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Modeling of deionization of aqueous compounds using electrodialysis process (data collection for enhanced clarity)

SEPARATION SCIENCE AND TECHNOLOGY 2024年第4期59卷 670-685页

作者： Moghadam, Mansour Kazemi Sharafi, Maryam Malek Ashtar Univ Dept Chem Engn Tehran Iran Malek Ashtar Univ Dept Chem Engn Tehran 158751774 Iran

The deionization process is increasingly recognized as a solution to global water shortages. This study aims to investigate the impact of various parameters on the deionization efficiency. Specifically, volume flow rates (F) were tested (1, 2.5, 5, 10, 15, and 20 ml min-1), solute concentrations (C) (200, 500, and 1000 mg l-1), temperatures (T) (50, 60, and 70 degrees C), voltages (V) (10, 20, and 30 V), and pressures (P) (200, 400, and 800 Pa). In another experiment examined F (100, 250, and 500 mol l-1 m2), V (0.1, 0.5, and 1 V), C (100, 200, 300, 400, and 500 moles m3), and time durations (D) (16, 32, 48, 64, 80, and 96 min). These experiments aimed to determine the separation percentage of potassium chloride cations and anions and were based on data extracted from scientific articles. In the subsequent step, the ANFIS model with the FCM network creation method and the error backpropagation training algorithm was employed to evaluate the impact of each cell input on the separation percentage, output flux, and the separation percentage of potassium chloride cations and anions. This evaluation was conducted through 31 and 15 iterations, respectively. based on the evaluation criteria, it was observed that the volume flow rate as the sole input yielded the highest prediction accuracy for the separation percentage and output flux. Additionally, the FCTVP model results indicated a slight reduction in the error criteria compared to the FCTV, FCT, FC, and single models. Consequently, it can be concluded that the cell pressure factor may play a decisive role, either reducing or increasing the separation process (R2 = 0.93,MBE = -0.3, and RMSE = 6.4), as well as the output flux (R2 = 0.85, MBE = 0.0003, and RMSE = 0.07). Furthermore, the statistical criteria values of the FVCT model for the separation percentage of potassium chloride cations and anions demonstrated a proper correlation (R2 = 0.96 and 0.95), slight underestimation, and low error MBE = -0.57 and -1.2) (RMSE

关键词： data-based methods outlet flux solute concentration separation percentage

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Network-theoretic modeling of fluid-structure interactions

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THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS 2023年第5期37卷 707-723页

作者： Nair, Aditya G. Douglass, Samuel B. Arya, Nitish Univ Nevada Dept Mech Engn 1664 N Virginia St Reno NV 89557 USA

The coupling interactions between deformable structures and unsteady fluid flows occur across a wide range of spatial and temporal scales in many engineering applications. These fluid-structure interactions (FSI) pose significant challenges in accurately predicting flow physics. In the present work, two multi-layer network approaches are proposed that characterize the interactions between the fluid and structural layers for an incompressible laminar flow over a two-dimensional compliant flat plate at a 35 degrees angle of attack. In the first approach, the network nodes are formed by wake vortices and bound vortexlets, and the edges of the network are defined by the induced velocity between these elements. In the second approach, coherent structures (fluid modes), contributing to the kinetic energy of the flow, and structural modes, contributing to the kinetic energy of the compliant structure, constitute the network nodes. The energy transfers between the modes are extracted using a perturbation approach. Furthermore, the network structure of the FSI system is simplified using the community detection algorithm in the vortical approach and by selecting dominant modes in the modal approach. Network measures are used to reveal the temporal behavior of the individual nodes within the simplified FSI system. Predictive models are then built using both data-driven and physics-based methods. Overall, thiswork sets the foundation for network-theoretic reduced-order modeling of fluid-structure interactions, generalizable to other multi-physics systems.

关键词： Fluid-structure interaction Reduced-order modeling Vortex dynamics data-based methods

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A non-intrusive data-based reformulation of a hybrid projection-based model reduction method 26

A non-intrusive data-based reformulation of a hybrid project...

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26th International Symposium on Mathematical Theory of Networks and Systems (MTNS)

作者： Gosea, Ion Victor Gugercin, Serkan Beattie, Christopher Max Planck Inst Dynam Complex Tech Syst Magdeburg Germany Virginia Tech Dept Math Blacksburg VA 24061 USA

We present a novel data-driven reformulation of the iterative SVD-rational Krylov algorithm (ISRK), in its original formulation a Petrov-Galerkin (two-sided) projection-based iterative method for model reduction combining rational Krylov subspaces (on one side) with Gramian/SVD based subspaces (on the other side). We show that in each step of ISRK, we do not necessarily require access to the original system matrices, but only to input/output data in the form of the system's transfer function, evaluated at particular values (frequencies). Numerical examples illustrate the efficiency of the new data-driven formulation. 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： Efficient strategies for large-scale complex systems Linear systems Identification and model reduction Time-invariant systems data-based methods Transfer function

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data-based stability analysis of strongly autonomous discrete nD systems 26

Data-based stability analysis of strongly autonomous discret...

