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CapitalCost_SurfacePlantCalculator.py
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CapitalCost_SurfacePlantCalculator.py
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import numpy as np
import CoolProp as CP
from PPIs import PPIs
class CapitalCost_SurfacePlantCalculator:
@staticmethod
def HEX(input, params):
# STEADY STATE COSTS
# USD to EURO Conversion (May 2023)
# 1 USD = 0.92 EURO
# Heat exchangers
# Description: Floating head type Shell & tube HEX. Carbon steel except
# tube-side Cr-Mo steel for corrosion resistance. sCO2 is tube-side;
# H2O is shell-side.
# REFERENCE: Product and Process Design Principles: Synthesis,
# Analysis, and Evaluation 4th Ed.
# Heat exchanger 1
# reference CEPCI; 2013 (average)
PPI_ref = PPIs.return_PPI('PPI_HX', 2013, params) # 2013 330.167
PPI_current = PPIs.return_PPI('PPI_HX', params.costYear, params) # 2022 494.764
# inputs
area = [input['A_1'], input['A_2']]
area = [x * 10.764 for x in area] # ft^2
P = [input['P_tubeside_1'], input['P_tubeside_2']]
# free-of-board costs
C_B = [np.exp(12.0310 - 0.8709 * np.log(x) + 0.09005 * (np.log(x)) ** 2) if x != 0 else 0 for x in area] # USD
# material factor -- S/T-side = carbon steel/Cr-Mo steel
a = 1.55
b = 0.05
F_M = [a + (x / 100) ** b for x in area]
# length factor L = 6069mm
F_L = 1.0
# tubeside pressure factor - cost correlation (P > 10.342 bar)
# REFERENCE: CAMARAZA-MEDINA et al. (2021) https://doi.org/10.52292/j.laar.2021.713
F_P = []
ID = 0.65 # m
for p in P:
if p > 10.342e5: # Pa
factor = 1 + ((p / 1e5) / 10.342 - 1) * (0.0035 - 0.022 * (ID - 0.3048)) # P in Pa, ID in m
else:
factor = 1.0
F_P.append(factor)
C_P = [f_p * f_m * F_L * c_b for f_p, f_m, c_b in zip(F_P, F_M, C_B)] # USD
C_P = [x * (PPI_current / PPI_ref) for x in C_P] # USD
# Guthrie Bare-Module Factor for STHEs
F_BM = 3.17
C_BM = [x * F_BM for x in C_P]
return C_BM
@staticmethod
def pump(input, params):
# Pump Cost Estimation
# reference CEPCI; 2013 (average)
PPI_ref = PPIs.return_PPI('PPI_Pump&Comp', 2013, params) # 2013 140.167
PPI_current = PPIs.return_PPI('PPI_Pump&Comp', params.costYear, params) # 2022 192.573
Q = input['m_dot'] / input['rho'] * 15850 # gallons per minute
H = (input['dP'] / 1e5) * 10.197 * 3.281 # ft
S = Q * (H) ** 0.5 # Size factor
C_B_pump = np.exp(12.1656 - 1.1448 * np.log(S) + 0.0862 * (np.log(S)) ** 2) # USD
# pump type factor; 1 stage, 1800rpm, 250 - 5000gpm range, 50 - 500ft head range, 250Hp max motor power
F_T_pump = 2.0
# material factor; cast iron
F_M_pump = 1.0
# Electric motor calculations for pump
mu_P = -0.316 + 0.24015 * (np.log(Q)) - 0.01199 * (np.log(Q)) ** 2
P_B = Q * H * (input['rho'] / 119.8) / 33000 / mu_P # Hp
mu_M = 0.80 + 0.0319 * (np.log(P_B)) - 0.00182 * (np.log(P_B)) ** 2
P_c = P_B / mu_M # Hp
C_B_driver = np.exp(5.9332 + 0.16829 * np.log(P_c) - 0.110056 * (np.log(P_c)) ** 2 +
0.071413 * (np.log(P_c)) ** 3 - 0.0063788 * (np.log(P_c)) ** 4) # USD
F_T_driver = 1.3 # 1800rpm enclosed, fan-cooled, 1 to 250 Hp
C_P = C_B_pump * F_T_pump * F_M_pump + C_B_driver * F_T_driver # USD
C_P = C_P * (PPI_current / PPI_ref) # USD
# Guthrie Bare-Module Factor for pumps
F_BM = 3.30
C_BM = C_P * F_BM
return C_BM
@staticmethod
def compressor(input, params):
PPI_ref = PPIs.return_PPI('PPI_Pump&Comp', 2013, params) # 2013 140.167
PPI_current = PPIs.return_PPI('PPI_Pump&Comp', params.costYear, params) # 2022 192.573
P_c = input['P_c'] / 745.7 # Hp
C_B = np.exp(9.1553 + 0.63 * np.log(P_c)) # USD
F_D = 1.0
F_M = 2.5
C_P = C_B * F_M * F_D # USD
C_P = C_P * (PPI_current / PPI_ref) # USD
F_BM = 2.15 # Guthrie Bare-Module Factor for gas compressors
C_BM = C_P * F_BM
return C_BM
@staticmethod
def turbine(input, params):
PPI_ref = PPIs.return_PPI('PPI_T-G', 2003, params) # 2003 154.033
PPI_current = PPIs.return_PPI('PPI_T-G', params.costYear, params) # 2022 245.235
S_T_fluid = 1.20 # Regular fluid (CO2)
C_P = 0.67 * (S_T_fluid * 2830 * (input['W_turbine'] / 1e3) ** 0.745 + 3680 * (input['W_turbine'] / 1e3) ** 0.617)
