The petroleum, chemical, and electrochemical industries operate a wide variety of multivariable processes that can benefit from advanced modeling, estimation, and control methods. Traditional linear methods can be applied to these systems, but this generally results in sub-optimal control. This dissertation presents new estimation, modeling, and control methods that address process nonlinearities and constraint handling capabilities under uncertainty. These methods are based on Moving Horizon Estimation (MHE), Nonlinear Model Predictive Control (NMPC), and Polynomial Chaos Theory (PCT).
When implementing control on real multivariable chemical or petrochemical processes such as distillation or separation operations, it is essential to ensure that the process remains within established safety limits and that each product meets certain quality constraints and specifications. Modeling and Control of a refinery simulation facility are presented using second order Volterra series models and a NMPC formulation that uses hard constraints on the actuator inputs. Realistic process data was generated using a dynamic refinery model simulated by Aspen HYSYS. This data was then used to determine the optimal model for use with NMPC. Advantages of incorporation of PCT to model a nonlinear process with parametric uncertainty and the use of the expanded model with NMPC are discussed in this work. It is shown that the proposed formulation can be applied with an adequate tuning to minimize the effect of parametric uncertainty on the process outputs. This work uses a two-tank model as a nonlinear case-study. A new algorithm is presented to estimate the uncertain parameters and states of a Lithium-Ion battery pack under various configurations of cells. Each configuration requires additional constraints on the process variables that are incorporated in a charge-discharge protocol. The empirical method is based on Moving Horizon Estimation and performs optimization to determine the optimal rates of change of the uncertain parameters. Past currents influence the uncertain parameters through an integral term, while the state of charge dependent open-circuit voltage is corrected by an ohmic term to account for the physical deterioration of the battery.
","abstract_html":"<p>The petroleum, chemical, and electrochemical industries operate a wide variety of multivariable processes that can benefit from advanced modeling, estimation, and control methods. Traditional linear methods can be applied to these systems, but this generally results in sub-optimal control. This dissertation presents new estimation, modeling, and control methods that address process nonlinearities and constraint handling capabilities under uncertainty. These methods are based on Moving Horizon Estimation (MHE), Nonlinear Model Predictive Control (NMPC), and Polynomial Chaos Theory (PCT).</p> <p>When implementing control on real multivariable chemical or petrochemical processes such as distillation or separation operations, it is essential to ensure that the process remains within established safety limits and that each product meets certain quality constraints and specifications. Modeling and Control of a refinery simulation facility are presented using second order Volterra series models and a NMPC formulation that uses hard constraints on the actuator inputs. Realistic process data was generated using a dynamic refinery model simulated by Aspen HYSYS. This data was then used to determine the optimal model for use with NMPC. Advantages of incorporation of PCT to model a nonlinear process with parametric uncertainty and the use of the expanded model with NMPC are discussed in this work. It is shown that the proposed formulation can be applied with an adequate tuning to minimize the effect of parametric uncertainty on the process outputs. This work uses a two-tank model as a nonlinear case-study. A new algorithm is presented to estimate the uncertain parameters and states of a Lithium-Ion battery pack under various configurations of cells. Each configuration requires additional constraints on the process variables that are incorporated in a charge-discharge protocol. The empirical method is based on Moving Horizon Estimation and performs optimization to determine the optimal rates of change of the uncertain parameters. Past currents influence the uncertain parameters through an integral term, while the state of charge dependent open-circuit voltage is corrected by an ohmic term to account for the physical deterioration of the battery.</p>","abstract_has_math":false,"creators":["Aliyev, Timur"],"institution":null,"degree_name":"Ph.D.","degree_level":"Campus Access Dissertation","degree_discipline":"Chemical Engineering","degree_department":null,"school":null,"contributors":["Edward Gatzke"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-01-01T08:00:00Z","date_published":"2010-01-01T08:00:00Z","updated_at":"2026-07-24T04:36:56Z","subjects":["Chemical Engineering","Engineering","Dynamic Optimization","Model Predictive Control","Moving Horizon Estimation","NMPC","Polynomial Chaos Theory"],"languages":[],"rights":["© 2010, Timur Aliyev"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarcommons.sc.edu/etd/125","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Edward Gatzke"]},{"key":"dc:creator","label":"Author","values":["Aliyev, Timur"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Chemical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Campus Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Chemical Engineering","Engineering","Dynamic Optimization","Model Predictive Control","Moving Horizon Estimation","NMPC","Polynomial Chaos Theory"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© 2010, Timur Aliyev"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarcommons.sc.edu/etd/125"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The petroleum, chemical, and electrochemical industries operate a wide variety of multivariable processes that can benefit from advanced modeling, estimation, and control methods. Traditional linear methods can be applied to these systems, but this generally results in sub-optimal control. This dissertation presents new estimation, modeling, and control methods that address process nonlinearities and constraint handling capabilities under uncertainty. These methods are based on Moving Horizon Estimation (MHE), Nonlinear Model Predictive Control (NMPC), and Polynomial Chaos Theory (PCT).
