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Intelligent data-based modeling and optimization approach for intensification of capacitive deionization system featuring low-cost carbon electrodes

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ELECTROCHIMICA ACTA 2025年 517卷

作者： Askari, Mahdi Salehi, Ehsan Velashjerdi, Mohammad Arak Univ Fac Engn Dept Chem Engn Arak *** Iran Arak Univ Fac Engn Dept Mat Sci & Engn Arak *** Iran

This work evaluates the capacitive deionization (CDI) performance of optimized peanut shell-derived porous carbon (PSPC) electrodes fabricated in our previous work. Investigation and optimization of the impact of CDI process parameters i.e., applied voltage, salt initial concentration, and feed flowrate, on salt adsorption capacity (SAC) was conducted via a novel intelligent data-based approach including design-of-experiment (DOE), response surface-methodology (RSM), Sobol's sensitivity analysis (SA), and genetic algorithm (GA). It was revealed that the SAC will be promoted as the voltage, salt initial concentration, and feed flowrate increase. The electrical voltage was found to be the most influential factor on SAC variation with more than 50 % effect. Process optimization via GA resulted the maximum SAC equal to 22.13 mg/g at the optimal conditions of electrical voltage = 1.6 V, salt initial concentration = 1500 mg/L, and feed flowrate = 27 mL/min. The PSPC electrode offered an appropriate long-term performance preserving 74 % of its capacity after 100 consecutive adsorption/regeneration cycles. Moreover, the adsorption kinetics followed the pseudo-first order pattern. This work uncovered the potential of intelligent data-based modeling and optimization techniques as a novel strategy for intensification of CDI process.

关键词： Capacitive deionization (CDI) Process intensification Optimization data-based modeling Sensitivity analysis (SA)

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data-based modeling, Multi-Objective Optimization, and Multi-Criterion Decision-Making to Maximize the Electro-Oxidation of Metoprolol over Boron-Doped Diamond Electrodes in a Flow-By Reactor

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PROCESSES 2024年第9期12卷 1958页

作者： Regalado-Mendez, Alejandro Vizarretea-Vasquez, Diego Robles-Gomez, Edson E. Natividad, Reyna Escudero, Carlos J. Peralta-Reyes, Ever Univ del Mar Res Labs Campus Puerto Angel Puerto Angel 70902 Oaxaca Mexico Univ Autonoma Estado Mexico UAEM UNAM Ctr Conjunto Invest Quim Sustentable Chem Engn Lab Km 14-5 Toluca Atlacomulco Rd Toluca 50200 Estado De Mexic Mexico Univ Autonoma Guadalajara Dept Biotechnol & Environm Ave Patria 1201 Zapopan 45129 Jalisco Mexico

Metoprolol is a cardioselective beta-blocker drug often used to treat hypertension, but it is considered as a hazardous organic persistent contaminant in wastewater. In this study, a 2.5 L solution of metoprolol (50 mg/L) underwent electro-oxidation in a flow-by reactor using boron-doped diamond electrodes in the batch recirculation mode. The study used multi-objective optimization and multi-criterion decision-making to determine the optimal operating parameters. The response surface methodology and a central composite rotatable design were used with three factors (pH0: 5-8, I: 2.5-4 A, and Q: 0.8-1.7 L/min) to model the chemical oxygen demand's (COD's) removal efficiency and the total organic carbon's (TOC's) removal efficiency. The experimental responses were modeled by reduced third- and second-order polynomials with determination coefficients (R2) of 0.9816 and 0.9430. The optimal operating parameters were found to be pH0 5, an I value of 3.84 A, and a Q value of 0.8 L/min with an electrolysis time of 7.5 h, resulting in a maximum COD removal efficiency of 60.8% and a TOC removal efficiency of 90.1%. The specific energy consumption was calculated as 9.61 kWh/mg of TOC, with a total operating cost of 0.77 USD/L. In conclusion, this study showed that the electrochemical process is efficient and reliable for treating wastewater containing metoprolol.

