1373 lines
47 KiB
Python
1373 lines
47 KiB
Python
|
|
|
|
import pandas as pd
|
|
import numpy as np
|
|
|
|
base_dir = "data/jrc-idees-2015"
|
|
|
|
# year for wich data is retrieved
|
|
year = 2015
|
|
year = year-2016
|
|
|
|
conv_factor=11.630 #ktoe/kton -> MWh/ton
|
|
|
|
country = 'EU28'
|
|
|
|
|
|
sub_sheet_name_dict = { 'Iron and steel':'ISI',
|
|
'Chemicals Industry':'CHI',
|
|
'Non-metallic mineral products': 'NMM',
|
|
'Pulp, paper and printing': 'PPA',
|
|
'Food, beverages and tobacco': 'FBT',
|
|
'Non Ferrous Metals' : 'NFM',
|
|
'Transport Equipment': 'TRE',
|
|
'Machinery Equipment': 'MAE',
|
|
'Textiles and leather':'TEL',
|
|
'Wood and wood products': 'WWP',
|
|
'Other Industrial Sectors': 'OIS'}
|
|
|
|
index = ['elec','biomass','methane','hydrogen','heat','naphtha','process emission']
|
|
|
|
df = pd.DataFrame(index=index)
|
|
|
|
|
|
## Iron and steel
|
|
#
|
|
#> There are two different approaches to produce iron and steel: i.e., integrated steelworks and electric arc.
|
|
#
|
|
#> Electric arc approach has higher efficiency and relies more on electricity.
|
|
#
|
|
#> We assume that integrated steelworks will be replaced by electric arc entirely.
|
|
|
|
sector = 'Iron and steel'
|
|
|
|
# read the input sheets
|
|
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
|
|
index_col=0,header=0,squeeze=True) # the summary sheet
|
|
|
|
excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
|
|
index_col=0,header=0,squeeze=True) # the final energy consumption sheet
|
|
|
|
excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
|
|
index_col=0,header=0,squeeze=True) # the used energy sheet
|
|
|
|
excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
|
|
index_col=0,header=0,squeeze=True) # the emission sheet
|
|
|
|
### Electric arc
|
|
|
|
sector = 'Electric arc'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[51:57,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec',sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat',sector] += s_fec['Low enthalpy heat']
|
|
|
|
#### Steel: Smelters
|
|
|
|
subsector = 'Steel: Smelters'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[61:67,year]
|
|
|
|
s_ued = excel_ued.iloc[61:67,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
# Efficiency changes due to transforming all the smelters into methane
|
|
eff_met=s_ued['Natural gas (incl. biogas)']/s_fec['Natural gas (incl. biogas)']
|
|
|
|
df.loc['methane', sector] += s_ued[subsector]/eff_met
|
|
|
|
#### Steel: Electric arc
|
|
|
|
subsector = 'Steel: Electric arc'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[67:68,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
# only electricity
|
|
df.loc['elec',sector] += s_fec[subsector]
|
|
|
|
#### Steel: Furnaces, Refining and Rolling
|
|
#> assume fully electrified
|
|
#
|
|
#> other processes are scaled by the used energy
|
|
|
|
subsector = 'Steel: Furnaces, Refining and Rolling'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[68:75,year]
|
|
|
|
s_ued = excel_ued.iloc[68:75,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
# this process can be electrified
|
|
eff = s_ued['Steel: Furnaces, Refining and Rolling - Electric']/s_fec['Steel: Furnaces, Refining and Rolling - Electric']
|
|
|
|
df.loc['elec',sector] += s_ued[subsector]/eff
|
|
|
|
#### Steel: Products finishing
|
|
#> assume fully electrified
|
|
|
|
subsector = 'Steel: Products finishing'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[75:92,year]
|
|
|
|
s_ued = excel_ued.iloc[75:92,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
# this process can be electrified
|
|
eff = s_ued['Steel: Products finishing - Electric']/s_fec['Steel: Products finishing - Electric']
|
|
|
|
df.loc['elec',sector] += s_ued[subsector]/eff
|
|
|
|
#### Process emissions (per physical output)
|
|
|
|
s_emi = excel_emi.iloc[51:93,year]
|
|
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = excel_out.iloc[7:8,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
df.loc['process emission',sector] = s_emi['Process emissions']/s_out[sector] # unit tCO2/t material
|
|
|
|
# final energy consumption per t
|
|
df.loc[['elec','heat','methane'],sector] = df.loc[['elec','heat','methane'],sector]*conv_factor/s_out[sector] # unit MWh/t material
|
|
|
|
|
|
|
|
## Integrated steelworks is converted to Electric arc
|
|
#
|
|
#> Electric arc uses scrap metal and Direct Reduced Iron
|
|
#
|
|
#> We assume that when substituting Integrated Steelworks by Electric arc furnaces.
