def test_map_data_use_ambient_pressure2():
# Try out inches of water column and barometric pressure in inHg.
data2 = os.path.join(_data_dir, "data2.csv")
data2_meta = os.path.join(_data_dir, "data2_meta.csv")
m = pyo.ConcreteModel("Data Test Model")
m.time = pyo.Set(initialize=[1, 2, 3])
m.pressure = pyo.Var(m.time, doc="pressure (Pa)", initialize=101325)
m.temperature = pyo.Var(m.time, doc="temperature (K)", initialize=300)
m.volume = pyo.Var(m.time, doc="volume (m^3)", initialize=10)
def retag(tag):
return tag.replace(".junk", "")
df, df_meta = da.read_data(
data2,
data2_meta,
model=m,
rename_mapper=retag,
unit_system="mks",
ambient_pressure="Pamb",
ambient_pressure_unit="inHg",
)
# Check that the unit conversions are okay, same pressures as data1
# differnt units, so the data read in should be the same
assert df["P"]["1901-3-3 10:00"] == pytest.approx(96526.6, rel=1e-2)
assert df["P"]["1901-3-3 12:00"] == pytest.approx(195886, rel=1e-2)
def test_map_data_use_ambient_pressure():
# Try out PSIG and barometric pressure in PSIA.
data1 = os.path.join(_data_dir, "data1.csv")
data1_meta = os.path.join(_data_dir, "data1_meta.csv")
m = pyo.ConcreteModel("Data Test Model")
m.time = pyo.Set(initialize=[1, 2, 3])
m.pressure = pyo.Var(m.time, doc="pressure (Pa)", initialize=101325)
m.temperature = pyo.Var(m.time, doc="temperature (K)", initialize=300)
m.volume = pyo.Var(m.time, doc="volume (m^3)", initialize=10)
def retag(tag):
return tag.replace(".junk", "")
df, df_meta = da.read_data(
data1,
data1_meta,
model=m,
rename_mapper=retag,
unit_system="mks",
ambient_pressure="Pamb",
ambient_pressure_unit="psi",
)
# Check that the unit conversions are okay
assert df["P"]["1901-3-3 12:00"] == pytest.approx(195886, rel=1e-4)
def test_map_data():
data1 = os.path.join(_data_dir, "data1.csv")
data1_meta = os.path.join(_data_dir, "data1_meta.csv")
m = pyo.ConcreteModel("Data Test Model")
m.time = pyo.Set(initialize=[1, 2, 3])
m.pressure = pyo.Var(m.time, doc="pressure (Pa)", initialize=101325)
m.temperature = pyo.Var(m.time, doc="temperature (K)", initialize=300)
m.volume = pyo.Var(m.time, doc="volume (m^3)", initialize=10)
def retag(tag):
return tag.replace(".junk", "")
df, df_meta = da.read_data(data1,
data1_meta,
model=m,
rename_mapper=retag,
unit_system="mks")
# Check for expected columns in data and meta data
assert "T" in df
assert "P" in df
assert "V" in df
assert "T" in df_meta
assert "P" in df_meta
assert "V" in df_meta
# Check that the unit strings updated after conversion
assert df_meta["T"]["units"] == "kelvin"
# this next unit is Pa
assert df_meta["P"]["units"] == "kilogram / meter / second ** 2"
assert df_meta["V"]["units"] == "meter ** 3"
# Check that the unit conversions are okay
assert df["T"]["1901-3-3 12:00"] == pytest.approx(300, rel=1e-4)
assert df["P"]["1901-3-3 12:00"] == pytest.approx(200000, rel=1e-4)
assert df["V"]["1901-3-3 12:00"] == pytest.approx(5.187286689, rel=1e-4)
# Check the mapping of the tags to the model (the 1 key is the time indexed
# from the model, because the reference is for a time-indexed variable)
assert pyo.value(df_meta["T"]["reference"][1]) == pytest.approx(300,
rel=1e-4)
assert pyo.value(df_meta["P"]["reference"][1]) == pytest.approx(101325,
rel=1e-4)
assert pyo.value(df_meta["V"]["reference"][1]) == pytest.approx(10,
rel=1e-4)
def read_data(model,
metadata="data/meta_data.csv",
data="data/data_small.csv"):
_log.info("Reading data...")