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26th International Symposium on Mathematical Theory of Networks and Systems (MTNS)

作者： Mukherjee, Mousumi Pal, Debasattam Indian Inst Engn Sci & Technol Sibpur India Indian Inst Technol Bombay Maharashtra India

The aim is to provide data-based conditions for checking stability of strongly autonomous nD systems. It is known that because of the lack of natural ordering of the domain, the problem of defining and studying stability of nD systems is not straightforward. We focus on the following two notions of stability: (i) stability with respect to a given direction, and more generally, (ii) conic stability, that is, stability with respect to subsets of the domain having the structure of a cone. For a strongly autonomous nD system, these two notions of stability have been studied in the literature from a model based perspective. Here, we analyze their data-based counterparts. We assume that the data is noise-free, and that it is measured from a linear system of partial difference equations, with constant real coefficients, having n independent variables. We further assume that the data-generating system is a strongly autonomous nD system, that is, the solution trajectories can be uniquely determined using finite "initial conditions". Given a finite set of data, that is, a finite set of multi-indexed sequences, we first define a notion of informativity of nD trajectories. Using informative nD trajectories, we provide data-based tests for checking stability of nD systems with respect to a given direction, and, more generally, for conic stability. 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： nD systems conic stability data-based methods algebraic analysis

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Black Box Model Analysis for Industrial Fault Monitoring 28

Black Box Model Analysis for Industrial Fault Monitoring

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Galve, Sergio Vilajosana, Xavier Puig, Vicenç Universitat Oberta de Catalunya Wireless Networks Research Lab Barcelona Spain Universitat Politècnica de Catalunya Advanced Control Systems Barcelona Spain

ISBN: (纸本)9798350339918

The aim of this paper is to present and discuss an analysis approach that can be deployed independently of the modelling tool capable of evaluating the samples of an assembled machine. By including an analysis of the results given the context of the input variables and the training performance, the method could be applied independently to the changes in modelling or the modelling approach. A real case study based on a water-cooling device has been used to illustrate the proposed approach. © 2023 IEEE.

关键词： black box interpretability data-based methods Fault

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data-based stability analysis of strongly autonomous discrete nD systems

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IFAC-PapersOnLine 2024年第17期58卷 439-444页

作者： Mousumi Mukherjee Debasattam Pal

The aim is to provide data-based conditions for checking stability of strongly autonomous nD systems. It is known that because of the lack of natural ordering of the domain, the problem of defining and studying stability of nD systems is not straightforward. We focus on the following two notions of stability: (i) stability with respect to a given direction, and more generally, (ii) conic stability, that is, stability with respect to subsets of the domain having the structure of a cone. For a strongly autonomous nD system, these two notions of stability have been studied in the literature from a model based perspective. Here, we analyze their data-based counterparts. We assume that the data is noise-free, and that it is measured from a linear system of partial difference equations, with constant real coefficients, having n independent variables. We further assume that the data-generating system is a strongly autonomous nD system, that is, the solution trajectories can be uniquely determined using finite “initial conditions”. Given a finite set of data, that is, a finite set of multi-indexed sequences, we first define a notion of informativity of nD trajectories. Using informative nD trajectories, we provide data-based tests for checking stability of nD systems with respect to a given direction, and, more generally, for conic stability.

关键词： nD systems conic stability data-based methods algebraic analysis

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A non-intrusive data-based reformulation of a hybrid projection-based model reduction method

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IFAC-PapersOnLine 2024年第17期58卷 226-231页

作者： Ion Victor Gosea Serkan Gugercin Christopher Beattie

关键词： Efficient strategies for large-scale complex systems Linear systems Identification and model reduction Time-invariant systems data-based methods Transfer function

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Unlocking the Potential of Wastewater Treatment: Machine Learning based Energy Consumption Prediction

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WATER 2023年第13期15卷

作者： Alali, Yasminah Harrou, Fouzi Sun, Ying King Abdullah Univ Sci & Technol KAUST Comp Elect & Math Sci & Engn CEMSE Div Thuwal 239556900 Saudi Arabia

