This document summarizes research on developing a nonlinear model predictive control (NMPC) strategy for optimizing the operation of a post-combustion carbon capture unit. Key points:
1) Researchers created a detailed Modelica model of an amine-based carbon capture process but reduced it to improve computational efficiency for real-time optimization.
2) The reduced model was validated against experimental plant data and found to accurately capture system dynamics and behavior.
3) JModelica.org was used to perform offline optimizations of the reduced model, minimizing costs while satisfying operational constraints.
4) Preliminary results showed the NMPC approach was able to optimize reboiler duty and maintain a target carbon
Production planning for distributed district heating with JModelica.orgModelon
This document describes a new approach for production planning of distributed district heating networks using JModelica.org. The current standard approach uses mixed integer linear programming to determine unit commitments and then solves an economic dispatch problem. The new approach uses Modelica models of the production units and network in a nonlinear optimization. This allows optimization of physically relevant signals like temperatures and flows. Case studies on a model of the Uppsala district heating network show the new approach better handles delays in the network and can optimize starting times of production units.
This document discusses the development of a new framework for modeling multi-component multi-phase mixtures in Modelica. The goals are to support both native Modelica media implementations and connections to external thermophysical property databases, address limitations of the existing Modelica.Media interface, and enable new applications involving multi-phase processes. A model-based interface structure is proposed to overcome restrictions of using pure functions. An example demonstrates the approach for simulating air separation processes using both a simple native Modelica air media and a connection to Refprop. Further testing and development is still needed to fully realize the new framework.
Modelon - Fuel System Modeling & Simulation SolutionModelon
The document discusses modeling and simulating aircraft fuel systems. It provides an overview of fuel system components and operating modes. It also introduces Modelon libraries that can be used to model fuel storage, pressurization, transfer, heat transfer, and other functions. Examples of applications include filling tank simulations, inerting systems, fuel transfer, and assessing flammability. The document discusses using the libraries for both offline and real-time simulations.
Performance prediction of PV & PV/T systems using Artificial Neural Networks ...Ali Al-Waeli
This presentation offers insight into use of ANN and machine learning for various applications in solar energy. Prepared and presented by Dr. Ali H. A. Alwaeli.
Aspen Plus is a process modeling and simulation tool used in various industries. It provides physical property models and a comprehensive library of unit operation models. The document introduces Aspen Plus and discusses its interface, properties analysis functions, and how to set up basic process simulations. It also outlines the topics that can be modeled in Aspen Plus, such as mass and energy balances, chemical reactions, and unit operations.
Nafems15 Technical meeting on system modelingSDTools
This presentation illustrates the main mechanisms of model reduction used in generating efficient system models that can be used in vibration design. Examples from automotive, aeronautics and train industries are used as illustrations.
This document discusses using reduced 3D models in vibrational design processes. It presents tools for model reduction including variable separation, parametric models, and domain decomposition. These tools combine finite element modeling, experimental modal analysis, and reduced order models to efficiently simulate complex systems for design studies while controlling accuracy.
This document presents a back-corona discharge model for predicting the efficiency and voltage-current characteristics of electrostatic precipitators. The model accounts for factors like the back-corona inception level current density, voltage-current characteristics under normal, moderate, and severe back-corona conditions. It also models how back-corona effects particle layer resistivity and the collection efficiency loss due to positive particle charging and voltage drop across the particle layer. The model is implemented in a program that can be used to simulate and compute the efficiency.
Production planning for distributed district heating with JModelica.orgModelon
This document describes a new approach for production planning of distributed district heating networks using JModelica.org. The current standard approach uses mixed integer linear programming to determine unit commitments and then solves an economic dispatch problem. The new approach uses Modelica models of the production units and network in a nonlinear optimization. This allows optimization of physically relevant signals like temperatures and flows. Case studies on a model of the Uppsala district heating network show the new approach better handles delays in the network and can optimize starting times of production units.
This document discusses the development of a new framework for modeling multi-component multi-phase mixtures in Modelica. The goals are to support both native Modelica media implementations and connections to external thermophysical property databases, address limitations of the existing Modelica.Media interface, and enable new applications involving multi-phase processes. A model-based interface structure is proposed to overcome restrictions of using pure functions. An example demonstrates the approach for simulating air separation processes using both a simple native Modelica air media and a connection to Refprop. Further testing and development is still needed to fully realize the new framework.
Modelon - Fuel System Modeling & Simulation SolutionModelon
The document discusses modeling and simulating aircraft fuel systems. It provides an overview of fuel system components and operating modes. It also introduces Modelon libraries that can be used to model fuel storage, pressurization, transfer, heat transfer, and other functions. Examples of applications include filling tank simulations, inerting systems, fuel transfer, and assessing flammability. The document discusses using the libraries for both offline and real-time simulations.
Performance prediction of PV & PV/T systems using Artificial Neural Networks ...Ali Al-Waeli
This presentation offers insight into use of ANN and machine learning for various applications in solar energy. Prepared and presented by Dr. Ali H. A. Alwaeli.
Aspen Plus is a process modeling and simulation tool used in various industries. It provides physical property models and a comprehensive library of unit operation models. The document introduces Aspen Plus and discusses its interface, properties analysis functions, and how to set up basic process simulations. It also outlines the topics that can be modeled in Aspen Plus, such as mass and energy balances, chemical reactions, and unit operations.
