os.makedirs(outDir, exist_ok = True) # parse inputs and outputs inputInfos, outputInfos = parse_config(configFile) 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 main():
# Set the input and output files/directories
aspenFile = os.path.abspath('DW1102A_AB.bkp')
excelFile = os.path.abspath('DW1102A_2016UPDATES.xlsm')
outDir = 'ethanol_output'
os.makedirs(outDir, exist_ok=True)
# ================================================================
# Create Aspen Plus communicator
# ================================================================
print('Opening Aspen Plus model... ', end='')
aspenModel = Aspen(aspenFile)
print('Success!')
# ================================================================
# Write backup file with value updates/replacements
# ================================================================
# aspenPath: str, path in ASPEN tree
# value: float or str, value to set
# ifFortran: bool, whether it is a Fortran variable
# ve_params['aspenPath'] = [value, ifFortran]
ve_params = {}
ve_params['path/in/aspen/tree'] = [0.75, False]
ve_params['path/in/aspen/tree2'] = [0.01, False]
DUMMY_OPERATION = True
print('Changing values in model backup definition tree... ', end='')
for key, value in ve_params.items():
if not DUMMY_OPERATION:
aspenModel.set_value(key, value[0], value[1])
else:
pass
print('Success!')
# ================================================================
# Run the Aspen Plus model
# ================================================================
print('Running Aspen Plus model... ', end='')
aspenModel.run_model()
print('Success!')
# ================================================================
# Save current model state
# ================================================================
print('Saving current model definition... ', end='')
tmpFile = '%s/%s.bkp' % (outDir, 'temp_backup')
aspenModel.save_model(tmpFile)
print('Success!')
# ================================================================
# Create Excel communicator and run calculator
# ================================================================
print('Opening Excel calculator... ', end='')
excelCalculator = Excel(excelFile)
print('Success!')
print('Running Excel analysis... ', end='')
if not DUMMY_OPERATION:
excelCalculator.load_aspenModel(tmpFile)
excelCalculator.run_macro('solvedcfror')
print('Success!')
# aspenModel.close()
excelCalculator.close()
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