52581466b6
Remove emissions from hydrogen production for ammonia (since H2 now comes from electrolysis). Allow process emissions from petrochemical production to be captured (the carbon is not necessarily fossil, but could come from CCU).
1376 lines
47 KiB
Python
1376 lines
47 KiB
Python
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import pandas as pd
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import numpy as np
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base_dir = "data/jrc-idees-2015"
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# year for wich data is retrieved
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year = 2015
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year = year-2016
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conv_factor=11.630 #ktoe/kton -> MWh/ton
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country = 'EU28'
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sub_sheet_name_dict = { 'Iron and steel':'ISI',
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'Chemicals Industry':'CHI',
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'Non-metallic mineral products': 'NMM',
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'Pulp, paper and printing': 'PPA',
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'Food, beverages and tobacco': 'FBT',
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'Non Ferrous Metals' : 'NFM',
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'Transport Equipment': 'TRE',
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'Machinery Equipment': 'MAE',
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'Textiles and leather':'TEL',
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'Wood and wood products': 'WWP',
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'Other Industrial Sectors': 'OIS'}
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index = ['elec','biomass','methane','hydrogen','heat','naphtha','process emission','process emission from feedstock']
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df = pd.DataFrame(index=index)
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## Iron and steel
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#
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#> There are two different approaches to produce iron and steel: i.e., integrated steelworks and electric arc.
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#
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#> Electric arc approach has higher efficiency and relies more on electricity.
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#
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#> We assume that integrated steelworks will be replaced by electric arc entirely.
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sector = 'Iron and steel'
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# read the input sheets
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excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
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index_col=0,header=0,squeeze=True) # the summary sheet
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excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
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index_col=0,header=0,squeeze=True) # the final energy consumption sheet
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excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
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index_col=0,header=0,squeeze=True) # the used energy sheet
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excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
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index_col=0,header=0,squeeze=True) # the emission sheet
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### Electric arc
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sector = 'Electric arc'
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df[sector] = 0
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# read the corresponding lines
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s_fec = excel_fec.iloc[51:57,year]
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assert s_fec.index[0] == sector
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# Lighting, Air compressors, Motor drives, Fans and pumps
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df.loc['elec',sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
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# Low enthalpy heat
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df.loc['heat',sector] += s_fec['Low enthalpy heat']
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#### Steel: Smelters
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subsector = 'Steel: Smelters'
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# read the corresponding lines
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s_fec = excel_fec.iloc[61:67,year]
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s_ued = excel_ued.iloc[61:67,year]
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assert s_fec.index[0] == subsector
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# Efficiency changes due to transforming all the smelters into methane
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eff_met=s_ued['Natural gas (incl. biogas)']/s_fec['Natural gas (incl. biogas)']
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df.loc['methane', sector] += s_ued[subsector]/eff_met
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#### Steel: Electric arc
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subsector = 'Steel: Electric arc'
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# read the corresponding lines
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s_fec = excel_fec.iloc[67:68,year]
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assert s_fec.index[0] == subsector
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# only electricity
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df.loc['elec',sector] += s_fec[subsector]
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#### Steel: Furnaces, Refining and Rolling
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#> assume fully electrified
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#
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#> other processes are scaled by the used energy
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subsector = 'Steel: Furnaces, Refining and Rolling'
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# read the corresponding lines
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s_fec = excel_fec.iloc[68:75,year]
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s_ued = excel_ued.iloc[68:75,year]
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assert s_fec.index[0] == subsector
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# this process can be electrified
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eff = s_ued['Steel: Furnaces, Refining and Rolling - Electric']/s_fec['Steel: Furnaces, Refining and Rolling - Electric']
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df.loc['elec',sector] += s_ued[subsector]/eff
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#### Steel: Products finishing
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#> assume fully electrified
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subsector = 'Steel: Products finishing'
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# read the corresponding lines
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s_fec = excel_fec.iloc[75:92,year]
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s_ued = excel_ued.iloc[75:92,year]
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assert s_fec.index[0] == subsector
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# this process can be electrified
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eff = s_ued['Steel: Products finishing - Electric']/s_fec['Steel: Products finishing - Electric']
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df.loc['elec',sector] += s_ued[subsector]/eff
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#### Process emissions (per physical output)
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s_emi = excel_emi.iloc[51:93,year]
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assert s_emi.index[0] == sector
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s_out = excel_out.iloc[7:8,year]
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assert sector in str(s_out.index)
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df.loc['process emission',sector] = s_emi['Process emissions']/s_out[sector] # unit tCO2/t material
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# final energy consumption per t
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df.loc[['elec','heat','methane'],sector] = df.loc[['elec','heat','methane'],sector]*conv_factor/s_out[sector] # unit MWh/t material
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## Integrated steelworks is converted to Electric arc
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#
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#> Electric arc uses scrap metal and Direct Reduced Iron
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#
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#> We assume that when substituting Integrated Steelworks by Electric arc furnaces.
