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Wastewater Aeration Optimization Agent with LangChain and OpenAI o3

Small PLC retrofit projects often lack advanced aeration control algorithms. Build a LangChain agent using OpenAI o3 to dynamically adjust wastewater aeration blowers based on telemetry data, achieving a 15% reduction in energy usage while keeping dissolved oxygen above 2.0 mg/L.

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What you are building

Develop a LangChain and OpenAI o3 driven controller to analyze real-time dissolved oxygen telemetry and dynamically adjust PLC blower speed setpoints.

How work is evaluated

Evaluation

Evaluates the agent's ability to lower energy usage while maintaining dissolved oxygen (DO) above 2.0 mg/L across simulated aeration scenarios.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

do_compliance_check

Ensures dissolved oxygen levels remain >= 2.0 mg/L during standard operations.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

energy_savings_percentage

Percentage of energy saved compared to fixed baseline blower operations. • target: 15 • range: 0-30

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Integrate LangChain stateful agents with simulated OPC UA PLC telemetry streams

  • Apply OpenAI o3 reasoning to evaluate non-linear dissolved oxygen dynamics

  • Implement feedback loops for real-time PLC register writebacks

  • Evaluate energy consumption reduction against dissolved oxygen compliance metrics

Resources

Dataset notes

Simulated time-series sensor stream for wastewater treatment aeration tanks, including DO, BOD, inflow rates, and blower power metrics.

Challenge setup

Evaluates the agent's ability to lower energy usage while maintaining dissolved oxygen (DO) above 2.0 mg/L across simulated aeration scenarios.

Challenge input

JSON object with time-series DO values, inflow rate, and current blower frequency.

LangChain

Orchestrates complex reasoning tool calls and state management.

OpenAI o3

Provides fast and reliable mathematical step-by-step reasoning for process parameter estimation.

Evaluated output

JSON object with target_blower_frequency_hz and reasoning_summary.

Evaluation checks
  • Ensures dissolved oxygen levels remain >= 2.0 mg/L during standard operations.
  • Percentage of energy saved compared to fixed baseline blower operations. • target: 15 • range: 0-30
Configured evaluation evidence
  • Benchmark: Industrial AI Aeration Benchmark
  • Energy Savings Percentage target: 15
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Langchain · Required
  • LangChain · Optional
  • OpenAI · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • do_compliance_check · Weight 1
  • energy_savings_percentage · Weight 1

Metrics

  • energy_savings_percentage · Target 15

Benchmarks

  • Industrial AI Aeration Benchmark
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with time-series DO values, inflow rate, and current blower frequency.

    Output: JSON object with target_blower_frequency_hz and reasoning_summary.

    Execution harness: Python

Evaluation tools

  • LangChain · Agent Framework
  • OpenAI o3 · LLM Reasoning Model

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Wastewater Aeration Optimization Agent with LangChain and OpenAI o3