Production of iron and steel releases seven percent of the global greenhouse gas (GHG) emissions. Incremental changes in present primary steel production technologies would not be sufficient to meet the emission reduction targets. Replacing coke, used in the blast furnaces as a reducing agent, with hydrogen produced from water electrolysis has the potential to reduce emissions from iron and steel production substantially. Mass and energy flow model based on an open-source software (Python) has been developed in this work to explore the feasibility of using hydrogen direct reduction of iron ore (HDRI) coupled with electric arc furnace (EAF) for carbon-free steel production. Modeling results show that HDRI-EAF technology could reduce specific emissions from steel production in the EU by more than 35%, at present grid emission levels (295 kgCO 2 /MWh). The energy consumption for 1 ton of liquid steel (tls) production through the HDRI-EAF route was found to be 3.72 MWh, which is slightly more than the 3.48 MWh required for steel production through the blast furnace (BF) basic oxygen furnace route (BOF). Pellet making and steel finishing processes have not been considered. Sensitivity analysis revealed that electrolyzer efficiency is the most important factor affecting the system energy consumption, while the grid emission factor is strongly correlated with the overall system emissions.Energies 2020, 13, 758 2 of 23 country, on the other hand, has a per capita steel consumption of 66.3 kg. As the standard of living in developing countries increases, demand for steel will grow further. The demand for steel will increase until 2050 [4]. Steel could be produced by reducing iron ore or by recycling steel scrap in an electric arc furnace (EAF). Iron and steel sector releases seven percent of the total CO 2 emission and 16% of the total industrial emission of CO 2 globally [5,6]. Limited availability of scrap and demand for special grades of steel, which can not be produced from steel recycling, would lead to an increased demand for ore based steel production in the future. More than 80% [3] of the ore based steel is produced through the BF-BOF route. The BF-BOF route uses approximately 18 GJ/t of energy supplied from coal [7], and has an emission intensity of approximately 1870 kgCO 2 /tls [4,8] (considering pellet making, steel rolling and finishing steps). Majority of the emissions is released from the blast furnace (61%) and coke making plant (27%) [9].Some of the alternative processes with significantly reduced carbon footprint are BF-BOF with carbon capture and storage (CCS), direct reduction of iron ore (DRI) with CCS, electrowining (electrolysis of iron ore) [10] and green hydrogen-based DRI production. Integration of CCS in steelmaking processes is being explored under the ultra-Low carbon dioxide(CO 2 ) steelmaking (ULCOS) [11,12] project. However, concerns over the safe transport and storage of captured makes CCS options less attractive. Electrowining or molten electrolysis of iron ore is a relatively new technol...

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To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring.

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As a renewable energy source, biogas produced from anaerobic digestion seems to play an important role in the energy market. Unlike wind and solar, which are intermittent, gas turbines fueled by biogas provide dispatchable renewable energy that can be ramped up and down to match the demand. If post-combustion carbon capture systems are implemented, they can also result in negative CO2 emissions. However, one of the major challenges here is the energy needed for CO2 chemical absorption in post-combustion capture, which is closely related to the concentration of CO2 in the exhaust gas upstream of the capture unit. This paper presents an evaluation of the effects of biogas and exhaust gas recirculation use on the performance of the gas turbine cycle for post-combustion CO2 capture application. The study is based on a combined heat and power micro gas turbine, Turbec T100, delivering 100kWe. The thermodynamic model of the gas turbine has been validated against experimental data obtained from test facilities in Norway and the United Kingdom. Based on the validated model, performance calculations for the baseline micro gas turbine (fueled by natural gas), biogas-fired cases and the cycle with exhaust gas recirculation have been carried out at various operational conditions and compared together. A wide range of biogas composition with varying methane content was assumed for this study. Necessary minor modifications to fuel valves and compressor were assumed to allow the engine operation with different biogas composition. The methodology and results are fully discussed in this paper.