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#> 50% of Integrated steelworks is substituted by scrap metal + electric furnaces
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#> 50% of Integrated steelworks is substituted by Direct Reduce Iron (with Hydrogen) + electric furnaces
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df['Integrated steelworks']=df['Electric arc']
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# adding the Hydrogen necessary for the Direct Reduction of Iron. consumption 1.7 MWh H2 /ton steel
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#(0.5 because only half of the steel requires DRI, the rest is scrap metal)
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df.loc['hydrogen', 'Integrated steelworks'] =snakemake.config["industry"]["H2_DRI"] * snakemake.config["industry"]["DRI_ratio"]
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## Chemicals Industry
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sector = 'Chemicals Industry'
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# read the input sheets
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excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
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index_col=0,header=0,squeeze=True) # the summary sheet
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excel_fec = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_fec',
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index_col=0,header=0,squeeze=True) # the final energy consumption sheet
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excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
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index_col=0,header=0,squeeze=True) # the used energy sheet
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excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
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index_col=0,header=0,squeeze=True) # the emission sheet
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### Basic chemicals
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sector = 'Basic chemicals'
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df[sector] = 0
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# read the corresponding lines
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s_fec = excel_fec.iloc[3:9,year]
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assert s_fec.index[0] == sector
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# Lighting, Air compressors, Motor drives, Fans and pumps
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df.loc['elec',sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
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# Low enthalpy heat
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df.loc['heat',sector] += s_fec['Low enthalpy heat']
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#### Chemicals: Feedstock (energy used as raw material)
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#> There are Solids, Refinery gas, LPG, Diesel oil, Residual fuel oil, Other liquids, Naphtha, Natural gas for feedstock.
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#
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#> Naphta represents 47%, methane 17%. LPG (18%) solids, refinery gas, diesel oil, residual fuel oils and other liquids are asimilated to Napthta
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#
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#> Following Lechtenbohmer 2016, the 85 TWh/year of methane for the ammonia industry are substited by hydrogen.
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subsector = 'Chemicals: Feedstock (energy used as raw material)'
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# read the corresponding lines
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s_fec = excel_fec.iloc[13:22,year]
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assert s_fec.index[0] == subsector
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# naphtha
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df.loc['naphtha',sector] += s_fec['Naphtha']
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# natural gas
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# 85 TWh/year of methane for the ammonia industry are substituted by hydrogen
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df.loc['methane',sector] += s_fec['Natural gas'] - snakemake.config["industry"]["H2_for_NH3"]/conv_factor
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df.loc['hydrogen',sector] += snakemake.config["industry"]["H2_for_NH3"]/conv_factor
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# 1 ktoe = 11630 MWh
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# LPG and other feedstock materials are assimilated to naphtha since they will be produced trough Fischer-Tropsh process
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df.loc['naphtha',sector] += (s_fec['Solids'] + s_fec['Refinery gas'] + s_fec['LPG'] + s_fec['Diesel oil']
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+ s_fec['Residual fuel oil'] + s_fec['Other liquids'])
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#### Chemicals: Steam processing
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#> All the final energy consumption in the Stem processing is converted to biomass.
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#
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#> The current efficiency of biomass is assumed in the conversion.
