Mazumder, Castaño
Chem. Eng. J. Year: 2022 DOI: https://doi.org/10.1016/j.cej.2021.133201


Steam reforming of hydrocarbons is and will be, in the short-medium run, the leading technology for producing hydrogen. At the same time, steam reforming has a large carbon footprint that can be decreased by implementing better kinetic models for process intensification. In this work, we have developed a methodology based on artificial neural networks to fit and improve the robustness of the kinetic model for steam reforming of naphtha surrogates (hexane and heptane) using a NiMgAl catalyst derived from hydrotalcite precursors. We analyze several strategies to obtain the fittest kinetic model and discuss the robustness of each. These models include hydrocarbon steam reforming, water gas shift and methanation reactions, and differ mainly in the type of adsorption term in the Langmuir-Hinshelwood formalism. The adsorption energies calculated by ab initio (DFT) provide insights on the different adsorption mechanisms of hydrocarbons and water on the catalyst surface sites. At the same time, validation of the kinetic model was conducted using wider range of experimental conditions and different model and real feeds (methane, naphtha, diesel and vegetable oil). In this way, the versatility of the model proposed and the strengths and weaknesses of using a data-driven approach for the kinetic model selection were proven.