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Chapter 5. Simultaneous Design and Control Optimization of PSA Systems Under Uncertainty Table 5.2: A brief overview of some recent work in the field of integrated design and control Authors Asteasuain et al. [5] Chawankul et al. [29] Asteasuain et al. [6] Flores- Tlacuahuac and Biegler [49] Chawankul et al. [30] Malcolm et al. [92] Ricardez- Sandoval et al. [130] Hamid et al. [59] IDC formulation Mixed integer dynamic optimization (MIDO) formulation at nominal con- ditions, controller performance indica- tors not included Lumped the capital and operating cost at steady state along with the con- troller performance cost into a single ob jective function Decomposition algorithm [113, 98] with outer approximation approach for integer variables and over estimator to simply the process dynamic feasibility test ISE treated as ob jective function, MIDO problem transformed to MINLP by employing finite element method on the state and control variables Lumped the capital and operating cost at steady state along with the con- troller performance cost into a single ob jective function. Furthermore, The steady state model and dynamic mod- els represented by simplified empirical correlations. Model mismatch treated as process uncertainty Decomposition algorithm [113, 98] with an extra step of steady state flexibility analysis before the dynamic one to reduce the search space of critical uncertain parameters scenar- ios. Furthermore, the optimal de- sign and control problem are decou- pled with the optimal control problem solved for fixed design. The solution of optimal control subproblem only ef- fects the master optimal design prob- lem if the operation fails in the flexi- bility tests Algorithm based on employing finite impulse response model of the original system at nominal conditions to evalu- ate the worst case disturbance and crit- ical parametric uncertainty simultane- ously. The resulting design is validated by performing closed loop simulation on the original model Four stage decomposition algorithm with multi-ob jective formulation incor- porating design and control objectives; used thermodynamics insights to nar- row down the feasible region Case study Styrene bulk polymerization reactor Multi- component distillation column separa- tion Design and control for grade transi- tion of styrene polymerization process CSTR Top and bot- tom purity control for depropanizer column Polymerization reactor design and control, and binary column separa- tion Tennessee Eastman pro- cess Ethylene glycol production (CSTR) and separation (distillation column) Modeling details DAE DAE DAE DAE Empirical corre- lations deduced from RADFRAC (Aspen PLUS) DAE DAE, lumped ob jective func- tion containing steady state capital and operating cost with real-time process output variability cost DAE Controller details Superstructure based MIMO formulation of feedforward- feedback controller Robust formulation of SISO control Superstructure based MIMO formulation for feedforward- feedback config- uration Superstructure based approach for process and control alternatives Robust formulation for MIMO based MPC controller Fixed controller structure with MIMO formulation Fixed controller structure of PI loops Controller structure based on analysis of control variable sensitivities to pro- cess disturbances and manipulative variables 102PDF Image | Operation and Control of Pressure Swing Adsorption Systems
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