(214c) Enhancing Regulatory Compliance: Assessing Hazardous Waste Standards with Low-Code Tools | AIChE

(214c) Enhancing Regulatory Compliance: Assessing Hazardous Waste Standards with Low-Code Tools

Authors 

Sabella, H. B. - Presenter, Pacific Northwest National Laboratory
Simpson, B., Pacific Northwest National Laboratory
Bottenus, C., Pacific Northwest National Laboratory
The Hanford Site, a former nuclear production complex, generated significant waste over its 30-year operational period, resulting in 56 million gallons currently stored in 177 tanks. This waste, comprising over 1,800 chemicals, poses substantial challenges for management in terms of storage, treatment, and disposal. Oversight is managed by the Department of Energy (DOE), Office of River Protection (ORP), with support from Washington River Protection Solutions (WRPS). Regulatory authority is delegated by the Nuclear Regulatory Commission (NRC) and Environmental Protection Agency (EPA) to Washington State Ecology and Washington State Department of Health during decommissioning. Understanding the waste's characteristics, such as ignitability and reactivity, is crucial for compliance with Washington State regulations under the ORP mission.

As waste is sampled and studied, classifications may evolve, necessitating periodic reviews. The most recent review involves analyzing over 2 million rows of sampling data and more than 50 report files (average page count: 133, average file size: 4.4 MB). To automate parts of this review, Data Scientists and Chemical Engineers at Pacific Northwest National Laboratory (PNNL) are exploring Tableau and Microsoft Power Platform products. Tableau facilitates data cleaning and visualization of sampling data to compare with regulatory standards. Meanwhile, the Power Platform extracts metadata (e.g., author, publish date) and content details (e.g., keywords) from reports to establish a structured document architecture. This architecture addresses gaps in sampling knowledge and offers historical context for waste code designations. This paper explores how incorporating these low-code tools can enhance efficiency and usability, providing comprehensive insights to support informed decision-making in waste management strategies.

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