This book presents a theoretical analysis of the modern methods used for modeling various chemical engineering processes. Currently, the two primary problems in the chemical industry are the optimal design of new devices and the optimal control of active processes. Both of these problems are often solved by developing new methods of modeling. These methods for modeling specific processes may be different, but in all cases, they bring the mathematical description closer to the real processes by using appropriate experimental data. In this book, the authors detail a new approach for the modeling of chemical processes in column apparatuses. Further, they describe the types of neural networks that have been shown to be effective in solving important chemical engineering problems. Readers are also presented with mathematical models of integrated bioethanol supply chains (IBSC) that achieve improved economic and environmental sustainability.
The integration of energy and mass processes is one of the most powerful tools for creating sustainable and energy efficient production systems. This book defines the main approaches for the thermal integration of periodic processes, direct and indirect, and the recent integration of small-scale solar thermal dryers with phase change materials as energy accumulators. An exciting overview of new approaches for the modeling of chemical engineering processes, this book serves as a guide for the important innovations being made in theoretical chemical engineering.
Author(s): Christo Boyadjiev
Series: Heat and Mass Transfer
Publisher: Springer
Year: 2021
Language: English
Pages: 213
City: Cham
Preface
Contents
Chapter 1: Introduction in the Chemical Engineering Processes Modeling
1 Prelude
2 Industrial Processes Kinetics
3 Modeling
4 Thermodynamic Approximation
5 Hydrodynamic Approximations
6 Boltzmann´s Approximation
7 Mechanism of Influence of Reaction Kinetics
8 Co-Current Absorption Column without Packings
8.1 Convection-Diffusion Model
8.2 Average-Concentration Model
9 Counter-Current Absorption Column with Random Packings
9.1 Fluid Flow along the Column Wall
9.2 Problems with Random Packings in the Columns
9.2.1 Experimental Data
9.2.2 Phase Volume Parts in the Column Volume
9.2.3 Pressure Drop of Random Packings Columns
9.2.4 Hydrodynamics of the Liquid Phase in the Column Volume
9.3 Liquid Layer Hydrodynamics
9.3.1 Parameters Identification
9.3.2 Gas Phase Hydrodynamics
10 Modeling of Processes with Unknown Mechanism
11 Conclusions
References
Chapter 2: Modeling of Ethanol Fermentation from Low-Grade Raw Materials, Including Cellulose and Hemicellulose in a Two-Step ...
1 Introduction
2 Mathematical MODELING
2.1 One-Step Fermentation
2.2 Two-Step Fermentation
3 Experimental
3.1 Alcohol Fermentation of Acid Hydrolyzate of Lignocellulosic Materials
3.2 Analyses
4 Results and Discussion
4.1 Results of the Mathematical Modeling
4.2 Results of Parameter Estimation from Experimental Data by the Mathematical Models
5 Conclusions
References
Chapter 3: Modeling and Simulation of Chemical Processes in Industrial Column Apparatuses
1 Introduction
2 Convection-Diffusion Model
3 Axial and Radial Velocity Components
4 Average-Concentration Model
5 Parameters Identification
6 Effect of the Chemical Reaction Rate
7 Conclusions
References
Chapter 4: Multi-Period Deterministic Model of Sustainable Integrated of Hybrid First and Second Generation Bioethanol Supply ...
