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Application of a Nonlinear Model Predictive controller to the Anaerobic Digestion of Readily Biodegradable Wastes 13th

Application of a Nonlinear Model Predictive Controller to th...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Garcia-Gen, Santiago Santos, Lino O. Vande Wouwer, Alain Univ Tecn Federico Santa Maria Dept Ingn Quim & Ambiental Ave Espana 1680 Valparaiso 2390123 Chile Univ Coimbra CIEPQPF Dept Chem Engn Rua Silvio LimaPolo 2 P-3030790 Coimbra Portugal Univ Mons UMONS Syst Estimat Control & Optimizat SECO B-7000 Mons Belgium

A nonlinear model predictive controller for the anaerobic digestion of readily biodegradable substrates is presented. The controller aims to achieve a planned methane production, following a reference trajectory for the whole operation. Using an existing dynamic model of anaerobic digestion, the controller optimizes the operation conditions by conveniently manipulating a set of process variables such that the methane flow rate follows the reference trajectory. The controller works in a sequential approach, i.e., the plant trajectory is estimated over a prediction horizon with a simplified dynamic model of the process that includes only two biological reactions: acidogenesis and methanogenesis;then, the model predictions are optimized via a sequential quadratic programming method to match the desired trajectory. Due to the simplicity of the process model, the iterative optimization process is solved in a relatively short time. Both the dynamic model of the process and the optimization algorithm are implemented in MATLAB. The controller is tested in a simulation case study treating a readily biodegradable liquid effluent, where the same process model is used to mimic the measurements of the real plant. Copyright (C) 2022 The Authors.

关键词： NMPC Anaerobic Digestion Biogas process control

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Simulation and 3-D Visualization of Crystallization process Based on Cellular Automata 13th

Simulation and 3-D Visualization of Crystallization Process ...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Ai, Jiali Dai, Jindong Zhai, Chi Sun, Wei Beijing Univ Chem Technol Coll Chem Engn Beijing 100029 Peoples R China Kunming Univ Sci & Technol Fac Chem Engn Kunming 650500 Yunnan Peoples R China

Crystallization is widely used for separation and purification. During this process, the different growth conditions, such as temperature and supersaturation, would result in different size and morphology, which not only affect the product quality, but also affect the subsequent process operations. Through the simulation of the crystallization process, a better understanding of crystallization process dynamics can be extracted. The population balance (PB) model has been used to simulate industrial crystallization processes, while the multi-dimensional population balance (MPB) model can be used to obtain the information of size and morphology distribution. However, the morphological distribution information provided by numerical solution of MPB is macroscopic, and the specific morphology of a crystal particle cannot be displayed at the microscopic level, so the difference among crystals morphology cannot be clearly displayed. Cellular automata (CA) is a method that simulates a process by considering simple cell changes and cell-to-cell interactions. It can be employed to conduct cross-scale simulation, i.e., both macro and micro. In this work, a CA method is introduced into an MPB model to simulate the batch cooling crystallization process from solution. The rule of crystal growth comes from classical diffusion theory, and whether the solution is transformed into a crystal is realized by the Monte Carlo method. The crystallization of potassium dihydrogen phosphate (KDP) is taken as a case-study and the results is verified with the results in the literature. Through CA simulation, not only the macroscopic crystal size and morphology distribution information can be obtained, but also the crystal morphology in microscopic can be exhibited. At the same time, the crystallization process can be visualized in 3-D environment, with size and morphology distribution being presented intuitively. In addition, the calculation time is reduced under certain accurate conditions. Thi

关键词： multi-dimensional population balance (MPB) equation batch cooling crystallization of solution dynamic simulation potassium dihydrogen phosphate (KDP) High-resolution finite volume method crystal morphology crystal size distribution

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Data-driven Linear Predictor based on Maximum Likelihood Nonnegative Matrix Decomposition for Batch Cultures of Hybridoma Cells 13th

Data-driven Linear Predictor based on Maximum Likelihood Non...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Pimentel, Guilherme A. Dewasme, Laurent Vande Wouwer, Alain Univ Mons Syst Estimat Control & Optimizat Grp SECO Mons Belgium

This paper presents an original design of low-rank linear predictors of nonlinear process state variables based on nonnegative matrix decomposition (NMD). Therefore, this predictor is datadriven and does not require an accurate model description of the process. In addition, measurement errors are considered, conferring maximum likelihood (ML) properties to the estimator and resulting in a maximum likelihood nonnegative matrix decomposition (MLNMD) formulation. The latter is validated in simulation with a model developed by the authors, describing monoclonal antibody (MAb) production from sequential batch hybridoma cell cultures that are further validated with real-life experimental data. To this end, two available experimental data sets are used for direct and cross-validation, highlighting the good predictive properties of the method. Copyright (C) 2022 The Authors.

