-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathlevel4_plot.py
218 lines (203 loc) · 9.42 KB
/
level4_plot.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#--------------- Style ---------------------
plt.rcParams['font.monospace'] = 'Ubuntu Mono'
plt.rcParams['font.size'] = 15
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['axes.labelweight'] = 'bold'
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
plt.rcParams['legend.fontsize'] = 15
plt.rcParams['legend.fancybox'] = True
plt.rcParams['legend.shadow'] = True
plt.rcParams['lines.linewidth'] = 2
#---------------- Graphs ------------------
def f1():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="Flashes", usecols='A:H', skiprows=1).to_numpy()
plt.plot(df[:, 0], df[:, 6]*100, label="Ethylene Vapour")
plt.plot(df[:, 0], df[:, 7]*100, label="Methanol Liquid")
plt.ylabel("Recovery (%)")
plt.xlabel("Temperatrue (°C)")
plt.legend()
plt.show()
def f2():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="Flashes", usecols='K:R', skiprows=1).to_numpy()
plt.plot(df[:, 0], df[:, 6]*100, label="Ethylene Vapour")
plt.plot(df[:, 0], df[:, 7]*100, label="Methanol Liquid")
plt.ylabel("Recovery (%)")
plt.xlabel("Temperatrue (°C)")
plt.legend()
plt.show()
def dec():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="Decanter", usecols='A:N', skiprows=1).to_numpy()
plt.plot(df[:, 0], df[:, 10]*100, label="Water Recovery")
plt.plot(df[:, 0], df[:, 11]*100, label="Toluene Recovery")
plt.plot(df[:, 0], df[:, 12]*100, label="P-Xylene Recovery")
plt.plot(df[:, 0], df[:, 13]*100, label="Benzene Recovery")
plt.ylabel("Recovery (%)")
plt.xlabel("Temperatrue (°C)")
plt.legend()
plt.show()
def dis1():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="AqueDis", usecols='A:N', skiprows=1).to_numpy()
fig, ax = plt.subplots(2, 1)
ax[0].plot(df[:, 0], df[:, 5]*100, label="Water Recovery")
ax[0].plot(df[:, 0], df[:, 6]*100, label="Methanol Recovery")
ax[0].set_xlabel("Feed Stage")
ax[0].set_ylabel("Recovery (%)")
ax[1].plot(df[:, 7], df[:, 12]*100, label="Water Recovery")
ax[1].plot(df[:, 7], df[:, 13]*100, label="Methanol Recovery")
ax[1].set_xlabel("Condenser Pressure(Bar)")
ax[1].set_ylabel("Recovery (%)")
ax[1].legend(loc='upper center', bbox_to_anchor=(0.5, 2.9), ncol=2)
plt.subplots_adjust(hspace=0.5)
plt.show()
def dis3():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="AqueDis", usecols='O:AB', skiprows=1).to_numpy()
fig, ax = plt.subplots(2, 1)
ax[0].plot(df[:, 0], df[:, 5]*100, label="Water Recovery")
ax[0].plot(df[:, 0], df[:, 6]*100, label="Methanol Recovery")
ax[0].set_xlabel("Feed Stage")
ax[0].set_ylabel("Recovery (%)")
ax[1].plot(df[:, 7], df[:, 12]*100, label="Water Recovery")
ax[1].plot(df[:, 7], df[:, 13]*100, label="Methanol Recovery")
ax[1].set_xlabel("Condenser Pressure(Bar)")
ax[1].set_ylabel("Recovery (%)")
ax[1].legend(loc='upper center', bbox_to_anchor=(0.5, 2.9), ncol=2)
plt.subplots_adjust(hspace=0.5)
plt.show()
def dis2():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="OrgDis", usecols='A:N', skiprows=1).to_numpy()
fig, ax = plt.subplots(2, 1)
