Production In Steel Industry Research Articles

Advancing towards sustainable and net-zero supply chains: a comprehensive analysis of knowledge capabilities and industry dynamism

Purpose This research aims to investigate the relationship between knowledge capabilities, engagement capabilities, operational capabilities and sustainable supply chain flexibility in the steel production industry. In addition, it seeks to understand how these elements contribute toward achieving a net-zero supply chain, under the moderation of industry dynamism. Design/methodology/approach In total, 371 people have responded to the self-administered survey that the authors developed. The nonresponse bias analysis was carried out before diving into fundamental assumptions, such as homoscedasticity and normality. The data's reliability and construct validity were assessed by using confirmatory factor analysis. The hypothesized conclusions were supported by subsequent regression outputs, strengthening the body of existing academic research. Findings The research's empirical results highlight the positive relationship among knowledge capabilities, operational capabilities, sustainable supply chain flexibility and net-zero supply chain, particularly under the influence of industry dynamism. Information obtained from the steel production industry corroborates these findings. Moderation role of industry dynamism in the relationship between operational capabilities, engagement capability and sustainable supply chain flexibility was found significant. Originality/value This conceptual framework clarifies how knowledge capability, dynamic capabilities and the flexibility of a sustainable supply chain interact. It highlights how these factors collectively work together to attain a sustainable supply chain with net-zero environmental impact.

An efficient molten steel slag gas quenching process: Integrating carbon solidification and waste heat recovery

The iron and steel-making industries have garnered significant attention in research related to low-carbon transitions and the reuse of steel slag. This industry is known for its high carbon emissions and the substantial amount of steel slag it generates. To address these challenges, a waste heat recovery process route has been developed for molten steel slag, which integrates CO2 capture and fixation as well as efficient utilization of steel slag. This process involves the use of lime kiln flue gas from the steel plant as the gas quenching agent, thereby mitigating carbon emissions and facilitating carbonation conversion of steel slag while simultaneously recovering waste heat. The established carbonation model of steel slag reveals that the insufficient diffusion of CO2 gas molecules within the product layer is the underlying mechanism hindering the carbonation performance of steel slag. This finding forms the basis for enhancing the carbonation performance of steel slag. The results of Aspen Plus simulation indicate that 1 t of steel slag (with a carbonation conversion rate of 15.169 %) can fix 55.19 kg of CO2, process 6.08 kmol of flue gas (with a carbon capture rate of 92.733 %), and recover 2.04 GJ of heat, 0.43 GJ of exergy, and 0.68 MWh of operating cost. These findings contribute to the development of sustainable and efficient solutions for steel slag management, with potential applications in the steel production industry and other relevant fields.

Analysis of The Performance Appraisal & Bonus Payments at Real Ispat

The goal of the research project "Analysis of The Performance Appraisal & Bonus Payments at Real Ispat" is to examine the complex workings of performance assessment systems and how they affect worker satisfaction, productivity, and motivation. Acknowledging the pivotal function that incentive payments and performance reviews play in molding organizational results, this study attempts to offer a thorough examination of these mechanisms in the context of Real Ispat, a prominent participant in the steel production industry. This study's primary goal is to examine how performance reviews affect workers' motivation and output. Performance reviews are meant to provide workers input on how they perform at work, point out their areas of strength and weakness, and establish goals for the future. This study investigates the relationship between these evaluations and workers' incentive to improve performance and total output. Through gathering information from workers at different levels, the study seeks to measure how much performance reviews impact employee engagement and productivity, which in turn boosts the effectiveness and production of the company. Finding out how employees feel about and are satisfied with the performance evaluation process is the second goal. The success of the system depends heavily on how fair, transparent, and successful the assessment process is by the workforce. This survey looks at how Real Ispat employees feel about the assessment process, whether they think it's fair and impartial, and if they're happy with how often these appraisals are done. Since these impressions have a direct influence on employee morale, engagement, and retention, it is important to understand them. Evaluating the efficacy of Real Ispat's present performance evaluation system is the third goal. This entails determining whether the system of appraisals successfully identifies top performers and appropriately represents the work performance of employees. The study also looks at the impact that appraisals have on career development and how they help define reasonable and attainable performance goals. It also looks for areas where the assessment system needs to be improved in order to better fit with employee expectations and company goals. The study uses a mixed-method approach, integrating quantitative and qualitative data-gathering approaches, to accomplish these goals. Likert scale surveys are used to collect quantifiable information about worker productivity, satisfaction, motivation, and views. To obtain qualitative insights into employees' experiences and ideas for improvement, focus groups, and in-depth interviews are held. It is anticipated that the study's conclusions will give Real Ispat's management insightful information that will help them improve their processes for incentive payments and performance reviews. The organization may improve employee motivation, contentment, and productivity by implementing focused interventions after determining the present strengths and weaknesses of these systems. This study also adds to the larger body of knowledge on performance reviews by presenting best practices that other companies may use and providing actual data from the manufacturing industry. Finally, the "Analysis of The Performance Appraisal & Bonus Payments at Real Ispat" hopes to establish a standard for other businesses looking to optimize their performance assessment procedures in addition to improving Real Ispat's internal operations. This research aims to promote a more driven, productive, and contented workforce—which will eventually drive corporate success and growth—through thorough analysis and practical recommendations. Submit Research Paper Submit Research Paper Publication Procedure Publication Ethics Call for Papers Publication Topics Guidelines Disclamiar Copyright Infragmentation FAQ Terms and Conditions Digital Object Identifier Editorial Board Health Care+ Download Manuscript Template Copyright Form Sample Certificates