|
|
#> 50% of Integrated steelworks is substituted by scrap metal + electric furnaces
|
|
#> 50% of Integrated steelworks is substituted by Direct Reduce Iron (with Hydrogen) + electric furnaces
|
|
|
|
df['Integrated steelworks']=df['Electric arc']
|
|
|
|
# adding the Hydrogen necessary for the Direct Reduction of Iron. consumption 1.7 MWh H2 /ton steel
|
|
#(0.5 becuase only half of the steel requires DRI, the rest is scrap metal)
|
|
df.loc['hydrogen', 'Integrated steelworks'] =1.7 * 0.5
|
|
|
|
|
|
## Chemicals Industry
|
|
|
|
sector = 'Chemicals Industry'
|
|
|
|
# read the input sheets
|
|
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
|
|
index_col=0,header=0,squeeze=True) # the summary sheet
|
|
|
|
excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
|
|
index_col=0,header=0,squeeze=True) # the final energy consumption sheet
|
|
|
|
excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
|
|
index_col=0,header=0,squeeze=True) # the used energy sheet
|
|
|
|
excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
|
|
index_col=0,header=0,squeeze=True) # the emission sheet
|
|
|
|
### Basic chemicals
|
|
|
|
sector = 'Basic chemicals'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[3:9,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec',sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat',sector] += s_fec['Low enthalpy heat']
|
|
|
|
#### Chemicals: Feedstock (energy used as raw material)
|
|
#> There are Solids, Refinery gas, LPG, Diesel oil, Residual fuel oil, Other liquids, Naphtha, Natural gas for feedstock.
|
|
#
|
|
#> Naphta represents 47%, methane 17%. LPG (18%) solids, refinery gas, diesel oil, residual fuel oils and other liquids are asimilated to Napthta
|
|
#
|
|
#> Following Lechtenbohmer 2016, the 85 TWh/year of methane for the ammonia industry are substited by hydrogen.
|
|
|
|
subsector = 'Chemicals: Feedstock (energy used as raw material)'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[13:22,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
# naphtha
|
|
df.loc['naphtha',sector] += s_fec['Naphtha']
|
|
|
|
# natural gas
|
|
# 85 TWh/year of methane for the ammonia industry are substituted by hydrogen
|
|
df.loc['methane',sector] += s_fec['Natural gas'] - 85000/conv_factor
|
|
df.loc['hydrogen',sector] += 85000/conv_factor
|
|
# 1 ktoe = 11630 MWh
|
|
|
|
# LPG and other feedstock materials are assimilated to naphtha since they will be produced trough Fischer-Tropsh process
|
|
df.loc['naphtha',sector] += (s_fec['Solids'] + s_fec['Refinery gas'] + s_fec['LPG'] + s_fec['Diesel oil']
|
|
+ s_fec['Residual fuel oil'] + s_fec['Other liquids'])
|
|
|
|
#### Chemicals: Steam processing
|
|
#> All the final energy consumption in the Stem processing is converted to biomass.
|
|
#
|
|
#> The current efficiency of biomass is assumed in the conversion.
|
|
|
|
subsector = 'Chemicals: Steam processing'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[22:33,year]
|
|
|
|
s_ued = excel_ued.iloc[22:33,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
# efficiency of biomass
|
|
eff_bio = s_ued['Biomass']/s_fec['Biomass']
|
|
|
|
# replace all fec by biomass
|
|
df.loc['biomass',sector] += s_ued[subsector]/eff_bio
|
|
|
|
#### Chemicals: Furnaces
|
|
#> assume fully electrified
|
|
|
|
subsector = 'Chemicals: Furnaces'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[33:41,year]
|
|
|
|
s_ued = excel_ued.iloc[33:41,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
#efficiency of electrification
|
|
eff_elec = s_ued['Chemicals: Furnaces - Electric']/s_fec['Chemicals: Furnaces - Electric']
|
|
|
|
df.loc['elec',sector] += s_ued[subsector]/eff_elec
|
|
|
|
#### Chemicals: Process cooling
|
|
#> assume fully electrified
|
|
|
|
subsector = 'Chemicals: Process cooling'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[41:55,year]
|
|
|
|
s_ued = excel_ued.iloc[41:55,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
eff_elec = s_ued['Chemicals: Process cooling - Electric']/s_fec['Chemicals: Process cooling - Electric']
|
|
|
|
df.loc['elec',sector] += s_ued[subsector]/eff_elec
|
|
|
|
#### Chemicals: Generic electric process
|
|
|
|
subsector = 'Chemicals: Generic electric process'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[55:56,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
df.loc['elec',sector] += s_fec[subsector]
|
|
|
|
#### Process emissions
|
|
|
|
s_emi = excel_emi.iloc[3:57,year]
|
|
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = excel_out.iloc[8:9,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
df.loc['process emission',sector] += s_emi['Process emissions']/s_out.values # unit tCO2/t material
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out.values# unit MWh/t material
|
|
# 1 ktoe = 11630 MWh
|
|
|
|
### Other chemicals
|
