Download this example as a Jupyter notebook or a Python script.

Perform a BoM sustainability summary query#

The following supporting files are required for this example:

bom-2301-assembly.xml

Run a BoM sustainability summary query#

First, connect to Granta MI.

[1]:

from ansys.grantami.bomanalytics import Connection

[2]:

server_url = "http://my_grantami_server/mi_servicelayer"
cxn = Connection(server_url).with_credentials("user_name", "password").connect()

Next, create a sustainability summary query. The query accepts a single BoM as argument, as well as optional configuration for units. If a unit is not specified, the default unit is used. Default units for the analysis are: MJ for energy, kg for mass, and km for distance.

[3]:

xml_file_path = "supporting-files/bom-2301-assembly.xml"
with open(xml_file_path) as f:
    bom = f.read()

from ansys.grantami.bomanalytics import queries

MASS_UNIT = "kg"
ENERGY_UNIT = "MJ"
DISTANCE_UNIT = "km"

sustainability_summary_query = (
    queries.BomSustainabilitySummaryQuery()
    .with_bom(bom)
    .with_units(mass=MASS_UNIT, energy=ENERGY_UNIT, distance=DISTANCE_UNIT)
)

[4]:

sustainability_summary = cxn.run(sustainability_summary_query)
sustainability_summary

[4]:

<BomSustainabilitySummaryQueryResult>

The BomSustainabilitySummaryQueryResult object returned implements a messages property, and properties showing the environmental impact of the items included in the BoM. Log messages are sorted by decreasing severity. The same messages are available on in the MI Service Layer log file, and are logged via the standard logging module. The next sections show examples of visualizations for the results of the sustainability summary query.

Summary per phase#

The sustainability summary result object contains a phases_summary property. This property summarizes the environmental impact contributions by lifecycle phase: materials, processes, and transport phases. The results for each phase include their absolute and relative contributions to the product as a whole.

[5]:

sustainability_summary.phases_summary

[5]:

[<SustainabilityPhaseSummaryResult('Material', EE%=31.56945748870393, CC%=40.05506081817221)>,
 <SustainabilityPhaseSummaryResult('Processes', EE%=59.95417367143312, CC%=52.40516764294889)>,
 <SustainabilityPhaseSummaryResult('Transport', EE%=8.476368839862962, CC%=7.539771538878892)>]

Use the pandas and plotly libraries to visualize the results. The data will first be translated from the BoM Analytics BomSustainabilitySummaryQueryResult to a pandas Dataframe

[6]:

import pandas as pd

EE_HEADER = f"EE [{ENERGY_UNIT}]"
CC_HEADER = f"CC [{MASS_UNIT}]"

phases_df = pd.DataFrame.from_records(
    [
        {
            "Name": item.name,
            "EE%": item.embodied_energy_percentage,
            EE_HEADER: item.embodied_energy.value,
            "CC%": item.climate_change_percentage,
            CC_HEADER: item.climate_change.value,
        }
        for item in sustainability_summary.phases_summary
    ]
)
phases_df

[6]:

	Name	EE%	EE [MJ]	CC%	CC [kg]
0	Material	31.569457	286.103262	40.055061	28.539686
1	Processes	59.954174	543.344296	52.405168	37.339278
2	Transport	8.476369	76.818449	7.539772	5.372173

[7]:

import plotly.graph_objects as go
from plotly.subplots import make_subplots


def plot_impact(df, title, textinfo="percent+label", hoverinfo="value+name"):
    fig = make_subplots(
        rows=1,
        cols=2,
        specs=[[{"type": "domain"}, {"type": "domain"}]],
        subplot_titles=["Embodied Energy", "Climate Change"],
    )
    fig.add_trace(go.Pie(labels=df["Name"], values=df[EE_HEADER], name=ENERGY_UNIT), 1, 1)
    fig.add_trace(go.Pie(labels=df["Name"], values=df[CC_HEADER], name=MASS_UNIT), 1, 2)
    fig.update_layout(title_text=title, legend=dict(orientation="h"))
    fig.update_traces(textposition="inside", textinfo=textinfo, hoverinfo=hoverinfo)
    fig.show()


plot_impact(phases_df, "BoM sustainability summary - By phase")

The transport phase#

The environmental contribution from the transport phase is summarized in the transport_details property. Results include the individual environmental impact for each transport stage included in the input BoM.

