Steel Prices Research Articles

Machine learning-based scrap steel price forecasting for the northeast Chinese market

Machine learning-based scrap steel price forecasting for the northeast Chinese market

Predictions of steel price indices through machine learning for the regional northeast Chinese market

Predictions of steel price indices through machine learning for the regional northeast Chinese market

Implementation of anti-dumping duties on iron and steel products: Effects on the Indonesian economy

This research aims to empirically study the impact of Indonesia's anti-dumping measures on the performance of related imported products in 2011. Using the GTAP-E model, the region is divided into 9 regions and the industrial sector is grouped into 2 industries. Empirical results indicate that the imposition of a 20% Anti-Dumping Duty (BMAD) is ineffective in restricting trade from targeted countries. The GDP of non-targeted countries increased, but international trade balances correlated negatively with each BMAD imposition. Additionally, domestic prices in Indonesia appear to be high due to the implementation of BMAD, which has resulted in increased prices of iron and steel in the domestic market.

Assessing environmental and market implications of steel decarbonisation strategies: a hybrid input-output model for the European union

As a key material for manufacturing clean energy technologies, steel is crucial for energy transition, but its production causes 2.6 Gton of CO2 emissions at global level each year. In 2020 the European Union (EU) set a net-zero emissions target by 2050, fostering innovation in the steel industry to reduce its environmental impact. However, a scenario-oriented and technologically comprehensive analysis assessing prospected environmental and market implications of steel decarbonisation strategies remains a gap, which is addressed in this paper. The analysis adopts a hybrid input-output-based life-cycle assessment model built in the MARIO framework, extending the Exiobase database to represent the supply chains of the most promising low-carbon steelmaking technologies in the EU, such as hydrogen- or charcoal-injected blast furnaces and natural gas- and hydrogen-based direct reduction routes. The penetration of these technologies is explored by formulating scenarios resembling European climate targets. The results show a reduction in the carbon footprint of steel across all scenarios, ranging up to −26% in 2030 and to −60% in 2050. However, the extent of footprint reduction is highly dependent on the share of clean electricity in the European supply mix, highlighting the relevance of holistic decarbonisation strategies. Economic implications affect steel prices, which rise up to 25% in 2030 and 56% in 2050, opening discussions on the need for suitable policies such as CBAM to avoid protectionism and encourage international technological progress.

OPTIMIZATION OF PURLINS CROSS-SECTION EXPOSED TO FIRE

<p>In practice, consideration of fire protection for structural elements mainly occurs after the adopted dimensions of sections. However, this procedure leads to not the most cost-effective solution in general. To find the optimal solution, it is necessary to apply one of the optimization methods. The presented optimization of purlins RHS cross-section is performed with nonlinear programming available in widely used program Excel. The objective function is defined as producing the purlin at a minimal price, considering the price of steel, work, and fire-resistant paint. Limits are introduced to ensure the cross-section satisfies the ultimate limit state for permanent and transient load situations, as well as in case of fire. Besides the ultimate limit states, the limits are defined for serviceability limit states and for cross-sectional geometry. Optimization analysis for different ISO 834 fire durations is followed by a result comparison. It provides an overview of cross-sectional parameters that most influence the bearing capacity in case of fire. It is concluded that by increasing the exposure time to fire, the optimal solution becomes a section with a smaller perimeter, larger surface area, and a thicker layer of fire-resistant coating.</p>

Calculation of production balances and costs in ladle furnace refining process with addition of scrap steel

As increasing the scrap steel ratio has become an important approach for energy conservation and emission reduction in the steel industry, the technology of multi-point scrap addition has emerged. Ladle furnace (LF) refining is a feasible step for scrap steel utilisation, which is often overlooked and relied solely on empirical adjustment, resulting in difficulty of precise control. It is necessary to conduct in-depth research on this process using scientific methods and explore the impact of scrap steel addition on other materials. A comprehensive calculation model was established by scientifically investigating material and thermal balances in the LF process in this article, and the impact of initial steel and scrap compositions, temperature, mass and price of scrap on production costs were analysed and compared based on the model. The results showed that as the carbon and silicon content in the initial steel increase by every 0.01% could on average reduce the production cost per ton of steel by 4.013 CNY and 0.8473 CNY, respectively, and increases by 0.123 CNY as the sulphur content increase every 0.001%. The impact of scrap composition on costs is similar to that of the initial molten steel. Increasing the carbon and sulphur content in scrap by every 0.01% will on average increase costs by 0.0765 CNY and 0.01 CNY, respectively. When the price difference between scrap and the crude steel price is within 450–500 CNY/t, increasing scrap addition will create more profit. The relative reduction in smelting time is 7.97 s as the scrap temperature increases every 50°C. The present study holds academic and industrial significance in improving LF efficiency and promoting the LF process towards a more sustainable direction.

