2 Components Data¶

In [1]:

import numpy as np
import time

from ml4pd import components
import dist_utils
from ml4pd.aspen_units import Distillation
from ml4pd.streams import MaterialStream

input_molecules = dist_utils.get_mol_labels()
components.set_components(input_molecules['name'].to_list())

raw_data = dist_utils.get_benchmark_data(comp=2, date='220505')
data = dist_utils.add_name_columns(raw_data, input_molecules[['name', 'mol']])
data = dist_utils.add_flow_perc(data)
data = dist_utils.rename_flowrate_columns(data)

import numpy as np import time from ml4pd import components import dist_utils from ml4pd.aspen_units import Distillation from ml4pd.streams import MaterialStream input_molecules = dist_utils.get_mol_labels() components.set_components(input_molecules['name'].to_list()) raw_data = dist_utils.get_benchmark_data(comp=2, date='220505') data = dist_utils.add_name_columns(raw_data, input_molecules[['name', 'mol']]) data = dist_utils.add_flow_perc(data) data = dist_utils.rename_flowrate_columns(data)

Benchmark Data Info¶

In [2]:

input_molecules.T

input_molecules.T

Out[2]:

	0	1	2	3	4	5	6	7	8	9	10	11	12	13
name	acetone	2-butanone	3-pentanone	3-hexanone	4-heptanone	4-octanone	5-nonanone	5-decanone	6-undecanone	6-dodecanone	6-tridecanone	7-tetradecanone	8-pentadecanone	water
mol	C3-ONE	C4-ONE	C5-ONE	C6-ONE	C7-ONE	C8-ONE	C9-ONE	C10-ONE	C11-ONE	C12-ONE	C13-ONE	C14-ONE	C15-ONE	H2O
smiles	CC(=O)C	CCC(=O)C	CCC(=O)CC	CCCC(=O)CC	CCCC(=O)CCC	CCCCC(=O)CCC	CCCCC(=O)CCCC	CCCCCC(=O)CCCC	CCCCCC(=O)CCCCC	CCCCCCC(=O)CCCCC	CCCCCCCC(=O)CCCCC	CCCCCCCC(=O)CCCCCC	CCCCCCCC(=O)CCCCCCC	O

In [3]:

raw_data.describe().T

raw_data.describe().T

Out[3]:

	count	mean	std	min	25%	50%	75%	max
temp_dist	12096.0	173.929239	105.194330	1.485171e+00	9.154284e+01	154.096750	250.946966	407.946556
temp_bott	12096.0	183.039972	104.974993	1.027061e+01	9.793269e+01	160.147656	259.470192	409.128699
temp_feed	12096.0	178.873940	105.192634	9.776815e+00	9.730620e+01	158.983986	256.880189	407.988078
duty_condensor	12096.0	-123319.488293	113010.590464	-4.928788e+05	-2.034318e+05	-72794.282000	-30670.924750	-4.151111
duty_reboiler	12096.0	123500.097689	113015.009374	-1.773669e-01	3.077638e+04	72978.959950	203765.868000	492886.176000
flowrate_bott_A	12096.0	0.065339	0.098728	0.000000e+00	3.100000e-07	0.014060	0.094588	0.438902
flowrate_bott_B	12096.0	0.482434	0.219551	2.692827e-10	3.285993e-01	0.514865	0.637710	0.975220
flowrate_feed_A	12096.0	0.242193	0.141914	5.000000e-02	7.692308e-02	0.261905	0.344828	0.454545
flowrate_feed_B	12096.0	0.757807	0.141914	5.454545e-01	6.551724e-01	0.738095	0.923077	0.950000
flowrate_dist_A	12096.0	0.176902	0.131861	2.253969e-04	5.282506e-02	0.161820	0.278296	0.538822
flowrate_dist_B	12096.0	0.275325	0.232881	1.747663e-04	5.904812e-02	0.228558	0.430709	0.949990
ratio_reflux	12096.0	10.000000	6.708481	1.000000e+00	5.500000e+00	10.000000	14.500000	19.000000
ratio_boilup	12096.0	10.000000	6.708481	1.000000e+00	5.500000e+00	10.000000	14.500000	19.000000
pressure_atm	12096.0	8.812709	12.348443	1.000000e-01	5.270747e-01	2.575419	10.861054	30.000000
feed_pressure	12096.0	8.812710	12.348443	1.000010e-01	5.270757e-01	2.575420	10.861055	30.000001
vapor_fraction	12096.0	0.000000	0.000000	0.000000e+00	0.000000e+00	0.000000	0.000000	0.000000
no_stages	12096.0	22.000000	14.697546	4.000000e+00	4.000000e+00	22.000000	40.000000	40.000000
feed_stage	12096.0	5.000000	0.000000	5.000000e+00	5.000000e+00	5.000000	5.000000	5.000000

