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Simulating and Analyzing Experiments in the Tennessee Eastman Process Simulator
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
2015 (English)In: ENBIS-15, 2015Conference paper, Presentation (Refereed)
Abstract [en]

In many of today’s continuous processes, the data collection is usually performed automatically yielding exorbitant amount of data on various quality characteristics and inputs to the system. Moreover, such data are usually collected at high frequency introducing significant serial dependence in time. This violates the independent data assumption of many industrial statistics methods used in process improvement studies. These studies often involve controlled experiments to unearth the causal relationships to be used for robustness and optimization purposes.

However real production processes are not suitable for studying new experimental methodologies, partly because unknown disturbances/experimental settings may lead to erroneous conclusions. Moreover large scale experimentation in production processes is frowned upon due to consequent disturbances and production delays. Hence realistic simulation of such processes offers an excellent opportunity for experimentation and methodological development.

One commonly used process simulator is the Tennessee Eastman (TE) challenge chemical process simulator (Downs & Vogel, 1993)[1]. The process produces two products from four reactants, containing 41 measured variables and 12 manipulated variables. In addition to the process description, the problem statement defines process constraints, 20 types of process disturbances, and six operating modes corresponding to different production rates and mass ratios in the product stream.

The purpose of this paper is to illustrate the use of the TE process with an appropriate feedback control as a test-bed for the methodological developments of new experimental design and analysis techniques.

The paper illustrates how two-level experimental designs can be used to identify how the input factors affect the outputs in a chemical process.

Simulations using Matlab/Simulink software are used to study the impact of e.g. process disturbances, closed loop control and autocorrelated data on different experimental arrangements.

The experiments are analysed using a time series analysis approach to identify input-output relationships in a process operating in closed-loop with multivariate responses. The dynamics of the process are explored and the necessary run lengths for stable effect estimates are discussed.

Place, publisher, year, edition, pages
2015.
National Category
Other Engineering and Technologies Other Engineering and Technologies not elsewhere specified Reliability and Maintenance
Research subject
Quality Technology and Management
Identifiers
URN: urn:nbn:se:ltu:diva-63238OAI: oai:DiVA.org:ltu-63238DiVA: diva2:1093003
Conference
15th Annual Conference of ENBIS, Prague, Czech Republic, 6-10 september 2015
Projects
Statistical Methods for Improving Continuous Production
Available from: 2017-05-04 Created: 2017-05-04 Last updated: 2017-05-10Bibliographically approved

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