Process monitoring is required for safety of operations, considerable reduction in downtime, and decrease in manufacturing costs. Chemical Engineers have a high responsibility in the proper functioning of a process plant as any deviations from normal operations might lead to a disastrous effect in loss of lives and infrastructure. The increased number of microprocessors due to the reduction in cost (Effect of Moores Law) has increased the speed of computers. This has led to the increase in the amount of data storage. Thus, creating a scope for us to train machines to identify representations of the data. Data clustering, an exploratory data analytics technique helps us in the process of unsupervised learning: unclassified datasets. Previously, process monitoring has been done using statistical techniques such as component analyses, however one of the challenge in practical applications is the difficulty to classify (cluster) the information from a high dimension data set commonly encountered in chemical process industry. There are many clustering algorithms such as K-Means, Mean Shift, Hierarchical, DBSCAN. In this study a detailed analysis of the alternative approaches for data classification was performed including conventional and novel techniques arising from Computer Science. This is the first reported instance of the use of HDBSCAN, a hybrid of Hierarchical and Density Based Spatial Clustering with Applications and noise, in a chemical process data. We have concluded that HDBSCAN outperforms the other clustering algorithms on chemical process dataset. The Chemical process dataset used was from the Tennessee Eastman Process. Since, in this study we compared clustering algorithms we realized the need for an easy automation for running clustering algorithms ,hence we built an application called Mi ClustoRoma, a graphical user interface built on python for classifying and visualizing chemical process datasets.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-01092017-172223 |
| Date | 26 January 2017 |
| Creators | Gowri Shankar, Vikram |
| Contributors | Benton,Michael, Romagnoli,Jose, Flake,John |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-01092017-172223/ |
| Rights | restricted, I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
Page generated in 0.0014 seconds