Vapor pressure-OpenMV.net Datasets

Vapor pressure

Description: Hourly process measurements from a distillation column that stabilises a hydrocarbon product stream in a refinery.
Data source: The goal is a soft sensor: predict the vapour pressure of the product from routinely measured process tags, between the infrequent laboratory analyses. The record spans hours of continuous operation. Over that time the process drifts to different operating points, so a model built once and left unchanged gradually loses accuracy; the data are well suited to studying adaptive / recursive models and multivariate process monitoring. The data tag identifiers, absolute dates and the site are removed. Time is given as hours elapsed from the start of the record, and all quantities are in SI units.
Data shape: 18743 rows and 30 columns
Usage restrictions: Unknown
Contact person: Kevin Dunn
Contact details: [email protected]
Added here on: 18 July 2026 6:08
Last updated: 18 July 2026 6:11

Load this dataset in Python

import pandas as pd

df = pd.read_csv("https://openmv.net/file/vapor-pressure.csv")
df.describe()

Preview (first 10 rows)

hours_elapsed	temp_01	temp_02	temp_03	temp_04	temp_05	temp_06	temp_07	temp_08	temp_09	temp_10	temp_11	temp_12	temp_13	valve_out_01	valve_out_02	flow_01	flow_02	flow_03	pres_01	analyzer_01	reb_temp_rise	reb_temp_avg	inv_reb_temp	inv_feed_temp	inv_bot_temp	inv_cond_pres	antoine_estimate	current_estimator
0	49.424	201.825	44.789	262.505	261.55	71.048	74.498	261.347	260.235	563.447	261.54	253.493	272.761	57.557	74.213	338.508	37.122	45.928	1723.164	4.162	9.012	261.409	1.871	2.105	1.899	0.548	28.141	34.285
1	49.067	201.42	44.511	262.122	261.073	70.359	73.855	260.846	259.756	561.856	260.897	253.007	272.369	56.827	73.773	336.351	36.778	45.919	1722.865	4.223	9.115	260.949	1.872	2.107	1.901	0.548	28.397	34.458
2	48.998	201.468	44.516	262.003	261.017	70.14	73.647	260.923	259.765	563.573	261.193	252.874	272.192	57.037	73.687	339.491	36.81	45.854	1722.905	3.962	9.129	260.927	1.872	2.107	1.901	0.548	28.469	34.527
3	48.617	201.584	44.521	261.296	260.404	69.329	72.822	260.614	259.544	559.84	261.164	252.104	271.771	59.467	74.487	348.488	36.881	45.835	1723.264	4.113	9.191	260.464	1.874	2.106	1.904	0.548	28.893	34.666
4	48.882	201.478	44.374	261.987	260.886	69.609	73.081	260.942	260.38	560.477	262.0	252.71	272.179	61.897	74.077	367.106	36.21	45.926	1724.082	3.912	9.277	261.049	1.872	2.107	1.902	0.548	28.576	34.671
5	48.707	201.685	44.326	262.045	261.208	70.005	73.362	261.052	260.322	561.431	262.254	252.864	272.639	61.961	73.77	368.383	36.428	46.028	1723.164	3.902	9.181	261.157	1.872	2.106	1.901	0.548	28.478	34.573
6	48.52	201.084	44.169	262.068	261.019	69.145	72.784	261.225	260.376	559.801	261.997	252.764	272.353	62.194	73.983	363.693	36.281	45.946	1723.145	3.882	9.303	261.172	1.872	2.109	1.901	0.548	28.532	34.583
7	48.548	201.368	44.279	262.029	261.079	69.24	72.744	261.087	260.412	560.404	262.428	252.822	272.366	62.048	74.207	364.731	36.216	46.12	1723.264	3.976	9.207	261.152	1.872	2.107	1.901	0.548	28.503	34.529
8	48.321	201.241	44.253	262.061	261.154	69.135	72.621	260.99	260.151	563.958	262.293	252.751	272.453	61.878	73.533	365.751	36.03	45.871	1723.264	3.774	9.31	261.089	1.872	2.108	1.901	0.548	28.541	34.55
9	48.263	201.516	44.313	262.215	261.294	68.888	72.509	261.042	260.093	565.617	261.084	252.955	272.504	60.01	73.965	351.846	36.948	45.907	1723.464	3.771	9.26	261.161	1.872	2.107	1.901	0.548	28.434	34.459