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What you’ll build in this exercise:
  • Add 2 new event types (Feedstock Receipt, Lab Analysis) with appropriate datapoints
  • Expand your model to a 5-step calculation pipeline with intermediate outputs
  • Test the pipeline with varied data and verify intermediate and final results
This exercise expands the single-formula model you built in Module 1 into a multi-step calculation pipeline that processes data from three event types. By the end, your model will calculate net carbon sequestered through 5 distinct intermediate steps. Prerequisites: Complete Lesson 2.1: Calculation Architecture and Lesson 2.2: Expanding Your Data Model. Ensure your Mangrove Biochar training project has the Biochar Delivery event type and simple model from Module 1.

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Part 1: Design (30 min)

Before building in Mangrove, design the expanded model on paper or in a spreadsheet.

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1. Identify additional event types

Your project needs data from two new operational activities:

Feedstock Receipt

What goes into the pyrolysis process.
  • Feedstock type (text)
  • Wet mass in tonnes (number)
  • Moisture content as % (number)
  • Receipt date (date)

Lab Analysis

Quality testing of produced biochar.
  • Sample ID (text)
  • Carbon content as % (number)
  • H:C molar ratio (number)
  • Analysis date (date)

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2. Map the 5-step calculation flow

For each step, identify the inputs and the formula:

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3. Identify validation rules

Think about what values should be flagged or rejected:
  • Moisture content must be between 0% and 100%
  • Carbon content must be between 0% and 100%
  • H:C ratio for biochar is typically 0.1–1.0
  • Wet mass must be positive

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Part 2: Build in Mangrove (1.5 hrs)

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Step 1: Add 2 new event types

  1. Open your Mangrove Biochar project and go to Data Inputs > Input Settings.
  2. Add a Feedstock Receipt event type (slug: feedstock-receipt) with 4 datapoints:
  1. Add a Lab Analysis event type (slug: lab-analysis) with 4 datapoints:
See configuring event types for details on setting up datapoints.

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Step 2: Expand your model with a 5-step pipeline

  1. Open the Models section and the Model Editor .
  2. Build the multi-step calculation tree:
1

Step 1: Dry feedstock mass

Operator: product Children:
  • Input node: wet-mass-tonnes (from Feedstock Receipt)
  • Calculated node: 1 - moisture/100 (use a Keisan expression or intermediate nodes)
Expected result: If wet mass = 12 tonnes and moisture = 15%, dry mass = 12 × 0.85 = 10.2 tonnes
2

Step 2: Feedstock carbon content

Operator: product Children:
  • Intermediate: dry feedstock mass (from Step 1)
  • Input node: carbon-content-pct (from Lab Analysis, divided by 100)
Expected result: If dry mass = 10.2 tonnes and carbon = 80%, carbon mass = 10.2 × 0.80 = 8.16 tonnes C
3

Step 3: Biochar yield efficiency

Operator: quotient Children:
  • Input node: biochar-mass-tonnes (from Biochar Delivery)
  • Intermediate: dry feedstock mass (from Step 1)
Expected result: If biochar mass = 8 tonnes and dry feedstock = 10.2 tonnes, yield = 8 / 10.2 = 0.784 (78.4% yield)
4

Step 4: Permanence factor

Operator: keisan Expression: Conditional based on H:C ratio from Lab Analysis
Expected result: If H:C = 0.35, permanence factor = 0.9
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Step 5: Net carbon sequestered (tCO2e)

Operator: product Children:
  • Intermediate: feedstock carbon content (from Step 2)
  • Intermediate: permanence factor (from Step 4)
  • Constant: 3.67
Output datapoint type: net-carbon-tco2eExpected result: 8.16 × 0.9 × 3.67 = 26.95 tCO2e
The biochar yield (Step 3) is calculated for informational purposes. It doesn’t feed directly into the net carbon calculation in this simplified model. You could use it in more advanced models to adjust carbon content based on conversion efficiency.

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Step 3: Test with varied data

  1. Create test events: add events for each of the 3 event types with the following test data:
  1. Run the model in the Console and verify each intermediate step:
    • Dry mass: 12 × 0.85 = 10.2 t
    • Carbon mass: 10.2 × 0.80 = 8.16 t C
    • Yield: 8 / 10.2 = 0.784
    • Permanence: 0.9 (H:C < 0.4)
    • Net carbon: 8.16 × 0.9 × 3.67 ≈ 26.95 tCO2e
  2. Test with edge cases: create additional events with:
    • High moisture (50%): dry mass should drop significantly
    • High H:C ratio (0.75): permanence should be 0.7 (lowest tier)
    • Different carbon content: verify proportional impact on output
If intermediate values don’t match expectations, check: (1) datapoint slugs match between event types and model nodes, (2) percentage values are divided by 100 where needed, (3) the Keisan expression handles all H:C ranges correctly.

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Success criteria

You have completed the Module 2 exercise when:
  • Two new event types exist (Feedstock Receipt and Lab Analysis), each with appropriate datapoints in Data Inputs > Input Settings
  • Model expanded to a 5-step pipeline with visible intermediate calculations
  • Intermediate outputs are verifiable: dry mass, carbon content, yield, permanence factor all produce expected values
  • Final output (net carbon in tCO2e) matches manual calculation for your test data
  • Edge cases tested: varied moisture, H:C ratios, and carbon content produce correct adjusted results

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What’s next

In Module 3 you’ll learn about batch partitioning: how to split your operational data into discrete, reportable batches, and add LCA emissions calculations to compute net carbon removal. Continue to Module 3: Batch Partitioning & LCA.