if varType == 'dis': inputData = extract_input_data(inputInfos) else: inputData = generate_input_data(inputInfos) # run simulation with Aspen try: aspenModel = Aspen(aspenFile) calculator = Excel(calculatorFile) simResults = simulate_using_aspen(aspenModel, calculator, inputData, outputInfos, outDir, nruns) finally: aspenModel.close() calculator.close() # save and plot results save_simulation_results(simResults, outDir) plot_hist(simResults, outputInfos[['Output', 'Unit']], outDir)
def calculate_margin(inputInfos, outputInfos, aspenFiles, calculatorFiles,
marketPriceFiles, rinPriceFile, capital, credits):
'''
Parameters
inputInfos: df, input infos for optimization, columns are ['Model', 'Input', 'Location', 'InputPath']
outputInfos: df, output infos, columns are ['Model', 'Output', 'Unit', 'Location']
aspenFiles: dict, keys are model names, values are aspen model path
calculatorFiles: dict, keys are model names, values are calculator path
marketPriceFiles: dict, keys are model names, values are market price file path
rinPriceFile: str or None, rin price file, None if no subtractor rin price
capital: str, whether to include the capital investment
credits: str, whether to include the by-product credits in sugar model
Returns
margins: dict, keys are model names, values are df (index are time, columns are ['output (unit)', 'market price (unit)', 'margin (unit)'])
totalMargins: df, index are time, columns are models
'''
margins = {}
totalMargins = {}
for model in inputInfos['Model'].unique():
if model is np.nan: continue
inputInfo = inputInfos[inputInfos['Model'] == model]
inputInfo.index = range(inputInfo.shape[0])
inputData = pd.DataFrame(index=inputInfo.index,
columns=['Input', 'Location', 'Data'],
dtype=object)
for i, (input, location,
inputPath) in inputInfo[['Input', 'Location',
'InputPath']].iterrows():
inputData.loc[i, :] = [
input, location,
pd.read_csv(inputPath, sep='\t',
index_col=0).values.reshape(-1)
]
if capital == 'no':
inputData.loc[inputData.shape[0], :] = [
'Capital', 'DCFROR!B18',
np.zeros(inputData.loc[0, 'Data'].size)
]
if credits == 'no' and model == 'sugar':
inputData.loc[inputData.shape[0], :] = [
'Credits', 'OPEX!I48',
np.zeros(inputData.loc[0, 'Data'].size)
]
outputInfo = outputInfos.loc[outputInfos['Model'] == model,
['Output', 'Unit', 'Location']]
outputInfo.index = [0] # index start from 0
output, unit = outputInfo.loc[0, :][['Output', 'Unit']]
###
if output == 'MESP':
outputInfo.loc[1, :] = ['Production', 'gal/yr', 'OPEX!B18']
elif output == 'MSSP':
outputInfo.loc[1, :] = ['Production', 'kg/yr', 'OPEX!B17']
###
aspenFile = aspenFiles[model]
calculatorFile = calculatorFiles[model]
aspenModel = Aspen(aspenFile)
calculator = Excel(calculatorFile)
simResults = simulate_using_calculator(aspenModel, calculator,
inputData, outputInfo)
aspenModel.close()
calculator.close()
priceFile = marketPriceFiles[model]
marketPrices = pd.read_csv(priceFile, sep='\t', index_col=0)
data = pd.DataFrame(index=marketPrices.index)
if rinPriceFile:
rinPrice = pd.read_csv(rinPriceFile, sep='\t',
index_col=0).values.reshape(-1)
data['%s (%s)' %
(output, unit)] = simResults[output].values - rinPrice
else:
data['%s (%s)' % (output, unit)] = simResults[output].values
data['market price (%s)' % unit] = marketPrices
data['margin (%s)' %
unit] = data['market price (%s)' % unit] - data['%s (%s)' %
(output, unit)]
margins[model] = data
###
totalMargin = data['margin (%s)' %
unit].values * simResults['Production'].values
if output == 'MESP':
totalMargin = totalMargin / 52
elif output == 'MSSP':
totalMargin = totalMargin / 52 * 2.2045
totalMargins[model] = totalMargin
totalMargins = pd.DataFrame(totalMargins, index=marketPrices.index)
###
return margins, totalMargins