Graphing and Interpretation

Purpose

Use this guide after a case has been solved and you need to connect graph shape to utility targets, target scope, and workflow decisions.

Prerequisites

Install openpinch[notebook] for Plotly figures or openpinch[dashboard] for the Streamlit review surface.

Sample Case

Use basic_pinch.json for process-level graphs and pulp_mill.json or zonal_site.json for Total Site profiles and SUGCC views.

Runnable Workflow

from OpenPinch import PinchProblem

problem = PinchProblem("basic_pinch.json")
problem.target.all_heat_integration()

summary = problem.summary_frame()
gcc = problem.plot.grand_composite_curve()
cc = problem.plot.composite_curve()
catalog = problem.plot.catalog()

Expected Output

summary_frame() gives the numerical context. problem.plot.* returns Plotly figures or graph data for the solved target family. catalog() helps confirm which graph families are available before exporting or displaying.

Interpretation

Use this order:

  1. read the summary row and target scope

  2. inspect the Grand Composite Curve for utility placement

  3. inspect Composite Curves or shifted curves for overlap and pinch behavior

  4. inspect Total Site profiles only after confirming the workflow is multizone

  5. inspect exergetic graphs only after running exergy post-processing

After exergy enrichment:

problem.target.exergy()
gcc_x = problem.plot.exergetic_grand_composite_curve()
nlp_x = problem.plot.exergetic_net_load_profiles()

For portable review artifacts:

paths = problem.plot.export(
    "graphs",
    plot=problem.plot.grand_composite_curve,
)

Common mistakes are comparing a process-level row to a site-level graph, reading graph shape before checking utility targets, or treating a graph change as sufficient without confirming the metrics.

Next Steps