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

Front Cover
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
			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
			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
		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
		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
			2.3.6. Model based OED dimension and complexity
		2.4. Summary
	3. Special aspects and challenges
		3.1. Static vs dynamic experimental conditions
			3.1.1. Continuous-flow reactors
			3.1.2. Batch- and fed-batch bioreactors
			3.1.3. Measurement frequency, measurement errors and optimal sampling
		3.2. Sequential planning and updating in mbOED
			3.2.1. Robustness issues
			3.2.2. Termination criteria
		3.3. Parameter identifiability
		3.4. Bayesian statistics
		3.5. Mathematical modeling, software and algorithms
	4. Industry view
		4.1. mbOED software, flexibility, usability, and required expert knowledge
	5. Discussion and conclusions
	References
Chapter 12: Product development in a multicriteria context
	1. Introduction
	2. Model fitting
		2.1. Generating the data: Design of experiments
	3. Multicriteria optimization and decision-making
	4. Approximating the set of efficient product designs
	5. Navigating the set of efficient product designs
	6. The role of Qritos in the design process
	7. Application: Designing an exterior paint recipe
		7.1. Chemical recipe
		7.2. DoE
		7.3. Laboratory measurements
		7.4. Modeling
		7.5. Optimization goals (product specifications)
		7.6. Decision-making process
	8. Outlook
	References
Chapter 13: Dispatching for batch chemical processes using Monte-Carlo simulations-A practical approach to scheduling in  ...
	1. Introduction
		1.1. Problem setting
		1.2. Literature
			1.2.1. Machine scheduling
			1.2.2. Scheduling chemical batch processes
	2. Proposed solution
		2.1. Production line simulation
			2.1.1. Random phase durations
			2.1.2. The simulation framework in a nutshell
		2.2. Scheduling
	3. Implementation
		3.1. Important steps for the implementation of our decision support tool in practice
		3.2. The final application
	4. Beyond real-time operative scheduling
		4.1. Use case 1: Prediction of future events and plant states
		4.2. Use case 2: What-if analyses for plant expansion/optimization
	5. Conclusions and outlook
	References
Chapter 14: Applications of the RTN scheduling model in the chemical industry
	Abbreviations
	1. Introduction
	2. Review of RTN model
		Discrete-time representation
			2.1.1. Resource balance
			2.1.2. Resource limits
			2.1.3. Operational constraints
			2.1.4. Variable domains
			2.1.5. Illustrative example
		2.2. Continuous-time representation
			2.2.1. Timing and sequencing
			2.2.2. Resource balance
			2.2.3. Variable domains
		2.3. Discrete-time vs continuous-time
			2.3.1. Representation of time
			2.3.2. Model size
			2.3.3. Linear programming relaxations
			2.3.4. Objective functions
			2.3.5. Discrete-continuous-time integration
	3. Industry-led developments
		3.1. Extended RTN model
			3.1.1. Quality-based changeovers
			3.1.2. External resource transfers with time windows
			3.1.3. Point orders
			3.1.4. Manipulation of resource limits
			3.1.5. Resource slacks
			3.1.6. Multiple task extents
			3.1.7. Industrial example: Multiple extents in a continuous processing plant
		3.2. State-space reformulation of extended RTN model
			3.2.1. Industrial example: Online scheduling of a mixed batch/continuous processing plant
		3.3. RTN for spatial packing problems
			3.3.1. Industrial example: Payload loading optimization
		3.4. RTN for transactional business process optimization
	4. Industrial impact
	5. Conclusions
	Acknowledgments
	References
Index
Back Cover