Nafems15 Technical meeting on system modelingSDTools
This presentation illustrates the main mechanisms of model reduction used in generating efficient system models that can be used in vibration design. Examples from automotive, aeronautics and train industries are used as illustrations.
This document discusses using reduced 3D models in vibrational design processes. It presents tools for model reduction including variable separation, parametric models, and domain decomposition. These tools combine finite element modeling, experimental modal analysis, and reduced order models to efficiently simulate complex systems for design studies while controlling accuracy.
This document presents a back-corona discharge model for predicting the efficiency and voltage-current characteristics of electrostatic precipitators. The model accounts for factors like the back-corona inception level current density, voltage-current characteristics under normal, moderate, and severe back-corona conditions. It also models how back-corona effects particle layer resistivity and the collection efficiency loss due to positive particle charging and voltage drop across the particle layer. The model is implemented in a program that can be used to simulate and compute the efficiency.
[Capella Day 2019] Model execution and system simulation in CapellaObeo
A common need in system architecture design is to verify that if the architect is correct and can satisfy its requirements. Execution of system architect model means to interact with state machines to test system’s control logic. It can verify if the logical sequences of functions and interfaces in different scenarios are desired.
However, only sequence itself is not enough to verify its consequence or output. So we need each function to do what it is supposed to do during model execution to verify its output, and that is what we called “system simulation”.
This presentation introduces how we do model execution in Capella, and how to embed digital mockup inside functions to do “system simulation” with a higher confidence.
Renfei Xu, Glaway
Renfei Xu is the technical manager of MBSE solution in Glaway. He has participated in many pilot projects of MBSE in areas like Engine Control, Avionics, Mechatronics and so on. In recent years, he is responsible for the deployment of MBSE using Capella and ARCADIA methodology in a Radar research institute.
Wenhua Fang, Glaway
Wenhua Fang is the Director of Systems Engineering in Glaway. He has more than 12 years of working experience in SE.
He is responsible for more than 10 implementation projects of MBSE in areas like Aircraft, Engine Control, Avionics, Automotive and so on. In recent years, he leads the team to deploy MBSE in China(including using Capella and ARCADIA methodology).
In the last few years energy efficiency of large scale infrastructures gained a lot of attention, as power consumption became one of the most impacting factors of the operative costs of a data-center and of its Total Cost of Ownership. Power consumption can be observed at different layers of the data-center: from the overall power grid, moving to each rack and arriving to each machine and system. Given the rise of application containers in the cloud computing scenario, it becomes more and more important to measure power consumption also at the application level, where power-aware schedulers and orchestrators can optimize the execution of the workloads not only from a performance perspective, but also considering performance/power trade-offs. DEEP-mon is a novel monitoring tool able to measure power consumption and attribute it for each thread and application container running in the system, without any previous knowledge regarding the characteristics of the application and without any kind of workload instrumentation. DEEP-mon is able to aggregate data for threads, application containers and hosts with a negligible impact on the monitored system and on the running workloads.
Information obtained with DEEP-mon open the way for a wide set of applications exploiting the capabilities offered by the monitoring tool, from power (and hence cost) metering of new software components deployed in the data center, to fine grained power capping and power-aware scheduling and co-location.
This document discusses exergetic and thermoeconomic analysis of a coal-fired power plant. It begins with definitions of exergy as usable work and explanations of energy and exergy analysis. It then describes various thermoeconomic analysis methods including Specific Exergy Costing (SPECO) and Modified Production Structural Analysis (MOPSA). SPECO and MOPSA are applied to a sample coal-fired power plant model to determine the unit exergy costs of each stream. The results of exergy and economic analyses of the plant are presented, identifying locations for potential efficiency improvements.
Optimization of Electrical Machines in the Cloud with SyMSpace by LCMcloudSME
Presented at NAFEMS DACH regional conference for numerical simulation methods by LCM and cloudSME in Wiesbaden on the 14th of November 2019.
The Linz Center of Mechatronics GmbH showcased how they easily optimize electrical drive engines in the cloud.
We supported LCM to work out the right cloud-based service solutions for their customers based on their existing software. By respecting the latest developments in the industry and science, including security and privacy compliance and hosting flexibility (free choice of data centre, no vendor lock-in).
Check out their cool System Model Space "SyMSpace" for electrical drive engines and trusted by industrial partners! (https://bit.ly/2CKGphb) #poweredbycloudSME
Yes, Cloud Computing is offering a broad range of actions and can be confusing. You want to dig deeper?
Write us an email or give us a call so that we can work out how to approach the perfect cloud solution for your needs.
SimScale is a web-based simulation platform that allows engineers to perform simulations like finite element analysis, computational fluid dynamics, and thermodynamics directly in their web browser. It has over 65,000 users worldwide and offers fully automated meshing and solving workflows. The document provides overviews of SimScale's features for structural analysis, fluid analysis, and thermal analysis and discusses its pricing plans and security measures to protect user data.
Numerical Simulation Slides for NBIL Presentation in Queens universityYashar Seyed Vahedein
The numerical simulation project conducted by NBIL aimed to predict the carbon nanotube manufacturing process using template-based chemical vapor deposition (TB-CVD). The simulation modeled the CVD reactor geometry, defined boundary conditions based on experimental data, and solved conservation equations to analyze flow behavior and species concentration over time. The results showed good agreement with experimental temperature data and provided insight into how varying process parameters like gas flow rate affected velocity profiles and mass fraction distributions within the reactor. This allows for optimization of the TB-CVD process to fabricate carbon nanotubes with higher efficiency.