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subsector = 'Chemicals: Steam processing'
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# read the corresponding lines
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s_fec = excel_fec.iloc[22:33,year]
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s_ued = excel_ued.iloc[22:33,year]
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assert s_fec.index[0] == subsector
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# efficiency of biomass
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eff_bio = s_ued['Biomass']/s_fec['Biomass']
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# replace all fec by biomass
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df.loc['biomass',sector] += s_ued[subsector]/eff_bio
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#### Chemicals: Furnaces
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#> assume fully electrified
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subsector = 'Chemicals: Furnaces'
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# read the corresponding lines
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s_fec = excel_fec.iloc[33:41,year]
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s_ued = excel_ued.iloc[33:41,year]
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assert s_fec.index[0] == subsector
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#efficiency of electrification
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eff_elec = s_ued['Chemicals: Furnaces - Electric']/s_fec['Chemicals: Furnaces - Electric']
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df.loc['elec',sector] += s_ued[subsector]/eff_elec
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#### Chemicals: Process cooling
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#> assume fully electrified
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subsector = 'Chemicals: Process cooling'
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# read the corresponding lines
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s_fec = excel_fec.iloc[41:55,year]
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s_ued = excel_ued.iloc[41:55,year]
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assert s_fec.index[0] == subsector
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eff_elec = s_ued['Chemicals: Process cooling - Electric']/s_fec['Chemicals: Process cooling - Electric']
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df.loc['elec',sector] += s_ued[subsector]/eff_elec
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#### Chemicals: Generic electric process
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subsector = 'Chemicals: Generic electric process'
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# read the corresponding lines
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s_fec = excel_fec.iloc[55:56,year]
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assert s_fec.index[0] == subsector
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df.loc['elec',sector] += s_fec[subsector]
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#### Process emissions
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s_emi = excel_emi.iloc[3:57,year]
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assert s_emi.index[0] == sector
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s_out = excel_out.iloc[8:9,year]
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assert sector in str(s_out.index)
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df.loc['process emission',sector] += (s_emi['Process emissions'] - snakemake.config["industry"]['petrochemical_process_emissions']*1e3 - snakemake.config["industry"]['NH3_process_emissions']*1e3)/s_out.values # unit tCO2/t material
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#these are emissions originating from feedstock, i.e. could be non-fossil origin
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df.loc['process emission from feedstock',sector] += (snakemake.config["industry"]['petrochemical_process_emissions']*1e3)/s_out.values # unit tCO2/t material
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# final energy consumption per t
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sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
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df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out.values# unit MWh/t material
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# 1 ktoe = 11630 MWh
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### Other chemicals
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sector = 'Other chemicals'
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df[sector] = 0
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# read the corresponding lines
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s_fec = excel_fec.iloc[58:64,year]
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# check the position
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assert s_fec.index[0] == sector
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# Lighting, Air compressors, Motor drives, Fans and pumps
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df.loc['elec',sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
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# Low enthalpy heat
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df.loc['heat',sector] += s_fec['Low enthalpy heat']
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#### Chemicals: High enthalpy heat processing
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#> assume fully electrified
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subsector = 'Chemicals: High enthalpy heat processing'
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# read the corresponding lines
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s_fec = excel_fec.iloc[68:81,year]
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s_ued = excel_ued.iloc[68:81,year]
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assert s_fec.index[0] == subsector
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eff_elec = s_ued['High enthalpy heat processing - Electric (microwave)']/s_fec['High enthalpy heat processing - Electric (microwave)']
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df.loc['elec',sector] += s_ued[subsector]/eff_elec
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#### Chemicals: Furnaces
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#> assume fully electrified
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subsector = 'Chemicals: Furnaces'
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# read the corresponding lines
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s_fec = excel_fec.iloc[81:89,year]
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s_ued = excel_ued.iloc[81:89,year]
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assert s_fec.index[0] == subsector
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eff_elec = s_ued['Chemicals: Furnaces - Electric']/s_fec['Chemicals: Furnaces - Electric']
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df.loc['elec',sector] += s_ued[subsector]/eff_elec
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#### Chemicals: Process cooling
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#> assume fully electrified
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subsector = 'Chemicals: Process cooling'
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# read the corresponding lines
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s_fec = excel_fec.iloc[89:103,year]
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s_ued = excel_ued.iloc[89:103,year]
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assert s_fec.index[0] == subsector
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eff = s_ued['Chemicals: Process cooling - Electric']/s_fec['Chemicals: Process cooling - Electric']
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df.loc['elec',sector] += s_ued[subsector]/eff
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#### Chemicals: Generic electric process
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subsector = 'Chemicals: Generic electric process'
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# read the corresponding lines
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s_fec = excel_fec.iloc[103:104,year]
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assert s_fec.index[0] == subsector
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df.loc['elec',sector] += s_fec[subsector]
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#### Process emissions
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s_emi = excel_emi.iloc[58:105,year]
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assert s_emi.index[0] == sector
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s_out = excel_out.iloc[9:10,year]
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assert sector in str(s_out.index)
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df.loc['process emission',sector] += s_emi['Process emissions']/s_out.values # unit tCO2/t material
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# final energy consumption per t
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sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
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df.loc[sources,sector] = df.loc[sources,sector]*11.630/s_out.values # unit MWh/t material
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# 1 ktoe = 11630 MWh
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### Pharmaceutical products etc.
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sector = 'Pharmaceutical products etc.'
