The article analyses conditions and factors, which affect realization of corporate ecologic responsibility at steelmaking enterprises in Russia. Special attention is paid to motivation of companies to keep to responsible work and assess its compliance with set standards. The research covers motivation of steel-making enterprises in respect of practices aimed at improving the ecologic situation, both internal and external factors are studied. The article analyzed current practices of big steel-making companies of Russia. The author investigates a wide range of factors that can influence company decisions in the field of ecologic responsibility. As a result the article provides a complete picture of the current situation and prospects of developing corporate ecologic responsibility at steelmaking enterprises in Russia.

The risk of accidents during simultaneous operations (SIMOPS) in plant maintenance has been increasing. However, research on methods to prevent such accidents has been limited. This study aims to develop a novel framework, hazard identification and risk assessment of simultaneous operations (HIRAS), for identifying and evaluating potential hazards during concurrent tasks. The framework developed herein is expected to be an effective safety management tool that can help prevent accidents during these operations. To this end, the job location and hazard information in job safety analysis (JSA) were standardized into four attributes. The standardized information was then synchronized spatially and temporally to develop a HIRAS model that identifies and assesses the impact of hazards between operations. The model was tested using 40 JSA documents corresponding to maintenance operations at Company P, a South Korean steel-making company. The model was tested in two scenarios: one with planned operations and the other with unplanned operations in addition to planned operations. The performance evaluation results of the first scenario showed an F1-score of 98.33%. In this case, a recall of 97.52% means that the model identified 97.52% of the hazard-inducing factors. The second scenario was compared with the results of a review by six subject matter experts (SMEs). The comparison of the results identified by the SMEs and the model showed an accuracy of 89.3%. This study demonstrates the potential of JSA, which incorporates the domain knowledge of workers and can be used not only for individual tasks but also as a safety management tool for surrounding operations. Furthermore, by improving the plant maintenance work environment, it is expected to prevent accidents, protect workers’ lives and health, and contribute to the long-term sustainable management of companies.

This article analyses a slab assignment problem for non-identical reheat furnaces in the steelmaking industry. We tackle the problem of assigning slabs to different types of reheat furnaces, a walking beam and a pusher type furnace. Furthermore, we need to determine the feed-in as well as the residence time for each slab. The aim is to (i) reduce the energy consumption, (ii) increase the production rate and (iii) the heating quality of the slabs which depends on the assigned furnace and to what extent the slabs are overheated. Moreover, the subsequent production stage (Hot Rolling Mill), needs to be synchronized with the furnaces which is essential for a smooth production. We specify the problem as a mixed integer optimization formulation that is solved with iterative parameter adjustment. Rapid convergence leads to a novel two-phase solution method that yields furnace schedules that are optimal for the adjusted parameter values in reasonable time. The results show that our proposed method performs better than an industry benchmark by increasing the production rate and increasing the reheating quality. Furthermore, the new generic problem formulation allows using standard software and can be applied to identical or non-identical furnaces highlighting its broad applicability across steel-making companies.

Scrap is the main raw material of the electric arc furnace (EAF) route of steelmaking and an important one in the primary route. The decarbonisation commitment acquired by the most relevant steelmaking companies, will only increase the amount of scrap used by both routes, making it a critical component to succeed in the worldwide challenge of reducing CO2 emissions. While being a very relevant material, scrap characterisation is complex due to its heterogeneity, but furthermore, its quality is worsening as its demand increases. This includes the sterile content, such as copper. Higher sterile content scrap comes at a lower price, while scrap with a lower content in undesired elements is most costly. Being able to balance the cost and benefit of purchasing scrap with a higher or lower content of copper with a respective, lower or higher purchase cost would be of interest to optimise the mix and produce the required quality steel at the lowest possible cost. Nowadays, this cost benefit analysis can only be done by performing Total Cost of Operation (TCO) calculations that require a considerable amount of information, including but not limited to the complete chemical composition of scrap, market parameters, production forecast and other relevant data; moreover, involving an arduous optimization process. Based on a parametric study of the TCO calculation, this work proposes a mathematical model to estimate the impact on the TCO of a predefined scrap mix when only modifying the specifics of one given scrap type. The results would facilitate the decision making by comparing the two scenarios as a factor of cost. This mathematical model can be easily programmed and requires only 5 parameters to be run, while providing a low error result.

