The European steel industry aims at a CO2 reduction of 80–95% by 2050, ensuring that Europe will meet the requirements of the Paris Agreement. As the reduction potentials of the current steelmaking routes are low, the transfer toward breakthrough‐technologies is essential to reach these goals. Hydrogen‐based steelmaking is one approach to realize CO2‐lean steelmaking. Therefore, the natural gas (NG)‐based direct reduction (DR) acts as a basis for the first step of this transition. The high flexibility of this route allows the gradual addition of hydrogen and, in a long‐term view, runs the process with pure hydrogen. Model‐based calculations are performed to assess the possibilities for injecting hydrogen. Therefore, NG‐ and hydrogen‐based DR models are developed to create new process know‐how and enable an evaluation of these processes in terms of energy demand, CO2‐reduction potentials, and so on. The examinations show that the hydrogen‐based route offers a huge potential for green steelmaking which is strongly depending on the carbon footprint of the electricity used for the production of hydrogen. Only if the carbon intensity is less than about 120 g CO2 kWh−1, the hydrogen‐based process emits less CO2 than the NG‐based DR process.
A B S T R A C TThis work looks at the application of neural networks in geophysical well-logging problems and specifically their utilization for inversion of nuclear downhole data. Simulated neutron and γ -ray fluxes at a given detector location within a neutron logging tool were inverted to obtain formation properties such as porosity, salinity and oil/water saturation. To achieve this, the forward particle-radiation transport problem was first solved for different energy groups (47 neutron groups and 20 γ -ray groups) using the multigroup code EVENT. A neural network for each of the neutron and γ -ray energy groups was trained to re-produce the detector fluxes using the forward modelling results from 504 scenarios. The networks were subsequently tested on unseen data sets and the unseen input parameters (formation properties) were then predicted using a global search procedure. The results obtained are very encouraging with formation properties being predicted to within 10% average relative error. The examples presented show that neural networks can be applied successfully to nuclear well-logging problems. This enables the implementation of a fast inversion procedure, yielding quick and reliable values for unknown subsurface properties such as porosity, salinity and oil saturation. I N T R O D U C T I O NIn many geophysical problems, the aim is to apply a technique which will enable the determination of subsurface properties (e.g. lithology, porosity, density, hydraulic conductivity, resistivity, salinity and water/oil saturation) through the use of either surface or borehole measurements. This constitutes what is known as a geophysical inverse problem in which a mathematical model is used to relate the measured/observed data to the subsurface model parameters. In order to recover correctly the unknown parameters in the mathematical model an error function, otherwise known as the objective function, is set up. This function measures the discrepancy between the observations and predictions from a forward-modelling calculation. Minimizing this error function leads to the recovery of the unknown parameters, yielding optimal solutions. *
The Midrex process produces metallurgical residues in the form of dust, sludge, and fines. As these have high iron content, herein, the aim is to recycle the residues and use them as an educt in the Midrex process, thus closing the material cycle and increasing raw material efficiency. Briquetting of these materials with binder is one possibility to prepare them for the use as an educt in the Midrex process. Experiments are conducted to test the suitability of the organic binders starch and cellulose for briquetting. Furthermore, tests with the inorganic bentonite are included for comparison. Briquettes are generally characterized by high strength. However, compared with iron oxide pellets, they have a low porosity and thus a higher apparent density, and consequently, a worse reducibility. The use of organic binders should improve the reducibility. The iron oxides are in close contact with the C‐carrier of the organic binder so that a solid–solid phase direct reduction can take place. Furthermore, the solid carbon reacts to CO, and thus, increases the presence of reducing gas in the enlarged pores of the briquettes, and should therefore increase the degree of reduction.
A substantial CO2-emmissions abatement from the steel sector seems to be a challenging task without support of so-called “breakthrough technologies”, such as the hydrogen-based direct reduction process. The scope of this work is to evaluate both the potential for the implementation of green hydrogen, generated via electrolysis in the direct reduction process as well as the constraints. The results for this process route are compared with both the well-established blast furnace route as well as the natural gas-based direct reduction, which is considered as a bridge technology towards decarbonization, as it already operates with H2 and CO as main reducing agents. The outcomes obtained from the operation of a 6-MW PEM electrolysis system installed as part of the H2FUTURE project provide a basis for this analysis. The CO2 reduction potential for the various routes together with an economic study are the main results of this analysis. Additionally, the corresponding hydrogen- and electricity demands for large-scale adoption across Europe are presented in order to rate possible scenarios for the future of steelmaking towards a carbon-lean industry.
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