An Overview Analysis of Current Research Status in Iron Oxides Reduction by Hydrogen

This paper focuses on the study of current knowledge regarding the use of hydrogen as a reducing agent in the metallurgical processes of iron and steel production. This focus is driven by the need to introduce environmentally suitable energy sources and reducing agents in this sector. This theoretical study primarily examines laboratory research on the reduction of Fe-based, metal-bearing materials. The article presents a critical analysis of the reduction in iron oxides using hydrogen, highlighting the advantages and disadvantages of this method. Most experimental facilities worldwide employ their unique original methodologies, with techniques based on Thermogravimetric analysis (TGA) devices, fluidized beds, and reduction retorts being the most common. The analysis indicates that the mineralogical composition of the Fe ores used plays a crucial role in hydrogen reduction. Temperatures during hydrogen reduction typically range from 500 to 900 °C. The reaction rate and degree of reduction increase with higher temperatures, with the transformation of wüstite to iron being the slowest step. Furthermore, the analysis demonstrates that reduction of iron ore with hydrogen occurs more intensively and quickly than with carbon monoxide (CO) or a hydrogen/carbon monoxide (H2/CO) mixture in the temperature range of 500 °C to 900 °C. The study establishes that hydrogen is a superior reducing agent for iron oxides, offering rapid reduction kinetics and a higher degree of reduction compared to traditional carbon-based methods across a broad temperature range. These findings underscore hydrogen’s potential to significantly reduce greenhouse gas emissions in the steel production industry, supporting a shift towards more sustainable manufacturing practices. However, the implementation of hydrogen as a primary reducing agent in industrial settings is constrained by current technological limitations and the need for substantial infrastructural developments to support large-scale hydrogen production and utilization.

Research on the Carbon Reduction Technology Path of the Iron and Steel Industry Based on a Multi-Objective Genetic Algorithm

This paper establishes a multi-objective optimization model based on an improved NSGA-II algorithm, aiming to study the carbon reduction technology path of specific enterprises in the steel industry under the background of China’s dual-carbon goal and fill the research gap in the carbon reduction technology path of steel enterprises, which has certain guiding significance for the realization of China’s dual-carbon goal and the low-carbon development of steel enterprises. Firstly, through the analysis of the list of extreme energy efficiency technologies in the steel industry and the main process flow of steel industry production, the multi-objective optimization model is constructed from the two objective dimensions of maximum CO2 emission reduction and maximum enterprise economic benefit. Then the improved NSGA-II algorithm is used to solve the model. And the empirical analysis of a Hebei iron and steel enterprise, based on the technology application of enterprises before the release of the technology list, the technology path of enterprises to reduce carbon is predicted. The actual application data of the enterprise is used for verification and analysis, and suggestions on the technical path for the future low-carbon development of the enterprise are provided. The experimental results show that: (1) The optimal solution set of Pareto is consistent with the practical application of enterprises, and the constructed model is accurate and efficient, which can be used for the research of carbon reduction technology path. (2) When introducing technology, enterprises can give priority to the solution of common set technology based on their own needs.