When implementing control on real multivariable chemical or petrochemical processes such as distillation or separation operations, it is essential to ensure that the process remains within established safety limits and that each product meets certain quality constraints and specifications. Modeling and Control of a refinery simulation facility are presented using second order Volterra series models and a NMPC formulation that uses hard constraints on the actuator inputs. Realistic process data was generated using a dynamic refinery model simulated by Aspen HYSYS. This data was then used to determine the optimal model for use with NMPC. Advantages of incorporation of PCT to model a nonlinear process with parametric uncertainty and the use of the expanded model with NMPC are discussed in this work. It is shown that the proposed formulation can be applied with an adequate tuning to minimize the effect of parametric uncertainty on the process outputs. This work uses a two-tank model as a nonlinear case-study. A new algorithm is presented to estimate the uncertain parameters and states of a Lithium-Ion battery pack under various configurations of cells. Each configuration requires additional constraints on the process variables that are incorporated in a charge-discharge protocol. The empirical method is based on Moving Horizon Estimation and performs optimization to determine the optimal rates of change of the uncertain parameters. Past currents influence the uncertain parameters through an integral term, while the state of charge dependent open-circuit voltage is corrected by an ohmic term to account for the physical deterioration of the battery.
"]},{"key":"dc:title","label":"Title","values":["Efficient Identification and Control Methods For Nonlinear Systems Under Uncertainty"]}]}],"canonical_facts":{"dc:contributor":["Edward Gatzke"],"dc:creator":["Aliyev, Timur"],"dc:description.abstract":["The petroleum, chemical, and electrochemical industries operate a wide variety of multivariable processes that can benefit from advanced modeling, estimation, and control methods. Traditional linear methods can be applied to these systems, but this generally results in sub-optimal control. This dissertation presents new estimation, modeling, and control methods that address process nonlinearities and constraint handling capabilities under uncertainty. These methods are based on Moving Horizon Estimation (MHE), Nonlinear Model Predictive Control (NMPC), and Polynomial Chaos Theory (PCT).
When implementing control on real multivariable chemical or petrochemical processes such as distillation or separation operations, it is essential to ensure that the process remains within established safety limits and that each product meets certain quality constraints and specifications. Modeling and Control of a refinery simulation facility are presented using second order Volterra series models and a NMPC formulation that uses hard constraints on the actuator inputs. Realistic process data was generated using a dynamic refinery model simulated by Aspen HYSYS. This data was then used to determine the optimal model for use with NMPC. Advantages of incorporation of PCT to model a nonlinear process with parametric uncertainty and the use of the expanded model with NMPC are discussed in this work. It is shown that the proposed formulation can be applied with an adequate tuning to minimize the effect of parametric uncertainty on the process outputs. This work uses a two-tank model as a nonlinear case-study. A new algorithm is presented to estimate the uncertain parameters and states of a Lithium-Ion battery pack under various configurations of cells. Each configuration requires additional constraints on the process variables that are incorporated in a charge-discharge protocol. The empirical method is based on Moving Horizon Estimation and performs optimization to determine the optimal rates of change of the uncertain parameters. Past currents influence the uncertain parameters through an integral term, while the state of charge dependent open-circuit voltage is corrected by an ohmic term to account for the physical deterioration of the battery.
"],"dc:identifier":["https://scholarcommons.sc.edu/etd/125"],"dc:rights":["© 2010, Timur Aliyev"],"dc:subject":["Chemical Engineering","Engineering","Dynamic Optimization","Model Predictive Control","Moving Horizon Estimation","NMPC","Polynomial Chaos Theory"],"dc:title":["Efficient Identification and Control Methods For Nonlinear Systems Under Uncertainty"],"thesis:degree_discipline":["Chemical Engineering"],"thesis:degree_level":["Campus Access Dissertation"],"thesis:degree_name":["Ph.D."]},"updated_at":"2026-07-24T04:36:56Z"}