Sep 2026· C – Journal of Carbon Research· 18 references
Catalysts for Methane Reforming
Abstract
CO2 hydrogenation by reverse water–gas shift (RWGS) directly combined with subsequent Fischer–Tropsch synthesis (FTS) in a single fixed-bed reactor represents a promising route for converting renewable hydrogen and captured carbon dioxide into hydrocarbons. However, the attainable CO2 conversion is limited by thermodynamic constraints of the RWGS, product inhibition of FTS, and intraparticle diffusion limitations. In the present work, intrinsic and effective reaction models were developed for a potassium-promoted FeCuZnK catalyst to investigate the interaction between intrinsic catalyst kinetics and internal diffusion phenomena. An intrinsic Langmuir–Hinshelwood–Hougen–Watson (LHHW) model was established using fine catalyst particles (dp ≤ 150 µm) and subsequently extended to coarse catalyst particles (dp ≈ 2 mm) by introducing an effectiveness factor. The intrinsic model identified water as the dominant inhibiting species, whereas the already high RWGS activity of the FeCuZnK catalyst leads to a rapid approach of the thermodynamic equilibrium, indicating that an increase in activity would only provide limited improvements. The effective model accurately reproduced the behavior of technical catalyst particles and was subsequently applied to multi-reactor concepts with intermediate water removal. A five-stage reactor cascade increased the attainable CO2 conversion from approximately 55% to 85% under otherwise identical operating conditions. The results demonstrate that reactor design and water management provide greater potential for process intensification than further increases in intrinsic catalyst activity alone.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new angle and explores waste in the Kanban-driven software development project context. A preliminary research model is presented for helping the consequent replication of the study. The results from the empirical analysis suggest Kanban can be an effective method in visualizing and organizing the current work, but does not prevent waste from creeping in, although the overall project outcome may be successful.
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduSep 14, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.