Eul-Bum Lee

The steel industry, as a large-scale equipment-intensive sector, emphasizes the importance of maintaining and managing equipment without failure. In line with the recent Fourth Industrial Revolution, there is a growing shift from preventive to predictive maintenance (PdM) strategies for cost-effective equipment management. This study aims to develop a PdM model for the Run-Out Table (ROT) equipment in hot rolling mills of steel plants, utilizing artificial intelligence (AI) technology, and to propose methods for contributing to energy efficiency through this model. Considering the operational data characteristics of the ROT equipment, an autoencoder (AE), capable of detecting anomalies using only normal data, was selected as the base model. Furthermore, Long Short-Term Memory (LSTM) networks were chosen to address the time-series nature of the data. By integrating the technical advantages of these two algorithms, a predictive maintenance model based on the LSTM-AE algorithm, named the Run-Out Table Predictive Maintenance Model (ROT-PMM), was developed. Additionally, the concept of an anomaly ratio was applied to identify equipment anomalies for each coil production. The performance evaluation of the ROT-PMM demonstrated an F1-score of 91%. This study differentiates itself by developing an optimized model that considers the specific environment and large-scale equipment operation of steel plants, and by enhancing its applicability through performance verification using actual failure data. Furthermore, it emphasizes the importance of PdM strategies in contributing to energy efficiency. It is expected that this research will contribute to increased energy efficiency and productivity in industrial settings, including the steel industry.

The by-product gases generated during steel manufacturing processes, including blast furnace gas, coke oven gas, and Linz–Donawitz gas, exhibit considerable variability in composition and supply. Consequently, achieving stable combustion control of these gases is critical for improving boiler efficiency. This study developed the advanced boiler combustion control model (ABCCM) by combining the random forest (RF) and classification and regression tree (CART) algorithms to optimize the combustion of steam power boilers using steel by-product gases. The ABCCM derives optimal combustion patterns in real time using the RF algorithm and minimizes fuel consumption through the CART algorithm, thereby optimizing the overall gross heat rate. The results demonstrate that the ABCCM achieves a 0.86% improvement in combustion efficiency and a 1.7% increase in power generation efficiency compared to manual control methods. Moreover, the model reduces the gross heat rate by 58.3 kcal/kWh, which translates into an estimated annual energy cost saving of USD 89.6 K. These improvements contribute considerably to reducing carbon emissions, with the ABCCM being able to optimize fuel utilization and minimize excess air supply, thus enhancing the overall sustainability of steelmaking operations. This study underscores the potential of the ABCCM to extend beyond the steel industry.

This case study presents an innovative fast-track reconstruction approach applied to a heavily trafficked LLPRS project on Interstate-15 (I-15) in southern California. A 4.5-km stretch of badly damaged concrete truck lanes, two in each direction, was rebuilt in only two 210-hour (about 9 days) one-roadbed continuous closures, using counter-flow traffic and around-the-clock operations as accelerated construction, an undertaking estimated to take 12 months using traditional nighttime closures. This post-construction case study validated pre-construction estimates of productivity and traffic delay, which closely matched performance data measured during construction. The I-15 Devore project provided a unique opportunity to validate, fine-tune, and enhance highway analysis tools for future LLPRS projects. The use of CA4PRS schedule analysis and other traffic models presented in this study facilitates teambuilding amongst engineers involved in the design, construction, and traffic phases to mutually arrive at an optimal solution.

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.

The engineering sector is undergoing digital transformation (DT) alongside shifts in labor patterns. This study concentrates on piping design within plant engineering, aiming to develop a system for optimal piping route design using artificial intelligence (AI) technology. The objective is to overcome limitations related to time and costs in traditional manual piping design processes. The ultimate aim is to contribute to the digitalization of engineering processes and improve project performance. Initially, digital image processing was utilized to digitize piping and instrument diagram (P&ID) data and establish a line topology set (LTS). Subsequently, three-dimensional (3D) modeling digital tools were employed to create a user-friendly system environment that visually represents piping information. Dijkstra’s algorithm was implemented to determine the optimal piping route, considering various priorities during the design process. Finally, an interference avoidance algorithm was used to prevent clashes among piping, equipment, and structures. Hence, an auto-routing system (ARS), equipped with a logical algorithm and 3D environment for optimal piping design, was developed. To evaluate the effectiveness of the proposed model, a comparison was made between the bill of materials (BoM) from Company D’s chemical plant project and the BoM extracted from the ARS. The performance evaluation revealed that the accuracy in matching pipe weight and length was 105.7% and 84.9%, respectively. Additionally, the accuracy in matching the weight and quantity of fittings was found to be 99.7% and 83.9%, respectively. These findings indicate that current digitalized design technology does not ensure 100% accurate designs. Nevertheless, the results can still serve as a valuable reference for attaining optimal piping design. This study’s outcomes are anticipated to enhance work efficiency through DT in the engineering piping design sector and contribute to the sustainable growth of companies.

