Compare SwolfPy scenarios and optimize waste allocation

get_project_summary(result_id=...)

Record the current parameters before changing them. Do not edit waste fractions from memory.

update_parameters(
  result_id=...,
  parameters=[...]
)

Fractions for each source must satisfy the project's sum constraints. Keep a copy of the baseline list before mutation.

The public direct-package tutorial demonstrates a residential-waste split with parameters named frac_of_RWC_from_SF_COl_to_LF and frac_of_RWC_from_SF_COl_to_WTE, set so the two fractions sum to 1. Always discover and preserve the current parameter list instead of assuming those names exist in every template.

create_scenario(
  result_id=...,
  scenario_name="baseline",
  input_dict={"SF_COl": {"Mixed_Paper": 1.0}}
)

The public tutorial uses this one-Mg/year Mixed_Paper scenario as a compact example. A scenario name is not enough for reproducibility; retain the exact input_dict that defines the scenario activity.

run_comparative_lca(
  result_id=...,
  functional_units=[{...}, {...}],
  methods=[[...], [...]]
)

Use run_comparative_lca_async for a larger functional-unit × method grid.

contribution_analysis(lca_result_id=...)
get_mass_flows(lca_result_id=..., process="...")

Keep the project result_id and the lca_result_id separate.

optimize(
  result_id=...,
  functional_unit={...},
  method=[...],
  constraints={...},
  n_iter=30,
  collection=false
)

The optimizer minimizes the selected LCIA score by changing waste fractions under the supplied constraints. Use optimize_async for longer searches.

• Allowed treatment routes and fraction constraints.
• Collection, facility-capacity, geography, and logistics assumptions.
• Functional unit and LCIA method.
• Process-model validity and real-world implementability.