关键词： BDD electrodes data-based modeling electro-oxidation flow reactor metoprolol optimization

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A Comparison of Physics- and data-based modeling of Rural Drainage Systems 3

A Comparison of Physics- and Data-based Modeling of Rural Dr...

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3rd IFAC Workshop on Integrated Assessment modeling for Environmental Systems (IAMES)

作者： Baumann, Henry Schaum, Alexander Ravn, Nanna Hoegh Meurer, Thomas Jensen, Lina Nybo MVM Karlsruhe Inst Technol Digital Proc Engn Grp Hertzstr 16 D-76187 Karisruhe Germany Univ Hohenheim Dept Proc Analyt Computat Sci Hub Garbenstr 23 D-7(1599 Stuttgart Germany LNH Water Aps Kathord 3 DK-3080 Tikob Denmark

Rural drainage systems are often large and complex systems, which are difficult to model. In this work, a physical model based on the hydrology and hydraulic laws and a data-driven modeling approach with an ARX structure are created for the rural drainage system in Eiderstedt, Germany. Thereupon, the prediction accuracy for real measurement data of both models is investigated and the respective advantages and disadvantages are discussed. It is shown that both models are able to predict the water level quite well, with the physical and data-based model being more accurate for long- and short-term predictions, respectively. Copyright (C) 2024 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： data-based modeling physical modeling rural drainage systems

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A Comparison of Physics- and data-based modeling of Rural Drainage Systems ⁎

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IFAC-PapersOnLine 2024年第2期58卷 25-30页

作者： Henry Baumann Alexander Schaum Nanna Høegh Ravn Thomas Meurer Lina Nybo Jensen Digital Process Engineering Group MVM Karlsruhe Institute of Technology Hertzstr. 16 76187 Karlsruhe Germany Department for Process Analytics Computational Science Hub University of Hohenheim Garbenstr. 23 70599 Stuttgart Germany LNH water Aps Kathøjvej 3 3080 Tikøb Denmark

关键词： data-based modeling physical modeling rural drainage systems

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data-based modeling of drug penetration relates human skin barrier function to the interplay of diffusivity and free-energy profiles

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2017年第14期114卷 3631-3636页

作者： Schulz, Robert Yamamoto, Kenji Klossek, Andre Flesch, Roman Hoenzke, Stefan Rancan, Fiorenza Vogt, Annika Blume-Peytavi, Ulrike Hedtrich, Sarah Schaefer-Korting, Monika Ruehl, Eckart Netz, Roland R. Free Univ Berlin Dept Phys D-14195 Berlin Germany Free Univ Berlin Dept Phys D-14195 Berlin Germany Free Univ Berlin Phys Chem D-14195 Berlin Germany Free Univ Berlin Inst Pharm D-14195 Berlin Germany Charite Clin Clin Res Ctr Hair & Skin Sci D-10117 Berlin Germany

based on experimental concentration depth profiles of the antiinflammatory drug dexamethasone in human skin, we model the time-dependent drug penetration by the 1D general diffusion equation that accounts for spatial variations in the diffusivity and free energy. For this, we numerically invert the diffusion equation and thereby obtain the diffusivity and the free-energy profiles of the drug as a function of skin depth without further model assumptions. As the only input, drug concentration profiles derived from X-ray microscopy at three consecutive times are used. For dexamethasone, skin barrier function is shown to rely on the combination of a substantially reduced drug diffusivity in the stratum corneum (the outermost epidermal layer), dominant at short times, and a pronounced free-energy barrier at the transition from the epidermis to the dermis underneath, which determines the drug distribution in the long-time limit. Our modeling approach, which is generally applicable to all kinds of barriers and diffusors, allows us to disentangle diffusivity from free-energetic effects. Thereby we can predict short-time drug penetration, where experimental measurements are not feasible, as well as long-time permeation, where ex vivo samples deteriorate, and thus span the entire timescales of biological barrier functioning.

