
Aatia contributed to the watertap-org/watertap repository by developing and refining process simulation features for water treatment applications. Over five months, Aatia built a generic surrogate crystallizer model with new property calculations, integrated and stabilized the OLI API client, and enhanced flowsheet modeling through constraint management and error handling improvements. Using Python, Jupyter Notebooks, and data visualization techniques, Aatia addressed both user-facing documentation and backend stability, including CI reliability and onboarding tutorials. The work demonstrated depth in chemical engineering modeling, robust error handling, and test-driven development, resulting in more accurate simulations, improved user experience, and maintainable, well-documented code.

September 2025 highlights focused on stabilizing CI for Windows and delivering user-facing WaterTAP enhancements. The work reduced test flakiness in Windows builds and improved onboarding for WaterTAP flows via a new LSRRO tutorial and notebook improvements.
September 2025 highlights focused on stabilizing CI for Windows and delivering user-facing WaterTAP enhancements. The work reduced test flakiness in Windows builds and improved onboarding for WaterTAP flows via a new LSRRO tutorial and notebook improvements.
July 2025 monthly summary for watertap: Delivered key features and stability improvements with a strong focus on robustness and maintainability.
July 2025 monthly summary for watertap: Delivered key features and stability improvements with a strong focus on robustness and maintainability.
May 2025: Reinforced documentation quality and user onboarding for watertap by delivering a focused tutorial docs fix and preserving codebase stability. The primary accomplishment was correcting a broken link in the tutorial index to ensure reliable access to the parmest_demo tutorial, improving user navigation and reducing potential support tickets.
May 2025: Reinforced documentation quality and user onboarding for watertap by delivering a focused tutorial docs fix and preserving codebase stability. The primary accomplishment was correcting a broken link in the tutorial index to ensure reliable access to the parmest_demo tutorial, improving user navigation and reducing potential support tickets.
In December 2024, delivered and stabilized the OLI API client integration for watertap, focusing on reliability, error handling, and maintainability. Core issues affecting API requests, file operations, and session database cleanup were addressed, with test flakiness managed to stabilize the integration flow. The work reduces downtime, improves data ingestion reliability, and accelerates downstream analytics.
In December 2024, delivered and stabilized the OLI API client integration for watertap, focusing on reliability, error handling, and maintainability. Core issues affecting API requests, file operations, and session database cleanup were addressed, with test flakiness managed to stabilize the integration flow. The work reduces downtime, improves data ingestion reliability, and accelerates downstream analytics.
2024-10 monthly summary for watertap-org/watertap: Delivered a generic surrogate crystallizer model in the MCAS property package, including new property calculations for specific enthalpy and saturation pressure and a new unit model. The update includes accompanying documentation and testing to validate the feature and maintain QA coverage. Overall impact: higher-fidelity crystallization modeling, enabling more accurate process design and faster scenario analysis. Technologies/skills demonstrated: Python modeling, unit model integration, property calculation logic, test-driven development, and documentation.
2024-10 monthly summary for watertap-org/watertap: Delivered a generic surrogate crystallizer model in the MCAS property package, including new property calculations for specific enthalpy and saturation pressure and a new unit model. The update includes accompanying documentation and testing to validate the feature and maintain QA coverage. Overall impact: higher-fidelity crystallization modeling, enabling more accurate process design and faster scenario analysis. Technologies/skills demonstrated: Python modeling, unit model integration, property calculation logic, test-driven development, and documentation.
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