1 Introduction
2 Literature Review
2.1 Summary of Literature Review
3 Objectives of the Present Study
4 Problem Definition
4.1 General Formulation of the Problem
5 Model Formulation
5.1 Mathematical Model Description
5.2 Supply Chain Structure
5.3 Basic Relationships
5.3.1 Model of Environmental Assessment of IBSC
Greenhouse Gases to Grow Biomass ELSt
Total GHG Emissions from Bioethanol (E100) Production ELBt
Total GHG Emissions from Gasoline Production ELDt
The Environmental Impact of Transportation ETTt
Total GHG Emissions from Utilization Solid Wastes ESWt, [kgCO2 - eqd-1]
Total GHG Emissions from Utilization Straw ESTRAWt
GHG Emissions from Bioethanol (E100) and Gasoline Usage in Vehicle Operations ECARt
5.3.2 Model of Economic Assessment of an IBSC TDCt, [$ year-1]
Model Investment Costs for Biorefineries by Year TICt
Model Investment Costs for Solid Waste Plants by Year TIWt
Total Production Cost Model of IBSC TPCt
Total Utilization Cost Model of Solid Waste TPWt
Total Transportation Cost Model TTCt
A Carbon Tax Levied Cost Model TTAXBt
Government Incentives for Bioethanol (E100) Production Cost Model
Total Costs of Selling Straw for Other Purposes
5.3.3 Model of Social Assessment of an IBSC Jobt, [Number of Jobs/year]
5.4 Restrictions
5.4.1 SC Design Constraints
Bioethanol Plants Capacity Limited by Upper and Lower Constrains
Solid Waste Plants Capacity Limited by Upper and Lower Constrains
Limits on IBSC Flow Acceptability
A Limitation Guaranteeing the Regions Needs for Straw for Technical Needs and Utilization
A Limitation Guaranteeing the Regions Needs for Grain for Technical Needs and Utilization
Mass Balances Between Bioethanol (E100) Plants and Biomass Regions
Mass Balances Between Bioethanol (E100) Plants and Customer Zones
5.4.2 Logical Constrains
Restriction Guarantees That a Given Region f F Installed Power Plant with Size p P for Bioethanol (E100) Production
Restriction Guarantees That a Given Region w W Installed Solid Waste Plant with Size s S
Limitation Ensure the Availability of at Least One Connection to a Region of Bioresources and Region for Biofuel
Limit Which Guarantees That Each Region Will Provide Only One Plant with a Biomass Type i I
Limitation of Assurance That At Least One Region f F Producing Bioethanol (E100) Is Connected to a Costumer Zones c C
Limitation of Assurance That At Least One Region f F Producing Bioethanol (E100) Is Connected to a Solid Waste Utilization Pl...
Condition Ensuring That the Solid Waste Produced from a Given Biorefinery Will Be Processed in Only One of the Plants for Use
Condition Ensuring That a Plant Used in a Given Region Will Be Connected to At Least One Plant in Which Solid Waste Is Generat...
Restrictions That Ensure That Only One Mode of Transport Is Used for the Transport of Bioraw Materials or Finished Products Be...
5.4.3 Transport Links
Restrictions on Transportation of Biomass Are
Restrictions on Transportation of Bioethanol (E100) Are
Restrictions on Transportation of Solid Waste Are
Restrictions on Transportation of Straw Are
Restrictions on Transportation of Wheat-Corn for Food Security Are
5.4.4 Restriction for Total Environmental Impact of All Regions
5.4.5 Limitation Guaranteeing Crop Rotation
5.4.6 Model of Constraints for Energy Balances/Energy Efficiency Constraints
Limitation Ensuring That the Overall Energy Balance in the Region Is Provided
Limitation Ensuring That the Overall Energy Balance in Each Customer Zones Is Provided
Limitation Ensuring That Each Region Will Be Provided in the Desired Proportions Fuels
5.4.7 Model of Constraints for Total Cost of a BSC Network
5.5 Optimization Objective Functions
5.5.1 Economic Objective Function
5.5.2 Environmental Objective Function
5.5.3 Integrated Economic and Environmental Objective Function
5.5.4 Social Objective Function
6 Optimal Synthesis Problem Formulation Using Mathematical Model
