UID:
almahu_9949984755502882
Umfang:
1 online resource (428 pages)
ISBN:
9780323850445
,
0323850448
Inhalt:
"Simulation and Optimization in Process Engineering: The Benefit of Mathematical Methods in Applications of the Process Industry brings together examples where the successful transfer of progress made in mathematical simulation and optimization has led to innovations in an industrial context that created substantial benefit. Containing introductory accounts on scientific progress in the most relevant topics of process engineering (substance properties, simulation, optimization, optimal control and real time optimization), the examples included illustrate how such scientific progress has been transferred to innovations that delivered a measurable impact, covering details of the methods used, and more."--
Anmerkung:
Intro -- Simulation and Optimization in Process Engineering: The Benefit of Mathematical Methods in Applications of the Chemical In ... -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1: Prediction and correlation of physical properties including transport and interfacial properties with the PC-S ... -- 1. Model equations of PC-SAFT -- 2. Parameterization -- 2.1. Pure-component parameters -- 2.2. Binary interaction parameters -- 3. Group-contribution methods for PC-SAFT -- 4. Transport properties -- 5. Interfacial properties -- References -- Chapter 2: Dont search-Solve! Process optimization modeling with IDAES -- 1. Introduction -- 1.1. Optimization evolution from systematic search to direct solution -- 2. Solution algorithms and optimization models -- 3. Advanced optimization for differential-algebraic applications -- 3.1. Complexity of dynamic optimization strategies -- 4. The IDAES optimization modeling software platform -- 5. Carbon capture optimization case study -- 5.1. Optimization problem formulation -- 5.2. Problem initialization and implementation -- 6. Conclusions and future perspectives -- Acknowledgments -- References -- Chapter 3: Thinking multicriteria-A jackknife when it comes to optimization -- 1. Introduction -- 1.1. Short account on multicriteria optimization -- 2. Process design -- 2.1. Continuous design variables -- 2.2. Discrete alternatives -- 2.3. The impact of uncertainties -- 2.4. Extension to optimal control -- 3. Model adjustment, model comparison and model-based design of experiments -- 4. Decision support -- Acknowledgments -- References -- Chapter 4: Integrated modeling and energetic optimization of the steelmaking process in electric arc furnaces: An industr ... -- 1. Introduction -- 2. Electric arc furnace process model -- 2.1. Hybrid EAF process model -- 2.1.1. Model dynamics.
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2.1.2. The electric arc model -- 2.1.3. Radiative heat exchange from the electric arc -- 2.1.4. Monte Carlo calculation of the view factors -- 2.1.5. Oxy-fuel burners -- 2.1.6. Combustion of coal -- 2.1.7. Oxidation of metals -- 2.1.8. Molten metal splashing -- 3. Dynamic optimization of the melting profiles -- 3.1. Problem statement -- 3.2. A general formulation of the dynamic optimization problem -- 3.3. Formulation of the dynamic optimization problem of the EAF process -- 4. Solution using control vector parametrization -- 4.1. Numerical solution of the model -- 4.2. Termination conditions -- 4.3. Model validation and parameter estimation -- 4.4. Numerical solution of the optimization problem -- 4.5. Batch time constraint -- 5. Results and discussions -- 5.1. Numerical case study -- 5.1.1. Batch simulation -- 5.1.2. Batch optimization -- 5.2. Results for the real industrial process -- 6. Conclusions -- References -- Chapter 5: Solvent recovery by batch distillation-Application of multivariate sensitivity studies to high dimensional mul ... -- 1. Introduction -- 1.1. Separation of acetone and methanol -- 1.2. Continuous separation processes -- 1.3. Batch processes for separation -- 2. Problem definition -- 2.1. Product specifications and constraints -- 2.2. Description of the plant -- 3. Literature review -- 4. Methodology -- 4.1. Heuristics for the selection of a suitable multipurpose plant -- 4.2. Tool for running flowsheet simulations -- 4.3. Algorithms for optimizing flowsheet simulations -- 4.4. Tool for running multivariate sensitivity studies -- 5. Set up of the flowsheet simulation -- 5.1. Thermodynamic models -- 5.2. Screening model -- 5.2.1. Number of equilibrium trays -- 5.2.2. Heat duty and molar vapor flow -- 5.2.3. Design variables -- 5.3. Low-fidelity model -- 5.4. High-fidelity model -- 5.4.1. Heat exchanger models.