C_P = C_P * (PPI_current / PPI_ref) # USD
F_BM = 1.5 # Guthrie Bare-Module Factor for gas-driven turbines
C_BM = C_P * F_BM
return C_BM
@staticmethod
def cooling_tower(input, params):
PPI_ref = PPIs.return_PPI('PPI_Cool', 2019, params) # 2013 131.650
PPI_current = PPIs.return_PPI('PPI_Cool', params.costYear, params) # 2022 272.893
TDC = 1.2 # Tower Design Coefficient - CO2: 1.2
if params.coolingMode == 'Wet':
a_cool = 5.58e3
b_cool = 0
c_cool = -1.77e1
d_cool = 1.96e2
c_cooling = a_cool * (1 / params.dT_approach) + b_cool * (params.T_surface_air_C + 273.15) + c_cool * (params.T_surface_air_C + 273.15) / params.dT_approach + d_cool * (1 / (params.dT_approach + input['dT_range']))
a_cond = 4.08e3
b_cond = -1.54e-2
c_cond = -1.24e1
d_cond = 0
c_condensing = a_cond * (1 / params.dT_approach) + b_cond * (params.T_surface_air_C + 273.15) + c_cond * (params.T_surface_air_C + 273.15) / params.dT_approach + d_cond * (1 / (params.dT_approach + input['dT_range']))
elif params.coolingMode == 'Dry':
a_cool = 7.31e3
b_cool = 0
c_cool = 0
d_cool = 1.23e3
c_cooling = a_cool * (1 / params.dT_approach) + b_cool * (params.T_surface_air_C + 273.15) + c_cool * (params.T_surface_air_C + 273.15) / params.dT_approach + d_cool * (1 / (params.dT_approach + input['dT_range']))
a_cond = 1.91e3
b_cond = 0
c_cond = 0
d_cond = 0
c_condensing = a_cond * (1 / params.dT_approach) + b_cond * (params.T_surface_air_C + 273.15) + c_cond * (params.T_surface_air_C + 273.15) / params.dT_approach + d_cond * (1 / (params.dT_approach + input['dT_range']))
else:
raise Exception('CapitalCost_CoolingTower:UnknownCoolingMode', 'Unknown Cooling Mode')
Q_Ref_BAC = 1e6 # Reference case 1000 kWth (1e6 Wth)
F_cooling = abs(input['Q_desuperheating']) / (abs(input['Q_desuperheating']) + abs(input['Q_condensing']))
C_Ref_BAC = Q_Ref_BAC * TDC * (F_cooling * (c_cooling / 1e3) + (1 - F_cooling) * (c_condensing / 1e3))
C_P = C_Ref_BAC * (abs(input['Q_desuperheating'] + input['Q_condensing']) / Q_Ref_BAC) ** 0.8
C_P = C_P * (PPI_current / PPI_ref) # USD
F_BM = 2.17 # Guthrie Bare-Module Factor for air-fin coolers
C_BM = C_P * F_BM # USD
return C_BM
@staticmethod
def tank(input, params):
PPI_ref = PPIs.return_PPI('PPI_Tank', 2013, params) # 2013 100.00
PPI_current = PPIs.return_PPI('PPI_Tank', params.costYear, params) # 2022 232.443
V = input['V_store'] # m^3
P = input['P_store'] / 6895 # psi
P_d = np.exp(0.60608 + 0.91615 * np.log(P) + 0.0015655 * (np.log(P) ** 2))
S = 15000 # psi
E = 0.85
N = 8 # number of vessels
D = ((V / N) * 4 / 3 / np.pi) ** (1 / 3) # m
D = D * 3.28084 # ft
L = D * 3 # ft
t_s = (P_d * (D * 12) / (2 * S * E - 1.2 * P_d)) # inches
rho_V = 0.284 # lb/in^3
W = np.pi * ((D * 12) + t_s) * ((L * 12) + 0.8 * (D * 12)) * t_s * rho_V # lb
C_V = np.exp(5.6336 + 0.4599 * np.log(W) + 0.00582 * (np.log(W)) ** 2)
C_V = C_V * N
C_PL = 2275 * D ** 0.2094
F_BM = 3.05 # Guthrie Bare-Module Factors for vertical pressure vessels
F_M = 1.0 # carbon steel
C_P = C_V * F_M + C_PL # USD
C_BM = C_P * F_BM # USD
C_BM = C_BM * (PPI_current / PPI_ref) # USD
return C_BM
# @staticmethod
# def cooling_water(input, params):
# CEPCI_current = PPIs.return_PPI('CEPCI', params.costYear, params) # 2022 816
# rho = CP.PropsSI('DMASS', 'P', P_pump_in, 'T', T_pump_in) # kg/m^3
# q = m_dot / rho # m^3/s
# a = 0.00007 + 2.5e-5 / q
# b = 0.003
# C_BM_unit = a * CEPCI_current + b # USD/m^3
# C_BM = C_BM_unit * q * 2.88e7 # USD/year
# return C_BM
# def refrigeration(input, params):
# CEPCI_current = PPIs.return_PPI('CEPCI', params.costYear, params) # 2022 816
# a = 0.5 * (Q_c / 1e3) ** (-0.9) / (T ** 3)
# b = 1.1e6 / T ** 5
# C_BM_unit = a * CEPCI_current + b # USD/kJ
# C_BM = C_BM_unit * (Q_c / 1e3) * 2.88e7 # USD/year
# return C_BM
def TCI(C_TBM):
C_site = 0.1 * C_TBM
C_buildings = 0.1 * C_TBM
C_offsite = 0.05 * C_TBM
C_TPI = 1.18 * (C_TBM + C_site + C_buildings + C_offsite)
F_ISF = 1.20
C_TPI = C_TPI * F_ISF
C_WC = 1.176 * C_TPI
C_TCI = C_TPI + C_WC
return C_TCI