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Challenge

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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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

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

Delivery guide

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

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

What you should walk away with

  • 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 and assets

Reference links and supporting material

Dataset notes

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

How this agent runs

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.

Checks for
  • 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
Proof of success
  • Benchmark: Industrial AI Aeration Benchmark
  • Energy Savings Percentage target: 15
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
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

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

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