关键词： nonnegative matrix decomposition maximum likelihood measurements errors hybridoma cells cultures

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Machine learning-based analysis of the physio-chemical properties for the predictive thickness control of atomic layer deposition 13th

Machine learning-based analysis of the physio-chemical prope...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Kim, Changsu Do, Thai Ngan Kim, Jiyong Sungkyunkwan Univ Sch Chem Engn Suwon 16419 South Korea

Atomic layer deposition (ALD) is an outstanding thin film deposition technique based on the surface chemical reaction. As the conventional device fabrication method is reported to be ineffective under the 5nm process, ALD drew attention to its ability to control the film growth. To develop an efficient ALD process, controlling film thickness is one of the most important factors. The key requirement for the film thickness control is to understand their chemical reactivity upon adsorption on different substrate surfaces. However, the current research on viable ALD development have remained inefficient because of the expensive and time-consuming experiments. In this study, we aim to analyze and suggest a strategy for identifying the contribution of ALD process features on the device film thickness based on principal component analysis (PCA) method. First, the features of ALD experiment (Precursor properties, reactant type, substrate properties, operating conditions) are compiled to define the chemical dimension of ALD process. Then, the contributions of ALD experiment features on the thickness growth are analyzed by projecting ALD chemical dimension into the information space using PCA technique. As a result, we could identify highly sensitive features for ALD film growth and suggest the strategy for controlling film thickness. From providing the analysis for the film growth, we elucidate the importance of high dimension analysis of ALD process and improve the understanding on film growth control. [GRAPHICS] .

关键词： Atomic layer processing Machine learning Artificial intelligence

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Nonlinear Model Predictive control of Post-combustion CO2 Capture process for Flexible Operation 13th

Nonlinear Model Predictive Control of Post-combustion CO2 Ca...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Jung, Howoun Lee, Jay H. Korea Adv Inst Sci & Technol Dept Chem & Biomol Engn Daejeon 305701 South Korea

A large amount of energy requirement for solvent regeneration is a major barrier to the widespread adoption of amine-based post-combustion CO2 capture (PCC). Flexible operation is one of the ways to lower the energy penalty by responding to changes in economic factors like the energy price. However, for effective implementation of flexible operation strategies, it is necessary to identify the most economic operating condition under various potential scenarios and to establish an appropriate control strategy to operate the process. As flexible operation will inherently involve a large operating envelope, we investigate the use of nonlinear model predictive control (NMPC) technology. To circumvent the problem of solving a large-scale nonlinear programming problem online, a simpler NARX model is identified and used. With the NARX model, an offset-free NMPC is designed and simulated under various dynamic scenarios. The developed NARX-based NMPC shows satisfactory control performance, stabilizing the CO2 capture rate faster than LMPC by 60-100 min. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license(https://***/licenses/by-nc-nd/4.0/)

关键词： Post-combustion CO(2)capture Dynamic simulation System identification Nonlinear model predictive control

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Optimal control of Mechanical Ventilators with Learned Respiratory dynamics

Optimal Control of Mechanical Ventilators with Learned Respi...

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Annual IEEE symposium on Computer-Based Medical systems

作者： Isaac R. Ward Dylan M. Asmar Mansur Arief Jana Krystofova Mike Mykel J. Kochenderfer Department of Aeronautics and Astronautics Stanford Intelligent Systems Laboratory Stanford University Stanford USA Department of Pediatrics UCSF Weill Institute for Neurosciences University of California San Francisco San Francisco USA

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Deciding on appropriate mechanical ventilator management strategies significantly impacts the health outcomes for patients with respiratory diseases. Acute Respiratory Distress Syndrome (ARDS) is one such disease that requires careful ventilator operation to be effectively treated. In this work, we frame the management of ventilators for patients with ARDS as a sequential decision making problem using the Markov decision process framework. We implement and compare controllers based on clinical guidelines contained in the ARDSnet protocol, optimal control theory, and learned latent dynamics represented as neural networks. The Pulse Physiology Engine’s respiratory dynamics simulator is used to establish a repeatable benchmark, gather simulated data, and quantitatively compare these controllers. We score performance in terms of measured improvement in established ARDS health markers (pertaining to improved respiratory rate, oxygenation, and vital signs). Our results demonstrate that techniques leveraging neural networks and optimal control can automatically discover effective ventilation management strategies without access to explicit ventilator management procedures or guidelines (such as those defined in the ARDSnet protocol).

关键词： Acute respiratory distress syndrome Ventilators Protocols Neural networks Optimal control Benchmark testing Ventilation

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Cell Metabolic Diagnosis and control in CHO Fed-batch process 13th

Cell Metabolic Diagnosis and Control in CHO Fed-batch Proces...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Kuang, Bingyu Duc Hoang Wang, Zhao Yoon, Seongkyu Univ Massachusetts Chem Engn Dept Lowell MA 01854 USA