Wastewater treatment plants (WWTPs) are energy-intensive facilities that fulfill stringent effluent quality norms. Energy consumption prediction in WWTPs is crucial for cost savings, process optimization, compliance with regulations, and reducing the carbon footprint. This paper evaluates and compares a set of 23 candidate machine-learning models to predict WWTP energy consumption using actual data from the Melbourne WWTP. To this end, Bayesian optimization has been applied to calibrate the investigated machine learning models. Random Forest and XGBoost (eXtreme Gradient Boosting) were applied to assess how the incorporated features influenced the energy consumption prediction. In addition, this study investigated the consideration of information from past data in improving prediction accuracy by incorporating time-lagged measurements. Results showed that the dynamic models using time-lagged data outperformed the static and reduced machine learning models. The study shows that including lagged measurements in the model improves prediction accuracy, and the results indicate that the dynamic K-nearest neighbors model dominates state-of-the-art methods by reaching promising energy consumption predictions.

关键词： machine learning consumption wastewater treatment plants data-based methods

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Energy consumption prediction in water treatment plants using deep learning with data augmentation

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RESULTS IN ENGINEERING 2023年 20卷

作者： Harrou, Fouzi Dairi, Abdelkader Dorbane, Abdelhakim Sun, Ying King Abdullah Univ Sci & Technol KAUST Comp Elect & Math Sci & Engn CEMSE Div Thuwal 239556900 Saudi Arabia Univ Sci & Technol Oran Mohamed Boudiaf USTO MB Dept Comp Sci BP 1505 Oran 31000 Algeria Belhadj Bouchaib Univ Ain Temouchent Dept Mech Engn SSL Ain Temouchent Algeria

Wastewater treatment plants (WWTPs) are energy-intensive facilities that play a critical role in meeting stringent effluent quality regulations. Accurate prediction of energy consumption in WWTPs is essential for cost savings, process optimization, regulatory compliance, and reducing carbon footprint. This paper introduces an efficient approach for predicting energy consumption in WWTPs, leveraging deep learning models, data augmentation, and feature selection. Specifically, Spline Cubic interpolation enriches the dataset, while the Random Forest model identifies important features. The study investigates the impact of lagged data to capture temporal dependencies. Comparative analysis of five deep learning models on original and augmented datasets from Melbourne WWTP demonstrates substantial performance improvement with augmented data. Incorporating lagged energy consumption data further enhances accuracy, providing valuable insights for effective energy management. Notably, the Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BiGRU) models achieve Mean Absolute Percentage Error (MAPE) values of 1.36% and 1.436%, outperforming state-of-the-art methods.

关键词： Deep learning data augmentation Features selection Energy consumption Wastewater treatment plants data-based methods

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Editorial: data-based resilience-oriented planning and operation of multi-energy systems

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Li, Zhengmao Guerrero, Josep M. Shahidehpour, Mohammad Lo, Edmond Y. M. Lei, Shunbo Aalto Univ Sch Elect Engn Otakaari Ostrobothnia Finland Aalborg Univ Dept Energy Technol Aalborg Denmark Illinois Inst Technol Elect & Comp Engn Dept Chicago IL USA Nanyang Technol Univ Sch Civil & Environm Engn Singapore Singapore Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China

Background and ObjectiveRecently, there is an increasing number of extreme events such as natural disasters, malicious attacks, etc all over the world [1]. For example, Hurricane Maria, struck the Caribbean in Sept 2017, leaving a trail of destruction and devastating impacts on various islands, particularly Puerto Rico [2]. It can be observed that those events could destroy the current emerging multi-energy systems and cause huge economic losses or even threaten the safety of human beings [3]. Besides, for extreme events, there can be massive historical data, so it is meaningful to take advantage of the fast-developing machine learning methods to obtain a both reliable and resilient operation framework [4]. This research topic aims to provide a data-based, cost-effective, and resilience-oriented operation scheme for multi-energy systems. To start with, the system modeling is designed with the intensive coordination of heterogeneous energy, then, a resilient operation scheme is developed to alleviate the negative effects of extreme events. In the end, machine learning-based algorithms will be employed to provide online smart operation solutions to reduce economic *** SummaryThe special issue currently includes 5 papers. 1 paper is on resilience-oriented operation, 3 papers are on reliable and robust operation and the last 1 is on dynamic stability. All contributions to this research topic focus on one or more of the research areas listed in this special ***-oriented OperationTo deal with the uncertainties from renewable energy sources for the multi-energy system in remote rural areas, An, et al, 2023 in [5] analyze the possible operational risks regarding renewable generation and then introduce the condition value at risk to quantify the risk cost. On this basis, stochastic programming based on a multi-energy microgrid planning model that minimizes the investment cost, operation cost, and risk cost, while considering the physical limitations is

关键词： data-based methods resilience oriented planning and operation multi-energy systems reconfiguration uncertainty sources

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