关键词： diffusion data-based modeling biological barriers skin Smoluchowski equation

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A Comparative data-based modeling Study on Respiratory CO₂ Gas Exchange during Mechanical Ventilation

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2016年第0期4卷 8-8页

作者： Kim, Chang-Sei Ansermino, J. Mark Hahn, Jin-Oh Univ Maryland Dept Mech Engn College Pk MD 20742 USA Univ British Columbia Dept Anesthesiol Pharmacol & Therapeut Vancouver BC Canada

The goal of this study is to derive a minimally complex but credible model of respiratory CO2 gas exchange that may be used in systematic design and pilot testing of closed-loop end-tidal CO2 controllers in mechanical ventilation. We first derived a candidate model that captures the essential mechanisms involved in the respiratory CO2 gas exchange process. Then, we simplified the candidate model to derive two lower-order candidate models. We compared these candidate models for predictive capability and reliability using experimental data collected from 25 pediatric subjects undergoing dynamically varying mechanical ventilation during surgical procedures. A two-compartment model equipped with transport delay to account for CO2 delivery between the lungs and the tissues showed modest but statistically significant improvement in predictive capability over the same model without transport delay. Aggregating the lungs and the tissues into a single compartment further degraded the predictive fidelity of the model. In addition, the model equipped with transport delay demonstrated superior reliability to the one without transport delay. Further, the respiratory parameters derived from the model equipped with transport delay, but not the one without transport delay, were physiologically plausible. The results suggest that gas transport between the lungs and the tissues must be taken into account to accurately reproduce the respiratory CO2 gas exchange process under conditions of wide-ranging and dynamically varying mechanical ventilation conditions.

关键词： respiratory CO2 gas exchange data-based modeling closed-loop mechanical ventilation control

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data-based instantaneous conditional progress variable dissipation rate modeling for turbulent premixed combustion

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COMBUSTION AND FLAME 2024年 259卷

作者： Lacey, Cristian E. VanderKam, Katie Sundaresan, Sankaran Mueller, Michael E. Princeton Univ Dept Mech & Aerosp Engn Princeton NJ 08544 USA Princeton Univ Dept Chem & Biol Engn Princeton NJ 08544 USA

Manifold-based turbulent combustion modeling reduces computational cost considerably by projecting the thermochemical state onto a lower-dimensional space of a few manifold variables and reconstructing the state from a set of manifold equations. In turbulent premixed combustion, a one-dimensional space is defined by the progress variable, and the progress variable dissipation rate appears as a parameter in the resulting one-dimensional manifold equations. Closure of the manifold equations requires a model for the dependence of this dissipation rate on the progress variable. Existing models for these profiles are often ad hoc, derived analytically with crude assumptions, and assumed to be spatially and temporally 'universal' - yet the resulting thermochemical state can be sensitive to these profiles. To evaluate the assumptions in traditional models, instantaneous conditional progress variable dissipation rate profiles are extracted from DNS databases of turbulent premixed jet flames. At low Karlovitz number, the instantaneous conditional progress variable dissipation rate profiles exhibit some variance but are relatively well-characterized by a 'universal' model. However, a large variance in the instantaneous conditional progress variable dissipation rate profiles is observed at high Karlovitz number, indicating the form of the conditional dissipation rate is certainly not 'universal'. To capture this variation at high Karlovitz number, a data-based model for the instantaneous conditional progress variable dissipation rate profiles is generated, and the resulting model predictions demonstrate excellent agreement with profiles extracted from DNS and far less error than 'universal' models. The sensitivity of the solutions of the one-dimensional premixed manifold equations to the dissipation rate profile is demonstrated a priori, and the error associated with the observed variance in the instantaneous conditional progress variable dissipation rate profiles (i.e., relat

关键词： data-based modeling Manifold modeling Scalar dissipation rate Turbulent premixed combustion Large Eddy Simulation (LES)

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A Markov data-based approach to system identification and output error covariance analysis for tensegrity structures

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NONLINEAR DYNAMICS 2024年第9期112卷 7215-7231页

作者： Shen, Yuling Chen, Muhao Skelton, Robert E. Soochow Univ Sch Future Sci & Engn Suzhou 215222 Jiangsu Peoples R China Texas A&M Univ Dept Aerosp Engn College Stn TX 77840 USA

