PHASuite: An Automated HAZOP Analysis Tool for Chemical Processes

Zhao, Chunhua; Bhushan, Mani; Venkatasubramanian, Venkat

doi:10.1205/psep.04056

Cited by 32 publications

(3 citation statements)

References 26 publications

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“…Venkatasubramanian and his group introduced digraphs to enhance graphical representation during HAZOP automation (Vaidhyanathan & Venkatasubramanian, 1996). Their work led to the development of PHASUITE, an automatic qualitative tool (C. Zhao et al, 2005) and the HAZOP Expert tool (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998). Another knowledge-based system called SERO was developed by (Leone, 1996) to enhance the expertise and creativity of HAZOP study teams.…”

Section: Threementioning

confidence: 99%

HAZard and OPerability Study Analysis as a Semi-Automatic Approach

Boutadjine,

korichi,

sellami

2024

Jou. Eng. Res.

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Risk analysis is crucial in industrial conception. HAZOP is the top risk analysis method for the oil and gas sector. This paper presents a semi-automatic method to address HAZOP's limitations and produce automatic results. The method uses a knowledge base, initially filled with gas liquefaction data, and is enhanced with subsequent case studies. An inference engine processes this data to conduct a HAZOP study. Propagation rules identify potential deviation paths, enabling risk analysis and consequence prediction based on the knowledge base. This method uniquely illustrates deviation paths and introduces nodes along these paths for further study. The findings derive from dynamic knowledge of each system in the knowledge base and can be reviewed and amended by experts.

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Section: Threementioning

confidence: 99%

HAZard and OPerability Study Analysis as a Semi-Automatic Approach

Boutadjine,

korichi,

sellami

2024

Jou. Eng. Res.

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“…• Combining first-principles with data-driven processing [25][26][27] • Causal models-based explanatory systems [7,16,17,[28][29][30][31] • Domain-specific knowledge engines [19,[32][33][34][35][36] Thus, I do not view AI methods as merely useful tools to extract patterns from large amounts of data, even though that benefit is very much there, but as a new knowledge modeling paradigm, the next natural evolutionary stage in the long history of developing formal methods -first applied math (i.e., differential and algebraic equations), then operations research (i.e., math programming), and now artificial intelligence. Conceptually, applied math models numerical relationships between variables and parameters, mathematical programming models relationships between constraints, and AI models relationships between symbolic variables and symbolic structures.…”

Section: Role Of Symbolic Ai In Knowledge Modeling and Representationmentioning

confidence: 99%

Teaching Artificial Intelligence to Chemical Engineers: Experience from a 35-year-old Course

Venkatasubramanian

2022

Chem. Eng. Ed.

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He considers himself an artist in science, whose natural tendency is to conduct curiosity-driven research in a style that might be considered impressionistic, emphasizing conceptual issues over mere techniques. Venkat's research interests are diverse, ranging from AI to systems engineering to theoretical physics to economics, but with a focus on understanding complexity and emergent behavior in different domains.

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“…Nevertheless, in PSE domain, most of the proposed ontology-based frameworks involve pseudo-ontologies or lightweight ontologies sometimes as mere technological update of previous works. Moreover many of these knowledge models (Vaidhyanathan & Venkatasubramanian, 1995;Venkatasubramanian & Vaidhyanathan, 1994;Zhao et al, 2005aZhao et al, , 2005b were designed following a semantic mediation or they were not formally implemented.…”

Section: Ontology-based Modeling In Process Engineeringmentioning

confidence: 99%