An Impact Assessment of the European Carbon Border Adjustment Mechanism (CBAM) on the Korean Economy: A CGE Approach

Purpose - This paper investigates the economic impact of the European Union Carbon Border Adjustment Mechanism (EU-CBAM) on Korea’s economy.
 Design/Methodology/Approach - The study employs the input-output model and Global Trade Analysis Project-Energy (GTAP-E) model by McDougal and Golub (2007), along with the GTAP-E version 10.1 Data Base developed by Aguiar et al. (2019). The input-output model estimates the carbon emissions of Korea’s exports to the EU27, evaluating potential carbon costs and a carbon tax (measured in US dollars per ton of emissions). Utilizing the static GTAP-E model, three policy scenarios are implemented, focusing on direct carbon emissions (Scope 1), emissions from electricity usage (Scope 2), and indirect emissions (Scope 3).
 Findings - Simulation outcomes reveal significant contractions in CBAM-related sectors of the Korean economy, with a decline in iron and steel production (US$ 68.00 million) and electronic industries (US$ 39.16 million). Korea’s export reductions to the EU27 in CBAM-affected sectors (iron and steel, cement, aluminum) indicate a subtle impact extending to the energy, energy-intensive, and services sectors.
 Research Implications - This paper provides valuable insights to policymakers and entrepreneurs in navigating the challenges and opportunities arising from the interlinkage of environmental policies and global trade dynamism. The analysis contributes to discerning perspectives essential for informed decision-making amid the complex interplay of regulatory frameworks and international trade dynamics.

Ordinary-Portland-cement solidification of Cs-137 contaminated electric arc furnace dust from steel production industry in Thailand

The cementation was used to immobilize the Cs-137 contaminated electric arc furnace dust (EAFD). Various mixing recipes were used to prepare the EAFD-cement waste form specimens. The strength test, the ANSI/ANS-16.1 leaching test and the immersion test were performed to judge whether the cured specimens satisfy the Radwaste disposal requirements. The strengths of all specimens were higher than the acceptable limit (3.45 MPa). The specimen's strength depended on the EAFD content, the water-to-binders ratio, and the curing time. Moreover, it could be affected by the leaching of the cement and EAFD components. The leaching results showed that the Cs-137 could be totally leached after ending of the test. There is a positive correlation between the quantity of Cs-137 in the leachate and the leachate pH. The Cs-137 leachability index (LI) decreased as the EAFD content and the ratio increased. The LI values ranged from 5.9 to 6.4. The Cs-137 leaching from the specimens could be controlled by the diffusion or the surface wash-off, depending on the recipes used. Additionally, the Cs-137 leaching might be controlled by multiple mechanisms. The findings reasonably recommend that the recipe with the ratio of 0.40 and 40 % EAFD could be used for the EAFD immobilizing.

Low-cycle fatigue testing and microstructure of high strength-ductility structural steel materials

The well-known tradeoff between strength and ductility is a key issue in the large-scale engineering application of steel materials to resist fatigue due to earthquakes and other vibrational excitations. The steel production industry provides a vast range of technologies to achieve the desired performances. Through experimental research, it was found that FeCrNi-based high-ductility steel (HD-S) can demonstrate remarkable hysteresis behavior due to extensive deformation capacity of strain-hardening until the ultimate fracture, compared to industrially manufactured high-strength steel (HS-S) with the level of 1 GPa in yield strength. The balance between strength and ductility can be realized by slightly adding the percentage of Ni by 5% to achieve a ductile hysteresis behavior. Moreover, the HD-S specimens exhibit greater resistance to low-cycle fatigue with large plastic amplitude. By developing a new damage evolution law based on instantaneous damage differential during nonstationary fatigue history, the fatigue life of materials is extended into the inelastic hinges of flexural beams/origami components. The proposed approach enables the fatigue design of steel structural components with desirable disaster-prevention capacities for complex steel structures.

Assessing radioactivity in soil in the vicinity of steel production industries: a pioneering investigation in Bangladesh

ABSTRACT The steel production process results in the emission of naturally occurring radioactive and toxic heavy metals, which are inherent in the raw materials employed as the initial components. To assess the public exposure through atmospheric dispersions of such radiotoxic metals originating from the production plants and the disposal of associated wastes in nearby landfills, a pioneering study was conducted to assess the radioactivity in forty-four soil samples collected from the environs of sixteen different steel mills in the megacity of Dhaka, Bangladesh, using high-purity germanium (HPGe) γ-ray spectrometry. The results indicated activity ranges of 6 ± 1 to 37 ± 3 Bq/kg, 6 ± 1 to 45 ± 4 Bq/kg, and 101 ± 9 to 470 ± 45 Bq/kg for 226Ra, 232Th, and 40K, respectively, in the soil samples. Radiological hazard parameters were assessed, and none surpassed the recommended limits set by various international organisations. These results underscore that soil samples obtained from the vicinity of the steel mills do not present an immediate and significant risk to the local community in terms of radiation hazards. Nevertheless, given the observed physical alterations in the soil samples, likely resulting from chemical reactions with pollutants released from the steel mills, it is advisable to refrain from using the examined soil samples for agricultural or construction purposes. This study aims to establish a foundational dataset, serving as a crucial reference for the upcoming commissioning of the Rooppur Nuclear Power Plant in the fiscal year 2024–25. This study advocates for the adoption of a systematic and regular monitoring programme to assess radiation levels in the vicinity of steel production industries across the country.