Understanding and Predicting CO2 Properties for CCS Transport, Richard Graham, University of Nottingham. Presented at CO2 Properties and EoS for Pipeline Engineering, 11th November 2014
Understanding and predicting CO2 properties - Presentation by Richard Graham in the Effects of Impurities on CO2 Properties session at the UKCCSRC Cardiff Biannual Meeting 10-11 September 2014
Upfront Thinking to Design a Better Lab Scale DoEplaced1
Presentation Given at AIChE 2009 and the Dynochem User meeting. Discussion on using mechanistic modeling to support DoE investigations and QbD initiatives for single reaction steps.
PID Tuning for Near Integrating Processes - Greg McMillan DeminarJim Cahill
Greg McMillan shares how to reduce tuning time for near integrating processes.
Recorded video version available for viewing at: http://www.screencast.com/t/NmUxZTBiNTg
LU-VE has experience producing heat exchangers for refrigeration and HVAC, especially with natural refrigerants like CO2. They presented on CO2 heat exchangers and their work on the NXTHPG project. Their CO2 test facility in Europe allows them to test heat exchangers under stationary conditions. Software predictions of capacity and pressure drop generally match test results well within 10%. Testing of CO2 evaporators showed capacity increases with Reynolds number as expected. Their 5mm tube CO2 coil for the NXTHPG project was smaller and lighter than the standard design while maintaining performance. CFD analysis helped optimize the fan configuration to increase COP by 2%.
1. Robert van Straalen presented a method using machine learning and reinforcement learning to optimize control of an industrial waste water treatment plant.
2. An initial neural network model was developed to predict air flow and energy consumption.
3. A reinforcement learning algorithm was then used to choose control settings that meet airflow needs while minimizing energy use.
The document discusses chemical process simulation and the use of process simulators. It provides an overview of process simulation techniques including sequential modular approach, equation oriented approach, and simultaneous modular approach. It also discusses typical process units that can be modeled, the development of simulation flowsheets, and commercial process simulators such as Aspen and ChemCad. Process simulation allows prediction of process performance, optimization of energy usage, and identification of bottlenecks.
The document discusses chemical process simulation. It provides background on using computer-aided simulation tools to analyze large-scale chemical processes under steady-state conditions. It describes the overall structure of process design and analysis using simulation, including flowsheet synthesis, material and energy balancing, equipment sizing and costing, and economic evaluation. It also discusses specific techniques for process simulation, including sequential modular, equation-oriented, and simultaneous modular approaches.
This document describes using ASPEN Plus dynamic simulation software to model and control a continuous stirred tank reactor (CSTR) process. It introduces key dynamic simulation concepts and outlines the steps to:
1) Build a process flowsheet model in steady-state, including reactions, streams and equipment.
2) Convert the model to dynamic mode and input dynamic parameters.
3) Add a level controller to the CSTR and tune it using open-loop testing and the Ziegler-Nichols method.
4) Simulate the dynamic behavior of the controlled process to evaluate controller performance.
Industrial plant optimization in reduced dimensional spacesCapstone
This document summarizes an industrial plant optimization lecture given in Toronto. It discusses the history of optimization in oil refining from early adoption in the 1950s to modern real-time optimization (RTO). RTO aims to capture opportunities from changing plant conditions by modeling the plant with engineering equations and optimizing the model in parallel with plant operation. While RTO provides benefits, reconciling measurements, non-linear constraints, and operator acceptance present technical and behavioral challenges. New approaches using projection methods to model plants from historical operating data in reduced dimensional spaces are discussed as alternatives to traditional modeling that may better represent operator preferences and familiarity.
The document discusses the development of an online calorific value sensor and models for optimizing control of municipal solid waste combustion (MSWC) systems. It describes the development of a sensor based on measurements of gases and humidity that can determine the calorific value and moisture content of waste in real time. Dynamic models of MSWC systems were also developed and validated. These models and the sensor allow optimization of control concepts for MSWC systems through advanced control methods like model predictive control and evolutionary control tested in EU projects.
Combustion tutorial ( Eddy Break up Model) , CFDA.S.M. Abdul Hye
This document provides a tutorial for using STAR-CCM+ to simulate three combustion models: an idealized CAN gas turbine combustion chamber, a flame tube, and methane on platinum. It describes setting up simulations for each model, including importing geometries, defining materials and reactions, setting boundary conditions and solver parameters, and visualizing results. Specific steps are outlined for a simulation of propane combustion in a CAN chamber using an eddy break-up model, including generating a PPDF table and specifying initial conditions and stopping criteria.
This document discusses the damping ratio of unit step responses in control systems. It defines damping ratio as the ratio of the actual damping coefficient to the critical damping coefficient. It describes the different types of damping including underdamped, overdamped, and critically damped systems. It discusses using a unit step function as a common test input and analyzing the step response to identify system properties. MATLAB coding examples are provided to simulate step responses and the document discusses applications in identification from step response testing.