[8]:

sustainability_summary.transport_details

[8]:

[<TransportSummaryResult('Port to airport by truck', EE%=6.809879274093459, CC%=6.440242424179394)>,
 <TransportSummaryResult('Country 1 to country 2 by air', EE%=90.75802098515888, CC%=91.25967099575654)>,
 <TransportSummaryResult('Airport to distributor by truck', EE%=2.432099740747663, CC%=2.3000865800640695)>]

[9]:

DISTANCE_HEADER = f"Distance [{DISTANCE_UNIT}]"

transport_df = pd.DataFrame.from_records(
    [
        {
            "Name": item.name,
            DISTANCE_HEADER: item.distance.value,
            "EE%": item.embodied_energy_percentage,
            EE_HEADER: item.embodied_energy.value,
            "CC%": item.climate_change_percentage,
            CC_HEADER: item.climate_change.value,
        }
        for item in sustainability_summary.transport_details
    ]
)
transport_df

[9]:

	Name	Distance [km]	EE%	EE [MJ]	CC%	CC [kg]
0	Port to airport by truck	350.0	6.809879	5.231244	6.440242	0.345981
1	Country 1 to country 2 by air	1500.0	90.758021	69.718904	91.259671	4.902627
2	Airport to distributor by truck	125.0	2.432100	1.868301	2.300087	0.123565

[10]:

plot_impact(transport_df, "Transport stages - environmental impact")

In some situations, it may be useful to calculate the environmental impact per distance travelled and add the results as new columns in the DataFrame.

[11]:

EE_PER_DISTANCE = f"EE [{ENERGY_UNIT}/{DISTANCE_UNIT}]"
CC_PER_DISTANCE = f"CC [{MASS_UNIT}/{DISTANCE_UNIT}]"
transport_df[EE_PER_DISTANCE] = transport_df.apply(lambda row: row[EE_HEADER] / row[DISTANCE_HEADER], axis=1)
transport_df[CC_PER_DISTANCE] = transport_df.apply(lambda row: row[CC_HEADER] / row[DISTANCE_HEADER], axis=1)
transport_df

[11]:

	Name	Distance [km]	EE%	EE [MJ]	CC%	CC [kg]	EE [MJ/km]	CC [kg/km]
0	Port to airport by truck	350.0	6.809879	5.231244	6.440242	0.345981	0.014946	0.000989
1	Country 1 to country 2 by air	1500.0	90.758021	69.718904	91.259671	4.902627	0.046479	0.003268
2	Airport to distributor by truck	125.0	2.432100	1.868301	2.300087	0.123565	0.014946	0.000989

[12]:

fig = make_subplots(
    rows=1, cols=2, specs=[[{"type": "domain"}, {"type": "domain"}]], subplot_titles=[EE_PER_DISTANCE, CC_PER_DISTANCE]
)
fig.add_trace(
    go.Pie(labels=transport_df["Name"], values=transport_df[EE_PER_DISTANCE], name=f"{ENERGY_UNIT}/{DISTANCE_UNIT}"),
    1,
    1,
)
fig.add_trace(
    go.Pie(labels=transport_df["Name"], values=transport_df[CC_PER_DISTANCE], name=f"{MASS_UNIT}/{DISTANCE_UNIT}"), 1, 2
)
fig.update_layout(
    title_text="Transport stages impact - Relative to distance travelled",
    legend=dict(orientation="h")
)
fig.update_traces(textposition="inside", textinfo="percent+label", hoverinfo="value+name")
fig.show()

The materials phase#

The environmental contribution from the material phase is summarized in the material_details property. The results are aggregated: each item in material_details represents the total environmental impact of a material summed from all its occurrences in the BoM. Listed materials contribute more than 2% of the total impact for the material phase. Materials that do not contribute at least 2% of the total are aggregated under the Other item.

[13]:

sustainability_summary.material_details

[13]:

[<MaterialSummaryResult('beryllium-beralcast191-cast', EE%=41.04868278333434, CC%=54.32870571288889)>,
 <MaterialSummaryResult('stainless-astm-cn-7ms-cast', EE%=40.06955268607085, CC%=31.595335871309544)>,
 <MaterialSummaryResult('steel-1010-annealed', EE%=18.881764530594808, CC%=14.075958415801562)>]

[14]:

materials_df = pd.DataFrame.from_records(
    [
        {
            "Name": item.identity,
            "EE%": item.embodied_energy_percentage,
            EE_HEADER: item.embodied_energy.value,
            "CC%": item.climate_change_percentage,
            CC_HEADER: item.climate_change.value,
            f"Mass before processing [{MASS_UNIT}]": item.mass_before_processing.value,
            f"Mass after processing [{MASS_UNIT}]": item.mass_after_processing.value,
        }
        for item in sustainability_summary.material_details
    ]
)
materials_df