Time Benchmark¶

In [4]:

times = []
for i in range(0, 10):
    start_time = time.time()
    feed_stream = MaterialStream(stream_type="feed")(
        vapor_fraction=data['vapor_fraction'].to_list(),
        pressure=data['feed_pressure'].to_list(),
        molecules=dist_utils.get_name_columns(data).to_dict('list'),
        flowrates=dist_utils.get_flowrate_columns(data).to_dict('list'),
    )
    
    dist_col = Distillation(
        no_stages = data['no_stages'].to_list(),
        feed_stage =  data['feed_stage'].to_list(),
        pressure = data['pressure_atm'].to_list(),
        reflux_ratio = data['ratio_reflux'].to_list(),
        boilup_ratio = data['ratio_boilup'].to_list(),
        verbose=False
    )

    bott_stream, dist_stream = dist_col(feed_stream)
    
    times.append(time.time() - start_time)

ordered_data = dist_utils.sort_targets_by_weight(data, feed_stream._mw_idx)

average = np.mean(times).round(2)
std = np.std(times).round(2)

print(f"{len(data)} data pts take {average} +/- {std} seconds to predict.")

times = [] for i in range(0, 10): start_time = time.time() feed_stream = MaterialStream(stream_type="feed")( vapor_fraction=data['vapor_fraction'].to_list(), pressure=data['feed_pressure'].to_list(), molecules=dist_utils.get_name_columns(data).to_dict('list'), flowrates=dist_utils.get_flowrate_columns(data).to_dict('list'), ) dist_col = Distillation( no_stages = data['no_stages'].to_list(), feed_stage = data['feed_stage'].to_list(), pressure = data['pressure_atm'].to_list(), reflux_ratio = data['ratio_reflux'].to_list(), boilup_ratio = data['ratio_boilup'].to_list(), verbose=False ) bott_stream, dist_stream = dist_col(feed_stream) times.append(time.time() - start_time) ordered_data = dist_utils.sort_targets_by_weight(data, feed_stream._mw_idx) average = np.mean(times).round(2) std = np.std(times).round(2) print(f"{len(data)} data pts take {average} +/- {std} seconds to predict.")

12096 data pts take 4.32 +/- 0.52 seconds to predict.

Classifier Benchmark¶

In [5]:

ok_idx = np.array(ordered_data[ordered_data['Status'] == 'OK'].index)
dist_utils.plot_confusion_matrix(ordered_data, dist_col.status)

ok_idx = np.array(ordered_data[ordered_data['Status'] == 'OK'].index) dist_utils.plot_confusion_matrix(ordered_data, dist_col.status)

Flowrates Benchmark¶

In [6]:

dist_utils.plot_flow(
    y_true=ordered_data.loc[ok_idx],
    y_pred=bott_stream.flow.loc[ok_idx]
)

dist_utils.plot_flow( y_true=ordered_data.loc[ok_idx], y_pred=bott_stream.flow.loc[ok_idx] )

Duty Benchmark¶

In [7]:

dist_utils.plot_duty(ordered_data, dist_col, ok_idx)

dist_utils.plot_duty(ordered_data, dist_col, ok_idx)

Temperature Benchmark¶

In [8]:

dist_utils.plot_temp(ordered_data, bott_stream, dist_stream, ok_idx)

dist_utils.plot_temp(ordered_data, bott_stream, dist_stream, ok_idx)

Mean Absolute Errors¶

In [9]:

dist_utils.evaluate_flow(
    ordered_data.loc[ok_idx], 
    bott_stream.flow.loc[ok_idx], 
    metric='mae'
)

dist_utils.evaluate_flow( ordered_data.loc[ok_idx], bott_stream.flow.loc[ok_idx], metric='mae' )

Out[9]:

	flowrate_bott_A	flowrate_bott_B	mean
0	0.01558	0.036244	0.025912

In [10]:

dist_utils.evaluate_duty(
    ordered_data, 
    dist_col, 
    ok_idx, 
    metric='mae'
)

dist_utils.evaluate_duty( ordered_data, dist_col, ok_idx, metric='mae' )

Out[10]:

	condensor_duty	reboiler_duty	mean
0	7781.799472	7681.774765	7731.787119

In [11]:

dist_utils.evaluate_temp(
    ordered_data, 
    bott_stream, 
    dist_stream, 
    ok_idx, 
    metric='mae'
)

dist_utils.evaluate_temp( ordered_data, bott_stream, dist_stream, ok_idx, metric='mae' )

Out[11]:

	bott_temp	dist_temp	mean
0	8.506879	5.823343	7.165111