|
|
|
sector = 'Other chemicals'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[58:64,year]
|
|
|
|
# check the position
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec',sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat',sector] += s_fec['Low enthalpy heat']
|
|
|
|
#### Chemicals: High enthalpy heat processing
|
|
#> assume fully electrified
|
|
|
|
subsector = 'Chemicals: High enthalpy heat processing'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[68:81,year]
|
|
|
|
s_ued = excel_ued.iloc[68:81,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
eff_elec = s_ued['High enthalpy heat processing - Electric (microwave)']/s_fec['High enthalpy heat processing - Electric (microwave)']
|
|
|
|
df.loc['elec',sector] += s_ued[subsector]/eff_elec
|
|
|
|
#### Chemicals: Furnaces
|
|
#> assume fully electrified
|
|
|
|
subsector = 'Chemicals: Furnaces'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[81:89,year]
|
|
|
|
s_ued = excel_ued.iloc[81:89,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
eff_elec = s_ued['Chemicals: Furnaces - Electric']/s_fec['Chemicals: Furnaces - Electric']
|
|
|
|
df.loc['elec',sector] += s_ued[subsector]/eff_elec
|
|
|
|
#### Chemicals: Process cooling
|
|
#> assume fully electrified
|
|
|
|
subsector = 'Chemicals: Process cooling'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[89:103,year]
|
|
|
|
s_ued = excel_ued.iloc[89:103,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
eff = s_ued['Chemicals: Process cooling - Electric']/s_fec['Chemicals: Process cooling - Electric']
|
|
|
|
df.loc['elec',sector] += s_ued[subsector]/eff
|
|
|
|
#### Chemicals: Generic electric process
|
|
|
|
subsector = 'Chemicals: Generic electric process'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[103:104,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
df.loc['elec',sector] += s_fec[subsector]
|
|
|
|
#### Process emissions
|
|
|
|
s_emi = excel_emi.iloc[58:105,year]
|
|
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = excel_out.iloc[9:10,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
df.loc['process emission',sector] += s_emi['Process emissions']/s_out.values # unit tCO2/t material
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
|
|
df.loc[sources,sector] = df.loc[sources,sector]*11.630/s_out.values # unit MWh/t material
|
|
# 1 ktoe = 11630 MWh
|
|
|
|
### Pharmaceutical products etc.
|
|
|
|
sector = 'Pharmaceutical products etc.'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[106:112,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec',sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat',sector] += s_fec['Low enthalpy heat']
|
|
|
|
#### Chemicals: High enthalpy heat processing
|
|
#> assume fully electrified
|
|
|
|
subsector = 'Chemicals: High enthalpy heat processing'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[116:129,year]
|
|
|
|
s_ued = excel_ued.iloc[116:129,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
eff_elec = s_ued['High enthalpy heat processing - Electric (microwave)']/s_fec['High enthalpy heat processing - Electric (microwave)']
|
|
|
|
df.loc['elec',sector] += s_ued[subsector]/eff_elec
|
|
|
|
#### Chemicals: Furnaces
|
|
#> assume fully electrified
|
|
|
|
subsector = 'Chemicals: Furnaces'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[129:137,year]
|
|
|
|
s_ued = excel_ued.iloc[129:137,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
eff = s_ued['Chemicals: Furnaces - Electric']/s_fec['Chemicals: Furnaces - Electric']
|
|
|
|
df.loc['elec',sector] += s_ued[subsector]/eff
|
|
|
|
#### Chemicals: Process cooling
|
|
#> assume fully electrified
|
|
|
|
subsector = 'Chemicals: Process cooling'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[137:151,year]
|
|
|
|
s_ued = excel_ued.iloc[137:151,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
eff_elec = s_ued['Chemicals: Process cooling - Electric']/s_fec['Chemicals: Process cooling - Electric']
|
|
|
|
df.loc['elec',sector] += s_ued[subsector]/eff_elec
|
|
|
|
#### Chemicals: Generic electric process
|
|
|
|
subsector = 'Chemicals: Generic electric process'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[151:152,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
df.loc['elec',sector] += s_fec[subsector]
|
|
|
|
# read the corresponding lines
|
|
s_out = excel_out.iloc[10:11,year]
|
|
|
|
# check the position
|
|
assert sector in str(s_out.index)
|
|
|
|
df.loc['process emission',sector] += 0 # unit tCO2/t material
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat', 'naphtha']
|
|
|
|
df.loc[sources,sector] = df.loc[sources,sector]*11.630/s_out.values # unit MWh/t material
|
|
# 1 ktoe = 11630 MWh
|
|
|
|
## Non-metallic mineral products
|
|
#
|
|
#> This includes cement, ceramic and glass production.