[14]:

	Name	EE%	EE [MJ]	CC%	CC [kg]	Mass before processing [kg]	Mass after processing [kg]
0	beryllium-beralcast191-cast	41.048683	117.441620	54.328706	15.505242	0.02700	0.024
1	stainless-astm-cn-7ms-cast	40.069553	114.640297	31.595336	9.017210	1.54595	1.450
2	steel-1010-annealed	18.881765	54.021344	14.075958	4.017234	2.74574	2.640

[15]:

plot_impact(materials_df, "Aggregated materials impact")

Mass before and mass after secondary processing can help determine if the material mass removed during processing contributes a significant fraction of the impact of the overall material phase.

[16]:

fig = go.Figure(
    data=[
        go.Bar(
            name="Mass before secondary processing",
            x=materials_df["Name"],
            y=materials_df[f"Mass before processing [{MASS_UNIT}]"],
        ),
        go.Bar(
            name="Mass after secondary processing",
            x=materials_df["Name"],
            y=materials_df[f"Mass after processing [{MASS_UNIT}]"],
        ),
    ],
    layout=go.Layout(
        xaxis=go.layout.XAxis(title="Materials"),
        yaxis=go.layout.YAxis(title=f"Mass [{MASS_UNIT}]"),
        legend=dict(orientation="h")
    ),
)
fig.show()

The material processing phase#

The environmental contributions from primary and secondary processing (applied to materials), and joining and finishing processes (applied to parts) are summarized in the primary_processes_details, secondary_processes_details, and joining_and_finishing_processes_details properties respectively. Each of these properties lists the unique process-material pairs (for primary and secondary processing) or individual processes (for joining and finishing) that contribute at least 5% of the total impact for that category of process. The percentage contributions are relative to the total contribution of all processes from the same category. Processes that do not meet the contribution threshold are aggregated under the Other item, with the material set to None.

Primary processing#

[17]:

sustainability_summary.primary_processes_details

[17]:

[<ProcessSummaryResult(process='Primary processing, Casting', material='stainless-astm-cn-7ms-cast', EE%=55.92433171310843, CC%=56.01916015300561)>,
 <ProcessSummaryResult(process='Primary processing, Casting', material='steel-1010-annealed', EE%=39.09683391559776, CC%=39.276605734704205)>,
 <ProcessSummaryResult(process='Other', material='None', EE%=4.97883437129381, CC%=4.704234112290182)>]

[18]:

primary_process_df = pd.DataFrame.from_records(
    [
        {
            "Process name": item.process_name,
            "Material name": item.material_identity,
            "EE%": item.embodied_energy_percentage,
            EE_HEADER: item.embodied_energy.value,
            "CC%": item.climate_change_percentage,
            CC_HEADER: item.climate_change.value,
        }
        for item in sustainability_summary.primary_processes_details
    ]
)
primary_process_df

[18]:

	Process name	Material name	EE%	EE [MJ]	CC%	CC [kg]
0	Primary processing, Casting	stainless-astm-cn-7ms-cast	55.924332	299.624263	56.019160	20.629294
1	Primary processing, Casting	steel-1010-annealed	39.096834	209.468038	39.276606	14.463777
2	Other	None	4.978834	26.674965	4.704234	1.732354

Add a Name to each item that represents the process-material pair name.

[19]:

primary_process_df["Name"] = primary_process_df.apply(
    lambda row: f"{row['Process name']} - {row['Material name']}", axis=1
)
plot_impact(
    primary_process_df, "Aggregated primary processes impact", textinfo="percent", hoverinfo="value+name+label"
)

Secondary processing#

[20]:

sustainability_summary.secondary_processes_details

[20]:

[<ProcessSummaryResult(process='Secondary processing, Grinding', material='steel-1010-annealed', EE%=44.943044109852245, CC%=44.943044109852245)>,
 <ProcessSummaryResult(process='Secondary processing, Machining, coarse', material='stainless-astm-cn-7ms-cast', EE%=31.06413363087522, CC%=31.064133630875222)>,
 <ProcessSummaryResult(process='Machining, fine', material='steel-1010-annealed', EE%=15.162837591750153, CC%=15.16283759175015)>,
 <ProcessSummaryResult(process='Other', material='None', EE%=8.82998466752239, CC%=8.829984667522393)>]