ax[0].plot(df[:, 0], df[:, 5]*100, label="Toluene Recovery")
ax[0].plot(df[:, 0], df[:, 6]*100, label="P-Xylene Recovery")
ax[0].set_xlabel("Feed Stage")
ax[0].set_ylabel("Recovery (%)")
ax[1].plot(df[:, 7], df[:, 12]*100, label="Toluene Recovery")
ax[1].plot(df[:, 7], df[:, 13]*100, label="P-Xylene Recovery")
ax[1].set_xlabel("Condenser Pressure(Bar)")
ax[1].set_ylabel("Recovery (%)")
ax[1].legend(loc='upper center', bbox_to_anchor=(0.5, 2.9), ncol=2)
plt.subplots_adjust(hspace=0.5, left = 0.15)
plt.show()
def dis4():
df1 = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="XyDis", usecols='A:M', skiprows=1).to_numpy()
df2 = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="XyDis", usecols='O:AA', skiprows=1).to_numpy()
fig, ax = plt.subplots(2, 1)
ax[0].plot(df1[:, 0], df1[:, 9]*100, label="Toluene Recovery")
ax[0].plot(df1[:, 0], df1[:, 10]*100, label="P-Xylene Recovery")
ax[0].plot(df1[:, 0], df1[:, 11]*100, label="M-Xylene Recovery")
ax[0].plot(df1[:, 0], df1[:, 12]*100, label="O-Xylene Recovery")
ax[0].set_xlabel("Feed Stage")
ax[0].set_ylabel("Recovery (%)")
ax[1].plot(df2[:, 0], df2[:, 9]*100, label="Toluene Recovery")
ax[1].plot(df2[:, 0], df2[:, 10]*100, label="P-Xylene Recovery")
ax[1].plot(df2[:, 0], df2[:, 11]*100, label="M-Xylene Recovery")
ax[1].plot(df2[:, 0], df2[:, 12]*100, label="O-Xylene Recovery")
ax[1].set_xlabel("Condenser Pressure(Bar)")
ax[1].set_ylabel("Recovery (%)")
ax[1].legend(loc='upper center', bbox_to_anchor=(0.5, 2.7), ncol=2)
plt.subplots_adjust(hspace=0.3)
plt.show()
def dis5():
df1 = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="XyDis", usecols='AC:AO', skiprows=1).to_numpy()
df2 = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="XyDis", usecols='AQ:BC', skiprows=1).to_numpy()
fig, ax = plt.subplots(2, 1)
ax[0].plot(df1[:, 0], df1[:, 9]*100, label="Toluene Recovery")
ax[0].plot(df1[:, 0], df1[:, 10]*100, label="P-Xylene Recovery")
ax[0].plot(df1[:, 0], df1[:, 11]*100, label="M-Xylene Recovery")
ax[0].plot(df1[:, 0], df1[:, 12]*100, label="O-Xylene Recovery")
ax[0].set_xlabel("Feed Stage")
ax[0].set_ylabel("Recovery (%)")
ax[1].plot(df2[:, 0], df2[:, 9]*100, label="Toluene Recovery")
ax[1].plot(df2[:, 0], df2[:, 10]*100, label="P-Xylene Recovery")
ax[1].plot(df2[:, 0], df2[:, 11]*100, label="M-Xylene Recovery")
ax[1].plot(df2[:, 0], df2[:, 12]*100, label="O-Xylene Recovery")
ax[1].set_xlabel("Condenser Pressure(Bar)")
ax[1].set_ylabel("Recovery (%)")
ax[1].legend(loc='upper center', bbox_to_anchor=(0.5, 2.7), ncol=2)
plt.subplots_adjust(hspace=0.3)
plt.show()
def dis6():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="XyDis", usecols='BE:BO', skiprows=1).to_numpy()
fig, ax = plt.subplots(2, 1)
ax[0].plot(df[:, 0], df[:, 3]*100, label="O-Xylene Purity")
ax[0].plot(df[:, 0], df[:, 4]*100, label="O-Xylene Recovery")
ax[0].set_xlabel("Feed Stage")
ax[0].set_ylabel("Recovery (%)")
ax[1].plot(df[:, 6], df[:, 9]*100, label="O-Xylene Purity")
ax[1].plot(df[:, 6], df[:, 10]*100, label="O-Xylene Recovery")
ax[1].set_xlabel("Condenser Pressure(Bar)")
ax[1].set_ylabel("Recovery (%)")