|
|
#
|
|
#> This sector includes process-emissions related to the fabrication of clinker.
|
|
|
|
sector = 'Non-metallic mineral products'
|
|
|
|
# read the input sheets
|
|
excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
### Cement
|
|
#
|
|
#> This sector has process-emissions.
|
|
#
|
|
#> Includes three subcategories: (a) Grinding, milling of raw material, (b) Pre-heating and pre-calcination, (c) clinker production (kilns), (d) Grinding, packaging. (b)+(c) represent 94% of fec. So (a) is joined to (b) and (d) is joined to (c).
|
|
#
|
|
#> Temperatures above 1400C are required for procesing limestone and sand into clinker.
|
|
#
|
|
#> Everything (except current electricity and heat consumption) is transformed into biomass
|
|
|
|
sector = 'Cement'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[3:25,year]
|
|
|
|
s_ued = excel_ued.iloc[3:25,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec',sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat',sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Efficiency changes due to biomass
|
|
eff_bio=s_ued['Biomass']/s_fec['Biomass']
|
|
|
|
df.loc['biomass', sector] += s_ued[['Cement: Grinding, milling of raw material', 'Cement: Pre-heating and pre-calcination']].sum()/eff_bio
|
|
|
|
#### Cement: Clinker production (kilns)
|
|
|
|
subsector = 'Cement: Clinker production (kilns)'
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[34:43,year]
|
|
|
|
s_ued = excel_ued.iloc[34:43,year]
|
|
|
|
assert s_fec.index[0] == subsector
|
|
|
|
# Efficiency changes due to biomass
|
|
eff_bio=s_ued['Biomass']/s_fec['Biomass']
|
|
|
|
df.loc['biomass', sector] += s_ued[['Cement: Clinker production (kilns)', 'Cement: Grinding, packaging']].sum()/eff_bio
|
|
|
|
#### Process-emission came from the calcination of limestone to chemically reactive calcium oxide (lime).
|
|
#> Calcium carbonate -> lime + CO2
|
|
#
|
|
#> CaCO3 -> CaO + CO2
|
|
|
|
s_emi = excel_emi.iloc[3:44,year]
|
|
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = excel_out.iloc[7:8,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
df.loc['process emission',sector] +=s_emi['Process emissions']/s_out.values # unit tCO2/t material
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out.values # unit MWh/t material
|
|
|
|
### Ceramics & other NMM
|
|
#
|
|
#> This sector has process emissions.
|
|
#
|
|
#> Includes four subcategories: (a) Mixing of raw material, (b) Drying and sintering of raw material, (c) Primary production process, (d) Product finishing. (b)represents 65% of fec and (a) 4%. So (a) is joined to (b).
|
|
#
|
|
#> Everything is electrified
|
|
|
|
sector = 'Ceramics & other NMM'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[45:94,year]
|
|
|
|
s_ued = excel_ued.iloc[45:94,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Ceramics: Microwave drying and sintering']/s_fec['Ceramics: Microwave drying and sintering']
|
|
df.loc['elec', sector] += s_ued[['Ceramics: Mixing of raw material','Ceramics: Drying and sintering of raw material']].sum()/eff_elec
|
|
|
|
eff_elec=s_ued['Ceramics: Electric kiln']/s_fec['Ceramics: Electric kiln']
|
|
df.loc['elec', sector] += s_ued['Ceramics: Primary production process']/eff_elec
|
|
|
|
eff_elec=s_ued['Ceramics: Electric furnace']/s_fec['Ceramics: Electric furnace']
|
|
df.loc['elec', sector] += s_ued['Ceramics: Product finishing']/eff_elec
|
|
|
|
s_emi = excel_emi.iloc[45:94,year]
|
|
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = excel_out.iloc[8:9,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
df.loc['process emission',sector] += s_emi['Process emissions']/s_out.values # unit tCO2/t material
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
|
|
df.loc[sources,sector] = df.loc[sources,sector]*11.630/s_out.values # unit MWh/t material
|
|
# 1 ktoe = 11630 MWh
|
|
|
|
### Glass production
|
|
#
|
|
#> This sector has process emissions.
|
|
#
|
|
#> Includes four subcategories: (a) Melting tank, (b) Forming, (c) Annealing, (d) Finishing processes. (a)represents 73%. (b), (d) are joined to (c).
|
|
#
|
|
#> Everything is electrified.
|
|
|
|
sector = 'Glass production'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[95:123,year]
|
|
|
|
s_ued = excel_ued.iloc[95:123,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Glass: Electric melting tank']/s_fec['Glass: Electric melting tank']
|
|
df.loc['elec', sector] += s_ued['Glass: Melting tank']/eff_elec
|
|
|
|
eff_elec=s_ued['Glass: Annealing - electric']/s_fec['Glass: Annealing - electric']
|
|
df.loc['elec', sector] += s_ued[['Glass: Forming','Glass: Annealing','Glass: Finishing processes']].sum()/eff_elec
|
|
|
|
s_emi = excel_emi.iloc[95:124,year]
|
|
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = excel_out.iloc[9:10,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
df.loc['process emission',sector] += s_emi['Process emissions']/s_out.values # unit tCO2/t material
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out.values # unit MWh/t material
|
|
|
|
## Pulp, paper and printing
|
|
#
|
|
#> Pulp, paper and printing can be completely electrified.
