Summary of wastewater treatment datasets

This file describes summary of datasets available in aqua_fetch package for wastewater treatment. The datasets are divided into following categories:

1. Adsorption

2. Photocatalysis

3. Membrane processes

4. Sonolysis

import os
import site

if __name__ == '__main__':
    wd_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath('__file__')))))
    # wd_dir = os.path.dirname(os.path.dirname(os.path.realpath('__file__')))
    #wd_dir = os.path.dirname(os.path.realpath('__file__'))  # for debugging
    print(wd_dir)
    site.addsitedir(wd_dir)

from aqua_fetch import (
    ec_removal_biochar,
    cr_removal,
    po4_removal_biochar,
    heavy_metal_removal,
    industrial_dye_removal,
    heavy_metal_removal_Shen,
    P_recovery,
    N_recovery,
    As_recovery,
    mg_degradation,
    dye_removal,
    dichlorophenoxyacetic_acid_removal,
    pms_removal,
    micropollutant_removal_osmosis,
    ion_transport_via_reverse_osmosis,
    cyanobacteria_disinfection
)

/home/docs/checkouts/readthedocs.org/user_builds/aquafetch/checkouts/latest

Adsorption

data, _ = ec_removal_biochar()
print(data.shape)

(3757, 29)

print(data.columns)

Index(['pyrolysis_temperature', 'pyrolysis_time', 'C', 'H', 'O', 'N',
       '(O+N)/C', 'ash', 'H/C', 'O/C', 'N/C', 'surface_area', 'pore_volume',
       'average_pore_size', 'adsorption_time', 'initial_concentration',
       'solution_ph', 'rpm', 'volume', 'adsorbent_dosage',
       'adsorption_temperature', 'ion_concentration', 'humic_acid',
       'adsorbent', 'pollutant', 'wastewater_type', 'adsorption_type',
       'capacity', 'final_concentration'],
      dtype='object')

data, _ = cr_removal()
print(data.shape)

(219, 20)

print(data.columns)

Index(['adsorbent', 'NaOH_conc_M', 'surface_area', 'pore_volume', 'C_%',
       'Al_%', 'Nb_%', 'O_%', 'Na_%', 'pore_size', 'adsorption_time',
       'initial_conc', 'loading_g/L', 'volume_l', 'loading_g', 'solution_ph',
       'cycle_number', 'final_conc', 'adsorption_capacity',
       'removal_efficiency'],
      dtype='object')

data, _ = po4_removal_biochar()
print(data.shape)

(5014, 33)

print(data.columns)

Index(['adsorbent', 'feedstock', 'activation', 'pyrolysis_temp',
       'heating_rate', 'pyrolysis_time', 'C_%', 'H_%', 'O_%', 'N_%', 'S_%',
       'Ca_%', 'ash', 'H/C', 'O/C', 'N/C', '(O+N/C)', 'surface_area',
       'pore_volume', 'avg_pore_size', 'adsorption_time_min', 'Ci_ppm',
       'solution_pH', 'rpm', 'volume_l', 'loading_g', 'loading_g/L',
       'adsorption_temp', 'ion_concentration_mM', 'ion_type', 'final_conf',
       'qe', 'efficiency'],
      dtype='object')

data, _ = heavy_metal_removal()
print(data.shape)

(219, 18)

print(data.columns)

Index(['adsorbent', 'NaOH_conc_M', 'surface_area', 'pore_volume', 'C_%',
       'Al_%', 'Nb_%', 'O_%', 'Na_%', 'pore_size', 'adsorption_time',
       'initial_conc', 'loading_g/L', 'volume_l', 'loading_g', 'solution_ph',
       'cycle_number', 'final_conc'],
      dtype='object')

data, _ = industrial_dye_removal()
print(data.shape)

(680, 29)

print(data.columns)

Index(['adsorbent', 'calcination_temperature', 'calcination_time_min', 'C_%',
       'H_%', 'O_%', 'N_%', 'ash', 'H/C', 'O/C', 'N/C', 'surface_area',
       'pore_volume', 'average_pore_size', 'dye', 'adsorption_time_min',
       'initial_concentration', 'solution_ph', 'rpm', 'volume_l',
       'loading_g/l', 'adsorption_temperature', 'ion_concentration_M',
       'humic_acid', 'wastewater_type', 'adsorption_type',
       'final_concentration', 'qe', 'adsorbent_loading'],
      dtype='object')

data, _ = heavy_metal_removal_Shen()
print(data.shape)

(353, 18)

print(data.columns)

Index(['heavy_metal', 'hm_label', 'ph_bichar', 'C_%', '(O+N)/C', 'O/C', 'H/C',
       'ash', 'PS', 'SA', 'CEC', 'temperature', 'solution_ph', 'C0', 'χ', 'r',
       'Ncharge', 'n'],
      dtype='object')

data, _ = P_recovery()
print(data.shape)

