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Build a Waste-to-Energy Plant Feedstock Routing Workflow with Mastra AI and Hyperbolic

Waste-to-energy facilities face variable caloric intake and municipal collection delays that disrupt boiler performance. Build an automated feedstock routing agent using Mastra AI workflows and Hyperbolic infrastructure to dispatch collection trucks based on real-time calorific values, achieving a 15% reduction in plant thermal instability.

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What you are building

Construct an automated feedstock routing system utilizing Mastra AI TypeScript workflows and Hyperbolic compute to optimize municipal waste delivery for energy generation.

How work is evaluated

Evaluation

Evaluates the Mastra AI routing agent on calorific value balance and route efficiency.

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

valid_json_structure

Ensure agent outputs structured dispatch JSON

Binary check

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

Dimension 2

thermal_stability_score

Ratio of resulting blended calorific value to target boiler requirement • target: 0.9 • range: 0-1

Binary check

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

Learning goals

  • Implement Mastra AI agent workflows for multi-node energy process automation

  • Integrate Hyperbolic API for fast inference on caloric analysis telemetry

  • Optimize truck routing models based on dynamic moisture and calorific value metrics

  • Establish pass/fail validation logic for municipal feedstock intake operations

Resources

Dataset notes

Simulated truck waste moisture logs and incinerator boiler temperature telemetry inspired by Casablanca WtE project data.

Challenge setup

Evaluates the Mastra AI routing agent on calorific value balance and route efficiency.

Challenge input

JSON containing batch telemetry of incoming truck loads and current boiler thermal state

Mastra AI

Required framework for workflow automation

Hyperbolic

Required fast inference tool for calorific classification

Evaluated output

JSON containing dispatched truck schedule, calculated thermal drift, and route cost

Evaluation checks
  • Ensure agent outputs structured dispatch JSON
  • Ratio of resulting blended calorific value to target boiler requirement • target: 0.9 • range: 0-1
Configured evaluation evidence
  • Benchmark: Industrial AI Process Optimization Benchmark
  • Thermal Stability Score target: 0.9
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript 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
  • Hyperbolic · Required
  • Mastra AI · Optional
  • Resemble AI · Optional
Orchestration
  • Mastra AI · Optional

Evaluation contract

  • valid_json_structure · Weight 1
  • thermal_stability_score · Weight 1

Metrics

  • thermal_stability_score · Target 0.9

Benchmarks

  • Industrial AI Process Optimization Benchmark
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing batch telemetry of incoming truck loads and current boiler thermal state

    Output: JSON containing dispatched truck schedule, calculated thermal drift, and route cost

    Execution harness: JavaScript

Evaluation tools

  • Mastra AI · Agent Framework
  • Hyperbolic · Model Runtime

Recipe freeze marker

No recipe freeze time is recorded.

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