|
|
#
|
|
#> There are no process emissions associated to this sector.
|
|
|
|
sector = 'Pulp, paper and printing'
|
|
|
|
# read the input sheets
|
|
excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
### Pulp production
|
|
#
|
|
#> Includes three subcategories: (a) Wood preparation, grinding; (b) Pulping; (c) Cleaning.
|
|
#
|
|
#> (b) Pulping is electrified. The efficiency is calculated from the pulping process that is already electric.
|
|
#
|
|
#> (a) Wood preparation, grinding and (c) Cleaning represent only 10% their current energy consumption is assumed to be electrified without any change in efficiency
|
|
|
|
sector = 'Pulp production'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[3:28,year]
|
|
|
|
s_ued = excel_ued.iloc[3:28,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Industry-specific
|
|
df.loc['elec', sector] += s_fec[['Pulp: Wood preparation, grinding', 'Pulp: Cleaning']].sum()
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Pulp: Pulping electric']/s_fec['Pulp: Pulping electric']
|
|
df.loc['elec', sector] += s_ued['Pulp: Pulping thermal']/eff_elec
|
|
|
|
# add electricity from process that is already electrified
|
|
df.loc['elec', sector] += s_fec['Pulp: Pulping electric']
|
|
|
|
s_out = excel_out.iloc[8:9,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Pulp production (kt)'] # unit MWh/t material
|
|
|
|
### Paper production
|
|
#
|
|
#> Includes three subcategories: (a) Stock preparation; (b) Paper machine; (c) Product finishing.
|
|
#
|
|
#> (b) Paper machine and (c) Product finishing are electrified. The efficiency is calculated from the pulping process that is already electric.
|
|
#
|
|
#> (a) Stock preparation represents only 7% and its current energy consumption is assumed to be electrified without any change in efficiency.
|
|
|
|
sector = 'Paper production'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[29:78,year]
|
|
|
|
s_ued = excel_ued.iloc[29:78,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Industry-specific
|
|
df.loc['elec', sector] += s_fec['Paper: Stock preparation']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Paper: Paper machine - Electricity']/s_fec['Paper: Paper machine - Electricity']
|
|
df.loc['elec', sector] += s_ued['Paper: Paper machine - Steam use']/eff_elec
|
|
|
|
eff_elec=s_ued['Paper: Product finishing - Electricity']/s_fec['Paper: Product finishing - Electricity']
|
|
df.loc['elec', sector] += s_ued['Paper: Product finishing - Steam use']/eff_elec
|
|
|
|
# add electricity from process that is already electrified
|
|
df.loc['elec', sector] += s_fec['Paper: Paper machine - Electricity']
|
|
|
|
# read the corresponding lines
|
|
s_out = excel_out.iloc[9:10,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out.values # unit MWh/t material\
|
|
|
|
### Printing and media reproduction
|
|
#
|
|
#> (a) Printing and publishing is assumed to be electrified without any change in efficiency.
|
|
|
|
sector='Printing and media reproduction'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[79:90,year]
|
|
|
|
s_ued = excel_ued.iloc[79:90,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec',sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
df.loc['elec',sector] += s_ued[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat',sector] += s_fec['Low enthalpy heat']
|
|
df.loc['heat',sector] += s_ued['Low enthalpy heat']
|
|
|
|
# Industry-specific
|
|
df.loc['elec', sector] += s_fec['Printing and publishing']
|
|
df.loc['elec', sector] += s_ued['Printing and publishing']
|
|
|
|
# read the corresponding lines
|
|
s_out = excel_out.iloc[10:11,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out.values # unit MWh/t material
|
|
|
|
## Food, beverages and tobaco
|
|
#
|
|
#> Food, beverages and tobaco can be completely electrified.
|
|
#
|
|
#> There are no process emissions associated to this sector.