CFD evaluation of lime addition in AMD Nabin Khadka
This document discusses using computational fluid dynamics (CFD) to evaluate lime addition for treating acid mine drainage (AMD) in a mixing tank. CFD can provide insights into flow patterns, velocities, and dead zones within the tank. The study models different propeller positions and numbers of blades to determine optimal mixing. Results show center positioning of the propeller increases flow velocity and mixing effectiveness. Two to three blades are suitable for the given flow rates but may differ at other rates. CFD analysis provides data to better understand and manage AMD treatment through lime neutralization in mixing tanks.
[Capella Day 2019] Model execution and system simulation in CapellaObeo
A common need in system architecture design is to verify that if the architect is correct and can satisfy its requirements. Execution of system architect model means to interact with state machines to test system’s control logic. It can verify if the logical sequences of functions and interfaces in different scenarios are desired.
However, only sequence itself is not enough to verify its consequence or output. So we need each function to do what it is supposed to do during model execution to verify its output, and that is what we called “system simulation”.
This presentation introduces how we do model execution in Capella, and how to embed digital mockup inside functions to do “system simulation” with a higher confidence.
Renfei Xu, Glaway
Renfei Xu is the technical manager of MBSE solution in Glaway. He has participated in many pilot projects of MBSE in areas like Engine Control, Avionics, Mechatronics and so on. In recent years, he is responsible for the deployment of MBSE using Capella and ARCADIA methodology in a Radar research institute.
Wenhua Fang, Glaway
Wenhua Fang is the Director of Systems Engineering in Glaway. He has more than 12 years of working experience in SE.
He is responsible for more than 10 implementation projects of MBSE in areas like Aircraft, Engine Control, Avionics, Automotive and so on. In recent years, he leads the team to deploy MBSE in China(including using Capella and ARCADIA methodology).
In the last few years energy efficiency of large scale infrastructures gained a lot of attention, as power consumption became one of the most impacting factors of the operative costs of a data-center and of its Total Cost of Ownership. Power consumption can be observed at different layers of the data-center: from the overall power grid, moving to each rack and arriving to each machine and system. Given the rise of application containers in the cloud computing scenario, it becomes more and more important to measure power consumption also at the application level, where power-aware schedulers and orchestrators can optimize the execution of the workloads not only from a performance perspective, but also considering performance/power trade-offs. DEEP-mon is a novel monitoring tool able to measure power consumption and attribute it for each thread and application container running in the system, without any previous knowledge regarding the characteristics of the application and without any kind of workload instrumentation. DEEP-mon is able to aggregate data for threads, application containers and hosts with a negligible impact on the monitored system and on the running workloads.
Information obtained with DEEP-mon open the way for a wide set of applications exploiting the capabilities offered by the monitoring tool, from power (and hence cost) metering of new software components deployed in the data center, to fine grained power capping and power-aware scheduling and co-location.
This document discusses exergetic and thermoeconomic analysis of a coal-fired power plant. It begins with definitions of exergy as usable work and explanations of energy and exergy analysis. It then describes various thermoeconomic analysis methods including Specific Exergy Costing (SPECO) and Modified Production Structural Analysis (MOPSA). SPECO and MOPSA are applied to a sample coal-fired power plant model to determine the unit exergy costs of each stream. The results of exergy and economic analyses of the plant are presented, identifying locations for potential efficiency improvements.
Optimization of Electrical Machines in the Cloud with SyMSpace by LCMcloudSME
Presented at NAFEMS DACH regional conference for numerical simulation methods by LCM and cloudSME in Wiesbaden on the 14th of November 2019.
The Linz Center of Mechatronics GmbH showcased how they easily optimize electrical drive engines in the cloud.
We supported LCM to work out the right cloud-based service solutions for their customers based on their existing software. By respecting the latest developments in the industry and science, including security and privacy compliance and hosting flexibility (free choice of data centre, no vendor lock-in).
Check out their cool System Model Space "SyMSpace" for electrical drive engines and trusted by industrial partners! (https://bit.ly/2CKGphb) #poweredbycloudSME
Yes, Cloud Computing is offering a broad range of actions and can be confusing. You want to dig deeper?
Write us an email or give us a call so that we can work out how to approach the perfect cloud solution for your needs.
SimScale is a web-based simulation platform that allows engineers to perform simulations like finite element analysis, computational fluid dynamics, and thermodynamics directly in their web browser. It has over 65,000 users worldwide and offers fully automated meshing and solving workflows. The document provides overviews of SimScale's features for structural analysis, fluid analysis, and thermal analysis and discusses its pricing plans and security measures to protect user data.
Numerical Simulation Slides for NBIL Presentation in Queens universityYashar Seyed Vahedein
The numerical simulation project conducted by NBIL aimed to predict the carbon nanotube manufacturing process using template-based chemical vapor deposition (TB-CVD). The simulation modeled the CVD reactor geometry, defined boundary conditions based on experimental data, and solved conservation equations to analyze flow behavior and species concentration over time. The results showed good agreement with experimental temperature data and provided insight into how varying process parameters like gas flow rate affected velocity profiles and mass fraction distributions within the reactor. This allows for optimization of the TB-CVD process to fabricate carbon nanotubes with higher efficiency.
Understanding and Predicting CO2 Properties for CCS Transport, Richard Graham, University of Nottingham. Presented at CO2 Properties and EoS for Pipeline Engineering, 11th November 2014
Understanding and predicting CO2 properties - Presentation by Richard Graham in the Effects of Impurities on CO2 Properties session at the UKCCSRC Cardiff Biannual Meeting 10-11 September 2014
Upfront Thinking to Design a Better Lab Scale DoEplaced1
Presentation Given at AIChE 2009 and the Dynochem User meeting. Discussion on using mechanistic modeling to support DoE investigations and QbD initiatives for single reaction steps.
