Tennesee Eastman Process Data

Module for getting Tennesee Eastman Process data 
from dPCA.lag import *
from dPCA.corrmat import *

We are now going to try and automate the analysis of PACM and ACM by simply utilising loops and eigenvalues. First however we are going to bring in some data from the Tennesee Eastman Process (TEP).

filepath = 'https://github.com/waterboy96/TEPData/blob/1ac08a54cb9d420ff4bc0c3f0076ca06dc2ec7e4/TEP.csv?raw=true'
TEP = pd.read_csv(filepath, index_col = [0]); TEP.head()
0 1 2 3 4 5 6 7 8 9 ... 42 43 44 45 46 47 48 49 50 51
0 0.24889 3702.3 4502.7 9.4170 26.996 42.183 2705.2 75.173 120.40 0.33611 ... 54.059 24.804 63.269 21.950 40.188 39.461 47.000 47.594 41.384 18.905
1 0.24904 3666.2 4526.0 9.2682 26.710 42.332 2705.5 74.411 120.41 0.33676 ... 53.781 24.790 62.171 22.239 40.108 43.710 46.128 47.508 41.658 18.976
2 0.25034 3673.3 4501.3 9.4212 26.842 42.360 2705.3 75.125 120.41 0.33739 ... 54.075 24.669 61.585 22.191 40.030 39.480 44.121 47.612 41.721 16.562
3 0.25109 3657.8 4497.8 9.3792 26.528 41.982 2707.3 73.992 120.38 0.33664 ... 54.117 24.595 61.561 21.959 40.121 32.848 45.858 47.459 40.836 20.094
4 0.24563 3698.0 4537.4 9.3746 26.736 42.354 2705.3 75.283 120.42 0.32521 ... 53.906 24.451 61.388 22.271 39.538 36.682 45.753 47.458 41.727 18.330

5 rows × 52 columns

namepath = 'https://github.com/waterboy96/TEPData/blob/main/TEPnames.xlsx?raw=true'
names = pd.read_excel(namepath, index_col = [0], header = None); names.head()
1 2 3 4 5 6 7 8 9 10 ... 43 44 45 46 47 48 49 50 51 52
0
Type Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous ... Manipulated Manipulated Manipulated Manipulated Manipulated Manipulated Manipulated Manipulated Manipulated Manipulated
Subtype Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous ... Manipulated Manipulated Manipulated Manipulated Manipulated Manipulated Manipulated Manipulated Manipulated Manipulated
Block Feed Feed Feed Feed Feed Feed Reactor Reactor Reactor Separator ... Feed Feed Compressor Separator Separator Stripper Stripper Reactor Stripper Reactor
Name A Feed D Feed E Feed A and C feed Recycle flow Reactor feed rate Reactor pressure Reactor level Reactor temperature Purge rate ... A feed flow A and C feed flow Compressor recycle valve Purge valve Separator pot liquid flow Stripper liquid product flow Stripper steam valve Reactor cooling water flow Condenser cooling water flow Agitator speed

4 rows × 52 columns

TEP.columns = names.loc['Name']; TEP.head()
Name A Feed D Feed E Feed A and C feed Recycle flow Reactor feed rate Reactor pressure Reactor level Reactor temperature Purge rate ... A feed flow A and C feed flow Compressor recycle valve Purge valve Separator pot liquid flow Stripper liquid product flow Stripper steam valve Reactor cooling water flow Condenser cooling water flow Agitator speed
0 0.24889 3702.3 4502.7 9.4170 26.996 42.183 2705.2 75.173 120.40 0.33611 ... 54.059 24.804 63.269 21.950 40.188 39.461 47.000 47.594 41.384 18.905
1 0.24904 3666.2 4526.0 9.2682 26.710 42.332 2705.5 74.411 120.41 0.33676 ... 53.781 24.790 62.171 22.239 40.108 43.710 46.128 47.508 41.658 18.976
2 0.25034 3673.3 4501.3 9.4212 26.842 42.360 2705.3 75.125 120.41 0.33739 ... 54.075 24.669 61.585 22.191 40.030 39.480 44.121 47.612 41.721 16.562
3 0.25109 3657.8 4497.8 9.3792 26.528 41.982 2707.3 73.992 120.38 0.33664 ... 54.117 24.595 61.561 21.959 40.121 32.848 45.858 47.459 40.836 20.094
4 0.24563 3698.0 4537.4 9.3746 26.736 42.354 2705.3 75.283 120.42 0.32521 ... 53.906 24.451 61.388 22.271 39.538 36.682 45.753 47.458 41.727 18.330

5 rows × 52 columns

source

GetTEP

 GetTEP ()
TEP = GetTEP()
TEP
Name A Feed D Feed E Feed A and C feed Recycle flow Reactor feed rate Reactor pressure Reactor level Reactor temperature Purge rate ... A feed flow A and C feed flow Compressor recycle valve Purge valve Separator pot liquid flow Stripper liquid product flow Stripper steam valve Reactor cooling water flow Condenser cooling water flow Agitator speed
0 0.24889 3702.3 4502.7 9.4170 26.996 42.183 2705.2 75.173 120.40 0.33611 ... 54.059 24.804 63.269 21.950 40.188 39.461 47.000 47.594 41.384 18.905
1 0.24904 3666.2 4526.0 9.2682 26.710 42.332 2705.5 74.411 120.41 0.33676 ... 53.781 24.790 62.171 22.239 40.108 43.710 46.128 47.508 41.658 18.976
2 0.25034 3673.3 4501.3 9.4212 26.842 42.360 2705.3 75.125 120.41 0.33739 ... 54.075 24.669 61.585 22.191 40.030 39.480 44.121 47.612 41.721 16.562
3 0.25109 3657.8 4497.8 9.3792 26.528 41.982 2707.3 73.992 120.38 0.33664 ... 54.117 24.595 61.561 21.959 40.121 32.848 45.858 47.459 40.836 20.094
4 0.24563 3698.0 4537.4 9.3746 26.736 42.354 2705.3 75.283 120.42 0.32521 ... 53.906 24.451 61.388 22.271 39.538 36.682 45.753 47.458 41.727 18.330
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
955 0.23955 3687.2 4581.0 9.3941 26.878 42.199 2705.2 75.573 120.41 0.33658 ... 54.571 24.129 62.237 22.005 41.145 38.419 45.451 47.510 41.466 16.998
956 0.23352 3625.4 4500.9 9.3884 26.754 42.477 2708.3 74.372 120.41 0.33708 ... 54.741 23.006 58.477 22.337 40.351 38.657 47.279 47.567 40.971 15.621
957 0.23440 3660.3 4535.7 9.3709 27.034 42.302 2707.3 75.292 120.40 0.34096 ... 54.324 22.919 61.946 22.227 39.877 41.288 44.007 47.338 41.891 21.744
958 0.23611 3645.0 4506.9 9.1996 26.769 42.252 2704.2 74.956 120.38 0.35081 ... 53.732 23.630 62.816 21.982 41.638 42.218 40.647 47.266 39.813 18.826
959 0.23729 3666.8 4511.1 9.2764 26.467 42.330 2702.0 75.270 120.41 0.34809 ... 53.682 23.514 60.518 21.642 41.970 34.258 41.110 47.165 40.500 18.353

960 rows × 52 columns