Mill Scale-Derived Magnetite Nanoparticles: A Novel Substrate for Lactate Oxidase-Based Biosensors.

Recycling and revalorization of waste are currently essential for sustainable growth. Mill scale, a waste product from steel production industries, which contains high levels of iron and minimal impurities, is proposed in this study as the source to synthesize magnetite nanoparticles (Fe3O4) for an enhancement of a lactate biosensor range. The synthesized Fe3O4 nanoparticles were coated with polydopamine (PDA) to prevent aggregation and degradation, creating a stable platform for immobilizing lactate oxidase enzyme (LOx) on their surfaces. The characterization of the Fe3O4@PDA material was carried out using transmission electron microscopy (TEM), dynamic light scattering (DLS), and measurement of the polydispersity index (PdI). The Fe3O4@PDA-LOx material was then deposited on a screen-printed carbon electrode modified with Prussian blue (SPCE-PB) for lactate detection. The biosensor exhibited a broad, dual linear concentration-response range, one from 0.1 to 4.62 mM with a limit of detection of 0.32 mM and sensitivity of 1.54 μAmM-1cm-2, and another one from 4.62 to 149.21 mM with a limit of detection of 6.31 mM and sensitivity of 0.08 μAmM-1cm-2. The dual-range concentration response of the biosensor makes it an ideal tool for lactate determination in various applications, including sports medicine, clinical diagnosis, and industrial bioprocessing.

Catalytic pyrolysis of lignite with clay catalyst activated with spent pickling liquor

AbstractSpent pickling liquor (SPL) is an acidic waste with rich iron content from the stainless steel production industry. Reusability of SPL as a catalytic activity promoter in bentonite clay is crucial for the catalytic pyrolysis of various materials like biomass, coal, and waste plastics. For this aim, the bentonite catalyst activated with the SPL nomenclatured as SPL‐treated bentonite (SPLAB) catalyst was employed in the catalytic pyrolysis of lignite for an oil product with high yield and quality. The SPL‐treated bentonite catalytic pyrolysis oil (SPLABO) for the conditions of the final temperature of 600°C, heating rate of 10°C/min, and catalyst ratio of 15 wt% had a yield of 26.35 ± 1.14 wt% and good fuel properties in view of the higher heating value of 35.23 MJ/kg and density of 1076.4 kg/m3 at 20°C. Moreover, it has a rich chemical content by means of monocyclic aromatics and shorter chain alkanes compared with that of the thermal activated bentonite oil (TABO). Based on the chemical structure analysis results of the SPLABO and TABO comparatively, it was concluded that the SPL agent used as a catalytic activity promoter led to a significant increase in the catalytic activity of bentonite catalyst.

Analisa Audit Energi Listrik di PT. Ispat Bukit Baja Kota Bekasi

PT. Ispat Bukit Baja is a company engaged in the steel production industry. Every component production uses electricalenergy as the main source. With the main load of lighting, air conditioning, and electric motors, the use of electricity in this company is verylarge and important. An audit energy needs to be carried out because the value of Energy Consumption Intensity in 2020 is 251 kWh / m2/ year and in 2021 it is 221 kWh / m2 / year and the value of Specific Energy Consumption in 2020 is 0.26 GJ / Ton and in 2021 is 0.29GJ/Ton. The energy audit method used is to calculate the value of Energy Consumption Intensity and Specific Energy Consumption. Thesavings opportunities made are on the air conditioning system, lighting system, and electric motor load. From the results of the analysisand calculation of savings in the air conditioning system, savings of 50.1% or 67.70 kWatt were obtained using a psychrometric diagramto determine the cooling system according to the needs of the room, then for the lighting system, savings of 21.9% or 6 kWatt by replacingthe installed lighting system with energy-saving lamps, then the load of the electric motor is using capacitor to improve the power factorvalue which was originally 0.77 to 0.85 with the aim that the motor speed does not change which will result in the precision of the productionobtained savings in the use of apparent power of 7.13% or 126.52 kVA. So that the results of the apparent power savings can be used forbackup. The total recommended power saving obtained from the calculation results is 73.7 kWatt. So based on the calculation of the energyaudit that has been done, it can reduce production costs and energy use efficiency

