Collection, storage and application of human knowledge in expert system development

Balch, Robert; Schrader, Sue; Ruan, Tongjun

doi:10.1111/j.1468-0394.2007.00439.x

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…Expert engineers frequently modify these conditions, relying on their specialized knowledge and on-site experimental data [5]. However, the limited scope of most operating variables can often hamper the efficiency of glycerin purification.…”

Section: Introductionmentioning

confidence: 99%

“…These adjustments are necessary not only to maintain the quality of refined glycerin but also to ensure that the controller actions are effective within the unit operation constraints. Expert engineers frequently modify these conditions, relying on their specialized knowledge and on-site experimental data [5]. However, the limited scope of most operating variables can often hamper the efficiency of glycerin purification.…”

Section: Introductionmentioning

confidence: 99%

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Production Capacity Prediction and Optimization in the Glycerin Purification Process: A Simulation-Assisted Few-Shot Learning Approach

Jitchaiyapoom,

Panjapornpon,

Bardeeniz

et al. 2024

Processes

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Chemical process control relies on a tightly controlled, narrow range of margins for critical variables, ensuring process stability and safeguarding equipment from potential accidents. The availability of historical process data is limited to a specific setpoint of operation. This challenge raises issues for process monitoring in predicting and adjusting to deviations outside of the range of operational parameters. Therefore, this paper proposes simulation-assisted deep transfer learning for predicting and optimizing the final purity and production capacity of the glycerin purification process. The proposed network is trained by the simulation domain to generate a base feature extractor, which is then fine-tuned using few-shot learning techniques on the target learner to extend the working domain of the model beyond historical practice. The result shows that the proposed model improved prediction performance by 24.22% in predicting water content and 79.72% in glycerin prediction over the conventional deep learning model. Additionally, the implementation of the proposed model identified production and product quality improvements for enhancing the glycerin purification process.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Production Capacity Prediction and Optimization in the Glycerin Purification Process: A Simulation-Assisted Few-Shot Learning Approach

Jitchaiyapoom,

Panjapornpon,

Bardeeniz

et al. 2024

Processes

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“…In this era, characterized by knowledge economy, as Toffler (1991) noted, it is knowledge, not cheap labour, and symbols, rather than raw materials, that embody and add value. Knowledge representation (Jones, 2006; Snidaro & Foresti, 2007; Balch et al , 2007) and knowledge management (Maliappis & Sideridis, 2004; Amaravadi, 2005; Bernard & Tichkiewitch, 2008) are hotly debated topics among academia and commercial enterprises. In scientific research, studies are carried out on the structure, organization, efficiency and source of knowledge.…”

Section: Introductionmentioning

confidence: 99%

Measurement of enterprise knowledge by state characterization

Bernard

2010

Expert Systems

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Enterprise production is becoming more and more knowledge oriented nowadays and knowledge management is playing a key role in making commercial profit and maintaining enterprise competence. Knowledge characterization and measurement are the bases of knowledge management, and this paper aims to develop a method for characterizing and measuring knowledge in a comprehensive and practical way. Knowledge in its static state is modelled as a vector, whose values vary with the situation, and changes in state are modelled as a finite state machine.

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“…This means the design of a physical and logical support, on which applications and users can rely in order to store and share the knowledge [4]. This involves using standard ways to communicate knowledge units and retrieve them [15].…”

Section: State-of-the-artmentioning

confidence: 99%

Automated knowledge base management: A survey

Martinez‐Gil¹

2017

Preprint

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A fundamental challenge in the intersection of Artificial Intelligence and Databases consists of developing methods to automatically manage Knowledge Bases which can serve as a knowledge source for computer systems trying to replicate the decision-making ability of human experts. Despite of most of tasks involved in the building, exploitation and maintenance of KBs are far from being trivial, significant progress has been made during the last years. However, there are still a number of challenges that remain open. In fact, there are some issues to be addressed in order to empirically prove the technology for systems of this kind to be mature and reliable.

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Collection, storage and application of human knowledge in expert system development

Cited by 12 publications

References 13 publications

Production Capacity Prediction and Optimization in the Glycerin Purification Process: A Simulation-Assisted Few-Shot Learning Approach

Production Capacity Prediction and Optimization in the Glycerin Purification Process: A Simulation-Assisted Few-Shot Learning Approach

Measurement of enterprise knowledge by state characterization

Automated knowledge base management: A survey

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