ax[1].legend(loc='upper center', bbox_to_anchor=(0.5, 2.9), ncol=2)
plt.subplots_adjust(hspace=0.5)
plt.show()
def dis7():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="BenDis", usecols='A:O', skiprows=1).to_numpy()
fig, ax = plt.subplots(2, 1)
ax[0].plot(df[:, 0], df[:, 5]*100, label="Benzene Recovery")
ax[0].plot(df[:, 0], df[:, 6]*100, label="Toluene Recovery")
ax[0].set_xlabel("Feed Stage")
ax[0].set_ylabel("Recovery (%)")
ax[1].plot(df[:, 8], df[:, 13]*100, label="Benzene Recovery")
ax[1].plot(df[:, 8], df[:, 14]*100, label="Toluene Recovery")
ax[1].set_xlabel("Condenser Pressure(Bar)")
ax[1].set_ylabel("Recovery (%)")
ax[1].legend(loc='upper center', bbox_to_anchor=(0.5, 2.9), ncol=2)
plt.subplots_adjust(hspace=0.5)
plt.show()
def dis8():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="BenDis", usecols='Q:AE', skiprows=1).to_numpy()
fig, ax = plt.subplots(2, 1)
ax[0].plot(df[:, 0], df[:, 5]*100, label="Benzene Recovery")
ax[0].plot(df[:, 0], df[:, 6]*100, label="Methanol Recovery")
ax[0].set_xlabel("Feed Stage")
ax[0].set_ylabel("Recovery (%)")
ax[1].plot(df[:, 8], df[:, 13]*100, label="Benzene Recovery")
ax[1].plot(df[:, 8], df[:, 14]*100, label="Methanol Recovery")
ax[1].set_xlabel("Condenser Pressure(Bar)")
ax[1].set_ylabel("Recovery (%)")
ax[1].legend(loc='upper center', bbox_to_anchor=(0.5, 2.9), ncol=2)
plt.subplots_adjust(hspace=0.5)
plt.show()
def dis9():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="BenDis", usecols='AG:AW', skiprows=1).to_numpy()
fig, ax = plt.subplots(2, 1)
ax[0].plot(df[:, 0], df[:, 5]*100, label="Benzene Purity")
ax[0].plot(df[:, 0], df[:, 6]*100, label="Toluene Recovery")
ax[0].plot(df[:, 0], df[:, 7]*100, label="Benzene Recovery")
ax[0].set_xlabel("Feed Stage")
ax[0].set_ylabel("Recovery (%)")
ax[1].plot(df[:, 9], df[:, 14]*100, label="Benzene Purity")
ax[1].plot(df[:, 9], df[:, 15]*100, label="Toluene Recovery")
ax[1].plot(df[:, 9], df[:, 16]*100, label="Benzene Recovery")
ax[1].set_xlabel("Condenser Pressure(Bar)")
ax[1].set_ylabel("Recovery (%)")
ax[1].legend(loc='upper center', bbox_to_anchor=(0.5, 2.7), ncol=2)
plt.subplots_adjust(hspace=0.3)
plt.show()
def dis10():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="BenDis", usecols='AY:BI', skiprows=1).to_numpy()
fig, ax = plt.subplots(2, 1)
ax[0].plot(df[:, 0], df[:, 3]*100, label="Benzene Purity")
ax[0].plot(df[:, 0], df[:, 4]*100, label="Benzene Recovery")
ax[0].set_xlabel("Feed Stage")
ax[0].set_ylabel("Recovery (%)")
ax[1].plot(df[:, 6], df[:, 9]*100, label="Benzene Purity")
ax[1].plot(df[:, 6], df[:, 10]*100, label="Benzene Recovery")
ax[1].set_xlabel("Condenser Pressure(Bar)")
ax[1].set_ylabel("Recovery (%)")
ax[1].legend(loc='upper center', bbox_to_anchor=(0.5, 2.9), ncol=2)
plt.subplots_adjust(hspace=0.5)
plt.show()
def crys():
df = pd.read_excel("Data/Separation Unit Sensitivity.xlsx", sheet_name="Crystal", usecols='B:E', skiprows=1).to_numpy()
plt.plot(df[:, 0], df[:, 3])
plt.ylabel("Recovery (%)")
plt.xlabel("Temperatrue (K)")
plt.subplots_adjust(left = 0.15)
plt.show()
#-------------- Execute -------------------
f1()
f2()