Therapeutic protein productivity and product quality highly rely on cell metabolism of the fed-batch process, which is a costly, time-consuming and lack of intracellular analytical diagnostic tools. Cell culture medium composition and feeding strategy is critical to regulate cell metabolism. In this study, we present an unorthodox approach to optimize CHO bioprocess by integrating conventional design-of-experiments (DOE) methodology with genome scale model (GEM) flux analysis. Generic CHO-K1 metabolic model was tailored and further integrated with CHO fed-batch metabolomic data to obtain a cell line- and process-specific model. In silico metabolic flux analysis was conducted via GEM to identify the critical medium components toward cellular growth and further evaluate their optimized flux values from thirty five simulated fed batch DOE conditions. Glucose and valine were projected as the most critical nutrients in the process from the flux simulation analysis. Using this approach, previously identified metabolic inhibitor cytidine monophosphate accumulated in extracellular environment was found to be regulated by glucose, glutamine, aspartate, and alanine and further experimentally validated through dose-dependent amino acid spiking study. A process diagnostic and control model was constructed from network topology modeling constructed through GEM and pathway enrichment analysis, which allowed optimization of medium components utilized in a fed-batch feeding process to better support cell metabolism and mitigate accumulation of metabolic inhibitors. Copyright (C) 2022 The Authors.

关键词： Therapeutic protein production Fed-batch bioprocess in silico model FBA Culture medium development Metabolism shift

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Dynamic simulator and model predictive control of a milk pasteurizer 13th

Dynamic simulator and model predictive control of a milk pas...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Thostrup, Thomas Marschall Pedersen, Katrine Esmann Tronci, Stefania Errico, Massimiliano Univ Southern Denmark Dept Green Technol DK-5230 Odense Denmark Univ Cagliari Dipartimento Ingn Meccan Chim & Mat I-09123 Cagliari Italy

In this study, the design, optimization and dynamic modelling of a milk pasteurization unit have been developed, using the pseudo-component approach for describing milk properties. The fluid has been regarded as a mixture of five major categories, namely water, fats, proteins, carbohydrates, and minerals. Exploiting the optimal pasteurizer configuration, selected based on the total annualized cost, a dynamic model of the process has been also derived. The simulation of the system is then used as a virtual plant to develop a nonlinear model predictive control (NMPC) designed for rejecting the more important disturbances that can enter the system. The predicted trajectories have been calculated with a simplified version of the dynamic model, obtained by neglecting parameters temperature dependence. The NMPC performance has been compared with a PI controller in terms of set-point tracking and disturbance rejection Similar results have been obtained when using the different control algorithms for the output responses, but the NMPC showed better behaviour of the manipulated variables. Copyright (C) 2022 The Authors.

关键词： Pasteurization optimal design process modelling automatic control NMPC

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A Discrete-Steepest Descent Framework for the Simultaneous process and control Design of Multigrade Reactive Distillation Columns 13th

A Discrete-Steepest Descent Framework for the Simultaneous P...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Linan, David A. Ricardez-Sandoval, Luis A. Univ Waterloo Waterloo ON N2L 3G1 Canada

The simultaneous optimization of continuous and discrete design variables, operating conditions, and controller's tuning parameters of reactive distillation (RD) columns is investigated in this work. For this purpose, the capabilities of a recently proposed modular economic optimization strategy based on a Discrete-Steepest Descent (D-SDA) framework are investigated. The D-SDA is a decomposition method that aims to improve an initial design by systematically modifying its discrete decisions, e.g., number of stages, until a design that optimizes the process economics while meeting the desired specifications is found. A case study involving the production of ethyl tert-butyl-ether (ETBE) in a RD unit was considered. The simultaneous design and control of the RD column was solved under two scenarios, i.e., product changeovers between four different grades and the production of a single grade of ETBE under a step disturbance in the feed composition. The results show that the modular strategy can specify economic design and control schemes in reasonable computational times. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/ licenses/ by-nc-nd/4.0/)

关键词： Multitasking Reactive Distillation Optimal design and control Discrete-Steepest Descent

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Computational Model of Ta2O5/TaOx Memristors: Predicting Resistive Switching Behavior and Filament Growth dynamics for Enhanced Device control and Optimization

Computational Model of Ta2O5/TaOx Memristors: Predicting Res...

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Midwest symposium on Circuits and systems (MWSCAS)

作者： Armin Gooran-Shoorakchaly Sarah Safura Sharif Yaser Mike Banad School of Electrical and Computer Engineering University of Oklahoma Norman USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Memristors have been suggested for various applications, including nonvolatile memory and neuromorphic systems. In contrast to traditional devices that rely purely on electron transport, memristors use resistive switching (RS), which is based on the redistribution of ions. Despite multiple experimental and modeling efforts to investigate the RS process, a full physical framework that can quantitatively explain the dynamic RS behavior remains difficult. A model that quantitatively outlines dynamic resistive switching, including set/reset cycling and retaining self-consistency from the initial formation process, has yet to be developed. This study presents a $\text{TazO}_{5}/\text{TaO}_{\mathrm{X}}$ device model that can accurately predict all crucial RS properties during the formation process and subsequent set and reset cycles. The simulation outcomes were also contrasted with experimental DC and pulse measurements in 1R structures and revealed excellent agreement.

关键词： Pulse measurements Nonvolatile memory Neuromorphics Computational modeling Memristors process control Switches

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