Biocarbon obtained from pyrolysis and then pelletized using pyrolysis oils can be a useful fuel to substitute coke and coal used in the steel industry as reductants. A reduction of emissions of exactly 30% can be achieved. This depends also on the pyrolysis and pelletizing processes which are taken into consideration and the national electricity mix. We estimate that the reduction in emission can be further increased through coupling carbon capture and storage with biocarbon use. On the other hand, another alternative fuel which can be used in direct iron reduction (for example) is hydrogen. The production of hydrogen not always has a low impact and the technology is an important aspect to be considered. If hydrogen is produced from electrolysis also in this case the electricity mix of the country has an important role. The authors propose in this contribution a comparison between the impact of the final functional unit of steel produced using biocarbon with that produced using hydrogen. The analysis is performed through LCA focusing on the carbon footprint impact.
Modern infrastructure reflects energy nexus of multiple sources: gas, electricity, water & air. Different energy systems are modelled by unique tools but suffer from complexity, high computational burden & low accuracy due to lack of measured data for modelling. Many involve closed tools, making them inapplicable for other energy systems. A unified modelling is needed which interfaces different energy carriers under common modelling variables. Energy & power are common parameters that are attributed with all energy systems: chemical, thermal, electrical & mechanical. This paper contributes towards the development of a unified modelling of the multi-energy systems followed by its direct application on an industrial HVAC system, which can further be used in efficient and optimal control of the system for cost, energy, and carbon savings.
Air conditioning systems consume a large amount of energy with the rising living standard of human beings. Indirect evaporative cooler, which is increasingly recognized as a promising alternative to partially substitute conventional air-conditioning devices, has been studied extensively to improve the cooling efficiency and save energy in buildings.
Using porous media in the indirect evaporative heat exchangers is a critical approach for performance enhancement. This paper established a two-dimensional plate-type counterflow indirect evaporative cooler model with porous media on the secondary air channel surface. On the one hand, the porous structure was incorporated in the model to alter the boundary layer and flow status. On the other hand, the water retention ability of porous media that potentially improves the surface wettability has been proven to enable the intermittent operation of the water pump. The influence of various porous parameters, i.e. porosity and pore diameter, on the time-independent dynamic variation of the outlet primary air temperature have been quantitatively analyzed. This study provided a theoretical foundation for the studies of the porous plate-type indirect evaporative cooling technology.
Building energy consumption prediction is important in energy system management, building operation, and energy supply planning. This study proposes a novel model with attention based seq2seq method, which is a deep learning algorithm, to improve the prediction performance. The developed model is performed with experiment on a real energy profile data of an office building in Shenzhen, China. The prediction performance of the proposed hybrid model is evaluated with indicators of MSE, RMSE, MAE and SMAPE. The results demonstrate that attention mechanism can improve the prediction performance of model whose input are time series. Compared with the metrics of prediction result of other models, the MSE, RMSE, MAE, SMAPE of prediction result of proposed model decrease by more than half percent.
New solutions to decarbonisation in the transport sector are prominently required to replace oil consumption. Full battery electric vehicles (BEVs) are usually limited to light-duty vehicles due to their energy density. The aim in this study is to evaluate the technical and economic feasibility of electric trucks that are supplemented by electrified highways (eHighways), instead of using conventional diesel, petrol, and full BEVs. The battery is the most expensive component of electric vehicles, especially for heavy-duty trucks. The principle of eHighway is that electricity is supplied to electric vehicles directly from the electric grid as they travel along the road. The eHighway concept is being developed with two primary methods to connect the roadway to the vehicle: conductive power transfer (CPT) where electric connection to the vehicle can be provided from above or below the vehicle and inductive power transfer that is in-motion wireless power transfer (WPT). If eHighways are installed on the major links that connect main cities, the eHighway technology can be suitable for long-distance journeys. This research evaluates the eHighway technologies of both CPT and in-motion WPT. A case study has been conducted. Various costs are calculated and analyzed. Results show that the driving cost (or selling price) of a heavy-duty electric truck on the eHighways using CPT technology ranges from $0.21 to 0.67 per km with varying daily traffic volume. The driving cost of a heavy-duty electric truck on the eHighways using in-motion WPT technology ranges from $0.22-1.03 depending on daily traffic volume. If fuel and vehicle prices evolve as predicted between now and 2050, eHighways could become an economically feasible form of road transport, especially for heavy-duty trucks, resulting in energy savings and thus reductions in CO2 emission.