The development of fast and efficient computer hardware technology has resulted in the rapid development of numerous computational software tools for making statistical inferences. The computational algorithms, which are the backbone of these tools, originate from distinct areas in science, mathematics and engineering. The main focus of this thesis is on computational tools which can be employed for estimating unmeasured variables in a process using all the available prior information. Specifically, this thesis demonstrates the application of a variety of tools for soft sensing of process variables and uncertain parameters of physiochemical process models, using routine data available from the process. The application examples presented in this thesis come from broad areas where process uncertainty is inherent and includes petrochemical processes, mechanical valve actuators, and upstream production processes in petroleum reservoirs. The mathematical models that are employed in different domains vary significantly in their structure and their level of complexity. In the petrochemical domain, the focus was on developing empirical soft sensors which are essentially nonparametric mathematical models identified using routine data from the process. The Support Vector Regression technique was applied for identifying such nonparametric empirical models. On the other hand, in all the other application examples in this thesis the physical parametric models of the process were utilized. The latter application examples, which cover a major portion of this thesis, demonstrate the application of modern state and parameter estimation algorithms which are firmly grounded on Bayesian theory and Monte Carlo techniques. Prior to the chapters on the application of state and parameter estimation techniques, a tutorial overview of the Monte Carlo simulation based state estimation algorithms is provided with an attempt to throw new light on these techniques. The tutorial is aimed at making these techniques simple to visualize and understand. The application case studies serve to illustrate the performance of the different algorithms. All case studies presented in this thesis are performed on processes that exhibit significant nonlinearity in terms of the relationship between the process input variables and output variables. / Process Control
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/1622 |
| Date | 06 1900 |
| Creators | Balakrishnapillai Chitralekha, Saneej |
| Contributors | Shah, Sirish (Chemical and Materials Engineering), Huang, Biao (Chemical and Materials Engineering), Prasad, Vinay (Chemical and Materials Engineering), Lynch, Alan (Electrical and Computer Engineering), Trivedi, Japan (Civil and Environmental Engineering), Foss, Bjarne (Engineering Cybernetics, Norwegian University of Science and Technology) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 7078454 bytes, application/pdf |
| Relation | Chitralekha, S. B. & Shah, S. L. (2010). Application of Support Vector Regression for developing soft sensors for nonlinear processes. The Canadian Journal of Chemical Engineering, Vol. 88, Issue 5, Pages 696709., Chitralekha, S. B., Shah, S. L., & Prakash, J. (2010). Detection and quantification of valve stiction by the method of unknown input estimation. Journal of Process Control, Vol. 20, Issue 2, Pages 206 216., Chitralekha, S. B., Prakash, J., Raghavan, H., Gopaluni, R., & Shah, S. L. (2010). A comparison of simultaneous state and parameter estimation schemes for a continuous fermentor reactor. Journal of Process Control, Vol. 20, Issue 8, Pages 934 943., Chitralekha, S. B., Trivedi, J. J., & Shah, S. L. (2010). Application of the ensemble Kalman filter for characterization and history matching of unconventional oil reservoirs. SPE-137480. |
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