STS – Steel Transportation Service

Currently, a prominent challenge faced by steel companies and retail shops is their dependence on intermediaries or brokers during transactions. This reliance leads to the imposition of additional fees that businesses must pay these intermediaries for facilitating deals. The proposed project aims to address these service- related challenges by establishing direct communication channels between steel companies and retail shops. This innovative approach allows them to forge direct connections without incurring any extra fees to brokers. In the conventional scenario where steel companies and shops rely on brokers for transactions, the negotiation process for steel prices and quantities often becomes time- consuming. Our project presents a solution to this existing problem by empowering steel companies and shops. Through our user-friendly website, we facilitate a streamlined and efficient process for negotiating and finalizing deals. By eliminating the need for intermediaries, businesses can enjoy a more convenient and cost-effective transaction experience, fostering direct communication and enhancing overall efficiency in the steel supply chain.

Analyzing time series to forecast hot rolled coil steel price in Spain by means of neural non-linear models

Abstract In the industrial context, steel is a broadly-used raw material with applications in many different fields. Due to its high impact in the activity of many industries all over the world, forecasting its price is of utmost importance for a huge amount of companies. In this work, non-linear neural models are applied for the first time to different datasets in order to validate their suitability when predicting the price of this commodity. In particular, the NAR, NIO and NARX neural network models are innovatively applied for the first time to forecast the price of hot rolled steel in Spain. Besides these variety of models, different datasets consisting of a set of heterogenous variables from the last seven years and related to the price of this commodity are benchmarked and analyzed. The results showed that NARX is the best performing model when the price of raw materials used to produce steel and the stock market prices of three major global steel producing companies are employed as input to this predictive model. Consequently, this result may boost the application of Machine Learning in companies, in order to schedule the supplying operations according to the price forecasting.

Machine learning predictions of regional steel price indices for east China

From 1 January 2010 to 15 April 2021, this study examines the challenging task of daily regional steel price index forecasting in the east Chinese market. We train our models using cross-validation and Bayesian optimisations implemented through the expected improvement per second plus algorithm, and utilise Gaussian process regressions to validate our findings. Investigated parameters as part of model training involve predictor standardisation status, basis functions, kernels and standard deviation of noises. The models that were built accurately predicted the price indices between 8 January 2019 and 15 April 2021, with an out-of-sample relative root mean square error of 0.57%, root mean square error of 0.84, mean absolute error of 0.48, and correlation coefficient of 99.81%.

Amorphous Core Distribution Transformer with Improved Efficiency and Low Loss for Power Sector Application

The amorphous transformer (AMTD) is an energy-efficient transformer that minimizes both airborne pollutants and the amount of energy required to create power. This research investigates the use of amorphous material in transformer construction rather than the usual silicon steel core. It outperforms silicon steel in terms of magmatic characteristics. Because of its high resistivity and low thickness, adopting amorphous as a transformer core instead of a traditional transformer can eliminate 70% of transformer no load losses. When deciding which transform to install and use in the electrical network, the total prices of silicon steel and amorphous steel are considered. The total owed cost (TOC) of any specific transformer is calculated by discounting future transformer losses (losses from both load and no load) to present value and leveling them across the transformer's lifetime. The characteristics of amorphous and conventional electrical cold-rolled grain-oriented(CRGO) materials were evaluated. Amorphous metal is a special type of alloy with an irregular molecular arrangement rather than a well-organized crystalline structure. Amorphous metal can be utilized for the transformer core to increase transformer efficiency. The material's increased magnetic characteristics and physical dimensions lead to much lower hysteresis and eddy current losses. Using amorphous metal cores can minimize transformer core loss by more than60 percent. High power-density transformers are in high demand in many applications where weight and available space are severely constrained, such as power electronics. While electromagnetic device weights and sizes normally decrease at higher frequencies, core loss may increase considerably.

Quantifying the Impact of the COVID-19 Pandemic on Steel Price and Production Trends Using Time Series Analysis

Quantifying the Impact of the COVID-19 Pandemic on Steel Price and Production Trends Using Time Series Analysis

Optimum investment strategy for hydrogen-based steelmaking project coupled with multiple uncertainties