This paper introduces a data-driven approach to address the long-standing challenge of modeling complex tensegrity systems. The proposed approach focuses on approximating unknown black box systems and estimating their output error covariance using input/output (IO) information. First, an approximation system that mirrors the input-output relation of the black box system is obtained. Next, output error covariance between the approximation and the black box system is calculated, which evaluates the accuracy of the identified model. This two-step approach relies exclusively on the black box system's Markov parameter sequence, eliminating the need for dynamics knowledge of the system. Nonlinear examples of a NACA 2412 tensegrity morphing airfoil and a 3D tensegrity prism are studied for validation. The proposed approach successfully identified approximation systems in state space realization in both cases with insignificant output error covariances. Compared to the widely-used Mode Displacement Method (MDM), the proposed approach exhibits an advantage in identifying velocity outputs for tensegrity systems. The developed approach in this paper applies to other tensegrity structures and structural identification problems.

关键词： Tensegrity Nonlinear dynamics Markov parameters data-based modeling System identification Covariance analysis

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Active data-Enabled Robot Learning of Elastic Workpiece Interactions

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2025年第3期147卷 031007页

作者： Mccann, Lance Yan, Leon (Liangwu) Hassan, Sarmad Garbini, Joseph Devasia, Santosh Univ Washington Dept Mech Engn Seattle WA 98195 USA

During manufacturing processes, such as clamping and drilling of elastic structures, it is essential to maintain tool-workpiece normality to minimize shear forces and torques, thereby preventing damage to the tool or the workpiece. The challenge arises in making precise model-based predictions of the relatively large deformations that occur as the applied normal force (e.g., clamping force) is increased. However, precision deformation predictions are essential for selecting the optimal robot pose that maintains force normality. Therefore, recent works have employed force-displacement measurements at each work location to determine the robot pose for maintaining tool normality. Nevertheless, this approach, which relies on local measurements at each work location and at each gradual increment of the applied normal force, can be slow and consequently time prohibitive. The main contributions of this work are: (i) to use Gaussian process (GP) methods to learn the robot-pose map for force normality at unmeasured workpiece locations;and (ii) to use active learning to optimally select and minimize the number of measurement locations needed for accurate learning of the robot-pose map. Experimental results show that the number of data points needed with active learning is 77.8% less than the case with a benchmark linear positioning learning for the same level of model precision. Additionally, the learned robot-pose map enables a rapid increase of the normal force at unmeasured locations on the workpiece, reaching force-increment rates up to eight times faster than the original force-increment rate when the robot is learning the correct pose.

关键词： data-based modeling Gaussian process regression active learning intelligent manufacturing system stiffness estimation predicted robot interactions experimental design

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Scalable Parametric-Identification Procedure for Kinematics of Automated N-Trailer Vehicles

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第6期73卷 7758-7770页

作者： Michalek, Maciej Marcin Poznan Univ Tech Inst Automatic Control & Robot IAR Poznan Poland

Kinematic description of a nonholonomic articulated N-Trailer vehicle includes two kinds of parameters: trailer lengths and hitching offsets. In the case of picking up of various trailers by an automated tractor in logistic hubs or transshipment terminals, the kinematic parameters of trailers can be uncertain or even unknown to a control system of the automated tractor. Since an accurate kinematic model is usually required to keep effective functionality of automated vehicles, it seems justified to provide a model-learning capability to the automated or intelligent tractors of N-Trailer vehicles. In this paper, we propose a scalable (with respect to any finite number of trailers) parametric identification procedure applicable to any type of N-Trailer kinematics with non-steerable trailers' wheels. The key idea results from a reformulation of joint-angles kinematics in an iterative form of linear regression models with only two parameters. The proposed estimation algorithm assumes availability of measurements of articulation angles and characteristic velocities of the tractor. Numerical results obtained for the 5-Trailer nonholonomic kinematics and for a high-fidelity TruckSim vehicle model equipped with three trailers illustrate effectiveness of the proposed data-based modeling approach and large-sample statistical properties of the applied estimation procedure.

关键词： Kinematics Agricultural machinery Wheels Numerical models Axles Task analysis Logistics N-Trailer vehicle kinematics parametric identification data-based modeling intelligent vehicles

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