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  1. from pyXSteam.XSteam import XSteam
  2. import matplotlib.pyplot as plt
  3.  
  4. steamTable = XSteam(XSteam.UNIT_SYSTEM_BARE)
  5.  
  6. batch_time = 5  # timmar
  7.  
  8. dm_eth = 11.37  # [m^3/h]
  9. T1 = [74.3, 72, 70, 75, 70]  # [C] dynamisk
  10. T2 = [13, 14, 15, 13, 12]  # [C] dynamisk
  11. P2 = 0.3  # MPa
  12.  
  13. dQ_C_list = []
  14. dQ_H_list = []
  15.  
  16.  
  17. def heat_sink(P2, h2, dm_h2o):  # Function heat sink
  18.     h3 = steamTable.hL_p(P2)
  19.     dQ_H = dm_h2o*(h2-h3)  # [kW]
  20.     return dQ_H
  21.  
  22.  
  23. def heat_exchange(T1, T2):
  24.     # density of ethanol
  25.     den = 785.3  # kg/m^3
  26.  
  27.     # specific heat capacity {isobaric gas} should be dynamic
  28.     c_eth = 2.18  # [kJ/kg K]
  29.  
  30.     # heat transferred to cooling agent
  31.     dQ_C = dm_eth * c_eth * (T1 - T2) * den
  32.  
  33.     # enthalpy at saturated steam
  34.     h_H = steamTable.hV_p(0.101325)
  35.  
  36.     s_L3 = steamTable.sL_p(0.3)
  37.     s_low = s_L3
  38.  
  39.     h_low = steamTable.h_ps(0.101325, s_low)
  40.  
  41.     dm_h2o = dQ_C / (h_H-h_low)
  42.  
  43.     # entropy of water at atmospheric pressure
  44.     s1 = steamTable.sV_p(0.101325)
  45.  
  46.     # entropy after compression given ideal conditions
  47.     s2 = s1
  48.  
  49.     # enthalpy entering heat exchanger
  50.     h2 = steamTable.h_ps(P2, s2)
  51.  
  52.     return dQ_C, h2, dm_h2o
  53.  
  54.  
  55. for x in range(batch_time):
  56.     dQ_C, h2, dm_h2o = heat_exchange(T1[x], T2[x])
  57.     dQ_C_list.append(dQ_C)
  58.     dQ_H_list.append(heat_sink(P2, h2, dm_h2o))
  59.  
  60.  
  61. fig, ax = plt.subplots()
  62. ax.plot(range(batch_time), dQ_C_list, '--o', label= 'Värmeöverföring Eth/kyl [kJ/timme]')
  63. ax.plot(range(batch_time), dQ_H_list, '--o', label= 'Värmeöverföring kyl/ånga [kJ/timme]')
  64. ax.set_xlabel('Tid [timme]')
  65. ax.set_ylabel('Värmeöverföring [kJ/timme]')
  66. ax.set_title('Kylbehov per timme under en körning')
  67. ax.axis([0, batch_time, 0, 1.1*max(dQ_C_list)])
  68. ax.legend()
  69. plt.show()
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