(504, 8)

print(data.columns)

Index(['stir_rpm', 'time_min', 'temperature_C', 'pH', 'N:P', 'Mg:P',
       'P_initial_mgl', 'P_recovery_%'],
      dtype='object')

data, _ = N_recovery()
print(data.shape)

(210, 8)

print(data.columns)

Index(['stir_rpm', 'time_min', 'temperature_C', 'pH', 'N:P', 'Mg:N',
       'P_initial_mgl', 'N_recovery_%'],
      dtype='object')

data, _ = As_recovery()
print(data.shape)

(684, 12)

print(data.columns)

Index(['material', 'biochar_modification', 'biochar_type', 'BET_surface_area',
       'pore_volume', 'solution_pH', 'reactor_temperature',
       'initial_As_concentration_mg_L', 'adsorbent_dosage',
       'equilibrium_reaction_time_h', 'pyrolysis_temperature', 'As_mg_g'],
      dtype='object')

Photocatalysis

data, _ = mg_degradation()
print(data.shape)

(1200, 14)

print(data.columns)

Index(['surface_area', 'pore_volume', 'catalyst_loading_g/l',
       'Light_intensity (W)', 'time_min', 'solution_pH', 'HA (mg/L)',
       'ini_conc_mg/l', 'final_conc_mg/l', 'catalyst_type', 'anions',
       'Efficiency (%)', 'k_first', 'k_2nd'],
      dtype='object')

data, _ = dye_removal()
print(data.shape)

(1527, 38)

print(data.columns)

Index(['catalyst', 'hydrothermal_synthesis_time_min', 'energy_band_gap_eV',
       'C_%', 'O_%', 'Fe_%', 'Al_%', 'Ni_%', 'Mo_%', 'S_%', 'Bi', 'Ag', 'Pd',
       'Pt', 'surface_area_m2/g', 'pore_volume_cm3/g', 'pore_size_nm',
       'volume_l', 'loading_g', 'light_intensity_watt', 'light_source_dist_cm',
       'time_m', 'dye', 'log_kw', 'hydrogen_bonding_accep_count',
       'hydrogen_bonding_donor_count', 'solubility_g/l', 'molecular_wt_g/M',
       'pka1', 'pka2', 'dye_conc_mg/l', 'solution_ph', 'ha_mg/l', 'anions',
       'final_concentration_mg/l', 'k_1st', 'k_2nd', 'efficiency_%'],
      dtype='object')

data, _ = dichlorophenoxyacetic_acid_removal()
print(data.shape)

(1044, 16)

print(data.columns)

Index(['catalyst', 'surface_area', 'pore_volume', 'energy_band_gap_eV', 'Au_%',
       'Bi_%', 'Fe_%', 'O_%', 'catalyst_loading_g/l', 'light_intensity_watt',
       'time_min', 'solution_ph', 'anions', 'ini_conc_mg/l', 'final_conc_mg/l',
       'efficiency_%'],
      dtype='object')

data, _ = pms_removal()
print(data.shape)

(2078, 25)

print(data.columns)

Index(['time_min', 'catalyst_type', 'magnetization_Ms_emu/g',
       'energy_band_gap_eV', 'calcination_temp_C', 'min_calcination_time',
       'surface_area', 'pore_size', 'pollutant', 'poll_mol_formula',
       'pms_concentration_g/l', 'light_intensity_watt', 'light_type',
       'catalyst_dosage_g/l', 'ini_conc_ppm', 'solution_ph', 'H2O2_Conc_ppm',
       'volume_ml', 'stirring_speed_rpm', 'radical_scavenger',
       'inorganic anions', 'water_type', 'cycle_num', 'final_conc_ppm',
       'removal_efficiency_%'],
      dtype='object')

Membrane processes

# data, _ = micropollutant_removal_osmosis()
# print(data.shape)

# # %%
# data, _ = ion_transport_via_reverse_osmosis()
# print(data.shape)

data, _ = cyanobacteria_disinfection()
print(data.shape)

(314, 146)

print(data.columns)

Index(['Time (min)', 'Cyanobacterial cell count',
       'wastewater concentration (Ci)', 'Sonicator power density',
       'Concentration of H2O2', 'Volume (mL)', 'Solution pH',
       'final count/0.5 mL', 'final count/mL', 'Particles/mL',
       ...
       'Transparency Max', 'Volume (ABD) Mean', 'Volume (ABD) Min',
       'Volume (ABD) Max', 'Volume (ESD) Mean', 'Volume (ESD) Min',
       'Volume (ESD) Max', 'Width Mean', 'Width Min', 'Width Max'],
      dtype='object', length=146)