6.1 Single-Criteria Objective Models
6.1.1 Minimizing GHG Emissions [kgCO2 - eq]
6.1.2 Minimizing Annualized Total Cost [$]
6.1.3 Maximize the Social Impact of the System Work of the Supply Chain
6.2 Multi-Criteria Objective Models
7 Optimal Renovation Problem Formulation Using Mathematical Model
8 Potential Bioethanol Production in Bulgaria for 2016-2020
8.1 Model Input Data
8.2 Computational Results and Analysis
9 Conclusion
Appendix: Notation
Sets, Subsets, and Indices
Sets/Indices
Subsets/Indices
Input Parameters for the Problem
Environmental Parameters
Monetary Parameters
Technical Parameters
Environmental Parameters Depending on the Time Interval
Monetary Parameters Depending on the Time Interval
Technical Parameters Depending on the Time Interval
Decision Variables for the Problem (Xt)
Positive Continuous Variables
Binary Variables
References
Chapter 5: Energy Integration of Production Systems with Batch Processes in Chemical Engineering
1 Introduction
1.1 Direct Heat Integration
1.1.1 Heat Integration between Two Periodic Reactors with Recirculation of the Main Fluids
1.1.2 Heat Integration Using Intermediate Heating and Cooling Agents
1.2 Indirect Heat Integration
1.2.1 Heat Integration Using Two Heat Storages
1.3 Heat Integration Using a Common Heat Storage
2 Conclusion
References
Chapter 6: Artificial Neural Networks: Applications in Chemical Engineering
1 Introduction
1.1 Artificial Neural Networks: Basic Concepts and Definitions
1.2 Choice of ANN Architecture
1.3 Feed-Forward Neural Networks
1.4 Recurrent Neural Networks
1.5 Radial Basis Function Network (RBFN)
1.6 Combined Neural Networks
1.7 Neural Network Training: Identification of its Parameters
1.8 Bias and Variance in ANN
1.9 ANN Model Validation
2 Concluding Remarks
References
Chapter 7: Approach for Parameter Identification of Multiparameter Models
1 Introduction
1.1 New Hierarchical Approach for Parameter Identification
1.2 Fermentation Systems Modeling
1.3 Microalgae Growth Kinetics Modeling
2 Conclusions
References
Chapter 8: Modeling and Simulation of Phase Change Material Based Thermal Energy Accumulators in Small-Scale Solar Thermal Dry...
1 Introduction
1.1 Sensible Heat Versus Latent Heat in TES for Integration with a Solar Dryer
1.2 Conditions for Successful Solar Drying
2 Small-Scale Solar Dryers with Paraffin
2.1 Why Paraffin as PCM in Solar Dryers
2.2 Thermal Conductivity Enhancement of Paraffin in Solar Dryers
2.2.1 Additives
2.2.2 Encapsulation
2.2.3 Extended Surfaces
2.3 PCM-Based Thermal Energy Accumulators in Small-Scale Solar Dryers
2.3.1 Passive (Natural Convection) Versus Active (Forced Convection) Solar Drying
2.3.2 Separate TES Unit with PCM
Examples of PCM Occupying the Volume of the Unit
Examples of PCM Encapsulated in Containers
2.3.3 TES Inbuilt in the SAH: Solutions for Better Thermo-Hydraulic Performance
Containers with Paraffin: Reducing inside and outside Conductive Resistance
Increasing the Heat Transfer Surface of the SAH with PCM
2.3.4 TES Inbuilt in the DC
3 Methods for MODELING and Simulation of Solar Dryers with Pcm
3.1 Energy Analysis
3.1.1 SAH Efficiency
3.1.2 Stored/Recovered Thermal Energy
3.1.3 Efficiency of the PCM Modules
3.1.4 Efficiency of the Solar Drying System Integrated with SAHs at Different Charging Modes
Natural Convection of Air through the SAHs without PCM Modules
Forced Convection of Air through the SAHs without PCM Modules
Forced Convection of Air through the SAHs with N PCM Modules
3.2 Exergy Analysis
3.2.1 Exergy Analysis of the SAH
3.2.2 Exergy Analysis of TES
3.2.3 Exergy Analysis of the DC
3.3 Examples of Efficiency Evaluation of Solar Dryers with PCM
3.4 Mathematical Modeling of the Thermal Behavior of a Solar Dryer with PCM
3.4.1 CFD with Enthalpy-Porosity Model
CFD Simulation of a Single Container with PCM
CFD Simulation of a PCM-Based TES Unit
CFD Simulation of the DC
4 Conclusions
References
Conclusions
Index
