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Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics

BIOTECHNOLOGY AND BIOENGINEERING 2024年第1期121卷 366-379页

作者： Espinel-Rios, Sebastian Morabito, Bruno Pohlodek, Johannes Bettenbrock, Katja Klamt, Steffen Findeisen, Rolf Max Planck Inst Dynam Complex Tech Syst Anal & Redesign Biol Networks Magdeburg Germany Yokogawa Insil Biotechnol GmbH Stuttgart Germany Tech Univ Darmstadt Control & Cyber Phys Syst Lab Darmstadt Germany

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity. Metabolic cybergenetic feedback in fed-batch bioreactors exploiting dynamic optimization and model predictive control: Key proteins, like enzyme preg ${p}_{reg}$, adjust using inducible gene systems to achieve various metabolic modes, influenced by fa

关键词： constraint-based modeling dynamic metabolic control metabolic cybergenetics model predictive control optogenetics state estimation

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Research on Hybrid Mechanistic and Data-Driven modeling of Wind Turbines Based on Particle Swarm optimization 8

Research on Hybrid Mechanistic and Data-Driven Modeling of W...

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8th IEEE international conference on Energy Internet, ICEI 2024

作者： Ji, Zhanyang Wang, Xiyang Wang, Shuai North China Electric Power University School of Control and Computer Engineering China

ISBN: (纸本)9798331523558

In response to the complex nonlinear operational characteristics of large wind turbines, a hybrid semi-mechanistic modeling method for multi-condition operations is proposed, based on a refined 5MW wind turbine model and co-simulation using FAST and Simulink. First, a complex nonlinear mechanistic model is constructed through mechanistic analysis, identifying the input and output variables, and completing data collection. Then, the aerodynamic system is linearized using a Taylor expansion, and an incremental model is built for key nonlinear components at steady-state operating points. Finally, guided by mechanistic insights, the particle swarm optimization (PSO) algorithm is applied to refine the aerodynamic partial derivatives, optimizing the results of the model linearization. The conclusions show that the proposed modeling method retains advantages of speed and precision under various operating conditions, while preserving the physical meaning of model parameters. This method effectively captures the complex nonlinear response characteristics of wind turbines across different operating conditions, laying the foundation for wind turbine condition monitoring and controller research. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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optimization of spare parts configuration scheme for ship equipment based on multi-agent simulation

Optimization of spare parts configuration scheme for ship eq...

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2024 international conference on Electronics, Electrical and Information Engineering, ICEEIE 2024

作者： Li, Wei Li, Hongjun Li, Hua 713th Research Institute of China State Shipbuilding Corporation Limited Henan Zhengzhou China

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper presents a comprehensive exploration of the optimization of spare parts configuration schemes for ship equipment using a multi-agent simulation methodology. The focus lies in leveraging the failure rates of life-limited parts and employing advanced multi-agent modeling technologies to conduct simulated calculations pertaining to a ship's offshore operations. Through these simulations, the paper aims to ascertain the optimal outcomes for the spare parts configuration scheme. The research methodology involves the development and application of a multi-agent simulation method, specifically tailored to address the challenges associated with the mathematical modeling of complex systems. The proposed approach not only enhances the accuracy of spare parts configuration but also contributes to overcoming difficulties in modeling intricate systems. To substantiate the effectiveness of the proposed multi-agent simulation method, the paper includes illustrative examples demonstrating its application in real-world scenarios. The findings of the research highlight the method's efficacy in optimizing spare parts configuration schemes for ship equipment. This paper contributes to the field by presenting a sophisticated and practical approach to address the complexities inherent in mathematical modeling within the context of maritime operations. Based on the multi-agent simulation method, this paper primarily discusses the optimization of spare parts configuration schemes for ship equipment. By means of failure rates of life-limited parts as well as multi-agent modeling technologies, this method can be employed to perform the simulating calculation of a ship's offshore operations, thus determining the optimization outcomes of the spare parts configuration scheme. Research findings demonstrate that the multi-agent simulation method proposed in this paper addresses the difficulties related to the mathematical modeling of complex systems, with an example presented to illust

关键词： Boat equipment

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SUPPLY CHAIN RESILIENCE optimization WITH AGENT-BASED modeling (SCROAM): A NOVEL HYBRID FRAMEWORK

SUPPLY CHAIN RESILIENCE OPTIMIZATION WITH AGENT-BASED MODELI...