The problem of using electrodes in the steel melting process

This article explores the critical role of electrode technology and energy efficiency in the process of electric arc welding for the purpose of melting and shaping steel. It delves into the challenges posed by electrodes that directly impact the technology’s effectiveness and energy consumption. Furthermore, the article conducts an analysis of various electrode types and classifications utilized in steel melting processes, emphasizing their efficient utilization in the steel production industry. As the demand for steel production continues to rise, so do the requirements for electrode strength and durability. However, modern metallurgical processes often encounter the recurring issue of electrode degradation and failure. Through comprehensive research, this article sheds light on the common problems associated with electrode failure during steel melting operations.

A possible contribution of carbon capture, geological utilization, and storage in the Chinese crude steel industry for carbon neutrality

The steel production industry faces tremendous pressure from carbon emission peaks and carbon neutrality targets. CO2 capture, geological utilization, and storage (CCUS) is crucial for CO2 mitigation in fossil-fuel-based industries at scale. Here, an integrated techno-economic assessment model is built to assess the potential of CCUS retrofitting in the crude steel industry. According to this assessment, the total CO2 emissions from 277 crude steel manufacturers represent about 1.76 billion tons. This systematic evaluation provides preliminary answers to critical questions, such as the factory inventory suitable for CCUS retrofitting, matched geological sites, CO2 mitigation magnitude, and related cost characteristics. Furthermore, the contribution of CCUS to the carbon neutralization of the crude steel industry is predicted. With a 60% and 85% net mitigation rate, more than 53% and 82% of CO2 emissions (942 Mt and 1466 Mt/a) in the crude steel plants can be mitigated with levelized costs of less than 60 USD/t. Suitable plants are mainly distributed in regions in or near the Bohai Bay, Junggar, Jianghan, and Ordos basins. The mitigation potential model predicts that the contribution of CCUS will be in the range of 17.6%–56.5% depending on carbon price or supporting policy, and the cumulative cost will be in the range of 189–633 billion USD. The stochastic analysis shows that the fraction of cumulative CO2 mitigation follows the normal distribution of N (39%, 3.2%), and the related incremental cost follows N (305, 39) billion USD. CCUS could greatly help green transition and the sustainable development of the Chinese steel industry.

On a Matlab/Simulink of Power Quality Issues in Distribution System with the Presence of Electric Arc Furnaces and Mitigation Solution Using Active Filter

The non-linearity of the conductance of the electric arc furnace causes voltage flicker and harmonics in power systems. Nowadays, the application of electric arc furnaces in the steel production industry in Vietnam also results in the aforementioned power quality issues and it has been critical to find appropriate solutions for mitigation of those issues recently. This paper develops a time-domain simulation of electric arc furnace that can show power quality issues such as voltage flicker and harmonics in the distribution system and the solution using an active filter for power quality improvement in MATLAB/SIMULINK and thus, it is possible to assess the influences of different parameters of electric arc furnace on the design of active filter. The comparison of simulation results and experimental results obtained from the distribution system of the factory of Thien Phat Company has been conducted.

THE TRANSITION TO A HYDROGEN ECONOMY

The article examines the problem of the hydrogen market – from production to how to use it, as well as the prospects for the development of these energy sources. To combat climate change, more and more countries are investing in low-carbon energy, which is produced from renewable sources and is therefore neutral throughout the chain. Thus, the global hydrogen market is growing. Hydrogen can help decarbonise industrial sectors and combat climate change, either as an environmentally friendly fuel in the transport sector or as a means of reducing emissions in steel production and many other industries. Hydrogen can also play a vital role in energy transport. But even though hydrogen does not pollute itself, because its combustion releases only water vapour, it emits CO2 when fossil fuels are used for its production (“Grey” hydrogen). “Green” hydrogen is still in its infancy: less than 1% of global hydrogen production, which is itself only 2% of global energy consumption. “Green” hydrogen is very expensive to produce, considerably more than “Grey” hydrogen.

Research on carbon productivity and its spatial convergence of steel industry in China.