PurposeThe purpose of this study is to evaluate the efficiency of a Brazilian steelmaking company’s reheating process of the hot rolling mill.Design/methodology/approachThe research method is a quantitative modeling. The main research techniques are data envelopment analysis, TOBIT regression and simulation supported by artificial neural networks. The model’s input and output variables consist of the average billet weight, number of billets processed in a batch, gas consumption, thermal efficiency, backlog and production yield within a specific period. The analysis spans 20 months.FindingsThe key findings include an average current efficiency of 81%, identification of influential variables (average billet weight, billet count and gas consumption) and simulated analysis. Among the simulated scenarios, the most promising achieved an average efficiency of 95% through increased equipment availability and billet size.Practical implicationsAdditional favorable simulated scenarios entail the utilization of higher pre-reheating temperatures for cold billets, representing a large amount of savings in gas consumption and a reduction in CO2 emissions.Originality/valueThis study’s primary innovation lies in providing steelmaking practitioners with a systematic approach to evaluating and enhancing the efficiency of reheating processes.

Abstract In the steel stockyard of the shipyard, the sorting work to relocate the steel plates already stacked to retrieve the target steel plate on the fabrication schedule is labor-consuming work requiring the operation of overhead cranes. To reduce the sorting work, there is a need for a method of stacking the steel plates in order of fabrication schedules when the steel plates arrive at the shipyard from the steel-making companies. However, the conventional optimization algorithm and heuristics have limitations in determining the optimal stacking location of steel plates because the real-world stacking problems in shipyards have vast solution space in addition to the uncertainty in the arrival order of steel plates. In this study, reinforcement learning is applied to the development of a real-time stacking algorithm for steel plates considering the fabrication schedule. Markov decision process suitable for the stacking problem is defined, and the optimal stacking policy is learned using an asynchronous advantage actor-critic algorithm. The learned policy is tested on several problems by varying the number of steel plates. The test results indicate that the proposed method is effective for minimizing the use of cranes compared with other metaheuristics and heuristics for stacking problems.

PurposeThe emission of greenhouse gases has become an increasingly relevant topic in supply chain management. The steel industry is a highly intensive manufacturing industry with significant emission levels, particularly Scope 3 emissions, which are the indirect emissions from suppliers. Since a supply chain is seen as a non-mandatory measurement item within GHG measurement protocols, this article contributes to the literature on assessing the suppliers of a focal company relative to their emissions for complying with Scope 3 (indirect emissions). It adds to the evolving literature on low-carbon supply chains.Design/methodology/approachThis study first conducted a survey with 110 suppliers from a focal transnational buyer company. A cluster analysis was performed, and ANOVA compared constructs relating to public or private ownership and country of origin. Finally, regression tested the relationship between the motivators and governance in the mitigation strategies.FindingsUsing cluster analysis, two groups of companies were found that have statistically significant differences. The influence of the country of origin was also found in relation to governance and mitigation strategies, as was the influence of the type of company on governance. Furthermore, the more motivated the suppliers and the more governance measures they adopt, the more companies adopt their own GHG mitigation strategies. These findings are summarized by way of an analytical framework that integrates the constructs with empirical evidence.Originality/valueThe steel industry is a sector that is particularly energy-intensive and produces millions of tons of CO2 per year. Emissions from its SC (Scope 3) are relevant but still seen as a non-mandatory item for measurement purposes within the GHG measurement protocols, which leads to less attention being paid to the subject. This study contributes by way of its analytical framework that is validated by empirical data that can be tested in further studies.

Greenhouse gas reduction and economic cost of technologies using green hydrogen in the steel industry

Spread is a major parameter in the steel wire rod rolling process since it is required for the calculation of material cross-sectional area and other rolling characteristics. Therefore, it is important to have a method to predict the spread with high accuracy and less computation time in wire rod rolling. In this study, multiple artificial intelligence (AI) methods including Multi-Layer Perceptron (MLP) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are employed to predict the spread. The 3D finite element (FE) analysis is used to generate the input data for the AI model and investigate the effect of different input parameters on the spread in one-stand and three-stand rolling setups of the wire rod rolling. The results demonstrate that the backward tension and the roll diameter are the most influencing parameters. Due to the use of dimensionless inputs and outputs, the model is independent of geometries and processing conditions which results in the transferability of the model. Furthermore, the ANFIS model provides some level of reasoning for the user by using a rule-based approach. Data fusion is also used to combine all outputs of the trained models and provide a single output for the prediction of spread in new data sets. The reasoning and transferability of the model result in the prediction of spread for a wide range of conditions in the steel wire rod rolling process. The generality and accuracy of the proposed approach are examined by comparing the results of the AI model with the FE analysis and experimental data obtained from the steelmaking company. The findings indicate that there is good agreement between the predicted and the measured values.