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2024 Winter simulation conference, WSC 2024

作者： Anagnostou, Anastasia Mintram, Kate Taylor, Simon J.E. Modelling and Simulation Group Dept. of Computer Science Brunel University London United Kingdom

ISBN: (纸本)9798331534202

Supply chains are vulnerable to an array of exogenous disruptions, including operational contingencies, natural disasters, terrorism, and political and geopolitical instability. In order to ensure resilience to these disruptions, supply chains can use mitigation strategies to minimize risk and maximize recovery. modeling approaches can be utilized to determine the most appropriate mitigation strategies for a specific scenario;however, there is currently no recognized modeling framework which can be applied to all supply chain sectors. This paper describes the key disruption risks to supply chains;the resilience and optimization strategies and performance metrics employed by supply chains to mitigate these risks;and the applications of simulation modeling in supply chain management. We present a hybrid framework for using agent-based modeling, alongside early warning systems, many objective optimization and option awareness analysis, to manage exogenous risks for a non-specific supply chain. © 2024 IEEE.

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modeling, simulation, optimization of the DLR Scout Rover to Enable Extraterrestrial Cave Exploration 6th

Modeling, Simulation, Optimization of the DLR Scout Rover to...

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6th Joint international conference on Multibody System Dynamics and 10th Asian conference on Multibody Systems, IMSD-ACMD 2020

作者： Pignède, Antoine Lichtenheldt, Roy Münchener Straße 20 Weßling82234 Germany

ISBN: (纸本)9789819775248

In-situ exploration of extraterrestrial area of scientific interest remains largely impossible with the state-of-the-art wheeled rovers. To combine the advantages of wheeled and legged robots, DLR’s Institute of System Dynamics and Control is developing a modular rover with rimless wheels, called Scout. To save time and money, the development of the Scout rover makes extensive usage of modeling and simulation. A detailed multibody model of the Scout rover accompanies the system through all phases following the V-shaped development process. This starts with the detailed modeling, analysis, and optimization of the rimless wheels and other central parts. It continues with simulation tests on the full system along with optimizations. For example, the properties of the "vertebrae" that connect segments together have been laid out following a digital optimization campaign. Subjects of this text are the details of the multibody Scout rover model, optimization on the rover and simulation analyses. A first prototype of the Scout rover has been built 1.5 years after the start of the project. It is already exceeding the requirements and expectations. Such good results would not have been possible this fast without simulation and optimization. Refinement of the simulation model and validation against the hardware are the next steps. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wheels

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Machine Learning Based on Antennas modeling for 5G and 6G Communication Systems: A Systematic Review

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international JOURNAL OF COMMUNICATION SYSTEMS 2025年第9期38卷

作者： Muttair, Karrar Shakir Shareef, Oras Ahmed Taher, Hazeem Baqir Middle Tech Univ Elect Engn Tech Coll Dept Comp Engn Baghdad Iraq Univ Kufa Fac Engn Nanotechnol & Adv Mat Res Unit Najaf Iraq Al Ayen Iraqi Univ Dept Med Devices Tech Engn AUIQ Nasiriyah Iraq Thi Qar Univ Coll Educ Pure Sci Dept Comp Sci Nasiriyah Iraq

Artificial intelligence (AI)-aided communications have gained significant traction in recent years due to the widespread application of machine learning (ML) and deep learning (DL) machines with algorithms to solve math problems in wireless communications. This study offers an overview of the use of ML models in antenna design and optimization. This incorporates DL on ML frameworks, categories, and structure to get practical and broad insights using ML techniques for high throughput, quick data analysis, and prediction. This article also comprehensively reviews recent research papers on antenna design via ML. This includes an analysis of several ML algorithms that have been applied to produce antenna parameters such as the reflection coefficient (S-parameters), efficiency and gain values, and radiation patterns of the antennas. However, the current antenna design's structure, variables, and external factors remain complex. In addition, the expense of time and processing resources is inescapable and unacceptable to most designers. ML-based antennas have been created to increase antenna modeling efficiency and accuracy to solve these challenges. Techniques for modeling data may be used to predict the performance of an antenna for a certain set of antenna factors of design. As a result, this study highlights the most sophisticated applied ML techniques that have been presented to increase antenna modeling efficiency and accuracy. The results demonstrate that AI, ML, and DL may minimize simulation needs, predict antenna behavior, and reduce time with high accuracy.

关键词： antenna design optimization antenna modeling artificial intelligence CST deep learning machines HFSS machine learning algorithms training techniques

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FDI Detection Method for Smart Grid Based on IBFO-CNN

FDI Detection Method for Smart Grid Based on IBFO-CNN

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9th international conference on Signal and Image Processing (ICSIP)

作者： Wang, Hao Zhang, Xiangcong Wang, Zhiying Zhang, Chan Zhang, Feifei Lu, Weizhi Wang, Lei Wang, Bingjie State Grid Henan Informat & Telecommun Co Data Ctr Zhengzhou Peoples R China