The Global-Malmquist-Luenberger (GML) index was applied to analyze the carbon productivity in steel industry (SICP) of 29 provinces in China from 2006 to 2019, and then, the SICP was decomposed into technical efficiency change index (TC) and technical progress index (EC). On this basis, the spatial effect is introduced into the traditional convergence model to investigate the spatial convergence of SICP. The empirical results show that: (1) the overall carbon productivity of China's steel industry is at a relatively low level, showing a slow growth trend. (2) The average value of the GML index of SICP is higher than 1, showing obvious inter-provincial and regional heterogeneity. Compared with TC, EC is the leading factor that promotes the increase of SICP. (3) The spatial absolute and condition β convergence of SICP exist in the whole country and the three major regions, but the σ convergence feature is not significant. The addition of spatial factors speeds up the convergence trend, and the speed of spatial absolute β convergence is about 3 times that of the classical convergence model. At the same time, the conditional convergence rate is significantly faster than the absolute convergence, which is closely related to the differences in influencing factors such as the industrial structure, economic development level, human capital, energy consumption intensity, and R&D investment among regions. There is still much room for improvement in carbon productivity in China's steel industry, and investment in scientific research must be increased in order to achieve the upgrading of the industrial structure and technological innovation. The existence of spatial convergence requires strengthening the joint reorganization of steel enterprises between provinces and regions, making full use of the spatial spillover effects of production technology, and realizing regional green and coordinated development.

Analysis of Energy Consumption and Its Influencing Factors in Hydrogen Metallurgy Process

Hydrogen metallurgy is an effective way for the high carbon emission steel industry to achieve low‐carbon green transformation and upgrading. According to energy conservation law, the article establishes gas utilization rate and thermal balance calculation models, discusses the influence of key factors such as reducing gas composition, reducing the temperature, and metallization rate on the gas utilization rate and energy consumption, and theoretically calculates the change of energy consumption in the process of steel industry production caused by using hydrogen. The optimal reduction temperature range is 1250–1350 K, which has theoretical minimum energy consumption, about 3.14 GJ, and maximum gas utilization rate, about 45%. The results show that when using 100% hydrogen reduction, increasing the reduction temperature properly lower theoretical energy consumption can be achieved and raise gas utilization rate and further achieve the goal of energy saving and consumption reduction, which is expected to provide beneficial help for hydrogen metallurgy developing in the steel industry.

Construction and Evolution of Fault Diagnosis Knowledge Graph in Industrial Process

The steel industry production line is complicated and contains a substantial amount of equipment, leading to serious problems in fault diagnosis such as wrong inspection strategies, fault location, maintenance, and so on. To realize accurate and efficient equipment fault diagnosis, this paper proposes a steel production line equipment fault diagnosis knowledge graph (SPLEFD-KG) based on a novel relation-oriented model with global context information for jointly extracting overlapping relations and entities (ROMGCJE). A low-level self-learning SPLEFD-KG is first constructed using the triples extracted by ROMGCJE. However, this low-level SPLEFD-KG is incomplete, and only contains sparse paths for fault reasoning. To overcome this problem, a reinforcement learning framework is applied to mine hidden semantic knowledge to complete the missing relation. Besides, the graph neural networks are introduced to compute the embedding vector of new entities outside of the SPLEFD-KG for continuously completing missing entities. Finally, the low-level self-learning SPLEFD-KG evolves to one high-level SPLEFD-KG, which can provide information-rich and accurate fault-related knowledge. Extensive experiments conducted on the steel production line equipment failure dataset indicate that the novel SPLEFD-KG significantly improves fault diagnosis results and provides effective maintenance programs.

Feature investigation with Digital Twin for predictive maintenance following a machine learning approach

Predictive Maintenance is gathering a lot of interest both from research and industries. The combination of Digital Twin models and Machine Learning provides the mixture of past and featured values for application in the prediction of failures in correlation with production plans. In this work, we explored the use of Machine Learning to extract, through the important features selection, information on which sensors/data - used in a steel industry production line - can be considered “principal” through data obtained from the integration of real-time monitoring and Digital Twin elaboration. The analysis of the data, collected from a period of six months, provided information on anomalies and main signal correlation. The data from Digital Twin and Machine Learning predicted normal and in need of observation states along with the anomalies. Further investigation using Machine Learning, provided the sensors that reported the anomalies and gathered principal components. The sensors’ signal data are currently used for real-time monitoring and Predictive Maintenance plans and integrated in a cloud based platform.