In this study, using natural gas instead of coke gas in the reheating furnace in a steelmaking company is investigated in terms of economic, social, and environmental impacts. A sample projection is prepared, and economic analyses are conducted in line with the production plan target for future planning periods of 144 months. The natural gas usage increases the production quantity by 914 tonnes and allows the company to produce 5,979,334 kWh of additional electric power from the metallurgical gases monthly. In the economic analysis, we use engineering economics techniques to examine the economic impacts of the modernization investment for reheating furnaces. Accordingly, a positive return for each month shows the feasibility of the renovation project. The self-paying time of the investment is calculated as a short period of 11 months. Besides, the social and environmental impacts are notable; the renovation project decreases occupational health and safety risks by using natural gas as a substitute fuel, preventing a potential explosion or poisoning risk in the production, storage, and distribution. The renovation project decreases the global warming potential of blast furnace gas constituents and carbon emissions by 0.84% per month.

How do states pursue industrial policies in the context of China’s rise? Examining Indonesia and Malaysia’s mineral processing sectors, we argue that these countries illustrate two different pathways that states take to bolster their industrial policies. Indonesia has followed the leading sector strategy to increase domestic nickel processing capacity and decrease reliance on resource exports. Chinese firms and the Indonesian government built the Indonesia Morowali Industrial Park to house nickel smelters, fostering a new leading sector. Chinese capital in smelting follows what Albert Hirschman has called ‘intermediate investments’, maximising forward and backward linkages across the Indonesian economy. In contrast, Malaysia has followed the dual economy strategy, where semi-finished goods are imported and assembled into finished ones to be exported abroad. Chinese firms and the Malaysian government established the Malaysia–China Kuantan Industrial Park to import, process and export steel products. However, due to the dual economy strategy, the industrial park impairs the activities of domestic steelmaking companies and inhibits the potential build-up of smelting capacity. In sum, through an examination of an industrial park in each country, our paper connects the literatures on industrial policy and Chinese capital.

In hot strip mills, prediction of the necking width of the hot strip is a fundamental step in the hot strip mill process. However, owing to the large number and complexity of the variables involved, this prediction remains a challenging problem. In this article, we propose a deep neural model with an attentional residual network that combines an attentional network to calculate feature importance and a residual network to estimate the necking value. When a hot strip mill dataset from a South Korean steelmaking company was evaluated, the proposed model showed a higher performance than several machine-learning methods. Furthermore, the importance of the features selected by the attentional network outperformed those by other feature selection methods. Our approach is useful for necking predictions and can be applied to determine feature importance.

Except for special grades of steel where it is used as an alloying element, phosphorus is regarded as an impurity that must be removed. Considering the conventional integrated iron and steelmaking, there are primarily two processes for phosphorus removal. The first is a hot metal dephosphorization (DeP) process that is applied to a blast furnace for hot metal before the steelmaking process. The second is the basic oxygen furnace steelmaking (BOS), a unique method primarily used for steelmaking, with the exception of stainless steels. Hot metal phosphorus content has a direct impact on BOS. An increase of phosphorus in hot metal is mainly related to the use of high P2O5 containing iron ores. In the current literature review, new trends of phosphorus removal in converter steelmaking are outlined. The double-slag practice was reported to be successful when hot metal P content was larger than 0.100%. It was indicated that the tapping temperature was critical for the production of low-phosphorus grades for which maximum allowable P content was 0.007% and that high tapping temperatures should be avoided. The tap-to-tap time for the double-slag process was slightly longer than the conventional converter steelmaking. It was further reported that the double-slag practice would be more economical than an establishment of a separate hot metal dephosphorization unit, if low-phosphorus grades did not have a significant share in the product mix of a steelmaking company. Endpoint phosphorus prediction was one of the important recent trends of converter steelmaking. A mixed injection of CO2-O2 to a basic oxygen furnace was applied to enhance dephosphorization, and promising results were reported. Unfortunately, a successful process for recycling of BOS dephosphorization slag has not been reported yet.

Major steel-making companies in Korea have recently been trying to advance into international markets for better profitability and new market shares. Even with strategic partnerships with local organizations, the Korean steel companies are facing and incurring significant risks which impact their ability to achieve a sustainable profit. The objective of this research is to determine an optimum combination of financial models, specifically Project (PF) and Mezzanine Financing (MF) with an option (convertible bond and bond with warrant). The results of the proposed model can lower interest rates of financing, thereby increasing the profitability of the project investors. To analyze the MF method’s effectiveness and proper use, the following three steps are applied: (1) Monte-Carlo Simulations (MCS) using Excel and @Risk software are performed for the Net Present Value (NPV) of the project and its volatility; (2) the Black-Scholes model (BSM) is applied to evaluate MF based on project value; and (3) interest rate of MF is calculated from its option value and is reapplied back to the NPV calculation of the project to determine the effects of MF. Assuming a 50% debt/equity ratio, these simulations were performed on five cases (50% senior debt, 0% MF for a base case then increasing MF and decreasing senior debt by 10% four times). Through this process, using the 10%, MF lowered the borrowing size by 20% and using MF continued to lower the borrowing size up to 40% borrowing when using 40% MF. Based on this result, the researchers support the use of MF to optimize Korean steel international financial models. The resultant data will serve as an effective method to increase net cash flow in overseas steel-plant project investments. This research was performed for a steel plant located in Iran as a case-study, but this optimized financing method using MF with an option product can be applied sustainably not only for overseas investment of steel plants but also any other business, such as oil & gas, power generation, and transportation industries.