The large-scale application of hydrogen steelmaking technology is expected to substantially accelerate the decarbonization process of the iron and steel industry. However, hydrogen steelmaking projects are still in the experimental or demonstration stage, and scientific investment decision-making methods are urgently needed to support the large-scale development of the technology. When assessing the investment value, existing studies usually only consider the intrinsic project value under a specific pathway, while ignoring the option value under realistic multiple uncertainties in terms of technology, market, and policy, leading to an underestimation of the investment value. To address this issue, this study constructs a real options model to explore the optimal investment timing and revenue of the hydrogen steelmaking project, by taking into account multi-dimensional uncertainties stemming from price fluctuations in the steel market, the development of the carbon market, and technological advances. Additionally, the impacts of various subsidy policies on the investment strategy are also investigated. Least Squares Monte Carlo method is applied to overcome computational challenges posed by dynamic programming under multi-dimensional uncertainties. The results show that: (i) Investment is not recommended based on current crude steel price and hydrogen price. (ii) When the annual reduction rate of hydrogen price reaches 5%, the optimal investment timing would advance to 2036. (iii) On this basis, with the introduction of a 20% green hydrogen subsidy policy, the optimal investment timing would be further brought forward to 2033. The implementation of tax incentives would significantly increase the investment value. The investment value would surge from 170 million CNY to 262 million CNY as the tax rate decreases from 20% to zero. The findings could provide reasonable suggestions for investment decisions under realistic volatile environments, as well as scientific references for policy design, thus facilitating the large-scale and high-level development of hydrogen-based steelmaking technology.

How to ensure the recovery of family businesses in a crisis situation? Case of the Moroccan family business Maghreb Steel

Learning outcomes After completion of the case study, students will be able to examine the financial implications of Maghreb Steel’s substantial investment in the Blad Assolb complex in 2007 within the restructuring plan; explore how this decision influenced the company’s financial health and strategic position in the steel market, within the context of the restructuring plan; assess the impact of the 2008 economic crisis within the restructuring plan; analyze how the crisis affected the company’s pricing strategies, profitability and overall business strategy; investigate the financial and strategic consequences of the hot rolling activity initiated as a result of the Blad Assolb project within the company’s restructuring plan; and critique how this venture impacted the company’s operations, cost structure and competitiveness in the steel industry, aligned with the restructuring plan. Case overview/synopsis This case study deals with the only flat steel producer in Morocco: Maghreb Steel, the Moroccan family-owned company created in 1975 by the Sekkat family. It was a leading steel company. At the beginning, the company was specialized in the field of steel tubes, but thanks to its growth ambitions, the Sekkat family had made Maghreb Steel a major player in the Moroccan steel sector. In the same logic of development, the top management of Maghreb Steel launched in 2007 in the adventure to create the first production complex of cold rolling in Morocco – an investment that pushed Maghreb Steel to resort to a debt of more than 6bn dirhams (DH) with a consortium of six banks and would have allowed the company a huge leap in growth, except that the decision-makers of the group Sekkat could not see coming the economic crisis of 2008 causing the fall of steel prices by 62% compared to 2007. Thus, from its effective launch in 2010, the activity of hot rolling would become, for the company, a regrettable orientation. Moreover, the national market could not absorb all the production of the complex that the company called Blad Assolb. In response to this difficult situation, Maghreb Steel decided to store its goods to avoid selling at a loss. Faced with this situation of sectoral crisis and deterioration of its activity, Maghreb Steel lost its ability to honor its financial commitments with the banking consortium. From then on, the company became a case of failure, and the recovery measures had not ceased to be duplicated by the various stakeholders: State, Sekkat family, creditors and management of the company, having only one objective in mind: Save Maghreb Steel! This said, the present case study is dedicated to the financial and strategic analysis of the current situation and the evolution of the company throughout the crisis period to finally propose a suitable recovery plan to save Maghreb Steel. Complexity academic level The case study can be taught to students of master’s degrees in financial management as a synthesis of finance courses. It can also be used to train executives and managers working in family businesses as part of professional certification training. Supplementary materials Teaching notes are available for educators only. Subject code CSS 1: Accounting and finance.

Regional Steel Price Index Predictions for North China through Machine Learning

Regional Steel Price Index Predictions for North China through Machine Learning

Regional steel price index predictions for North China through machine learning

Regional steel price index predictions for North China through machine learning

Research on the Improved Combinatorial Prediction Model of Steel Price Based on Time Series

Research on the Improved Combinatorial Prediction Model of Steel Price Based on Time Series

Are there inextricable connections among automobile stocks, crude oil, steel, and the US dollar?

