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12th IFAC Symposium on advanced control of Chemical processes (ADCHEM)

作者： Rangan, Keshav Kasturi Durand, Helen Wayne State Univ 42 W Warren Ave Detroit Detroit MI 48202 USA

Prior research from our group developed a control-integrated active actuator cyberattack detection strategy. This strategy continuously probed for cyberattacks by updating target steady-states at every sampling time and then moving the process state toward these over the subsequent sampling period. Attacks were flagged if a Lyapunov function around the target steady-state did not decrease over a sampling period. This strategy had the benefit of ensuring safety of the process until an attack was detected. However, the continuous probing for attacks could decrease profit from the process compared to not probing for the attacks, which could limit the attractiveness of the method in practice. This work marks our first step toward attempting to develop a framework for modifying this detection strategy to make guarantees that the profit over a sampling period would be no worse than that of a stabilizing controller. This is achieved through utilizing two auxiliary controllers, in addition to the one which facilitates the attackprobing, with constraints on profits in the various controllers to enable the profit proofs over a sampling period (in the absence of disturbances) to be developed. A process reactor example is used to demonstrate the implementation of the detection strategy.

关键词： advanced process control Nonlinear predictive control Cyberattack detection Lyapunov methods Profitability of stabilized nonlinear systems

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Machine Learning Modeling for process control and Electrochemical Reactor Operation

Machine Learning Modeling for Process Control and Electroche...

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作者： Luo, Junwei University of California Los Angeles

学位级别：Ph.D., Doctor of Philosophy

Electrochemical reaction processes attract increasing attention as a promising chemical process alternative to achieve green and sustainable chemical manufacturing due to its property that can produce chemical products without directly burning fossil fuels. Among various electrochemical reaction processes, CO2 reduction provides a possibility to capture the CO2 gas in the atmosphere and effectively relieve the global warming crisis. However, due to the complex, stochastic, and nonlinear nature of the electrochemical reactions, modeling an electrochemical process using first-principles is very challenging. To investigate the physical-chemical phenomena of the electrochemistry of reduction of CO2, the Laboratory of Electrochemical System Engineering at UCLA has developed a gastight rotating cylinder electrode (RCE) cell to decouple mass transport phenomena from the intrinsic kinetics of the electrochemical reactions. Specifically, this RCE cell allows manipulation of two variables, the applied potential and rotation speed of the cylindrical electrode, which determines the mass transport profile and reaction kinetics of the electrochemical reactions, respectively. This design further enables the development of an advanced process control system for the reactor to control the desired output states by adjusting the two key input ***, the absence of the first-principle model for the electrochemical reactor is a significant challenge to develop the process control system. To this end, considering the complexity of the process, neural network (NN) models are developed and used in this thesis work to capture the input-output relationship of the reactor and provide a data-based alternative to the unavailable, first-principle models. On the other hand, in a data-driven approach, the performance of the NN model is determined by the quality of the training data. Therefore, the inevitable noise, caused by experimental uncertainty, can corrupt the collected data and

关键词： advanced process control Electrochemical reaction Koopman operator Machine learning Smart manufacturing

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Integrated Dynamic Modeling and advanced process control of Carbon Capture Systems

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Energy Procedia 2014年 63卷 1354-1367页

作者： Priyadarshi Mahapatra Jinliang Ma Brenda Ng Debangsu Bhattacharyya Stephen E. Zitney David C. Miller National Energy Techology Laboratory Morgantown WV USA Lawrence Livermore National Laboratory Livermore CA USA Department of Chemical Engineering West Virginia University Morgantown WV USA National Energy Technology Laboratory Pittsburgh PA USA

Multi-scale dynamic capture models are often too computationally expensive for use in real-time applications, such as operator training and online process control. As a result, innovative methods are required to reduce the complexity of PDE-based and rate-based dynamic models while preserving input-output behavior. In this study, dynamic reduced models (D-RMs) approximate the high-fidelity capture models, thereby offering a trade-off between accuracy, range of applicability, and computational cost. In this paper, we highlight a D-RM builder tool that automatically generates fast D-RMs using pre-computed results from repeated simulation of high-fidelity dynamic process models. Dynamic uncertainty quantification (UQ) analysis is provided that quantifies confidence in the generated D-RM when used as a surrogate ‘plant’ model. In addition, an advanced process control (APC) framework tool is presented, which uses these reduced-order surrogate models as fast predictive-model(s) for implementing various nonlinear model-predictive control (NMPC) strategies. The developed framework is targeted towards superior disturbance rejection and better responsiveness to demand-changes for otherwise slow capture processes.