This paper focuses on an investment decision-making process for sustainable development based on the profitability impact factors for overseas projects. Investors prefer to use the discounted cash-flow method. Although this method is simple and straightforward, its critical weakness is its inability to reflect the factor volatility associated with the project evaluation. To overcome this weakness, the Value-at-Risk method is used to apply the volatility of the profitability impact factors, thereby reflecting the risks and establishing decision-making criteria for risk-averse investors. Risk-averse investors can lose relatively acceptable investment opportunities to risk-neutral or risk-amenable investors due to strict investment decision-making criteria. To overcome this problem, critical factors are selected through a Monte Carlo simulation and a sensitivity analysis, and solutions to the critical-factor problems are then found by using the Theory of Inventive Problem Solving and a business version of the Project Definition Rating Index. This study examines the process of recovering investment opportunities with projects that are investment feasible and that have been rejected when applying the criterion of the Value-at-Risk method. To do this, a probabilistic alternative approach is taken. To validate this methodology, the proposed framework for an improved decision-making process is demonstrated using two actual overseas projects of a Korean steel-making company.

Multi-Criteria Decision Making (MCDM) problems have received considerable attention from various researchers over the past decades. A great variety of methods and approaches has been developed in this field. The aim of this paper is to use a new COmbinative Distance-based ASsessment (CODAS) method to handle MCDM problems for a steelmaking company in Libya. So far no literature dealing with supplier selection using the (CODAS) method in the steelmaking company in Libya has been found. The concept of this method is based on computing the Euclidean distance and the Taxicab distance in order to determine the desirability of an alternative. The Euclidean distance is used as a primary measure, while the Taxicab distance as a secondary one. The developed method was applied to a real-world case study for ranking the suppliers in the Libyan Iron and Steel Company (LISCO). An attempt in this regard could enhance a decision-making technique for selecting the best suppliers for the selected case company. The results showed that the proposed method was effectively able to select the best supplier among six alternative ones.

Engineering strategies for enhancing C. vulgaris ESP-31 lipid production using effluents of coke-making wastewater

Abstract This work aims to reduce the energy consumption and thus increase the availability of blast furnace compressors of a steelmaking company, located in Alto Paraopeba region in Minas Gerais state, Brazil, through the elimination of waste points in the compressed air distribution. In order to develop this work, an ultrasound test in the compressed air line was performed to identify and quantify leaks in the flow. Once the leaks were identified, they were eliminated through corrective maintenance and improvements, and then the energy consumption scenarios before and after the improvements were compared. As a result, the average monthly electricity consumption in the reporting period decreased by 57.2%. In addition, one compressor was set aside in stand-by condition, as in the original plant. Thus, one can prove the efficiency in eliminating of waste points in compressed air distribution, since the reduction of energy consumption is important for the company to remain competitive, as the cost of electric energy affects the final price of the final products.

PurposeThe purpose of this paper is to propose a supplier selection method using grey system theory for a steelmaking company in Libya.Design/methodology/approachIn order to tackle incompleteness and imprecision of human’s judgments, grey numbers were used. This work uses a grey-based approach to represent decision makers’ comparison judgments and extent analysis method to select the best supplier. Therefore, an example of a selection problem of a steelmaking company in Libya was used to illustrate the proposed approach.FindingsSupplier selection in a supply chain is a critical strategic decision for company’s success and has attracted much attention of both academic scholars and decision makers. The authors have found that the Grey model can play an important role in improving supplier selection strategy, especially when it is in a situation where complex sustainability environments (i.e. Libya) exist.Originality/valueNo literature has been found till date for selection of supplier using grey system theory in a steelmaking company in Libya. An attempt in this regard could enhance a decision-making technique for selecting the best suppliers for the selected case company.

An investigation was conducted into a failure of convection section tubes in a process gas heater in a steel-making company. Thorough study using various characterisation techniques showed that the failure of the tubes, which were made of low-alloyed steel ASTM A335 P22, was attributed to metal dusting. The attack was accelerated because of the use of incompatible tube material for the intended operating conditions. Moreover, thermal fatigue cracks were discovered on the tube surfaces. Such cracks might have been caused by the high number of plant start-ups and shutdowns. Failure contributing factors and recommendations to avoid similar failure are discussed in this article.