PID Tuning for Near Integrating Processes - Greg McMillan DeminarJim Cahill
Greg McMillan shares how to reduce tuning time for near integrating processes.
Recorded video version available for viewing at: http://www.screencast.com/t/NmUxZTBiNTg
LU-VE has experience producing heat exchangers for refrigeration and HVAC, especially with natural refrigerants like CO2. They presented on CO2 heat exchangers and their work on the NXTHPG project. Their CO2 test facility in Europe allows them to test heat exchangers under stationary conditions. Software predictions of capacity and pressure drop generally match test results well within 10%. Testing of CO2 evaporators showed capacity increases with Reynolds number as expected. Their 5mm tube CO2 coil for the NXTHPG project was smaller and lighter than the standard design while maintaining performance. CFD analysis helped optimize the fan configuration to increase COP by 2%.
1. Robert van Straalen presented a method using machine learning and reinforcement learning to optimize control of an industrial waste water treatment plant.
2. An initial neural network model was developed to predict air flow and energy consumption.
3. A reinforcement learning algorithm was then used to choose control settings that meet airflow needs while minimizing energy use.
The document discusses chemical process simulation and the use of process simulators. It provides an overview of process simulation techniques including sequential modular approach, equation oriented approach, and simultaneous modular approach. It also discusses typical process units that can be modeled, the development of simulation flowsheets, and commercial process simulators such as Aspen and ChemCad. Process simulation allows prediction of process performance, optimization of energy usage, and identification of bottlenecks.
The document discusses chemical process simulation. It provides background on using computer-aided simulation tools to analyze large-scale chemical processes under steady-state conditions. It describes the overall structure of process design and analysis using simulation, including flowsheet synthesis, material and energy balancing, equipment sizing and costing, and economic evaluation. It also discusses specific techniques for process simulation, including sequential modular, equation-oriented, and simultaneous modular approaches.
This document describes using ASPEN Plus dynamic simulation software to model and control a continuous stirred tank reactor (CSTR) process. It introduces key dynamic simulation concepts and outlines the steps to:
1) Build a process flowsheet model in steady-state, including reactions, streams and equipment.
2) Convert the model to dynamic mode and input dynamic parameters.
3) Add a level controller to the CSTR and tune it using open-loop testing and the Ziegler-Nichols method.
4) Simulate the dynamic behavior of the controlled process to evaluate controller performance.
Industrial plant optimization in reduced dimensional spacesCapstone
This document summarizes an industrial plant optimization lecture given in Toronto. It discusses the history of optimization in oil refining from early adoption in the 1950s to modern real-time optimization (RTO). RTO aims to capture opportunities from changing plant conditions by modeling the plant with engineering equations and optimizing the model in parallel with plant operation. While RTO provides benefits, reconciling measurements, non-linear constraints, and operator acceptance present technical and behavioral challenges. New approaches using projection methods to model plants from historical operating data in reduced dimensional spaces are discussed as alternatives to traditional modeling that may better represent operator preferences and familiarity.
The document discusses the development of an online calorific value sensor and models for optimizing control of municipal solid waste combustion (MSWC) systems. It describes the development of a sensor based on measurements of gases and humidity that can determine the calorific value and moisture content of waste in real time. Dynamic models of MSWC systems were also developed and validated. These models and the sensor allow optimization of control concepts for MSWC systems through advanced control methods like model predictive control and evolutionary control tested in EU projects.
Combustion tutorial ( Eddy Break up Model) , CFDA.S.M. Abdul Hye
This document provides a tutorial for using STAR-CCM+ to simulate three combustion models: an idealized CAN gas turbine combustion chamber, a flame tube, and methane on platinum. It describes setting up simulations for each model, including importing geometries, defining materials and reactions, setting boundary conditions and solver parameters, and visualizing results. Specific steps are outlined for a simulation of propane combustion in a CAN chamber using an eddy break-up model, including generating a PPDF table and specifying initial conditions and stopping criteria.
This document discusses the damping ratio of unit step responses in control systems. It defines damping ratio as the ratio of the actual damping coefficient to the critical damping coefficient. It describes the different types of damping including underdamped, overdamped, and critically damped systems. It discusses using a unit step function as a common test input and analyzing the step response to identify system properties. MATLAB coding examples are provided to simulate step responses and the document discusses applications in identification from step response testing.
CFD evaluation of lime addition in AMD Nabin Khadka
This document discusses using computational fluid dynamics (CFD) to evaluate lime addition for treating acid mine drainage (AMD) in a mixing tank. CFD can provide insights into flow patterns, velocities, and dead zones within the tank. The study models different propeller positions and numbers of blades to determine optimal mixing. Results show center positioning of the propeller increases flow velocity and mixing effectiveness. Two to three blades are suitable for the given flow rates but may differ at other rates. CFD analysis provides data to better understand and manage AMD treatment through lime neutralization in mixing tanks.
Kieran Laxen - Assessing the impacts of short-term power generation - DMUG17IES / IAQM
An unapologetically technical conference, DMUG remains the key annual event for experts in this field. Unmissable speakers will be examining topical issues in emissions, exposure and dispersion modelling.