关键词： Dynamic Modeling Reduced-Order Model advanced process control Optimization Carbon Capture MPC CCSI

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Energy Consumption Analysis of a Continuous Flow Ohmic Heater with advanced process controls

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ENERGIES 2023年第2期16卷 868-868页

作者： Oluwole-ojo, Oluwaloba Zhang, Hongwei Howarth, Martin Xu, Xu Sheffield Hallam Univ Natl Ctr Excellence Food Engn Howard St Sheffield S1 1WB England

This paper presents an analysis of the energy consumption of a continuous flow ohmic heater (CFOH) with advanced process controls for heating operations in the food and drinks industry. The study was carried out by using operational data collected from a CFOH pilot plant that was designed and constructed at the National Centre of Excellence for Food Engineering (NCEFE), Sheffield Hallam University. The CFOH is controlled by a PC and includes an onboard Programmable Logic controller (PLC) and a Human Machine Interface (HMI) so that it can be operated as a stand-alone unit with basic on/off and power setting control but without any advanced control features. The technical solution presented in this paper for heating foods demonstrates significant energy saving compared with conventional heating methods. Using the CFOH, the electric current generated in the food products by the Joule effect produces a rapid temperature increase with very high energy efficiency. This technique eliminates the low efficiency of heat transfer from the surface of vessels typically used to heat and cook food products. The analysis presented in this paper describes the energy consumption of the CFOH and compares the efficiency of the CFOH when different advanced process control techniques are used. Experimental results and analysis have shown that the CFOH can achieve an energy efficiency conversion of at least 87.9%. It has also shown that the energy conversion percentage can be increased by applying advanced controllers such as model predictive control (MPC) or adaptive model predictive control (AMPC).

关键词： ohmic heating (OH) energy efficiency continuous flow ohmic heater (CFOH) advanced process control

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Optimizing Diesel Production Using advanced process control and Dynamic Simulation

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IFAC Proceedings Volumes 2014年第3期47卷 358-363页

作者： Márcio R.S. Garcia Renato N. Pitta Gilvan G. Fischer Enéas R.N. Neto Radix Engenharia e Desenvolvimento de Software Ltda Rio de Janeiro RJ Brazil Refinaria Henrique Lage São José dos Campos SP Brazil

This paper describes the economical and operational benefits achieved with the use of advanced process control techniques and dynamic simulation applied to a Naphtha Splitter Column. The project consists in optimizing the Diesel blending system of Henrique Lage Refinery (REVAP) located in the state of São Paulo, Brazil. The control strategy was designed to maximize production rate, respecting the operational constraints. The results include an increase in the Naphtha flow stream to the Diesel blending system and improvement of the operational stability, leading to valuable economic gains. The project is also a step forward in the use of Dynamic simulation for modelling and identification, where the simulation models have shown to be representative for the inferential variables integration, adding value to the final result.

关键词： advanced process control Optimization Dynamic Simulation Naphtha Splitter Distillation

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Improving extrapolation capabilities of a data-driven prediction model for control of an air separation unit

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COMPUTERS & CHEMICAL ENGINEERING 2025年 194卷

作者： Krespach, Valentin Blum, Nicolas Pottmann, Martin Rehfeldt, Sebastian Klein, Harald Tech Univ Munich Inst Plant & Proc Technol TUM Sch Engn & Design Dept Energy & Proc Engn Boltzmannstr 15 D-85748 Garching Germany Linde GmbH Linde Engn Dr Carl von Linde Str 6-14 D-82049 Pullach Germany

In model predictive control, fully data-driven prediction models can be used besides common (non-)linear prediction models based on first-principles. Although no process knowledge is required while relying only on sufficient data, they suffer in their extrapolation capability which is shown in the present work for the control of an air separation unit. In order to compensate for the deficits in the extrapolation behavior, a further data source, here a digital twin, is deployed for additional data generation. The plant data set is augmented with the artificially generated data giving rise to a hybrid model in terms of data generation. It is shown that this model can significantly improve the prediction quality informer extrapolation areas of the plant data set. Even conclusions about the uncertainty behavior of the prediction model can be found.

关键词： advanced process control Extrapolation Data augmentation Data-driven prediction model Air separation

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Toward intelligent food drying: Integrating artificial intelligence into drying systems

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DRYING TECHNOLOGY 2024年第8期42卷 1240-1269页

作者： Ashtiani, Seyed-Hassan Miraei Martynenko, Alex Dalhousie Univ Fac Agr Dept Engn Halifax NS B3H 4R2 Canada

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, and optimization within the drying industry. This paper presents a comprehensive overview of progress made in ML from shallow to deep learning and its implications for food drying. Theoretical foundations, advantages, and limitations of various ML approaches employed in this domain are explored. Additionally, advancements in ML models, particularly those enhanced by optimization algorithms, are reviewed. The review underscores the role of intelligent configuration of ML models, which affects their accuracy and ability to solve problems of high energy consumption, nutrient degradation, and uneven drying. Drawing upon research achievements, integrating of AI models with real-time measuring methods is discussed, enabling dynamic determination of optimal drying conditions and parameter adjustments. This integration facilitates automated decision-making, reducing human errors and enhancing operational efficiency in food drying. Moreover, AI models demonstrate proficiency in predicting drying times and analyzing energy usage patterns, thereby enabling optimization to minimize resource consumption while preserving product quality. Finally, this paper identifies current obstacles in technology development and proposes novel research avenues for sustainable drying technologies. HIGHLIGHTS center dot The strengths and weaknesses of various AI methodologies are examined center dot Artificial neural networks are extensively used for modeling drying phenomena center dot Machine learning models can simulate complex processes of food drying center dot Deep learning has significant potential for real-time monitoring of drying center dot Intelligent control systems can optimize food drying