|
|
|
|
sector = 'Food, beverages and tobacco'
|
|
|
|
# read the input sheets
|
|
excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[3:78,year]
|
|
|
|
s_ued = excel_ued.iloc[3:78,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Food: Direct Heat - Electric']/s_fec['Food: Direct Heat - Electric']
|
|
df.loc['elec', sector] += s_ued['Food: Oven (direct heat)']/eff_elec
|
|
|
|
eff_elec=s_ued['Food: Process Heat - Electric']/s_fec['Food: Process Heat - Electric']
|
|
df.loc['elec', sector] += s_ued['Food: Specific process heat']/eff_elec
|
|
|
|
eff_elec=s_ued['Food: Electric drying']/s_fec['Food: Electric drying']
|
|
df.loc['elec', sector] += s_ued['Food: Drying']/eff_elec
|
|
|
|
eff_elec=s_ued['Food: Electric cooling']/s_fec['Food: Electric cooling']
|
|
df.loc['elec', sector] += s_ued['Food: Process cooling and refrigeration']/eff_elec
|
|
|
|
# Steam processing is electrified without change in efficiency
|
|
df.loc['elec', sector] += s_fec['Food: Steam processing']
|
|
|
|
# add electricity from process that is already electrified
|
|
df.loc['elec', sector] += s_fec['Food: Electric machinery']
|
|
|
|
# read the corresponding lines
|
|
s_out = excel_out.iloc[3:4,year]
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Physical output (index)'] # unit MWh/t material
|
|
|
|
## Non Ferrous Metals
|
|
|
|
sector = 'Non Ferrous Metals'
|
|
|
|
# read the input sheets
|
|
excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
|
|
index_col=0,header=0,squeeze=True) # the emission sheet
|
|
|
|
### Alumina
|
|
#
|
|
#> High enthalpy heat is converted to methane. Process heat at T>500ºC is required here.
|
|
#
|
|
#> Refining is electrified.
|
|
#
|
|
#> There are no process emissions associated to Alumina manufacturing
|
|
|
|
sector = 'Alumina production'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[3:31,year]
|
|
|
|
s_ued = excel_ued.iloc[3:31,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# High-enthalpy heat is transformed into methane
|
|
s_fec = excel_fec.iloc[13:24,year]
|
|
|
|
s_ued = excel_ued.iloc[13:24,year]
|
|
|
|
assert s_fec.index[0] == 'Alumina production: High enthalpy heat'
|
|
|
|
eff_met=s_ued['Natural gas (incl. biogas)']/s_fec['Natural gas (incl. biogas)']
|
|
df.loc['methane', sector] += s_fec['Alumina production: High enthalpy heat']/eff_met
|
|
|
|
# Efficiency changes due to electrification
|
|
s_fec = excel_fec.iloc[24:30,year]
|
|
|
|
s_ued = excel_ued.iloc[24:30,year]
|
|
|
|
assert s_fec.index[0] == 'Alumina production: Refining'
|
|
|
|
eff_elec=s_ued['Electricity']/s_fec['Electricity']
|
|
df.loc['elec', sector] += s_ued['Alumina production: Refining']/eff_elec
|
|
|
|
# read the corresponding lines
|
|
s_out = excel_out.iloc[9:10,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Alumina production (kt)'] # unit MWh/t material
|
|
|
|
### Aluminium primary route
|
|
#
|
|
#> Production through the primary route is divided into 50% remains as today and 50% is transformed into secondary route
|
|
|
|
sector = 'Aluminium - primary production'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[31:66,year]
|
|
|
|
s_ued = excel_ued.iloc[31:66,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Add aluminium electrolysis (smelting
|
|
df.loc['elec', sector] += s_fec['Aluminium electrolysis (smelting)']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Aluminium processing - Electric']/s_fec['Aluminium processing - Electric']
|
|
df.loc['elec', sector] += s_ued['Aluminium processing (metallurgy e.g. cast house, reheating)']/eff_elec
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Aluminium finishing - Electric']/s_fec['Aluminium finishing - Electric']
|
|
df.loc['elec', sector] += s_ued['Aluminium finishing']/eff_elec
|
|
|
|
s_emi = excel_emi.iloc[31:67,year]
|
|
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = excel_out.iloc[11:12,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
df.loc['process emission',sector] = s_emi['Process emissions']/s_out['Aluminium - primary production'] # unit tCO2/t material
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Aluminium - primary production'] # unit MWh/t material
|
|
|
|
### Aluminium secondary route
|
|
#
|
|
#> All is coverted into secondary route fully electrified
|
|
|
|
sector = 'Aluminium - secondary production'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[68:109,year]
|
|
|
|
s_ued = excel_ued.iloc[68:109,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Secondary aluminium - Electric']/s_fec['Secondary aluminium - Electric']
|
|
df.loc['elec', sector] += s_ued['Secondary aluminium (incl. pre-treatment, remelting)']/eff_elec