This document summarizes a workshop on PV performance modeling. It discusses developing models to estimate yearly PV yield in Germany for regulatory reporting. Models are selected based on existing algorithms, computational efficiency, accuracy, and using satellite and weather data. Models are validated using irradiation, temperature, and generation data. The best irradiation model was Perez Model. A ZIPcode-based model called ZIPSoP estimates yield by classifying systems, optimizing parameters like temperature coefficients and comparing to measured generation. Current work focuses on improving ZIPSoP by determining the best aggregation level and parameter fitting.
Numarical simulation of a "Swirling jet" expanding inside a combust...numenor80
1) A numerical simulation was conducted of a swirling jet expanding inside a combustion reactor to analyze velocity and pressure fields.
2) Computational fluid dynamics (CFD) software was used to model the cold fluid dynamics of a swirl burner and compare results to literature.
3) The simulation accurately reflected the swirling jet behavior, with a reverse flow zone developing near the burner outlet as seen in previous studies. Further analysis will introduce combustion reactions and thermal modeling.
This document discusses computational fluid dynamics (CFD). CFD uses numerical analysis and algorithms to solve and analyze fluid flow problems. It can be used at various stages of engineering to study designs, develop products, optimize designs, troubleshoot issues, and aid redesign. CFD complements experimental testing by reducing costs and effort required for data acquisition. It involves discretizing the fluid domain, applying boundary conditions, solving equations for conservation of properties, and interpolating results. Turbulence models and discretization methods like finite volume are discussed. The CFD process involves pre-processing the problem, solving it, and post-processing the results.
1. The NextGen solution implemented at the Braunschweig WWTP includes a two-stage digestion system with thermal pressure hydrolysis (TPH) between stages, and systems for struvite and ammonium sulfate production.
2. Testing showed the TPH led to a 25% increase in methane production and improved dewatering, while struvite and ammonium sulfate recovery removed phosphorus and nitrogen.
3. Further optimization is planned to increase struvite particle size and phosphorus recovery rate, and to optimize ammonium sulfate production and heat management across the new systems.
Similar to NONLINEAR MODEL PREDICTIVE CONTROL FOR OPERATION OF A POST COMBUSTION ABSORPTION UNIT (20)
Vehicle Dynamics Library provides an open and user-extensible environment for full vehicle and vehicle subsystem analysis. Designed with a hierarchical structure and an extensive library of predefined vehicle components, the configuration of any class of wheeled vehicle – cars, trucks, motorsport vehicles, heavy vehicles – is convenient and straight-forward.
The library allows you to optimize and verify the design of your vehicle systems from the early design phases through control design and implementation. It is unique in that it provides true multi-body, multi-domain simulation with real-time performance, and model export capabilities allowing distribution across your organization.
The Modelica Vapor Cycle Library is used to design vapor cycle systems for heating, cooling and waste-heat recovery.
The library enables component interaction and dynamic system behavior to be simulated and analyzed at an early design stage. It can be used as an integrated part of energy management design for both mobile and residential applications.
The document discusses Modelon's Thermal Power Library, which provides modeling and simulation capabilities for thermal power systems including power plants, district heating networks, and components that use energy sources like solar, gas, waste, coal, and nuclear. The library allows modeling processes for design, analysis, control design, and optimization of plant operations. It features pre-configured templates, efficient component models, and capabilities like transient simulation and control strategies. Example applications are also summarized.
Modelon’s Pneumatics Library is used to verify and optimize the design of complete pneumatic systems throughout a product lifecycle.
Applications include suspension and brake systems, machine tools, heavy-duty pneumatic tools such as jackhammers, impact wrenches and drills.
The document describes the Modelon Liquid Cooling Library, which provides Modelica models for simulating liquid cooling systems. The library contains components for pipes, pumps, valves, heat exchangers, and more. It supports various liquids and can be used for applications like engine cooling, battery thermal management, and process industry equipment. The library is compatible with other Modelon libraries and supports batch simulation in tools like MATLAB, Python, and Excel.
The document provides an overview of the Jet Propulsion Library, which contains components and models for simulating jet engines. The library includes extensive subsystems and thermodynamic cycles, steady state and dynamic capabilities, heat exchanger models, and interfaces to other physical domains. It supports modeling of various jet engine types and applications like flight envelope studies. The latest release added a geared turbofan example and models for variable geometry and natural gas applications.
Heat Exchanger Library provides an environment for heat-exchanger design and analysis with interfaces for convenient system model integration. It contains a large number of geometry-based heat exchangers with a focus on compact designs, such as plate and micro-channel types. Both two-phase media such as refrigerants, and single-phase fluids like incompressible liquids, air or pressurized gases are supported.
The Hydro Power Library is designed for modeling and simulating hydroelectric power plants. It contains over 100 components for modeling hydro systems, mechanical systems, electrical systems, and control systems. The library allows users to analyze issues like water hammer effects, grid synchronization, pump storage, surge tank oscillations, and more. It is compatible with Modelon's Electric Power and Hydraulics Libraries.
Modelon’s Hydraulics Library is valuable for all industries that develop hydraulic components and applications, including automotive, aerospace and industrial equipment.
The Hydraulics Library provides models of pumps, motors and cylinders, restrictions and valves, hydraulic lines, lumped volumes and sensors. No special components for splits or mergers are required — users connect hydraulic components by simply drawing connection lines, making it easy to model non-standard configurations and component designs.