关键词： advanced process control machine learning real-time monitoring technology roadmap sustainable drying

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Part-Level Fault Classification of Mass Flow controller Drift in Plasma Deposition Equipment

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2024年第3期37卷 373-380页

作者： Kim, Min Ho Sim, Hye Eun Hong, Sang Jeen Myongji Univ Dept Semicond Engn Yongin 17058 South Korea

Semiconductor manufacturing processing can be jeopardized due to process fluctuations, and the degradation of equipment parts can significantly influence process variation. Timely diagnosing equipment faults causing process variations is desired in current high-end product manufacturing. This paper proposes a diagnostic method for the SiH4 gas flow rate drift using N2 vibrational transition in oxide deposition. In this research, optical emission spectroscopy (OES) and quadrupole mass spectrometer (QMS) are employed as condition monitoring sensors serving as a reference model to compare the diagnostic performance for gas flow rate drift. The study observes that the OES model exhibits much higher performance for minor diagnoses of less than 5% drift. The diagnostic model performance can be enhanced by incorporating plasma condition and gas indicators compared to when these indicators are used individually. This suggests that when conducting diagnostics for equipment and processes, it is crucial to consider indirect indicators like plasma indicators along with direct indicators such as gas radical density. The comprehensive use of both types of indicators enhances the diagnostic performance, providing a more accurate assessment of the conditions and potential problem in semiconductor manufacturing.

关键词： Plasmas Degradation process control Fluid flow Monitoring Semiconductor process modeling Semiconductor device manufacture Diagnosis advanced process control advanced equipment control in situ monitoring mass flow controller

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Digital Twin Enabled process Development, Optimization and control in Lyophilization for Enhanced Biopharmaceutical Production

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processES 2024年第1期12卷 211页

作者： Juckers, Alex Knerr, Petra Harms, Frank Strube, Jochen Tech Univ Clausthal Inst Separat & Proc Technol D-38678 Clausthal Zellerfeld Germany Martin Christ Gefriertrocknungsanlagen GmbH D-37520 Osterode Germany

Digital twins have emerged as a powerful concept for real-time monitoring and analysis, facilitating Quality by Design integration into biopharmaceutical manufacturing. Traditionally, lyophilization processes are developed through trial-and-error, incorporating high security margins and inflexible process set points. Digital twins enable the integration of adaptable operating conditions and implementation of automation through advanced process control (APC) with process Analytical Technology (PAT) and validated physicochemical models that rely on heat and mass transfer principles, allowing us to overcome the challenges imposed by the lyophilization process. In this study, a digital twin for freeze-drying processes is developed and experimentally validated. Using the digital twin, primary drying conditions were optimized for controlled nucleation and annealing methods by carrying out a few laboratory tests beforehand. By incorporating PAT and modeling, the digital twin accurately predicts the product's temperature and drying endpoint, showing smaller errors than the experiments. The digital twin significantly increases productivity by up to 300% while reducing the costs by 74% and the Global Warming Potential by 64%.

关键词： lyophilization process Analytical Technology process modeling Quality by Design digital twin process optimization advanced process control controlled nucleation

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Profit Considerations For Nonlinear control-Integrated Cyberattack Detection On process Actuators ⁎

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IFAC-PapersOnLine 2024年第14期58卷 592-597页

作者： Keshav Kasturi Rangan Helen Durand Wayne State University 42 W. Warren Ave. Detroit MI 48202 USA

Prior research from our group developed a control-integrated active actuator cyberattack detection strategy. This strategy continuously probed for cyberattacks by updating target steady-states at every sampling time and then moving the process state toward these over the subsequent sampling period. Attacks were fagged if a Lyapunov function around the target steady-state did not decrease over a sampling period. This strategy had the benefit of ensuring safety of the process until an attack was detected. However, the continuous probing for attacks could decrease profit from the process compared to not probing for the attacks, which could limit the attractiveness of the method in practice. This work marks our first step toward attempting to develop a framework for modifying this detection strategy to make guarantees that the profit over a sampling period would be no worse than that of a stabilizing controller. This is achieved through utilizing two auxiliary controllers, in addition to the one which facilitates the attack-probing, with constraints on profits in the various controllers to enable the profit proofs over a sampling period (in the absence of disturbances) to be developed. A process reactor example is used to demonstrate the implementation of the detection strategy.

关键词： advanced process control Nonlinear predictive control Cyberattack detection Lyapunov methods Profitability of stabilized nonlinear systems

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