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Aluminium processing - Electric']/s_fec['Aluminium processing - Electric']
|
|
df.loc['elec', sector] += s_ued['Aluminium processing (metallurgy e.g. cast house, reheating)']/eff_elec
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Aluminium finishing - Electric']/s_fec['Aluminium finishing - Electric']
|
|
df.loc['elec', sector] += s_ued['Aluminium finishing']/eff_elec
|
|
|
|
# read the corresponding lines
|
|
s_out = excel_out.iloc[12:13,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Aluminium - secondary production'] # unit MWh/t material
|
|
# 1 ktoe = 11630 MWh
|
|
|
|
# primary route is divided into 50% remains as today and 50% is transformed into secondary route
|
|
df.loc[sources,'Aluminium - primary production'] = 0.5*df.loc[sources,'Aluminium - primary production'] + 0.5*df.loc[sources,'Aluminium - secondary production']
|
|
df.loc['process emission','Aluminium - primary production'] = 0.5*df.loc['process emission','Aluminium - primary production']
|
|
|
|
### Other non-ferrous metals
|
|
|
|
sector = 'Other non-ferrous metals'
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[110:152,year]
|
|
|
|
s_ued = excel_ued.iloc[110:152,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Metal production - Electric']/s_fec['Metal production - Electric']
|
|
df.loc['elec', sector] += s_ued['Other Metals: production']/eff_elec
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Metal processing - Electric']/s_fec['Metal processing - Electric']
|
|
df.loc['elec', sector] += s_ued['Metal processing (metallurgy e.g. cast house, reheating)']/eff_elec
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Metal finishing - Electric']/s_fec['Metal finishing - Electric']
|
|
df.loc['elec', sector] += s_ued['Metal finishing']/eff_elec
|
|
|
|
s_emi = excel_emi.iloc[110:153,year]
|
|
|
|
assert s_emi.index[0] == sector
|
|
|
|
s_out = excel_out.iloc[13:14,year]
|
|
|
|
assert sector in str(s_out.index)
|
|
|
|
df.loc['process emission',sector] = s_emi['Process emissions']/s_out['Other non-ferrous metals (kt lead eq.)'] # unit tCO2/t material
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Other non-ferrous metals (kt lead eq.)'] # unit MWh/t material
|
|
|
|
## Transport Equipment
|
|
|
|
sector = 'Transport Equipment'
|
|
# read the input sheets
|
|
excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
|
|
index_col=0,header=0,squeeze=True) # the emission sheet
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[3:45,year]
|
|
|
|
s_ued = excel_ued.iloc[3:45,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Trans. Eq.: Electric Foundries']/s_fec['Trans. Eq.: Electric Foundries']
|
|
df.loc['elec', sector] += s_ued['Trans. Eq.: Foundries']/eff_elec
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Trans. Eq.: Electric connection']/s_fec['Trans. Eq.: Electric connection']
|
|
df.loc['elec', sector] += s_ued['Trans. Eq.: Connection techniques']/eff_elec
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Trans. Eq.: Heat treatment - Electric']/s_fec['Trans. Eq.: Heat treatment - Electric']
|
|
df.loc['elec', sector] += s_ued['Trans. Eq.: Heat treatment']/eff_elec
|
|
|
|
df.loc['elec', sector] += s_fec['Trans. Eq.: General machinery']
|
|
df.loc['elec', sector] += s_fec['Trans. Eq.: Product finishing']
|
|
|
|
# Steam processing is supplied with biomass
|
|
eff_biomass=s_ued['Biomass']/s_fec['Biomass']
|
|
df.loc['biomass', sector] += s_ued['Trans. Eq.: Steam processing']/eff_biomass
|
|
|
|
# read the corresponding lines
|
|
s_out = excel_out.iloc[3:4,year]
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Physical output (index)'] # unit MWh/t material
|
|
# 1 ktoe = 11630 MWh
|
|
|
|
## Machinery Equipment
|
|
|
|
sector = 'Machinery Equipment'
|
|
|
|
# read the input sheets
|
|
excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
|
|
index_col=0,header=0,squeeze=True) # the emission sheet
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[3:45,year]
|
|
|
|
s_ued = excel_ued.iloc[3:45,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Mach. Eq.: Electric Foundries']/s_fec['Mach. Eq.: Electric Foundries']
|
|
df.loc['elec', sector] += s_ued['Mach. Eq.: Foundries']/eff_elec
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Mach. Eq.: Electric connection']/s_fec['Mach. Eq.: Electric connection']
|
|
df.loc['elec', sector] += s_ued['Mach. Eq.: Connection techniques']/eff_elec
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Mach. Eq.: Heat treatment - Electric']/s_fec['Mach. Eq.: Heat treatment - Electric']
|
|
df.loc['elec', sector] += s_ued['Mach. Eq.: Heat treatment']/eff_elec
|
|
|
|
df.loc['elec', sector] += s_fec['Mach. Eq.: General machinery']
|
|
