{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/34089"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/34089","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Intelligent Parameter Adaptation for Chemical Processes","abstract":"Reducing the operating costs of chemical processes is very beneficial in decreasing a company's bottom line numbers. Since chemical processes are usually run in steady-state for long periods of time, saving a few dollars an hour can have significant long term effects. However, the complexity involved in most chemical processes from nonlinear dynamics makes them difficult processes to optimize. A nonlinear, open-loop unstable system, called the Tennessee Eastman Chemical Process Control Problem, is used as a test-bed problem for minimization routines. A decentralized controller is first developed that stabilizes the plant to set point changes and disturbances. Subsequently, a genetic algorithm calculates input parameters of the decentralized controller for minimum operating cost performance. Genetic algorithms use a directed search method based on the evolutionary principle of \"survival of the fittest\". They are powerful global optimization tools; however, they are typically computationally expensive and have long convergence times. To decrease the convergence time and avoid premature convergence to a local minimum solution, an auxiliary fuzzy logic controller was used to adapt the parameters of the genetic algorithm. The controller manipulates the input and output data through a set of linguistic IF-THEN rules to respond in a manner similar to human reasoning. The combination of a supervisory fuzzy controller and a genetic algorithm leads to near-optimum operating costs for a dynamically modeled chemical process.","abstract_html":"Reducing the operating costs of chemical processes is very beneficial in decreasing a company's bottom line numbers. Since chemical processes are usually run in steady-state for long periods of time, saving a few dollars an hour can have significant long term effects. However, the complexity involved in most chemical processes from nonlinear dynamics makes them difficult processes to optimize. A nonlinear, open-loop unstable system, called the Tennessee Eastman Chemical Process Control Problem, is used as a test-bed problem for minimization routines. A decentralized controller is first developed that stabilizes the plant to set point changes and disturbances. Subsequently, a genetic algorithm calculates input parameters of the decentralized controller for minimum operating cost performance. Genetic algorithms use a directed search method based on the evolutionary principle of "survival of the fittest". They are powerful global optimization tools; however, they are typically computationally expensive and have long convergence times. To decrease the convergence time and avoid premature convergence to a local minimum solution, an auxiliary fuzzy logic controller was used to adapt the parameters of the genetic algorithm. The controller manipulates the input and output data through a set of linguistic IF-THEN rules to respond in a manner similar to human reasoning. The combination of a supervisory fuzzy controller and a genetic algorithm leads to near-optimum operating costs for a dynamically modeled chemical process.","abstract_has_math":false,"creators":["Sozio, John Charles"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical Engineering","degree_department":"Electrical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["VanLandingham, Hugh F."],"committee_members":["Rony, Peter R.","Bay, John S."],"year":1999,"date_issued":"1999-07-08","date_published":"1999-07-08","updated_at":"2026-07-22T22:20:31Z","subjects":["Tennessee Eastman","decentralized process control","genetic algorithm","fuzzy logic"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-072099-122832"],"render_values":[{"text":"etd-072099-122832","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/34089","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["VanLandingham, Hugh F."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Rony, Peter R.","Bay, John S."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Sozio, John Charles"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:41:44Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:41:44Z","2000-07-23"]},{"key":"dc:date.issued","label":"Date","values":["1999-07-08"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Tennessee Eastman","decentralized process control","genetic algorithm","fuzzy logic"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-072099-122832"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/34089"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Reducing the operating costs of chemical processes is very beneficial in decreasing a company's bottom line numbers. Since chemical processes are usually run in steady-state for long periods of time, saving a few dollars an hour can have significant long term effects. However, the complexity involved in most chemical processes from nonlinear dynamics makes them difficult processes to optimize. A nonlinear, open-loop unstable system, called the Tennessee Eastman Chemical Process Control Problem, is used as a test-bed problem for minimization routines. A decentralized controller is first developed that stabilizes the plant to set point changes and disturbances. Subsequently, a genetic algorithm calculates input parameters of the decentralized controller for minimum operating cost performance. Genetic algorithms use a directed search method based on the evolutionary principle of \"survival of the fittest\". They are powerful global optimization tools; however, they are typically computationally expensive and have long convergence times. To decrease the convergence time and avoid premature convergence to a local minimum solution, an auxiliary fuzzy logic controller was used to adapt the parameters of the genetic algorithm. The controller manipulates the input and output data through a set of linguistic IF-THEN rules to respond in a manner similar to human reasoning. The combination of a supervisory fuzzy controller and a genetic algorithm leads to near-optimum operating costs for a dynamically modeled chemical process."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Intelligent Parameter Adaptation for Chemical Processes"]}]}],"canonical_facts":{"dc:contributor.committeechair":["VanLandingham, Hugh F."],"dc:contributor.committeemember":["Rony, Peter R.","Bay, John S."],"dc:contributor.department":["Electrical Engineering"],"dc:creator":["Sozio, John Charles"],"dc:date.accessioned":["2014-03-14T20:41:44Z"],"dc:date.available":["2014-03-14T20:41:44Z","2000-07-23"],"dc:date.issued":["1999-07-08"],"dc:description.abstract":["Reducing the operating costs of chemical processes is very beneficial in decreasing a company's bottom line numbers. Since chemical processes are usually run in steady-state for long periods of time, saving a few dollars an hour can have significant long term effects. However, the complexity involved in most chemical processes from nonlinear dynamics makes them difficult processes to optimize. A nonlinear, open-loop unstable system, called the Tennessee Eastman Chemical Process Control Problem, is used as a test-bed problem for minimization routines. A decentralized controller is first developed that stabilizes the plant to set point changes and disturbances. Subsequently, a genetic algorithm calculates input parameters of the decentralized controller for minimum operating cost performance. Genetic algorithms use a directed search method based on the evolutionary principle of \"survival of the fittest\". They are powerful global optimization tools; however, they are typically computationally expensive and have long convergence times. To decrease the convergence time and avoid premature convergence to a local minimum solution, an auxiliary fuzzy logic controller was used to adapt the parameters of the genetic algorithm. The controller manipulates the input and output data through a set of linguistic IF-THEN rules to respond in a manner similar to human reasoning. The combination of a supervisory fuzzy controller and a genetic algorithm leads to near-optimum operating costs for a dynamically modeled chemical process."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-072099-122832"],"dc:identifier.uri":["http://hdl.handle.net/10919/34089"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Tennessee Eastman","decentralized process control","genetic algorithm","fuzzy logic"],"dc:title":["Intelligent Parameter Adaptation for Chemical Processes"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:31Z"}