Thorin Daniel

Carbon emission reduction in the UK’s iron and steel industry, which is responsible for approximately 26% of national industrial emissions, is essential for the UK’s commitment to meet its net zero promise. Hydrogen, as a promising substitute for fossil reductant/fuel, can be utilized in the iron and steel industry to achieve low carbon emissions. In this study, 12 various technical routes that integrate different hydrogen technologies into iron-making processes are modelled and an environ-economic analysis is conducted looking at the carbon emission reduction potential and cost. Considered hydrogen production methods are alkaline electrolysis (AEL), proton exchange membrane (PEM) electrolysis, anion exchange membrane (AEM) electrolysis, bipolar membrane (BPM) electrolysis and seawater (SW) electrolysis and steam methane reforming combined with carbon capture, utilization, and storage (SMR+CCUS), while the considered iron-making processes are hydrogen injection into blast furnace (H2+BF) and hydrogen-based direct reduction (H-DR). It is found that the only technical route that is unable to reduce carbon emission under any scenario is SMR+H2+BF. Hydrogen from electrolysis can achieve more effective carbon abatement, but its economic feasibility is significantly influenced by electricity costs and grid carbon intensity. H-DR shows a larger carbon emission reduction potential compared to H2+BF. Evaluated comprehensively from the aspect of carbon emission reduction effectiveness and cost, SMR+H-DR is the most promising technical route. As the power grid carbon intensity decreased, shifting from SMR+H-DR to Electrolysis+H-DR became a more effective transition route, especially for countries currently relying on high-carbon intensity grids. The impact of the inflation rate on the technical routes is also examined in this study.

Generative Artificial Intelligence is a rapidly developing area being used to create powerful tools which have the potential to change a wide range of professional practices in chemical engineering. As this area develops, new principles on responsible use of Generative AI in chemical engineering are required to ensure that traditional engineering ethics are able to accommodate the new landscape. In this perspective, we assess the current state of engineering ethics, responsible AI principles and suggest how they can combine to ensure that Generative AI can be used responsibly within the chemical engineering sector. Whilst there are many aspect to engineering ethics and responsible AI use, the core principles which include transparency, integrity, and accountability are omnipresent and provide a shared foundation of good practice on which new regulations may be built as the need arises. Future breakthrough will require development on the AI technology itself, the people-centre approach and regulation changes.

•Novel TEA of AE7 surfactant production using CO2 from steel industry flue gas•CO2 conversion rates around 3% in different processing capacities•Green hydrogen costs are the biggest factor influencing minimum selling price (MSP)•Lowest MSP of $8.77/kg exceeds the forecasted $3.75/kg for fossil-based AE7•Monte Carlo simulation shows a 21% chance of positive NPV vs. bio-based surfactants Successfully transitioning to a net-zero and circular carbon economy requires adopting innovative technologies and business models to capture CO2 and convert it into valuable chemicals and materials. Given the high economic costs and limited funding available for this transition, robust economic modelling of potential circular carbon pathways is essential to identify economically viable routes. This study introduces a novel techno-economic analysis (TEA) of producing alcohol ethoxylate (AE7), a valuable surfactant, from industrial flue gas. Traditionally, AE7 is produced by reacting fatty alcohols with ethylene oxide derived from fossil or bio-based sources. This research explores a method using CO2 captured from steel industry flue gas to produce AE7, addressing a notable gap in the literature. It evaluates a thermo-catalytic pathway involving Fischer-Tropsch (FT) synthesis with syngas generated by the reverse-water gas-shift reaction, where CO2 reacts with H2. CO2 conversion rates range around 3% across processing capacities of 25 kt/a, 100 kt/a, and 1000 kt/a. The study finds that the CO2 mass fraction concentration in the process emission is 2.47 × 10–5, compared to 0.13 in the incoming flue gas, highlighting the system's positive environmental impact. A radial basis function neural network was built to forecast the long-term average price of fossil-based and bio-based surfactants to benchmark the results against. Economic analysis reveals that the cost of green hydrogen significantly impacts the minimum selling price (MSP), making cost parity with existing fossil-based surfactants challenging. The lowest MSP of $8.77/kg remains above the long-term forecasted price of $3.75/kg for fossil-based C12–14 AE7. However, Monte Carlo simulations show a 21% probability of achieving a positive net present value (NPV) compared to leading bio-based surfactant alternatives. Sensitivity analyses identify capital costs, the price of low-carbon hydrogen (LCOH), and diesel prices as the most influential factors affecting the MSP. Continued advancements in Fischer-Tropsch catalyst technologies, reductions in green hydrogen costs and growing consumer demand for environmentally friendly products could significantly enhance the economic feasibility of this sustainable approach, paving the way for broader adoption and contributing to a circular carbon economy. [Display omitted]

Green hydrogen from water electrolysis is a key driver for energy and industrial decarbonization. The prediction of the future green hydrogen cost reduction is required for investment and policy-making purposes but is complicated due to a lack of data, incomplete accounting for costs, and difficulty justifying trend predictions. A new AI-assisted data-driven prediction model is developed for an in-depth analysis of the current and future levelized costs of green hydrogen, driven by both progressive and disruptive innovations. The model uses natural language processing to gather data and generate trends for the technological development of key aspects of electrolyzer technology. Through an uncertainty analysis, green hydrogen costs have been shown to likely reach the key target of