BioSiting

Identifies optimal locations for anaerobic digesters and biogas infrastructure by balancing feedstock access with environmental, land-use, and siting constraints.

The Problem

“AI-driven siting for anaerobic digesters and biogas infrastructure”

Organizations face these key challenges:

1

Feedstock sources are fragmented and vary seasonally and geographically

2

Environmental exclusions and setbacks are complex and jurisdiction-specific

3

Land-use, zoning, and permitting constraints are difficult to normalize

4

Manual GIS workflows do not scale across counties or states

Impact When Solved

Cuts site screening time from weeks to hoursImproves consistency of parcel and corridor evaluation across regionsReduces risk of selecting sites with hidden environmental or land-use conflictsIncreases confidence in feedstock access and hauling economics

The Shift

Before AI~85% Manual

Human Does

  • •Collect parcel, feedstock, environmental, and infrastructure data from multiple sources
  • •Review maps and spreadsheets to exclude unsuitable areas and compare candidate parcels
  • •Estimate feedstock hauling access and infrastructure proximity for shortlisted sites
  • •Interpret zoning, land-use, and permitting constraints by jurisdiction

Automation

    With AI~75% Automated

    Human Does

    • •Set siting priorities, weighting assumptions, and scenario objectives
    • •Review ranked sites and approve which candidates move forward
    • •Handle exceptions involving ambiguous permitting, zoning, or local constraints

    AI Handles

    • •Ingest and normalize geospatial, feedstock, environmental, land-use, and infrastructure inputs
    • •Apply exclusions, setbacks, and jurisdiction-specific screening rules across territories
    • •Score and rank parcels based on feedstock access, hauling economics, compatibility, and connectivity
    • •Detect high-potential site clusters and generate scenario comparisons

    Operating Intelligence

    How it works

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence95%
    ArchetypeRecommend & Decide
    Shape6-step converge
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapeconverge

    Step 1

    Assemble Context

    Step 2

    Analyze

    Step 3

    Recommend

    Step 4

    Human Decision

    Step 5

    Execute

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

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