df, df_meta = mdata.read_data(
model=model,
csv_file=data,
csv_file_metadata=metadata,
ambient_pressure="BAROMETRIC-PRESS",
ambient_pressure_unit="inHg",
unit_system="mks",
)
# drop tags
# FWH1 Shell Pressure
df.drop("S105", axis=1, inplace=True)
del df_meta["S105"]
# FWH2 Extraction Temperature
#df.drop("S302", axis=1, inplace=True)
#del df_meta["S302"]
# FWH2 Extraction Temperature
#df.drop("S202", axis=1, inplace=True)
#del df_meta["S202"]
# for now drop attemperator flow
#df.drop("FW463", axis=1, inplace=True)
#del df_meta["FW463"]
df.drop("BG3503", axis=1, inplace=True)
del df_meta["BG3503"]
df.drop("BG3505", axis=1, inplace=True)
del df_meta["BG3505"]
# Second hot reheat pressure measure (don't double weight)
df.drop("S640", axis=1, inplace=True)
del df_meta["S640"]
df.drop("S202", axis=1, inplace=True)
del df_meta["S202"]
# want the data for number of mills in service, but don't want it in
# objective and such
del df_meta["num_mills"]
# Calculated tags can go here
# keep paper mill and attemperator flow nonnegative.
df["5C0002FLOW"] = np.maximum(df["5C0002FLOW"], 0)
#df["FW463"] += 0.265
#df["FW463"] = np.maximum(df["FW463"], 0)
# Average the O2 readings
df["BG3506"] = (df["BG3506A"] + df["BG3506B"]) / 2.0
df.drop("BG3506A", axis=1, inplace=True)
df.drop("BG3506B", axis=1, inplace=True)
del df_meta["BG3506A"]
del df_meta["BG3506B"]
# Add the two main aux cooling water volumetric flows to get total mol flow
df["CWCOND_MOL"] = (df["CW119"] + df["CW129"]) * 997000 / 18.015
df["CWAUXCOND_MOL"] = df["CW317-COND"] * 997000 / 18.015
df["PAPERFLOW_MOL"] = np.sqrt(
df["5C0002FLOW"]) * 24.29 * 0.4816 * 1000 / 2.2046 / 3600 / 0.018015
df["gross_mw"] = df["1JT66801S"] * 1e-6
df_meta["CWCOND_MOL"] = {
"description": "Condenser cooling water mole flow",
"reference_string": "m.fs_main.fs_stc.condenser.inlet_2.flow_mol[:]",
"units": "mol/s",
}
df_meta["CWAUXCOND_MOL"] = {
"description": "Aux condenser cooling water mole flow",
"reference_string":
"m.fs_main.fs_stc.aux_condenser.inlet_2.flow_mol[:]",
"units": "mol/s",
}
df_meta["PAPERFLOW_MOL"] = {
"description": "Paper mill mole flow",
"reference_string":
"m.fs_main.fs_stc.turb.hp_split[14].outlet_3.flow_mol[:]",
"units": "mol/s",
}
df_meta["gross_mw"] = {
"description": "Gross power in MW",
"reference_string": None,
"units": "MW",
}
df_meta["BG3506"] = {
"description": "Dry Flue gas O2 fraction",
"reference_string": "m.fs_main.fs_blr.aBoiler.fluegas_o2_pct_dry[:]",
"units": "None",
}
mdata.update_metadata_model_references(model, df_meta)
# bin the data and calculate the standard deviation
mdata.bin_data(df,
bin_by="gross_mw",
bin_no="bin_number",
bin_nom="bin_nominal_mw",
bin_size=5,
min_value=85,
max_value=235)
bin_stdev = mdata.bin_stdev(df, bin_no="bin_number")
# drop data from bins where there isn't enough data to calculate stdev
idx = df.index[~df["bin_number"].isin(bin_stdev)]
df.drop(idx, axis=0, inplace=True)
_log.info("Done reading data")
return df, df_meta, bin_stdev