According to the International Organization of Motor Vehicle Manufacturers (OICA), Automobile manufacturers use 40% of steel to make the body structure, 23% to make engines and gears, and 12% for suspensions. We untangled the perceived inextricable connections between crude oil, steel, the US dollar, and stocks of Toyota, Daimler, and Volkswagen as the automobile firms with the largest capitalization by using the time series daily data from 1998 to 2023. We used an augmented version of the Diebold and Yilmaz (2012) connectedness approach, which is based on the Time-Varying Parameter Vector Auto-regression (TVP-VAR) approach as introduced by Antonakakis et al. (2020). Findings suggested that Volkswagen, Daimler, and Toyota are more affected by shocks in steel prices than by USD and oil price shocks, with a spillover of 9%, 11.72%, and 8.72% respectively from steel. The next transmitter is USD, which contributes 4.94%, 6.37% and 3.96% of shocks to Volkswagen, Daimler, and Toyota stocks, and the third variable is the oil price shocks, which contributes 4.25%, 5.41% and 3.50% of spillover effects to Volkswagen, Daimler, and Toyota stocks, respectively. This shows the heterogeneous responses of automobile stocks with the highest capitalization and sales to the error variances transmitted from steel, USD as well as oil. Most notably, overall steel is a net transmitter and USD as net receiver in TYP-VAR system for Toyota and Daimler and can therefore be used as a diversifier. Our findings are impactful for the shareholders as well as portfolio managers for risk reduction and efficient portfolio management.

Evaluation of Project Budget Due to Price Fluctuations Based on Digital Technology in Factory Construction Projects

The development of industrial construction requires adequate resources to avoid the failure and risk of the project. The resources required are materials (building materials), human resources (human resources), machines (equipment), capital (money), methods (methods of implementation), timing, and information on the implementation of the construction. According to data from the Central Statistical Agency (BPS), wages of labor in Indonesia of Rs. 2.89 million increased by 1.12% by 2022. In addition, the price of construction materials has also increased, such as steel, steel bones, concrete, and other construction materials. Especially steel price increases, starting in the second year of the pandemic, have risen about 30%–40%. Generally, the construction of factory buildings uses more steel profiles for the construction of warehouses—storage of raw materials or warehousing of production sites. This has a huge impact on the project budget that has been estimated prior to material price fluctuations. Therefore, the re-evaluation of the project budget in PT. XYZ, which has been estimated by the company at Rp. 219.5 billion, has a total budget change to Rp. 276.1 billion.

Analysis on Debt-paying Ability of Shagang Group

In recent years, the overall performance of steel market can be described as "hot", which has far exceeded expectations. The supply of steel industry is less than the demand. The continuous implementation of environmental protection measures has led to the continuous increase of steel price, which has reached its peak in recent years. The steel industry, which has already obtained a lot of profits, further develops the market and obtains more profits. However, with the good performance of the economy in recent years, the management's business philosophy and management awareness are also being updated, and the debt paying ability is also increasingly valued by the operators, investors and creditors. Whether it is a small company, a listed company or any other organization, the solvency is more and more valued and treated as the same level as the future sustainable operation of the company. Shagang Group, as a very large private iron and steel enterprise in China, is mainly engaged in: iron and steel smelting, steel rolling, and has been the number one single steel mill in the country in steel export for many years. However, with the continuous increase in revenue, many problems have also been brought about. The debt scale of the company has grown rapidly, among which the short-term debt is large in scope and large in proportion. When there is a shortage of daily capital requirements, a single borrowing channel cannot obtain sufficient funds in time. Under such a background, it is of great significance to explore ways to improve the debt paying ability of Shagang Group and put forward reasonable improvement measures to realize the strategic objectives of Shagang Group in the next ten years.

Cost Forecasting for Building Materials under Conditions of Uncertainty: Methodology and Practice

Most large construction projects face the problem of cost overruns and failures to meet deadlines mainly due to changes in the cost of building materials. A lot of studies proved the high importance of the cost of building materials for the project budget and highlighted a number of factors that determine the cost of materials. However, modern unstable economic dynamics lead to the need not only to observe sufficient accuracy of quantity and cost calculations regarding primary building materials but also to carefully predict the cost, taking into account uncertainty factors (changes in the geopolitical situation, the impact of the pandemic, changes in the technological structure, etc.). This article proposes the use of a calculation and expert methodology for forecasting the cost of building materials on the example of building bars for two regions of the Russian Federation. This study includes a review of literature, which showed the dependence of the dynamics of the cost of construction on the prices of building materials, confirmed the impact of economic and noneconomic factors of the dynamics of prices of building materials and the impact of risk and uncertainty. Based on the literature review, it is also concluded that it is necessary to expertly adjust the results of the economic and mathematical modeling of the building materials’ price trend line under the influence of noneconomic factors of uncertainty. The statistics of the prices of building materials in Russia were analyzed, and the main causes of price dynamics (economic and noneconomic) were identified. The ARIMA model was selected to build a series of dynamics of prices of reinforcement steel, an expert adjustment of the forecast was made taking into account uncertainty factors. The method of calculation and expert forecasting of prices of building materials was proposed, and the forecast of prices of steel reinforcement in the regions of Russia was calculated on its basis. In conclusion, this study demonstrates the algorithm and practical results of forecasting the cost of building materials under the conditions of uncertainty, as well as recommendations for the implementation of predictive analytics tools in construction practice.