df.loc['elec', sector] += s_fec['Mach. Eq.: Product finishing']
|
|
|
|
# Steam processing is supplied with biomass
|
|
eff_biomass=s_ued['Biomass']/s_fec['Biomass']
|
|
df.loc['biomass', sector] += s_ued['Mach. Eq.: Steam processing']/eff_biomass
|
|
|
|
# read the corresponding lines
|
|
s_out = excel_out.iloc[3:4,year]
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Physical output (index)'] # unit MWh/t material
|
|
|
|
## Textiles and leather
|
|
|
|
sector = 'Textiles and leather'
|
|
# read the input sheets
|
|
excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
|
|
index_col=0,header=0,squeeze=True) # the emission sheet
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[3:57,year]
|
|
|
|
s_ued = excel_ued.iloc[3:57,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Textiles: Electric drying']/s_fec['Textiles: Electric drying']
|
|
df.loc['elec', sector] += s_ued['Textiles: Drying']/eff_elec
|
|
|
|
df.loc['elec', sector] += s_fec['Textiles: Electric general machinery']
|
|
df.loc['elec', sector] += s_fec['Textiles: Finishing Electric']
|
|
|
|
# Steam processing is supplied with biomass
|
|
eff_biomass=s_ued[15:26]['Biomass']/s_fec[15:26]['Biomass']
|
|
df.loc['biomass', sector] += s_ued['Textiles: Pretreatment with steam']/eff_biomass
|
|
df.loc['biomass', sector] += s_ued['Textiles: Wet processing with steam']/eff_biomass
|
|
|
|
# read the corresponding lines
|
|
s_out = excel_out.iloc[3:4,year]
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Physical output (index)'] # unit MWh/t material
|
|
|
|
## Wood and wood products
|
|
|
|
sector = 'Wood and wood products'
|
|
# read the input sheets
|
|
excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
|
|
index_col=0,header=0,squeeze=True) # the emission sheet
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[3:46,year]
|
|
|
|
s_ued = excel_ued.iloc[3:46,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Wood: Electric drying']/s_fec['Wood: Electric drying']
|
|
df.loc['elec', sector] += s_ued['Wood: Drying']/eff_elec
|
|
|
|
df.loc['elec', sector] += s_fec['Wood: Electric mechanical processes']
|
|
df.loc['elec', sector] += s_fec['Wood: Finishing Electric']
|
|
|
|
# Steam processing is supplied with biomass
|
|
eff_biomass=s_ued[15:25]['Biomass']/s_fec[15:25]['Biomass']
|
|
df.loc['biomass', sector] += s_ued['Wood: Specific processes with steam']/eff_biomass
|
|
|
|
# read the corresponding lines
|
|
s_out = excel_out.iloc[3:4,year]
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Physical output (index)'] # unit MWh/t material
|
|
|
|
## Other Industrial Sectors
|
|
|
|
sector = 'Other Industrial Sectors'
|
|
# read the input sheets
|
|
excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
|
|
index_col=0,header=0,squeeze=True)
|
|
|
|
excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
|
|
index_col=0,header=0,squeeze=True) # the emission sheet
|
|
|
|
df[sector] = 0
|
|
|
|
# read the corresponding lines
|
|
s_fec = excel_fec.iloc[3:67,year]
|
|
|
|
s_ued = excel_ued.iloc[3:67,year]
|
|
|
|
assert s_fec.index[0] == sector
|
|
|
|
# Lighting, Air compressors, Motor drives, Fans and pumps
|
|
df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
|
|
|
|
# Low enthalpy heat
|
|
df.loc['heat', sector] += s_fec['Low enthalpy heat']
|
|
|
|
# Efficiency changes due to electrification
|
|
eff_elec=s_ued['Other Industrial sectors: Electric processing']/s_fec['Other Industrial sectors: Electric processing']
|
|
df.loc['elec', sector] += s_ued['Other Industrial sectors: Process heating']/eff_elec
|
|
|
|
eff_elec=s_ued['Other Industries: Electric drying']/s_fec['Other Industries: Electric drying']
|
|
df.loc['elec', sector] += s_ued['Other Industrial sectors: Drying']/eff_elec
|
|
|
|
eff_elec=s_ued['Other Industries: Electric cooling']/s_fec['Other Industries: Electric cooling']
|
|
df.loc['elec', sector] += s_ued['Other Industrial sectors: Process Cooling']/eff_elec
|
|
|
|
# Diesel motors are electrified
|
|
df.loc['elec', sector] += s_fec['Other Industrial sectors: Diesel motors (incl. biofuels)']
|
|
df.loc['elec', sector] += s_fec['Other Industrial sectors: Electric machinery']
|
|
|
|
# Steam processing is supplied with biomass
|
|
eff_biomass=s_ued[15:25]['Biomass']/s_fec[15:25]['Biomass']
|
|
df.loc['biomass', sector] += s_ued['Other Industrial sectors: Steam processing']/eff_biomass
|
|
|
|
# read the corresponding lines
|
|
s_out = excel_out.iloc[3:4,year]
|
|
|
|
# final energy consumption per t
|
|
sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
|
|
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Physical output (index)'] # unit MWh/t material
|
|
|
|
|
|
df.to_csv('resources/industry_sector_ratios.csv', sep=';')
|