Fuel System Library is a Modelica library targeting the design and verification of fuel systems on civil and military aircraft. The library is designed to analyze and verify the system behavior during various dynamic operating modes and flight conditions.
Aircraft are characterized by large variations in acceleration and orientation. The Fuel System Library provides simulation results accounting for these effects on fuel-air mixtures and includes full support of bidirectional flow.
The Modelica Fuel Cell Library (FCL) is used to model, simulate, analyze and control fuel cell design and operation, especially for PEMFC (Polymer Electrolyte Membrane) and SOFC (Solid Oxide) fuel cell systems.
It contains the essential components needed to research, design and configure fuel cell systems, including components, subsystems, templates and media.
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Modelon’s FMI Composer offers users the ability to build system models, save in an open ssp format, export files as FMU, and simulate in their tool of choice. By leveraging the power of open standards users can connect FMUs, therefore optimizing the use of models across organizations and industries.
Regression testing tool for Modelica and FMI
Framework to define individual tests, test suites, and execute them either from a GUI or from Python scripts
Uses OPTIMICA Compiler Toolikt compiler to facilitate test design and usage
Tool-agnostic approach for test execution (currently supports Dymola, OpenModelica and OPTIMICA Compiler Toolkit)
Execution on local machines or via a server
Full HTML report provides dashboard overview with links to individual test results
NONLINEAR MODEL PREDICTIVE CONTROL FOR OPERATION OF A POST COMBUSTION ABSORPTION UNIT
1. NONLINEAR MODEL PREDICTIVE CONTROL FOR
OPERATION OF A POST COMBUSTION
ABSORPTION UNIT
J. Åkessona,b, G. Lavedana, K. Prölßa,
H. Tummescheita, S. Veluta
a) Modelon AB
b) Department of Automatic Control, Lund University
ICEPE Conference 2011, Frankfurt
2. Motivation: Optimal control for CCS
• Carbon dioxide separation reduces power plant efficiency significantly
• Find control strategy that handles dynamically changing boundary
conditions, and changing regulatory conditions while minimizing
operational costs
• Solve this task with non-linear model-predictive control (NMPC)
• Challenge: model needs to be sufficiently accurate, yet simple enough
to be optimized in real time!
power plant
legal restrictions
emission costs
dynamic load demands
electricity market price
fluegas
process steam
post-combustion
carbon capture
cleaned gas
3. Overview
Detailed model
development
Model reduction
Formulation of the
optimal control problem
State estimation from
measurements
Solution of the optimal
control problem
Application of control
signal to process
offline
online
Process toward NMPC Used tools
Dymola
Dymola/
JModelica.org
JModelica.org
JModelica.org
Presentation outline
1
2
3
4
Example
Offline
example
Modelica is modeling language for physical
systems modeling, simulation and
optimization defined by an open specification.
Both Dymola and JModelica.org use
Modelica for describing the model.
4. Flowsheet of the amine scrubbing process
pressure
reboiler
duty
flue gas
rate
recirculation
rate
Optional
intercooler
5. Model development
1. Medium properties and reactions
MEA-water-CO2 system incl. ions (electrolyte solution)
Gas mixture, flue gas (absorber) and water/CO2 (stripper)
Phase equilibrium at liquid/gas interface and in reboiler
2. Distributed bulk flow with dynamic mass and energy
balances, algebraic flow correlation (pressure drop and
hold-up)
3. Heat and mass transfer, semi-empirical correlations
liquid/gas, liquid/liquid, liquid/solid
enhancement due to reactions
6. Assumptions and dynamics
• Discretization only in bulk flow direction
• Gas/Liquid mass transfer with transfer coefficents and
enhancement factors instead of discretized multi-
component diffusion
• Instantaneous reactions in the liquid phase reduce the
number of states to four per liquid volume (amounts of
carbon dioxide, MEA and water, temperature)
• Incompressible liquid and ideal gas yield index 1 system
7. Focus on column model
• Same base models for both absorber and stripper
MEA solution
steam
and
CO2
flue
gas
8. Chemical system in liquid phase
Ion speciation in the liquid phase
missing in the plot:
H2O, OH-, H3O+, CO3--
• Chemical equilibrium
:activity coefficient
x : mole fraction
: stoichiometry coeffficient
T: temperature
• Assumed because of
relatively fast reactions
limited amount of literature
data available for kinetics
trade-off in order to cut
down the number of
numerical states
9. Model validation – Complete system
• Experimental data from pilot plant in Esbjerg, also presented in Faber et al. ,
Proceedings of GHGT 10
Constant boundary
conditions(open loop):
• Solution recirculation
rate
• Reboiler duty
• Flue gas temperature
and composition
• Gas exit pressures
• 30% MEA solution
Step variation: Flue gas flow rate
10. Model validation – Complete system
Removal rate (absorber)
• Very good agreement in
lower load case
• Experimental data
apparently not in steady-
state
Temperatures (stripper)
• good overall agreement
• simulation model reveals
faster dynamics
• total liquid system volume
was unknown and
probably underestimated
Not measured: Liquid
composition
11. Model validation – Complete system
Gas temperature profile in
absorber
• two (steady-state) operating
points
• assumption: even distribution
of measurement points
• good agreement of simulation
with measurements
• conclusions on model quality
possible concerning
heat of sorption
heat and mass transport
12. Model validation - stripper
Experimental data from: Tobiesen et al., Chem. Eng. Sci., 63 (2008) 2641-2656
Given:
• Solution flow rate
• Inlet temperature
• Inlet loading
• Reboiler duty
• Packing and
packing height
Caution:
• Extremely small
loading range,
usually larger
13. Model reduction – Ion speciation
• Computing the speciation in the liquid phase results in
large non-linear equation systems
• Eliminating ion speciation increases robustness and
simulation speed
• Liquid solution consists of
three species only: total CO2,
total H2O, total MEA
• Mole fraction of molecular
CO2, is computed from a
spline interpolation of the
speciation map:
xCO2 = f (T, NCO2/NMEA)Xmea=const.