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df[sector] = 0
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# read the corresponding lines
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s_fec = excel_fec.iloc[106:112,year]
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assert s_fec.index[0] == sector
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# Lighting, Air compressors, Motor drives, Fans and pumps
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df.loc['elec',sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
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# Low enthalpy heat
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df.loc['heat',sector] += s_fec['Low enthalpy heat']
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#### Chemicals: High enthalpy heat processing
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#> assume fully electrified
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subsector = 'Chemicals: High enthalpy heat processing'
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# read the corresponding lines
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s_fec = excel_fec.iloc[116:129,year]
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s_ued = excel_ued.iloc[116:129,year]
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assert s_fec.index[0] == subsector
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eff_elec = s_ued['High enthalpy heat processing - Electric (microwave)']/s_fec['High enthalpy heat processing - Electric (microwave)']
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df.loc['elec',sector] += s_ued[subsector]/eff_elec
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#### Chemicals: Furnaces
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#> assume fully electrified
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subsector = 'Chemicals: Furnaces'
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# read the corresponding lines
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s_fec = excel_fec.iloc[129:137,year]
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s_ued = excel_ued.iloc[129:137,year]
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assert s_fec.index[0] == subsector
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eff = s_ued['Chemicals: Furnaces - Electric']/s_fec['Chemicals: Furnaces - Electric']
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df.loc['elec',sector] += s_ued[subsector]/eff
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#### Chemicals: Process cooling
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#> assume fully electrified
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subsector = 'Chemicals: Process cooling'
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# read the corresponding lines
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s_fec = excel_fec.iloc[137:151,year]
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s_ued = excel_ued.iloc[137:151,year]
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assert s_fec.index[0] == subsector
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eff_elec = s_ued['Chemicals: Process cooling - Electric']/s_fec['Chemicals: Process cooling - Electric']
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df.loc['elec',sector] += s_ued[subsector]/eff_elec
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#### Chemicals: Generic electric process
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subsector = 'Chemicals: Generic electric process'
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# read the corresponding lines
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s_fec = excel_fec.iloc[151:152,year]
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assert s_fec.index[0] == subsector
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df.loc['elec',sector] += s_fec[subsector]
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# read the corresponding lines
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s_out = excel_out.iloc[10:11,year]
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# check the position
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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'] = (1-snakemake.config["industry"]["Al_to_scrap"])*df.loc[sources,'Aluminium - primary production'] + snakemake.config["industry"]["Al_to_scrap"]*df.loc[sources,'Aluminium - secondary production']
|
|
df.loc['process emission','Aluminium - primary production'] = (1-snakemake.config["industry"]["Al_to_scrap"])*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)
|
|
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excel_ued = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_ued',
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index_col=0,header=0,squeeze=True)
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excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector],
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index_col=0,header=0,squeeze=True)
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excel_emi = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(base_dir,country), sheet_name=sub_sheet_name_dict[sector]+'_emi',
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index_col=0,header=0,squeeze=True) # the emission sheet
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df[sector] = 0
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# read the corresponding lines
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s_fec = excel_fec.iloc[3:67,year]
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s_ued = excel_ued.iloc[3:67,year]
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assert s_fec.index[0] == sector
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# Lighting, Air compressors, Motor drives, Fans and pumps
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df.loc['elec', sector] += s_fec[['Lighting','Air compressors','Motor drives','Fans and pumps']].sum()
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# Low enthalpy heat
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df.loc['heat', sector] += s_fec['Low enthalpy heat']
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# Efficiency changes due to electrification
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eff_elec=s_ued['Other Industrial sectors: Electric processing']/s_fec['Other Industrial sectors: Electric processing']
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df.loc['elec', sector] += s_ued['Other Industrial sectors: Process heating']/eff_elec
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eff_elec=s_ued['Other Industries: Electric drying']/s_fec['Other Industries: Electric drying']
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df.loc['elec', sector] += s_ued['Other Industrial sectors: Drying']/eff_elec
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eff_elec=s_ued['Other Industries: Electric cooling']/s_fec['Other Industries: Electric cooling']
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df.loc['elec', sector] += s_ued['Other Industrial sectors: Process Cooling']/eff_elec
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# Diesel motors are electrified
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df.loc['elec', sector] += s_fec['Other Industrial sectors: Diesel motors (incl. biofuels)']
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df.loc['elec', sector] += s_fec['Other Industrial sectors: Electric machinery']
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# Steam processing is supplied with biomass
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eff_biomass=s_ued[15:25]['Biomass']/s_fec[15:25]['Biomass']
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df.loc['biomass', sector] += s_ued['Other Industrial sectors: Steam processing']/eff_biomass
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# read the corresponding lines
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s_out = excel_out.iloc[3:4,year]
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# final energy consumption per t
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sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
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df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Physical output (index)'] # unit MWh/t material
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df.to_csv('resources/industry_sector_ratios.csv', sep=';')
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