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The publication presents heat and electricity management in the Polish steel industry. The paper is based on actual data on heat and electricity consumption and intensity by processes in the steel industry in Poland in Industry 4.0 conditions. Two steel production processes are used in Poland: EAF Electric Arc Furnace and BOF Basic Oxygen Furnace. The analysis is an analysis of actual data is used to characterise the electricity and heat consumption by processes in the Polish steel industry. The analysis shows that the EAF technology is always more electricity intensive and the BOF technology more heat intensive. On the basis of conducted analysis, it can be concluded that pro-environmental innovations in the steel industry should first aim to reduce the electricity consumption of EAF technology and the heat consumption of BOF. An analysis of data for Poland for the period 2004–2020 shows that both cases occurred. The study shows that the heat consumption of BOF technologies has been steadily decreasing since 2010, and the electricity consumption of EAF technologies has been decreasing throughout the period under review. It can be concluded from this that the Polish steel industry is adapting to pro-environmental requirements and, through the introduction of technological innovations, is moving towards the concept of sustainable steel production according to green steel principles. The decrease in energy intensity (means electricity) of steel produced according to EAF technology is an important issue, as the high energy intensity of EAF processes affects the overall energy intensity of the steel production in Poland. In the future, the use of new innovative technological solutions, including solutions based on Industry 4.0 principles, should help the Polish steel industry to further reduce the level of electricity and heat consumption. The driving force behind the investment is the boom in the steel market. The authors made a short-term forecasts of steel production (2022–2025). The annual forecasts determined and analyses made were used to determine the heat and energy consumption of the Polish steel industry up to 2025.

Human action recognition is still a challenging topic in the computer vision field that has attracted a large number of researchers. It has a significant importance in varieties of applications such as intelligent video surveillance, sports analysis, and human–computer interaction. Recent works attempt to exploit the progress in deep learning architecture to learn spatial and temporal features from action video. However, it remains unclear how to combine spatial and temporal information with convolutional neural network. In this paper, we propose a novel human action recognition method by fusing spatial and temporal features learned from a simple unsupervised convolutional neural network called principal component analysis network (PCANet) in combination with bag-of-features (BoF) and vector of locally aggregated descriptors (VLAD ) encoding schemes. Firstly, both spatial and temporal features are learned via PCANet using a subset of frames and temporal templates for each video, while their dimensionality is reduced using whitening transformation (WT). The temporal templates are calculated using short-time motion energy images (ST-MEI) based on frame differencing. Then, the encoding scheme is applied to represent the final dual spatiotemporal PCANet features by feature fusion. Finally, the support vector machine (SVM) classifier is exploited for action recognition. Extensive experiments have been performed on two popular datasets, namely KTH and UCF sports, to evaluate the performance of proposed method. Our experimental results using leave-one-out evaluation strategy demonstrate that the proposed method presents satisfactory and comparable results on both datasets.

Every second, millions of users across the world share and download an enormous quantity of multimedia content produced by different image capture devices. The substantial amount of computing is incurred to provide visually similar results to the user’s query. Content-based image retrieval (CBIR) is an automated mechanism for retrieving similar/closer images from an image collection depending on the extraction of the visual content from the images themselves. Research in this area generally involves two directions. The first direction focuses on the effectiveness of the description of the visual content of images, namely features, by a technique that leads to the discernment of similar and dissimilar images, and eventually the retrieval of the closer images to the query image. Meanwhile, effectiveness refers to improving the precision rate that leads to discriminate similar and dissimilar images accurately. The second direction concerns the retrieval efficiency in terms of time consumption. This paper mainly focuses on effectiveness rather than efficiency. Generally, there are two kinds of visual features: the global features and the local features. This paper takes advantage from both global and local features, and hence, a hybrid CBIR technique is developed in which it contains two layers of search (so-called bi-layer). The first layer aims to eliminate/exclude the dissimilar images, as much as possible, that leads to decrease the range of the search, by comparing all images in the dataset to the query image depending on the well-known local feature descriptor called Speed-Up-Robust-Features, which is a type of descriptor used in a bag of features technique for image clustering purposes. The second layer aims to compare the query image to the images attained/remained in the first layer on the bases of extracting global features like shape, texture, and color to retrieve the certain number of the similar/closer images to the query image. Additionally, this proposed approach also introduces the idea of producing the cases to make the system more dynamic in order to increase the precision rate. The performance of the proposed CBIR approach has been assessed utilizing the Corel-lK dataset. The experimental findings demonstrated the influence of exploring the concept of bi-layer in enhancing precision rate in contrast to state-of-the-art approaches, that reached 86.06% and 80.72% for top-10 and top-20, respectively.

