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Waste-to-Energy Operational Dispatch Optimizer using LangChain and Ludwig

Casablanca waste-to-energy projects face variable feedstock caloric values and complex thermal conversion constraints. Build a LangChain and Ludwig workflow agent that processes daily municipal waste telemetry to predict optimal furnace operating parameters while maintaining a thermal conversion accuracy within 5 percent.

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What you are building

Implement a hybrid AI agent utilizing LangChain orchestration and Ludwig model training to automate thermal dispatch for municipal waste incinerators.

How work is evaluated

Evaluation

Evaluates furnace parameter dispatch predictions and agent action correctness against real plant baseline operational data.

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

Safety Threshold Test

Ensures furnace temperature recommendations remain between 850C and 1100C.

Binary check

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

Dimension 2

Efficiency MAPE

Mean Absolute Percentage Error between predicted conversion efficiency and gold benchmark. • target: 5 • range: 0-100

Binary check

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

Learning goals

  • Orchestrate waste feedstock predictive modeling using Ludwig and LangChain

  • Integrate IoT telemetry data into stateful LangGraph execution graphs

  • Automate thermal furnace operational parameter dispatch in real time

  • Evaluate operational efficiency gains against baseline combustion standards

Resources

Dataset notes

Simulated daily Casablanca municipal solid waste streams including moisture content, caloric values, tonnage, and historical incinerator sensor metrics.

Challenge setup

Evaluates furnace parameter dispatch predictions and agent action correctness against real plant baseline operational data.

Challenge input

JSON object with daily waste composition, moisture level, and target power output.

LangChain

Agent workflow orchestration

Ludwig

Declarative tabular model inference

Evaluated output

JSON object with recommended airflow, furnace target temperature, and estimated thermal conversion efficiency.

Evaluation checks
  • Ensures furnace temperature recommendations remain between 850C and 1100C.
  • Mean Absolute Percentage Error between predicted conversion efficiency and gold benchmark. • target: 5 • ra...
Configured evaluation evidence
  • Benchmark: GCC Energy Operational Optimization Benchmark
  • Efficiency MAPE target: 5
  • 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
  • Ludwig · Required
  • LangChain · Optional
  • All Hands AI · Optional
Orchestration
  • LangChain · Optional

Evaluation contract

  • Safety Threshold Test · Weight 1
  • Efficiency MAPE · Weight 1

Metrics

  • Efficiency MAPE · Target 5

Benchmarks

  • GCC Energy Operational Optimization Benchmark
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with daily waste composition, moisture level, and target power output.

    Output: JSON object with recommended airflow, furnace target temperature, and estimated thermal conversion efficiency.

    Execution harness: Python

Evaluation tools

  • LangChain · Agent Framework
  • Ludwig · ML Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Waste-to-Energy Operational Dispatch Optimizer using LangChain and Ludwig