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Linked challenge: Build a Desalination Plant Reverse Osmosis Membrane Cleaning Scheduler
Format
Code-aware
Lines
66
Sections
11
Linked challenge
Build a Desalination Plant Reverse Osmosis Membrane Cleaning Scheduler
Prompt source
Original prompt text with formatting preserved for inspection.
66 lines
11 sections
No variables
1 code block
## Build
Monitor reverse osmosis membrane pressure differentials and permeate quality to schedule clean-in-place operations. Optimize cleaning chemical usage while maintaining continuous baseline potable water production across the plant.
## User
Desalination Plant Maintenance Manager
## Workflow
1. Ingest SWRO membrane pressure drop telemetry and permeate conductivity readings.
2. Evaluate membrane fouling rates using WaterTAP module performance models.
3. Schedule staggered clean-in-place cycles across multiple RO trains to avoid complete shutdown.
4. Generate a chemical dosing and train isolation manifest for operators.
## Interface
Maintenance scheduling dashboard highlighting membrane fouling trends and recommended chemical wash timelines.
## Data
Utilize WaterTAP RO flowsheet modeling data to generate synthetic membrane fouling degradation profiles.
Starter examples below are synthetic. They are not downloaded records.
## Source
[WaterTAP treatment modeling](https://raw.githubusercontent.com/watertap-org/watertap/main/README.md)
Applies WaterTAP membrane unit models to correlate pressure differential increases with expected fouling severity.
## Tools
agno, pandas, fastapi.
Agno provides agentic reasoning capabilities to evaluate multi-train maintenance schedules against continuous production constraints.
Local or cloud LLM capable of evaluating tabular telemetry logic and generating valid JSON maintenance manifests.
## Deliverables
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.
## Acceptance
- Calculates membrane fouling progression from differential pressure telemetry.
- Determines the optimal clean-in-place cycle timing based on a pressure threshold limit.
- Ensures scheduled RO train outages maintain plant production constraints.
- Generates a human-reviewable chemical cleaning dispatch schedule.
## Starter Examples
```json
[
{
"input": {
"train_id": "RO-TRAIN-A",
"delta_p_bar": 2.8,
"baseline_delta_p_bar": 1.5,
"plant_demand_m3h": 5000
},
"expected_output": {
"fouling_status": "SEVERE",
"cip_required": true,
"pressure_diff_bar": 1.3,
"chemical_dosage_liters": 130,
"_note": "Simplified synthetic app record; chemical_dosage_liters = pressure_diff_bar * 100"
}
},
{
"input": {
"train_id": "RO-TRAIN-B",
"delta_p_bar": 1.7,
"baseline_delta_p_bar": 1.5,
"plant_demand_m3h": 5000
},
"expected_output": {
"fouling_status": "NORMAL",
"cip_required": false,
"pressure_diff_bar": 0.2,
"chemical_dosage_liters": 0,
"_note": "Simplified synthetic app record"
}
}
]
```
## Safety
Maintenance schedules are advisory and require human review by a certified chemical engineer before executing physical clean-in-place operations.
Use mock external writes. Require human review before any real deployment.Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.