In the process of basic oxygen furnace steelmaking, the high-velocity jet impacts the molten metal pool, resulting in the generation of splashing metal droplets with varying particle sizes. The expansion decarburization of metal droplets in the emulsion phase is one of the main steel-slag reaction behaviors. In this paper, firstly, a theoretical decarburization model of expansion droplet was established, finding biases in their calculation results. Then, the solid phase ratio in different FeO content of slag was analyzed, demonstrating its significant impact on the reactivity performance of slag. Finally, an improved decarburization comprehensive model for expanded droplets, based on the solid phase ratio of slag and data fitting, was developed and validated using experimental data. The results indicate that the improved droplet decarburisation comprehensive model shows a good agreement with the experimental data in predicting changes in carbon content, with Pearson correlation coefficient exceeding 0.94 for different FeO contents. ‌For FeO contents of 3%, 10%, 20%, and 30%, the model achieves MAE (0.035%, 0.024%, 0.056%, 0.059%) and RMSE (0.0034%, 0.0019%, 0.011%, 0.018%), respectively. This model can accurately calculate the change of carbon content of metal droplets under various complex multiphase slag conditions.

The production of steel in the world is dominated by two types of technologies: BF + BOF (the blast furnace and basic oxygen furnace, also known as integrated steel plants) and EAF (the electric arc furnace). The BF + BOF process uses a lot of natural resources (iron ore is a feedstock for steel production) and fossil fuels. As a result, these steel mills have a significantly negative impact on the environment. In turn, EAF technology is characterised by very low direct emissions and very high indirect emissions. The raw material for steel production is steel scrap, the processing of which is highly energy-consuming. This paper analyses the energy intensity of steel production in Poland as a function of investments made in the steel industry in the years 2000–2019. Statistical data on steel production in the EAF process in Poland (which represents an approximately 50% share of the steel produced, as the rest is produced utilising the BF + BOF process) was used. Slight fluctuations are caused by the periodic switching of technology for economic or technical reasons. The hypothesis stating that there is a relationship between the volume of steel production utilising the EAF process and the energy consumption of the process, which is influenced by investments, was formulated. Econometric modelling was used as the research method and three models were constructed: (1) a two-factor power model; (2) a linear two-factor model; and (3) a linear one-factor model. Our findings show that the correlation is negative, that is, along with the increase in technological investments in electric steel plants in Poland, a decrease in the energy consumption of steel produced in electric furnaces was noted during the analysed period.

Basic oxygen furnace slag (BOFS) is a high-volume waste resulting from the production of steel from pig iron. Due to its high free lime content, BOFS is difficult to recycle and/or include into conventional cement systems. Alkali-activation technology offers a pathway to transform industrial wastes such as BOFS into low-carbon cements. Alternative precursors for cement systems are needed as the reliance on commonly used materials like ground granulated blast furnace slag (GGBFS) is becoming unsustainable due to decreasing availability. This study investigates alkali-activated cements incorporating 20 and 30 wt.% of naturally weathered BOFS as a replacement for GGBFS, in both sodium silicate- and sodium carbonate-activated systems. A fraction of BOFS subject to mechanical activation is compared against the untreated BOFS in the 20 wt.% systems. It is observed that in naturally weathered BOFS, a significant portion of the free-lime is found to convert to portlandite, which accelerates alkali-activation kinetics. In sodium silicate-activated systems, the high pH of the activator results in incomplete reaction of the portlandite present in BOFS. The sodium carbonate-activated system shows near complete conversion of portlandite, causing an acceleration in the kinetics of reaction, setting, and hardening. These findings confirm the viability of sodium carbonate activated GGBFS-based systems with only a minor loss in strength properties. BOFS can be utilised as a valuable cement additive for the production of sustainable alkali-activated cements utilising sodium carbonate as a less carbon-intensive activator solution than the more commonly used sodium silicate. Mechanical activation of BOFS offers further optimisation potential for alkali-activation.