ISBN: (纸本)9798350350920

In recent years, false data injection (FDI) poses a serious security threat to the operational status of smart grid. Aiming at the current difficulty of smart grid to detect FDI attacks, this paper proposes a FDI detection method for smart grid based on IBFO-CNN. Firstly, the simulation modeling of FDI is completed based on the state estimation of smart grid. Then the convolutional neural network (CNN) is introduced as the basic FDI detection model, and for the problem of inefficient parameter optimization, the bacterial foraging optimization (BFO) algorithm improved by the multivariate updating strategy is used to realize automatic parameter optimization of the CNN, so as to obtain the FDI detection model of smart grid based on IBFO-CNN. Finally, the designed model is applied to the IEEE 14 bus system to test the model's detection performance for FDI. The experimental results show that the IBFO-CNN model proposed in this paper has high precision for FDI detection in the IEEE 14 bus system, which has a better detection performance and anti-noise performance compared with other models. The proposed method has good performance in engineering practice can effectively guarantee the data security of the smart grid.

关键词： false data injection smart grid convolutional neural network bacterial foraging optimization algorithm multivariate updating strategy

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An Optimal Resilience Dispatch method Integrating Hybrid Energy Resources for a Steel Factory Campus 13

An Optimal Resilience Dispatch method Integrating Hybrid Ene...

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13th international conference on Renewable Energy Research and Applications

作者： Wu, Yi-Syuan Liao, Jian-Tang Yang, Hong-Tzer Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan

ISBN: (纸本)9798350375596;9798350375589

When a power outage occurs, it may cause significant economic losses for industries. However, previous studies on power system resilience have seldom focused on user-side microgrids. This study proposes composite resilience indices for user-side microgrids to quantify their ability to withstand extreme events. The proposed resilience indices consider load value, minimum power supply, energy shortages, and the degradation characteristics of islanded microgrids. Additionally, this study introduces a two-stage resilience optimization and reconfiguration strategy, including energy-level scheduling and grid-level feeder reconfiguration. A genetic algorithm based multiobjective optimization approach is used for energy scheduling, followed by feeder reconfiguration using particle swarm algorithm integrated with DIgSILENT system modeling to meet the grid codes and maximize demand supply. The proposed methods are validated using a realistic steel corporation as a test system, showing the flexibility and practical feasibility of the system. Userside microgrid resilience can be effectively enhanced by the proposed method considering load priority.

关键词： microgrid power system resilience power system simulation pareto optimization feeder reconfiguration

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Research on Carrying Capacity of the High-speed Rail Station Based on Multi-objective optimization and simulation Verification 7

Research on Carrying Capacity of the High-speed Rail Station...

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7th international conference on Traffic Engineering and Transportation System (ICTETS)

作者： Xu, Feng China Railway Design Corp Tianjin Pilot Free Trade Zone Airport Econ Zone 109 East Seventh Rd Tianjin Peoples R China

ISBN: (纸本)9781510674479

With the rapid construction of China's high-speed rail, the gradual improvement of network layout, and the increasing track connections in the station, issues emerge such as the difficulty in selecting the stop train strategy of the station and the ambiguity of checking the calculation of station carrying capacity. In view of this situation, a carrying capacity calculation model based on multi-objective optimization and simulation verification is proposed for the railroad station integration scheme. Firstly, a multi- objective optimization calculation model is constructed to determine the objective function and constraints related to the issues. Secondly, the train tracking scenario simulation is applied and realized, and then the train operation plan is inferred based on the algorithm to realize the construction of the simulation environment for the cluster operation of the EMU. Finally, a high-speed railway intermediate station in southern China is taken as the simulation target, and the track connection scheme of the station is used as the simulation basis, so as to map the line information and abstract it for verification based on the real railroad network environment and station equipment data. The test results show that the multi-objective optimization and simulation verification model can effectively help develop a reasonable stop train strategy for the station and thereby obtain the station carrying capacity.

关键词： high-speed rail track connection in railroad station train operation simulation stop train strategy of the station station carrying capacity

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Reinforcement Learning applied to the optimization of Power Delivery Networks with Multiple Voltage Domains

Reinforcement Learning Applied to the Optimization of Power ...

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IEEE MTT-S international conference on Numerical Electromagnetic and Multiphysics modeling and optimization (NEMO)

作者： Han, Seunghyup Bhatti, Osama Waqar Na, Woo-Jin Swaminathan, Madhavan Georgia Inst Technol Atlanta GA 30332 USA Samsung Elect Memory Div Suwon South Korea

ISBN: (纸本)9798350347401

This paper proposes a reinforcement learning (RL)based method to optimize power plane design with multiple voltage domains. The proposed method enables the simultaneous selection of the plane for expansion and its expansion direction, determining the size and shape of multiple power planes. The results show that the proposed method provides various optimized power plane designs that satisfy the target impedance and ensure the reference plane for the input/output (I/O) interface.

关键词： reinforcement learning (RL) proximal policy optimization (PPO) power plane power delivery network

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