[21]:

secondary_process_df = pd.DataFrame.from_records(
    [
        {
            "Process name": item.process_name,
            "Material name": item.material_identity,
            "EE%": item.embodied_energy_percentage,
            EE_HEADER: item.embodied_energy.value,
            "CC%": item.climate_change_percentage,
            CC_HEADER: item.climate_change.value,
        }
        for item in sustainability_summary.secondary_processes_details
    ]
)
secondary_process_df

[21]:

	Process name	Material name	EE%	EE [MJ]	CC%	CC [kg]
0	Secondary processing, Grinding	steel-1010-annealed	44.943044	1.959488	44.943044	0.127255
1	Secondary processing, Machining, coarse	stainless-astm-cn-7ms-cast	31.064134	1.354376	31.064134	0.087957
2	Machining, fine	steel-1010-annealed	15.162838	0.661090	15.162838	0.042933
3	Other	None	8.829985	0.384982	8.829985	0.025002

Add a Name to each item that represents the process-material pair name.

[22]:

secondary_process_df["Name"] = secondary_process_df.apply(
    lambda row: f"{row['Process name']} - {row['Material name']}", axis=1
)
plot_impact(
    secondary_process_df, "Aggregated secondary processes impact", textinfo="percent", hoverinfo="value+name+label"
)

Joining and finishing#

Joining and finishing processes apply to parts or assemblies and therefore don’t include a material identity.

[23]:

sustainability_summary.joining_and_finishing_processes_details

[23]:

[<ProcessSummaryResult(process='Joining and finishing, Welding, electric', material='None', EE%=100.0, CC%=100.0)>]

[24]:

joining_and_finishing_processes_df = pd.DataFrame.from_records(
    [
        {
            "Name": item.process_name,
            "EE%": item.embodied_energy_percentage,
            EE_HEADER: item.embodied_energy.value,
            "CC%": item.climate_change_percentage,
            CC_HEADER: item.climate_change.value,
        }
        for item in sustainability_summary.joining_and_finishing_processes_details
    ]
)
joining_and_finishing_processes_df

[24]:

	Name	EE%	EE [MJ]	CC%	CC [kg]
0	Joining and finishing, Welding, electric	100.0	3.217094	100.0	0.230705

[25]:

plot_impact(
    joining_and_finishing_processes_df, "Aggregated secondary processes impact",
    textinfo="percent", hoverinfo="value+name+label"
)

Hierarchical view#

Finally, aggregate the sustainability summary results into a single DataFrame and present it in a hierarchical chart. This highlights the largest contributors at each level. In this example, two levels are defined: first the phase and then the contributors in the phase.

First, rename the processes Other rows, so that they remain distinguishable after all processes have been grouped under a general Processes.

Use assign to add a parent column to each DataFrame being concatenated. The join argument value inner specifies that only columns common to all dataframes are kept in the result.

[26]:

primary_process_df.loc[(primary_process_df["Name"] == "Other - None"), "Name"] = "Other primary processes"
secondary_process_df.loc[(secondary_process_df["Name"] == "Other - None"), "Name"] = "Other secondary processes"
joining_and_finishing_processes_df.loc[
    (joining_and_finishing_processes_df["Name"] == "Other - None"), "Name"] = "Other joining and finishing processes"

summary_df = pd.concat(
    [
        phases_df.assign(Parent=""),
        transport_df.assign(Parent="Transport"),
        materials_df.assign(Parent="Material"),
        primary_process_df.assign(Parent="Processes"),
        secondary_process_df.assign(Parent="Processes"),
        joining_and_finishing_processes_df.assign(Parent="Processes"),
    ],
    join="inner",
)
summary_df

# A sunburst chart presents hierarchical data radially.

fig = go.Figure(
    go.Sunburst(
        labels=summary_df["Name"],
        parents=summary_df["Parent"],
        values=summary_df[EE_HEADER],
        branchvalues="total",
    ),
    layout_title_text=f"Embodied Energy [{ENERGY_UNIT}]",
)
fig.show()

# An icicle chart presents hierarchical data as rectangular sectors.

fig = go.Figure(
    go.Icicle(
        labels=summary_df["Name"],
        parents=summary_df["Parent"],
        values=summary_df[EE_HEADER],
        branchvalues="total",
    ),
    layout_title_text=f"Embodied Energy [{ENERGY_UNIT}]",
)
fig.show()