Simulation and Optimization in Process Engineering: The Benefit of Mathematical Methods in Applications of the Chemical Industry

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Simulation and Optimization in Process Engineering
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Contents
Contributors
Preface
Prediction and correlation of physical properties including transport and interfacial properties with the PC-S ...
Model equations of PC-SAFT
Parameterization
Pure-component parameters
Binary interaction parameters
Group-contribution methods for PC-SAFT
Transport properties
Interfacial properties
References
Dont search-Solve! Process optimization modeling with IDAES
Introduction
Optimization evolution from systematic search to direct solution
Solution algorithms and optimization models
Advanced optimization for differential-algebraic applications
Complexity of dynamic optimization strategies
The IDAES optimization modeling software platform
Carbon capture optimization case study
Optimization problem formulation
Problem initialization and implementation
Conclusions and future perspectives
Acknowledgments
References
Thinking multicriteria-A jackknife when it comes to optimization
Introduction
Short account on multicriteria optimization
Process design
Continuous design variables
Discrete alternatives
The impact of uncertainties
Extension to optimal control
Model adjustment, model comparison and model-based design of experiments
Decision support
Acknowledgments
References
Integrated modeling and energetic optimization of the steelmaking process in electric arc furnaces: An industr ...
Introduction
Electric arc furnace process model
Hybrid EAF process model
Model dynamics
The electric arc model
Radiative heat exchange from the electric arc
Monte Carlo calculation of the view factors
Oxy-fuel burners
Combustion of coal
Oxidation of metals
Molten metal splashing
Dynamic optimization of the melting profiles
Problem statement
A general formulation of the dynamic optimization problem
Formulation of the dynamic optimization problem of the EAF process
Solution using control vector parametrization
Numerical solution of the model
Termination conditions
Model validation and parameter estimation
Numerical solution of the optimization problem
Batch time constraint
Results and discussions
Numerical case study
Batch simulation
Batch optimization
Results for the real industrial process
Conclusions
References
Solvent recovery by batch distillation-Application of multivariate sensitivity studies to high dimensional mul ...
Introduction
Separation of acetone and methanol
Continuous separation processes
Batch processes for separation
Problem definition
Product specifications and constraints
Description of the plant
Literature review
Methodology
Heuristics for the selection of a suitable multipurpose plant
Tool for running flowsheet simulations
Algorithms for optimizing flowsheet simulations
Tool for running multivariate sensitivity studies
Set up of the flowsheet simulation
Thermodynamic models
Screening model
Number of equilibrium trays
Heat duty and molar vapor flow
Design variables
Low-fidelity model
High-fidelity model
Heat exchanger models
Hydraulic column model
Liquid hold-up model
Flow control model
Results
Screening model
Highest possible acetone concentration in distillate
Impact of the design variables
Low-fidelity model
Acetone recovery
Methanol recovery
Water purification
Consecutive simulation of all steps
High-fidelity model
Applying the low-fidelity model results
Trajectory and switch point tuning
Economic evaluation
Summary
References
Modeling and optimizing dynamic networks: Applications in process engineering and energy supply
Introduction
AD-Net
Applications in energy supply
Power transmission
District heating
Applications in batch distillation
Forward simulation
Parameter identification
Optimal control
Conclusion
Acknowledgment
References
The use of digital twins to overcome low-redundancy problems in process data reconciliation
Introduction
Data reconciliation
Variable classification
Steady-state data reconciliation (DR)
Gross error detection
Gross error effect and how to handle
Gross error detection: Statistical methods
GE statistical detection algorithms
Numerical method for low-redundant system
Clever mean and clever variance (cm and cv)
Median and mad
Dynamic data reconciliation
Moving time-window approach
Solution of DDR with orthogonal matrix
Implementation and the role of digital twin
Industrial case study: Itelyum Regeneration amine washing unit
Process description
Assumptions
Results
Steady-state data reconciliation results discussion
Gross error detection results discussion
Dynamic data reconciliation case study: Amine tank dynamics
Conclusions
Steady-state data reconciliation
Dynamic data reconciliation (DDR)
Acknowledgments
References
Real-time optimization of batch processes via optimizing feedback control
Introduction
Representation of batch processes
Distinguishing features
Mathematical models
Static view of a batch process
Numerical optimization of batch processes
Problem formulation: Dynamic optimization
Reformulation of a dynamic optimization problem as a static optimization problem
Batch-to-batch solution: Static optimization
