Build an Industrial Desalination RO Membrane Cleaning Outage Planner
Schedule clean-in-place maintenance across multi-train reverse osmosis desalination plants. Coordinate membrane outages against finished water storage reserves and power tariff fluctuations.
What you are building
The core problem, expected build, and operating context for this challenge.
Schedule clean-in-place maintenance across multi-train reverse osmosis desalination plants. Coordinate membrane outages against finished water storage reserves and power tariff fluctuations.
Seawater desalination operations engineer
Parse normalized transmembrane pressure, salt passage telemetry, and finished water potable tank levels.
Model specific energy consumption increases caused by membrane scaling using WaterTAP RO membrane equations.
Formulate a staged clean-in-place maintenance schedule that preserves municipal water delivery quotas during off-peak power hours.
Generate a chemical staging manifest and clear operating instructions for the plant maintenance crew.
A tabular train scheduling console displaying fouling trends, clean-in-place chemical batches, and potable buffer projections.
Develop synthetic hourly sensor data for six SWRO trains over 90 days, capturing normalized flux decline and pressure differentials. Starter examples below are synthetic. They are not downloaded records.
WaterTAP treatment modeling Leverages reverse osmosis transport models and membrane resistance degradation relationships from WaterTAP.
agno, numpy, pydantic. Enables multi-agent coordination between energy tariff monitoring, chemical dosing calculators, and tank reserve monitors. Fast hosted model capable of parsing multi-variable engineering limits and drafting operational procedures.
Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.
Flags RO trains for chemical cleaning when normalized salt passage exceeds 1.5% or pressure differential rises 20%.
Ensures aggregate plant output remains above the minimum municipal contract delivery quota of 85,000 m3/day.
Restricts clean-in-place wash heater and flushing pump power draw to designated low-tariff grid periods.
```json [ { "input": { "train_id": "RO-Train-03", "normalized_pressure_drop_bar": 2.8, "clean_pressure_drop_bar": 2.1, "salt_passage_pct": 1.62, "potable_storage_m3": 45000, "daily_demand_m3": 80000 }, "expected_output": { "cip_recommended": true, "recommended_window": "22:00-06:00", "chemical_recipe": "2% Citric Acid followed by 0.1% NaOH", "potable_reserve_margin_hours": 13.5 } }, { "input": { "train_id": "RO-Train-01", "normalized_pressure_drop_bar": 2.2, "clean_pressure_drop_bar": 2.1, "salt_passage_pct": 0.95, "potable_storage_m3": 42000, "daily_demand_m3": 80000 }, "expected_output": { "cip_recommended": false, "recommended_window": "none", "chemical_recipe": "none", "potable_reserve_margin_hours": 12.6 } } ] ```
Simulation and scheduling output only. All chemical recipes and valve actuations must be verified by a licensed water treatment technician. Use mock external writes. Require human review before any real deployment.
Participation guide
Simulation and scheduling output only. All chemical recipes and valve actuations must be verified by a licensed water treatment technician. Use mock external writes and human review.
How work is evaluated
Flags RO trains for chemical cleaning when normalized salt passage exceeds 1.5% or pressure differential rises 20%. Ensures aggregate plant output remains above the minimum municipal contract delivery quota of 85,000 m3/day. Restricts clean-in-place wash heater and flushing pump power draw to designated low-tariff grid periods.
Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Complete workflow
Parse normalized transmembrane pressure, salt passage telemetry, and finished water potable tank levels. Model specific energy consumption increases caused by membrane scaling using WaterTAP RO membrane equations. Formulate a staged clean-in-place maintenance schedule that preserves municipal water delivery quotas during off-peak power hours. Generate a chemical staging manifest and clear operating instructions for the plant maintenance crew.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Correctness and evidence
Flags RO trains for chemical cleaning when normalized salt passage exceeds 1.5% or pressure differential rises 20%. Ensures aggregate plant output remains above the minimum municipal contract delivery quota of 85,000 m3/day. Restricts clean-in-place wash heater and flushing pump power draw to designated low-tariff grid periods.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Review interface
A tabular train scheduling console displaying fouling trends, clean-in-place chemical batches, and potable buffer projections.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Safe execution
Simulation and scheduling output only. All chemical recipes and valve actuations must be verified by a licensed water treatment technician.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Parse normalized transmembrane pressure, salt passage telemetry, and finished water potable tank levels.
Model specific energy consumption increases caused by membrane scaling using WaterTAP RO membrane equations.
Formulate a staged clean-in-place maintenance schedule that preserves municipal water delivery quotas during off-peak power hours.
Generate a chemical staging manifest and clear operating instructions for the plant maintenance crew.
Reference links and supporting material
Develop synthetic hourly sensor data for six SWRO trains over 90 days, capturing normalized flux decline and pressure differentials. Supplied starter examples are synthetic, not source extracts.
Challenge setup
Configuration summary for this challenge.
Challenge input
Input details are not configured.
Agno
Framework for multimodal agents.
Evaluated output
No output checks are configured.
- Parse normalized transmembrane pressure, salt passage telemetry, and finished water potable tank levels. Mo...
- Flags RO trains for chemical cleaning when normalized salt passage exceeds 1.5% or pressure differential ri...
- A tabular train scheduling console displaying fouling trends, clean-in-place chemical batches, and potable...
- 2 public reference cases
- 2 configured tools
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- Agno · Optional
- Agno · Optional
Evaluation contract
- Complete workflow · Weight 40
- Correctness and evidence · Weight 30
- Review interface · Weight 20
- Safe execution · Weight 10
Reference cases
- 2 public reference cases
Workflow tasks
No workflow tasks are configured.
Recipe freeze marker
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