• Energy Research

The complexity and age of industrial plants have prompted a rapid increase in equipment maintenance and replacement activities in recent years. Consequently, plant owners are challenged to reduce the process and review time of equipment purchase order (PO) documents. Currently, traditional keyword-based document search technology generates unintentional errors and omissions, which results in inaccurate search results when processing PO documents of equipment suppliers. In this study, a purchase order knowledge retrieval model (POKREM) was designed to apply knowledge graph (KG) technology to PO documents of steel plant equipment. Four data domains were defined and developed in the POKREM: (1) factory hierarchy, (2) document hierarchy, (3) equipment classification hierarchy, and (4) PO data. The information for each domain was created in a graph database through three subprocesses: (a) defined in a hierarchical structure, (b) classified into nodes and relationships, and (c) written in triples. Ten comma-separated value (CSV) files were created and imported into the graph database for data preprocessing to create multiple nodes. Finally, rule-based reasoning technology was applied to enhance the model’s contextual search performance. The POKREM was developed and implemented by converting the Neo4j open-source graph DB into a cloud platform on the web. The accuracy, precision, recall, and F1 score of the POKREM were 99.7%, 91.7%, 100%, and 95.7%, respectively. A validation study showed that the POKREM could retrieve accurate answers to fact-related queries in most cases; some incorrect answers were retrieved for reasoning-related queries. An expert survey of PO practitioners indicated that the PO document review time with the POKREM was reduced by approximately 40% compared with that of the previous manual process. The proposed model can contribute to the work efficiency of engineers by improving document search time and accuracy; moreover, it may be expandable to other plant engineering documents, such as contracts and drawings.

To meet the United Nation’s sustainable development energy goal, the Korean Ministry of Commerce announced they would increase renewable energy generation to 5.3% by 2029. These energy sources are often produced in small-scale power plants located close to the end users, known as distributed generation (DG). The use of DG is an excellent way to reduce greenhouse gases but has also been found to reduce power quality and safety reliability through an increase in voltage volatility. This paper performs a life-cycle cost analysis on the use of step voltage regulators (SVR) to reduce said volatility, simulating the impact they have on existing Korean solar photovoltaic (PV) DG. From the data collected on a Korean Electrical Power Corporation 30 km/8.2 megawatts (MW) feeder system, SVRs were found to increase earnings by one million USD. SVR volatile voltage mitigation increased expected earnings by increasing the estimated allowable PV power generation by 2.7 MW. While this study is based on Korean PV power generation, its findings are applicable to any DG sources worldwide.

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.

In the Fourth Industrial Revolution, artificial intelligence technology and big data science are emerging rapidly. To apply these informational technologies to the engineering industries, it is essential to digitize the data that are currently archived in image or hard-copy format. For previously created design drawings, the consistency between the design products is reduced in the digitization process, and the accuracy and reliability of estimates of the equipment and materials by the digitized drawings are remarkably low. In this paper, we propose a method and system of automatically recognizing and extracting design information from imaged piping and instrumentation diagram (P&ID) drawings and automatically generating digitized drawings based on the extracted data by using digital image processing techniques such as template matching and sliding window method. First, the symbols are recognized by template matching and extracted from the imaged P&ID drawing and registered automatically in the database. Then, lines and text are recognized and extracted from in the imaged P&ID drawing using the sliding window method and aspect ratio calculation, respectively. The extracted symbols for equipment and lines are associated with the attributes of the closest text and are stored in the database in neutral format. It is mapped with the predefined intelligent P&ID information and transformed to commercial P&ID tool formats with the associated information stored. As illustrated through the validation case studies, the intelligent digitized drawings generated by the above automatic conversion system, the consistency of the design product is maintained, and the problems experienced with the traditional and manual P&ID input method by engineering companies, such as time consumption, missing items, and misspellings, are solved through the final fine-tune validation process.

Contractors responsible for the whole execution of engineering, procurement, and construction (EPC) projects are exposed to multiple risks due to various unbalanced contracting methods such as lump-sum turn-key and low-bid selection. Although systematic risk management approaches are required to prevent unexpected damage to the EPC contractors in practice, there were no comprehensive digital toolboxes for identifying and managing risk provisions for ITB and contract documents. This study describes two core modules, Critical Risk Check (CRC) and Term Frequency Analysis (TFA), developed as a digital EPC contract risk analysis tool for contractors, using artificial intelligence and text-mining techniques. The CRC module automatically extracts risk-involved clauses in the EPC ITB and contracts by the phrase-matcher technique. A machine learning model was built in the TFA module for contractual risk extraction by using the named-entity recognition (NER) method. The risk-involved clauses collected for model development were converted into a database in JavaScript Object Notation (JSON) format, and the final results were saved in pickle format through the digital modules. In addition, optimization and reliability validation of these modules were performed through Proof of Concept (PoC) as a case study, and the modules were further developed to a cloud-service platform for application. The pilot test results showed that risk clause extraction accuracy rates with the CRC module and the TFA module were about 92% and 88%, respectively, whereas the risk clause extraction accuracy rates manually by the engineers were about 70% and 86%, respectively. The time required for ITB analysis was significantly shorter with the digital modules than by the engineers.

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.

Life-cycle cost analysis (LCCA) is a decision-making tool that allows governing agencies the ability to assess several long-term alternative investment options. This paper presents a LCCA analysis process which integrates the Federal Highway Administration (FHWA) program, RealCost (a road user cost calculation program), the FHWA-endorsed Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) and Caltrans specific design tools (CalFP and CalAC), into the existing Caltrans LCCA process (a modified version of the FHWA LCCA process). In using tools backed by the FHWA and validated through previous agency use, the presented process has a potential to be replicated on urban corridor improvement projects across the US while aiding agencies in achieving economical sustainability throughout the infrastructure maintenance phases. This paper also fills the gap identified by Ozbay et al. in 2004, incorporating road user cost calculations into the LCCA process. Validation was achieved through the execution of the recently completed $1.4 B US California SR-91 Corridor Improvement Project. The SR-91 team used the presented tool to choose one of the two alternatives (maintain HOV SR-91 lane and add I-15 HOV lane using long-life Portland Cement Concrete Pavement or add Express Lane to SR-91 and I-15 using long-life Continuously Reinforced Concrete Pavement and Asphalt Concrete Pavement), equating to an estimated life-cost savings of $32 M.

Many existing financial models for power plants chose a design based on the maximum thermal efficiency excluding the operational (OPEX) and capital (CAPEX) cost variations of technical factors. These factors are often fixed because including them in financial assessments can be burdensome and it is assumed that maximum efficiency equals maximum profit. However, this assumption may not always be right. Through 19,440 power plant steam-cycle design solutions and their associated OPEX and CAPEX, this study found the eighth most thermally-efficient solution to be $1.284 M more profitable than the traditional thermally-optimized design solution. As such, this paper presents a model incorporating technical factors through parametric estimation by minimizing the burden on decision makers. While this may reduce precision, it allows for quick cost assessments across differing design solutions. The data for model development was collected from a Korean-constructed, operational 600 MW coal-fired power plant in the Philippines. Using the Thermoflex software, nearly all design configurations’ heat rate outputs are simulated. Profitability is then optimized based on the resultant design configuration’s impact on revenue and CAPEX and OPEX costs. The simulation inputs included variables found to be most impactful on the steam generated power efficiency per existing literature. Lastly, the model includes an assessment of cost impacts among recent environmental regulations by incorporating carbon tax costs and a sensitivity analysis. The economic analysis model discussed in this paper is non-existent in current literature and will aid the power-plant project investment industry through their project feasibility analyses.