14. Model reduction
Further assumptions
• Heat of sorption
Important for correct reboiler duty
Simplification: concentration independent
• Heat and mass transfer
Constant transfer coefficients, incl. enhancement
• Constant heat capacities for each species in system
• Constant density of liquid phase
• Simplified model of absorber (desorber more important for energy savings)
• Overall effect: simulation time reduced by factor of 200
16. Stripper unit
• Stripper + reboiler, comparison of detailed and reduced
model, steady-state cases
• Given reboiler duty and inlet solvent loading
Conclusion:
• phase and chemical
equilibrium and heat of
sorption are captured
sufficiently well in
reduced model
17. Optimization
Problem
Goal: minimize the separation and emission cost while satisfying
operation and regulatory constraints
Potential degrees of freedom: reboiler duty, circulation rate,
(stripper pressure)
Constraints: pump capacity, reboiler pressure, CO2 emission...
Boundary conditions: flue gas rate and composition, electricity
price
Solution
Method: Nonlinear Model Predictive Control
Tool: JModelica.org platform
18. Towards NMPC
NMPC=sequence of open loop control problems
1. Estimate process states from measurements
2. Solve optimal control problem on the prediction horizon
3. Apply the first sample of the optimal input
4. Update internal data, go back to step 1 and repeat sequence
Current project status
Solve the NMPC problem offline, using detailed simulation model
instead of real process
Using an extended Kalman filter for state estimation
Using one or two degrees of freedom (control signals to optimize)
On a simplified system
19. Interaction Structure
CCS Plant or
Simulation model
State observer:
Extended
Kalman Filter
Optimization problem,
simplified model
Local linearized
model
uk yk
1
2
3
4
1. Estimate states from
measurements
2. Solve optimal control problem on
the prediction horizon
3. Apply the first sample of the
optimal input
4. Update linear part of Kalman filter,
go back to step 1 and repeat
sequence
20. Simplified problem
• Stripper unit:1493 equ., 50 states
• Absorber: equilibrium with flue gas
• Fixed circulation rate
• Pressure control at top
• DOF=reboiler duty
• Target removal efficiency ᶯ
• Minimize quadratic cost
𝐽 𝑢, 𝑥0 = 𝛼 ᶯ(𝑡) − ᶯ 𝑟𝑒𝑓
2
+ 𝛽
𝑑𝑢
𝑑𝑡
2
𝑑𝑡
𝐻 𝑝
0
• Constraint on reboiler pressure
𝑝 𝑟𝑒𝑏𝑜𝑖𝑙𝑒𝑟 𝑡 ≤ 𝑝 𝑚𝑎𝑥
Reboiler
Desorber
From
Absorber
To
Absorber
To
Storage
Condenser
Pressure
control
Heat injection
G
desorber
L
G L
reb?
LV
F
conden?
r?
r?
valve
gasSink
T_gs
k=313
p_gs
k=1.3e5
p_set
k=1?
valve.p?
headPressure
T_c?
T=3?
K
leanSolut?
richSolut?
flow sou?
T_so?
k=356
Vflo?
k=16.?
liquidSink
T_ls
k=313
p_ls
k=2e5
summary
Q_reb?
k=2e6
gain
pI1
vol2
vol1
dQ
heat_der
I
k=1
1
s
• Overall model: 1600 equations, 55 states
• Simplified absorber, interpolating for different L/G ratios.
• Pressure control at top of column
• 1 DOF: reboiler duty (DOF: degree of freedom)
• 2 DOF: reboiler duty and circulation rate
• Given target removal efficiency
21. Jmodelica.org
Extensible open-source platform for simulation and
optimization of Modelica models
• Dynamic optimization
• Modelica extension: Optimica
• Direct collocation method
• Large scale NLP
• Solver: IPOPT
• Python interface
23. Results: 1 DOF
• Prediction horizon: 1000s
• Sampling: 100s
• NLP: 29824 equations
• Initialization with constant
trajectories
• Solved in 73 iterations, less
than 100 s
• Consistency between
Jmodelica.org and Dymola
25. Summary
• Results and Conclusions
A validated Modelica model library for transient simulation of amine-
based post-combustion capture
The plant model was able to capture measured behavior of
absorption process
Physical model reduction leads to a model suitable for optimization
JModelica.org was used to perform optimization on this model with
1600 equations and 55 state variables
• Future work
Consider parts of power generation in model/cost function (LP turbines)
Moving horizon estimation instead of Extended Kalman Filter for state
estimation
Stripper pressure as degree of freedom
Higher discretization of stripper column
Maintain real-time for more complex model (input blocking, sampling,
initialization, Hessian...)
• New evaluation framework CasADi
• Methods for real-time NMPC, e.g., Advanced step method by V. Zavala