An ontological approach for planning and scheduling in primary steel production

Dobrev, M.; D, Gocheva; Batchkova, Idilia

doi:10.1109/is.2008.4670433

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…Numerous research studies have been carried out to develop ontological models for the steel industry. These inquiries cover a range of topics including knowledge modeling and information management in the steelmaking process [20], planning and scheduling in primary steel production [21], scheduling and control decision frameworks for process industries [22], and supply chain decision support for steel manufacturers [23]. However, the focus of the majority of these studies is primarily on supply chain and scheduling issues, with limited attention given to normal production tasks.…”

Section: The Application Of Ikgs In the Bfipmentioning

confidence: 99%

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Huang,

Yang

2024

Electronics

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Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.

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Section: The Application Of Ikgs In the Bfipmentioning

confidence: 99%

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Huang,

Yang

2024

Electronics

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“…Ontologies are also used for planning and scheduling of steel production. In [23], an ontological approach is proposed for the goal of optimal planning and scheduling. Within the proposed approach, a set of ontologies are integrated to form an ontological framework.…”

Section: Ontologies For the Steel Industrymentioning

confidence: 99%

SCRO: A Domain Ontology for Describing Steel Cold Rolling Processes towards Industry 4.0

2021

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This paper introduces the Steel Cold Rolling Ontology (SCRO) to model and capture domain knowledge of cold rolling processes and activities within a steel plant. A case study is set up that uses real-world cold rolling data sets to validate the performance and functionality of SCRO. This includes using the Ontop framework to deploy virtual knowledge graphs for data access, data integration, data querying, and condition-based maintenance purposes. SCRO is evaluated using OOPS!, the ontology pitfall detection system, and feedback from domain experts from Tata Steel.

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“…For this purpose, in the last decade, ontologies have been developed for one specific industrial domain such as aviation (Keller, 2016), aeropsace (Kossmann et al, 2009), construction (Liao et al, 2009), steel production (Dobrev et al, 2008), chemical engineering (Vinoth & Sankar, 2016;Feng et al, 2018), oil industry (Du et al, 2010;Guo & Wu, 2012), energy (Santos et al, 2018), and electronics (Liu et al, 2005a). Other ontologies have been used for one specific manufacturing process such as packaging (Liu et al, 2005b), process engineering (Wiesner et al, 2010), process compliance (Disi & Zualkernan, 2009), risk management (Atkinson et al, 2006), safety management (Hooi et al, 2012), customer feedback analysis (Kim and Lee, 2013;Daly et al, 2015), organizational management (Grangel-Gonzalez et al, 2016;Izhar and Apduhan, 2017), project management (Cheah et al, 2011), product development (Zhang et al, 2017), maintenance (Haupert et al, 2014), resource reconfiguration (Wan et al, 2018b), and production scheduling (Kourtis et al, 2019).…”

Section: As Well As System Diagnosismentioning

confidence: 99%

“…The fourth industrial revolution is initiating the use of CPS (Lee et al, 2015) and is focused on the development of a new generation of intelligent and integrated technologies for smart manufacturing (Ivezic & Ljubicic, 2016), seeking to optimize its planning and usage across different industrial domains such as oil and gas industry (Du et al, 2010), (Guo & Wu, 2012), mining (Xue & Chang, 2012), energy (Teixeira et al, 2017), steel production (Dobrev et al, 2008), construction (Sorli et al, 2006), aviation (Hoppe et al, 2017;Lehmann et al, 2018), automotive industry (Phutthisathian et al, 2013), electronic industry (Liu et al, 2005a), chemical industry (Natarajan et al, 2011), and process engineering (Wiesner et al, 2010). In addition, the concept of virtual production is considered to be the key factor for modeling production aiming for zero defects (MacDonald, 2016).…”

Section: Challenges Of Industry 40mentioning

confidence: 99%

Ontologies for Industry 4.0

Ragavan

Khamis

Fiorini³

et al. 2019

The Knowledge Engineering Review

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The current fourth industrial revolution, or 'Industry 4.0' (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.

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An ontological approach for planning and scheduling in primary steel production

Cited by 10 publications

References 4 publications

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

SCRO: A Domain Ontology for Describing Steel Cold Rolling Processes towards Industry 4.0

Ontologies for Industry 4.0

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