# AI use cases for Auto WWTP ASM Demo (jsdsg666/AutoWWTP-ASM-Demo)

Repo: [jsdsg666/AutoWWTP-ASM-Demo](https://github.com/jsdsg666/AutoWWTP-ASM-Demo) | Use cases: 3 | Page: https://dedupedai.com/use-cases/jsdsg666-autowwtp-asm-demo/

AutoWWTP-ASM is a Python LLM agent workflow that builds and calibrates ASM models for wastewater treatment plants from a task text and spreadsheet data.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Calibrate an ASM model from a task file: https://dedupedai.com/use-cases/jsdsg666-autowwtp-asm-demo/calibrate-asm-model-from-task-file/
- Review the ASM plan before calibration runs: https://dedupedai.com/use-cases/jsdsg666-autowwtp-asm-demo/review-asm-plan-before-calibration/
- Run the ASM workflow from the Codex project package: https://dedupedai.com/use-cases/jsdsg666-autowwtp-asm-demo/run-asm-workflow-from-codex-project/

## How to calibrate an ASM model from a task file with Auto WWTP ASM Demo

Repo: [jsdsg666/AutoWWTP-ASM-Demo](https://github.com/jsdsg666/AutoWWTP-ASM-Demo) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Run the full ASM workflow on plant data and get a report, calibration results and a final summary.

**Who it is for:** A wastewater engineer who has plant data in a spreadsheet and needs an ASM model calibrated for an anaerobic, anoxic or aerobic tank or the full process section.

**The problem:** Building an ASM model, running sensitivity analysis and calibrating parameters by hand takes repeated setup and manual checks.

**The outcome:** A run folder with the process context, plan, configuration, sensitivity and calibration results, figures, an asm_report.md and asm_report.pdf, and final_result.json.

### Steps

1. Install the dependencies with `pip install langgraph langchain-core langchain-openai langchain-anthropic langchain-google-genai pandas numpy scipy matplotlib openpyxl markdown-pdf pymoo`.
2. Set the LLM variables in PowerShell, starting with `$env:AUTOWWTP_LLM_API_KEY="your-api-key"`, then set AUTOWWTP_LLM_BASE_URL, AUTOWWTP_LLM_MODEL and AUTOWWTP_LLM_MESSAGE_CLASS as shown in the LLM Configuration section.
3. Open a terminal in the code/langgraph-en folder of your clone, since the README path is a local Windows path you must replace.
4. Confirm that your task file in task/ names the data path such as input/data1.xlsx, the modeled object, the target state variables, the boundary conditions and the calibration targets.
5. Run `python main.py --task-file task/task1.md`.
6. Open the new output/task-YYYYMMDD-HHMMSS-ffffff folder and read asm_report.md and final_result.json.

### Prompt to paste into your agent

```text
Use the AutoWWTP-ASM repo at <repo-folder>/code/langgraph-en to run the task file <task-file> with the LLM variables already set. Expected output: a new output/task-* folder containing asm_report.md, final_result.json and midoutput/calibration.json. Verify that midoutput/sensitivity.json and midoutput/calibration.json both exist before reporting success.
```

### Caveat

The README gives a local Windows path and a placeholder LLM base URL, so you must adapt both before the first run.

Risk, credentials: Keep your LLM API key in environment variables as the README advises and do not paste it into task files or source code.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/jsdsg666-autowwtp-asm-demo/calibrate-asm-model-from-task-file/

## How to review the ASM plan before calibration runs with Auto WWTP ASM Demo

Repo: [jsdsg666/AutoWWTP-ASM-Demo](https://github.com/jsdsg666/AutoWWTP-ASM-Demo) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Pause after the plan is written, check or edit asm_plan.md, then let the run continue.

**Who it is for:** A process modeler who wants to approve the modeling plan before any sensitivity analysis or calibration runs on plant data.

**The problem:** An automatically generated ASM plan may use the wrong modeled object, variables or boundary conditions, and a full run wastes time if the plan is wrong.

**The outcome:** A checked or edited asm_plan.md and a run that continues from your approved plan.

### Steps

1. Install the dependencies with `pip install langgraph langchain-core langchain-openai langchain-anthropic langchain-google-genai pandas numpy scipy matplotlib openpyxl markdown-pdf pymoo`.
2. Set the AUTOWWTP_LLM_* variables as described in the LLM Configuration section.
3. Run `python main.py --task-file task/task1.md --hitl-plan`.
4. When the run pauses after asm_plan.md is generated, open midoutput/asm_plan.md in the new output folder and check the modeled object, target variables and boundary conditions.
5. Edit the plan if needed, then continue the run as the pause prompt allows.

### Prompt to paste into your agent

```text
Use the AutoWWTP-ASM repo in <repo-folder>/code/langgraph-en to run <task-file> with the --hitl-plan option. Expected output: a paused run with midoutput/asm_plan.md ready for review, then the completed asm_report.md. Verify that the plan names the target tank or process section and the target state variables before continuing.
```

### Caveat

The README says the run pauses after asm_plan.md but does not describe the exact prompt to resume, so check the terminal output when it pauses.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/jsdsg666-autowwtp-asm-demo/review-asm-plan-before-calibration/

## How to run the ASM workflow from the Codex project package with Auto WWTP ASM Demo

Repo: [jsdsg666/AutoWWTP-ASM-Demo](https://github.com/jsdsg666/AutoWWTP-ASM-Demo) | Difficulty: Advanced | Time: 1 hour 30 minutes | Basis: From the README

Open the Codex project version and ask the agent to run an ASM modeling task.

**Who it is for:** A researcher who already works in Codex and wants the AutoWWTP-ASM workflow inside that project.

**The problem:** Switching between a chat agent and a separate command-line run slows down iterative ASM modeling work.

**The outcome:** A Codex session that reads AGENTS.md and the agent definitions, then runs an ASM modeling task on your data.

### Steps

1. Open the code/autowwtp-asm-codex folder as the project in Codex. (inferred, check the README)
2. Read AGENTS.md and the .codex/agents/*.toml files to see which agents the project defines. (inferred, check the README)
3. Set the LLM and Python dependencies from the README setup steps before asking Codex to run the workflow. (inferred, check the README)
4. Ask Codex to run the modeling task on the input spreadsheet and check the output folder for asm_report.md. (inferred, check the README)

### Prompt to paste into your agent

```text
Open the autowwtp-asm-codex project at <repo-folder>/code/autowwtp-asm-codex in Codex and read AGENTS.md. Run the ASM modeling task described in <task-file> on the data in <input-file>. Expected output: a report and a final_result.json. Verify that the process checks file exists before reporting success.
```

### Caveat

The README only says this folder is organized for Codex and does not state how to launch its agents, so the steps above are inferred.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/jsdsg666-autowwtp-asm-demo/run-asm-workflow-from-codex-project/