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5.4.2. Hydraulic column model -- 5.4.3. Liquid hold-up model -- 5.4.4. Flow control model -- 6. Results -- 6.1. Screening model -- 6.1.1. Highest possible acetone concentration in distillate -- 6.1.2. Impact of the design variables -- 6.2. Low-fidelity model -- 6.2.1. Acetone recovery -- 6.2.2. Methanol recovery -- 6.2.3. Water purification -- 6.2.4. Consecutive simulation of all steps -- 6.3. High-fidelity model -- 6.3.1. Applying the low-fidelity model results -- 6.3.2. Trajectory and switch point tuning -- 6.4. Economic evaluation -- 7. Summary -- References -- Chapter 6: Modeling and optimizing dynamic networks: Applications in process engineering and energy supply -- 1. Introduction -- 2. AD-Net -- 3. Applications in energy supply -- 3.1. Power transmission -- 3.2. District heating -- 4. Applications in batch distillation -- 4.1. Forward simulation -- 4.2. Parameter identification -- 4.3. Optimal control -- 5. Conclusion -- Acknowledgment -- References -- Chapter 7: The use of digital twins to overcome low-redundancy problems in process data reconciliation -- 1. Introduction -- 2. Data reconciliation -- 2.1. Variable classification -- 2.2. Steady-state data reconciliation (DR) -- 2.3. Gross error detection -- 2.4. Gross error effect and how to handle -- 2.5. Gross error detection: Statistical methods -- 2.6. GE statistical detection algorithms -- 2.7. Numerical method for low-redundant system -- 3. Clever mean and clever variance (cm and cv) -- 4. Median and mad -- 4.1. Dynamic data reconciliation -- 4.2. Moving time-window approach -- 4.3. Solution of DDR with orthogonal matrix -- 4.4. Implementation and the role of digital twin -- 5. Industrial case study: Itelyum Regeneration amine washing unit -- 5.1. Process description -- 5.2. Assumptions -- 6. Results -- 6.1. Steady-state data reconciliation results discussion.
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6.2. Gross error detection results discussion -- 6.3. Dynamic data reconciliation case study: Amine tank dynamics -- 7. Conclusions -- 7.1. Steady-state data reconciliation -- 7.2. Dynamic data reconciliation (DDR) -- Acknowledgments -- References -- Chapter 8: Real-time optimization of batch processes via optimizing feedback control -- 1. Introduction -- 2. Representation of batch processes -- 2.1. Distinguishing features -- 2.2. Mathematical models -- 2.3. Static view of a batch process -- 3. Numerical optimization of batch processes -- 3.1. Problem formulation: Dynamic optimization -- 3.2. Reformulation of a dynamic optimization problem as a static optimization problem -- 3.3. Batch-to-batch solution: Static optimization -- 3.4. Effect of plant-model mismatch -- 4. Feedback-based optimization of uncertain batchprocesses -- 4.1. Offline activity: Determine the feedback structure -- 4.1.1. Characterization of the solution -- 4.1.2. Design of the feedback structure -- 4.2. Real-time activities: Implement feedback control -- 4.2.1. Within-batch feedback -- 4.2.2. Batch-to-batch feedback -- 5. Illustrative example: Batch distillation column -- 5.1. Industrial batch distillation column -- 5.2. Process model -- 5.3. Input parameterization of the impurity fraction -- 5.4. Control design and performance -- 5.4.1. Constraint tracking -- 5.4.2. NCO tracking -- 5.4.3. Practical aspects -- 6. Conclusions -- References -- Chapter 9: On economic operation of switchable chlor-alkali electrolysis for demand-side management -- Abbreviations -- 1. Introduction -- 2. Operational mode switching of chlor-alkali electrolysis -- 3. Mathematical formulation for optimal sizing and operation of switchable chlor-alkali electrolysis -- 3.1. Operational mode transition -- 3.2. Mass balance -- 3.3. Power demand -- 3.4. Ramping constraints -- 3.5. Cost function -- 4. Case study.
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4.1. Optimal operational behavior of switchable chlor-alkali electrolysis -- 4.2. Comparison of switchable chlor-alkali electrolysis to other flexibility options -- 4.3. Simultaneous optimization of plant oversizing and operation -- 5. Conclusion -- Acknowledgments -- References -- Chapter 10: Optimal experiment design for dynamic processes -- 1. Introduction -- 2. Optimal experiment design for model structure discrimination -- 2.1. OED/SD in practice -- 3. Optimal experiment design for parameter estimation -- 3.1. Computing parameter variance-covariance matrix -- 3.1.1. Fisher information matrix approach -- 3.1.2. Direct mapping through parameter estimation -- 3.1.3. Other computational approaches to approximate variance-covariance matrix -- 3.2. OED/PE as an optimal control problem -- 3.2.1. Optimization criteria for OED/PE -- 3.3. OED/PE in practice -- 4. Advanced developments in optimal experiment design -- 4.1. Robust optimal experiment design for parameter estimation -- 4.2. Multicriterion optimal experiment design -- 5. Conclusions -- References -- Chapter 11: Characterization of reactions and growth in automated continuous flow and bioreactor platforms-From linear Do ... -- 1. Introduction -- 2. Miniaturized platforms and applications -- 2.1. Continuous-flow microreactor platforms in synthetic chemistry -- 2.1.1. Operation -- 2.2. Bioreactor platforms with automatic liquid handling -- 2.3. Applications of DoE, self-optimization, and mbOED-A bibliographical review -- 2.3.1. DoE for continuous flow reactions in synthetic chemistry -- 2.3.2. Sequential self-optimization of continuous-flow reactions -- 2.3.3. DoE for the development of biotechnological processes -- DoE for parallel cultivation platforms -- 2.3.4. Model-based OED for continuous flow reactions -- 2.3.5. Model-based OED for bioreactor cultures.
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2.3.6. Model based OED dimension and complexity.
Weitere Ausg.:
Print version: Bortz, Michael Simulation and Optimization in Process Engineering San Diego : Elsevier,c2022 ISBN 9780323850438
Sprache:
Englisch
Schlagwort(e):
Llibres electrònics
;
Llibres electrònics
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