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','process emission from feedstock'] 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 because only half of the steel requires DRI, the rest is scrap metal) df.loc['hydrogen', 'Integrated steelworks'] =snakemake.config["industry"]["H2_DRI"] * snakemake.config["industry"]["DRI_ratio"] ## 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'] - snakemake.config["industry"]["H2_for_NH3"]/conv_factor df.loc['hydrogen',sector] += snakemake.config["industry"]["H2_for_NH3"]/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'] - snakemake.config["industry"]['petrochemical_process_emissions']*1e3 - snakemake.config["industry"]['NH3_process_emissions']*1e3)/s_out.values # unit tCO2/t material #these are emissions originating from feedstock, i.e. could be non-fossil origin df.loc['process emission from feedstock',sector] += (snakemake.config["industry"]['petrochemical_process_emissions']*1e3)/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'] = (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) 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=';')