The BOF steelmaking process involves complex physical and chemical reactions, making precise control challenging when relying solely on human experience. Therefore, understanding the reaction mechanisms and developing simulation models for the BOF process are crucial for enhancing control accuracy and advancing intelligent steelmaking. In this study, the physical and chemical behaviors in various reaction zones were first analyzed under actual production conditions using the multi-zone reaction theory. Then, a comprehensive mechanism model for BOF steelmaking was established, and an integrated simulation of metallurgical reactions during the BOF steelmaking process was performed using FactSage thermodynamic software. Finally, the validity of this comprehensive model was verified through actual production data. The results show that the relative deviation of the cumulative decarburization rate ranges from −0.66% to 1.68%, while the absolute deviation of the calculated carbon content curve compared to the actual curve is less than 0.12%. This research helps clarify the variation patterns of key process parameters in BOF steelmaking, playing a significant role in advancing the intelligence of the BOF steelmaking process.

In order to examine the geometric characteristics of BOF (blast oxygen furnace) slag coarse aggregate, the aggregate image measurement system (AIMS) was used to analyze the sphericity, gradient angularity and micro texture. Both volumetric and mechanical properties were studied to evaluate the influence of geometric characteristics of BOF slag coarse aggregate on asphalt concrete. The experimental results show that the BOF slag coarse aggregate has the characteristics of high sphericity, good angular performance and rough surface texture. The geometric characteristics of BOF slag has obvious influence on the volume performance of asphalt concrete. the higher sphericity of BOF slag causes an increase of the air voids of asphalt mixture. BOF slag coarse aggregate can effectively improve the road performances of asphalt concrete. BOF slag’s higher sphericity and angularity improve the moisture damage resistance and rutting resistance of asphalt concrete. Results indicate that better angularity can slightly enhance the moisture resistance property of asphalt concrete, but excessively high angularity of BOF slag coarse aggregates reduces the anti-rutting properties of asphalt mixture.

In this study, the decarburization behavior in basic oxygen furnace (BOF) steelmaking was investigated based on the multi-zone reaction mechanism. The contributions of the main reaction zones to decarburization were clarified, and the effects of key factors—including the effective reaction amount in the main reaction zones, the post combustion ratio (PCR) in auxiliary reaction zones, and the carbon content of scrap steel—on decarburization behavior were quantitatively analyzed. The results indicate that decarburization predominantly occurs in the jet impact reaction zone (approximately 76% of the total decarburization), followed by the emulsion and metal droplet reaction zone (approximately 14%) and the bulk metal and slag reaction zone (approximately 10%). Variations in the effective reaction amount for the main reaction zones significantly affect both the decarburization rate and the endpoint carbon content, with the direct oxidation decarburization reaction in the jet impact reaction zone being the dominant factor. In addition, the PCR in the gas homogenization zone of the auxiliary reaction zones determines the distribution ratio of effective reaction oxygen, while the melting behavior of scrap steel in the metal homogenization zone plays a critical role in the precise control of the endpoint carbon content. This study provides a quantitative elucidation of the effects of different reaction zones on decarburization behavior, offering a foundation for the precise control of endpoint carbon content in BOF steelmaking.

The steel-making process in a Basic Oxygen Furnace (BOF) must meet a combination of target values such as the final melt temperature and upper limits of the carbon and phosphorus content of the final melt with minimum material loss. An optimal blow end time (cut-off point), where these targets are met, often relies on the experience and skill of the operators who control the process, using both collected sensor readings and an implicit understanding of how the process develops. If the precision of hitting the optimal cut-off point can be improved, this immediately increases productivity as well as material and energy efficiency, thus decreasing environmental impact and cost. We examine the usage of standard machine learning models to predict the end-point targets using a full production dataset. Various causes of prediction uncertainty are explored and isolated using a combination of raw data and engineered features. In this study, we reach robust temperature, carbon, and phosphorus prediction hit rates of 88, 92, and 89 pct, respectively, using a large production dataset.