Effect of plant-model mismatch
Feedback-based optimization of uncertain batchprocesses
Offline activity: Determine the feedback structure
Characterization of the solution
Design of the feedback structure
Real-time activities: Implement feedback control
Within-batch feedback
Batch-to-batch feedback
Illustrative example: Batch distillation column
Industrial batch distillation column
Process model
Input parameterization of the impurity fraction
Control design and performance
Constraint tracking
NCO tracking
Practical aspects
Conclusions
References
On economic operation of switchable chlor-alkali electrolysis for demand-side management
Abbreviations
Introduction
Operational mode switching of chlor-alkali electrolysis
Mathematical formulation for optimal sizing and operation of switchable chlor-alkali electrolysis
Operational mode transition
Mass balance
Power demand
Ramping constraints
Cost function
Case study
Optimal operational behavior of switchable chlor-alkali electrolysis
Comparison of switchable chlor-alkali electrolysis to other flexibility options
Simultaneous optimization of plant oversizing and operation
Conclusion
Acknowledgments
References
Optimal experiment design for dynamic processes
Introduction
Optimal experiment design for model structure discrimination
OED/SD in practice
Optimal experiment design for parameter estimation
Computing parameter variance-covariance matrix
Fisher information matrix approach
Direct mapping through parameter estimation
Other computational approaches to approximate variance-covariance matrix
OED/PE as an optimal control problem
Optimization criteria for OED/PE
OED/PE in practice
Advanced developments in optimal experiment design
Robust optimal experiment design for parameter estimation
Multicriterion optimal experiment design
Conclusions
References
Characterization of reactions and growth in automated continuous flow and bioreactor platforms-From linear Do ...
Introduction
Miniaturized platforms and applications
Continuous-flow microreactor platforms in synthetic chemistry
Operation
Bioreactor platforms with automatic liquid handling
Applications of DoE, self-optimization, and mbOED-A bibliographical review
DoE for continuous flow reactions in synthetic chemistry
Sequential self-optimization of continuous-flow reactions
DoE for the development of biotechnological processes
DoE for parallel cultivation platforms
Model-based OED for continuous flow reactions
Model-based OED for bioreactor cultures
Model based OED dimension and complexity
Summary
Special aspects and challenges
Static vs dynamic experimental conditions
Continuous-flow reactors
Batch- and fed-batch bioreactors
Measurement frequency, measurement errors and optimal sampling
Sequential planning and updating in mbOED
Robustness issues
Termination criteria
Parameter identifiability
Bayesian statistics
Mathematical modeling, software and algorithms
Industry view
mbOED software, flexibility, usability, and required expert knowledge
Discussion and conclusions
References
Product development in a multicriteria context
Introduction
Model fitting
Generating the data: Design of experiments
Multicriteria optimization and decision-making
Approximating the set of efficient product designs
Navigating the set of efficient product designs
The role of Qritos in the design process
Application: Designing an exterior paint recipe
Chemical recipe
DoE
Laboratory measurements
Modeling
Optimization goals (product specifications)
Decision-making process
Outlook
References
Dispatching for batch chemical processes using Monte-Carlo simulations-A practical approach to scheduling in ...
Introduction
Problem setting
Literature
Machine scheduling
Scheduling chemical batch processes
Proposed solution
Production line simulation
Random phase durations
The simulation framework in a nutshell
Scheduling
Implementation
Important steps for the implementation of our decision support tool in practice
The final application
Beyond real-time operative scheduling
Use case 1: Prediction of future events and plant states
Use case 2: What-if analyses for plant expansion/optimization
Conclusions and outlook
References
Applications of the RTN scheduling model in the chemical industry
Abbreviations
Introduction
Review of RTN model
Discrete-time representation
Resource balance
Resource limits
Operational constraints
Variable domains
Illustrative example
Continuous-time representation
Timing and sequencing
Resource balance
Variable domains
Discrete-time vs continuous-time
Representation of time
Model size
Linear programming relaxations
Objective functions
Discrete-continuous-time integration
Industry-led developments
Extended RTN model
Quality-based changeovers
External resource transfers with time windows
Point orders
Manipulation of resource limits
Resource slacks
Multiple task extents
Industrial example: Multiple extents in a continuous processing plant
State-space reformulation of extended RTN model
Industrial example: Online scheduling of a mixed batch/continuous processing plant
RTN for spatial packing problems
Industrial example: Payload loading optimization
RTN for transactional business process optimization
Industrial impact
Conclusions
Acknowledgments
References
Index
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