The global steel industry is rapidly transitioning towards energy decarbonization to address the climate crisis. Sintering is one of the main sources of greenhouse gas emissions from steel mills. Traditional sintering processes use straight inclined chutes to feed raw materials into the sinter machine. However, this design suffers from insufficient horizontal momentum, resulting in poor segregation of the layered materials. This study proposes an improved charging chute design profile that uses a cycloid curve and rolls to enhance segregation, thus reducing coal consumption and increasing productivity. To achieve this, we first modeled a charging chute using the cycloid curve. Secondly, building upon the cycloid concept, we created a roll-type chute by strategically placing rollers along the cycloid trajectory. Finally, the cycloid roll-type charging chute, integrating the cycloid trajectory with the roll-shaped charging chute, was simulated. Pilot tests comparing the cycloid roll-type and straight chute models demonstrated a significant increase in dispersion for the cycloid roll-type design, with a 65% improvement in the Strand-ward segregation (Sw) index compared to the straight chute. Furthermore, actual filed implementation in a sintering process achieved a 2.9% increase in operational productivity and a 6% reduction in fuel consumption. This study is significant not only for proposing an optimal chute design, but also for successfully implementing it in a full-scale steel mill, contributing to a reduction in fuel consumption and carbon reduction in steel mills.

The energy-intensive steel industry, which consumes substantial amounts of electricity, meets its power demands through external electricity purchases and self-generation through the operation of its own generators. This study aimed to optimize boiler combustion efficiency and increase power generation output by deriving optimal operational values for O₂ and CO within the boiler flue gas using machine learning (ML) with the aim of achieving maximum boiler efficiency. This study focuses on the power-generation boilers at steel mill P in Korea. First, 361 types of operation data from power generation equipment were collected and preprocessed. Subsequently, a partial least squares regression (PLSR) algorithm was used to develop a prediction model for O₂ and CO values, known as the Boiler Flue Gas Prediction Model (BFG-PM). The prediction accuracy for O₂ was notably high (83.2%), whereas that for CO was lower (53.4%). Nonetheless, the model’s reliability was high because more than 90% of the predicted values were within a 10% error range. Finally, the correlation of the BFG-PM model was applied to the performance test code (PTC) 4.0 for the boiler efficiency calculations formula, deriving the optimal O₂ and CO control points. Through a simulation, it was verified that the boiler efficiency was improved by controlling the combustion air. In addition, an average increase in boiler efficiency of 0.29% was confirmed by applying it directly to the generator operating on-site. The results of this study are expected to contribute to annual cost savings, with a reduction of USD 217,000 in electricity purchasing costs and USD 19,700 in greenhouse gas emissions trading expenses.

Recently, equipment replacement and maintenance repair and operation (MRO) optimization have substantially increased owing to the aging and deterioration of industrial plants, such as steel-making factories in Korea. Therefore, plant owners are required to quickly review equipment supply contracts, i.e., purchase order (PO) documents, with suppliers and vendors. Currently, there is inconsistency in the time and quality required for the PO document review process by engineers, depending on their manual skills and practice. This study developed a general provisions question-answering model (GPQAM) by combining knowledge graph (KG) and question-answering (QA) techniques to search for semantically connected contract clauses through the definition of relationships between entities during the review of equipment purchase contracts. The PO documents analyzed in this case study were based on one steel-making company’s general provisions (GP). GPQAM is a machine learning (ML)-based model with two sub-models (i.e., KG and QA) that automatically generates the most relevant answers to semantic search questions through a cypher query statement in GP for the PO engineers. First, based on the developed GP lexicon and its classifying taxonomy to be stored in the Neo4j graph database (GDB), the KG sub-model finds the corresponding synonyms and consequently shows GP-related information in a graphic form. Second, the QA sub-model is a function to find and answer contract information within the KG and applies pattern-matching technology based on the Aho–Corasick (AC) algorithm. Third, nodes with the meaning most similar to the question are selected using similarity measurement if a response cannot be extracted through the pattern-matching process. Forty-five pilot test questions were created and applied to the GPQAM model evaluation. The F1 score was 82.8%, indicating that the unsupervised training methods developed in this study could be better applied to a semantic QA process in plant engineering documents, where sufficient training data are limited and bargained. An expert survey of PO practitioners confirmed that the semantic QA capability of GPQAM might be efficient and useful for their work. As the first case of applying KG technology to semantic QA for plant equipment PO contracts, this study might be a meaningful contribution to the steel plant industry and, therefore, extended to construction and engineering contract applications.