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How useful are data-driven methods for building-level greywater treatment from a process engineering perspective?

WATER RESEARCH X 2025年 28卷

作者： Wang, Siyuan Schneider, Mariane Y. Volcke, Eveline I. P. Wu, Di Univ Ghent Dept Green Chem & Technol Ghent Belgium Univ Ghent Ctr Green Chem & Environm Biotechnol GREAT Global Campus Incheon South Korea Ctr Adv Proc Technol Urban Resource Recovery CAPTU Ghent Belgium Univ Ghent Dept Data Anal & Math Modelling Ghent Belgium Hong Kong Univ Sci & Technol Water Technol Ctr Dept Civil & Environm Engn Hong Kong Branch Hong Kong Peoples R China Hong Kong Univ Sci & Technol Chinese Natl Engn Res Ctr Control & Treatment Heav Hong Kong Branch Hong Kong Peoples R China

data-driven methods are increasingly explored in various fields, demonstrating their potential. Compared with reviewing case-specific applications, this article critically assesses the usefulness of data-driven methods application from a process engineering perspective. Specifically, we focus on building-level greywater treatment, a highly decentralized treatment option. The application of data-driven methods in this field presents both operational constraints (e.g., lack of professional staff for maintenance) and technical challenges (e.g., highly variable influent loadings). Therefore, we review data-driven methods applied to greywater treatment processes suitable for building-level applications to explore their effectiveness. This review includes both regression and classification-based methods applied in lab- and pilot-scale studies across the following treatment processes: (i) filtration, (ii) electrocoagulation, (iii) nature-based solutions, (iv) membrane bioreactors, and (v) adsorption. We further evaluate the practical usefulness of these data-driven methods from a process engineering aspect based on their ability to meet study objectives, their motivations, and the added value they provide to process engineers. For instance, these methods aid in identifying key operational factors for treatment optimization and improving water safety by developing early-warning systems based on data-driven monitoring methods, reducing the need for chemical additives and labor-intensive laboratory analyses. However, practical applications could be hindered by ill-defined model boundaries, insufficient sampling resolution, and poor input selection. Additionally, compared with applying data-driven methods in centralized wastewater treatment plants, emphasizing model transferability (including both intra- and inter-building transferability) is necessary to enhance the scalability and practical applicability of data-driven models at the building level. This review, grounded in p

关键词： Building-level greywater reuse Machine learning models data-driven method Usefulness Model transferability

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Hybrid approach for online capacity estimation of lithium-lon batteries: Integrating model-driven and data-driven methods

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JOURNAL OF ENERGY STORAGE 2025年 113卷

作者： Qiao, Xin Wang, Zhixue Su, Xianzheng Shen, Gang Hou, Enguang Cai, Yinghao Shandong Jiaotong Univ 5001 Haitang RdSci Pk Jinan 250357 Peoples R China Chinese Acad Sci Inst Automat 95 East Zhongguancun Rd Beijing 100190 Peoples R China

With the widespread adoption of electric vehicles (EVs) and energy storage systems (ESS), estimating lithium- ion batteries (LIBs) capacity has become crucial for academia and industry. EVs typically operate under variable current and low temperature conditions, making capacity estimation challenging compared to ESS. This paper introduces a novel method integrating model-based and data-driven approaches for battery capacity estimation. A novel feature, unit state of charge (SOC) interval capacity, is extracted during SOC estimation using the model-based method. Continuous features are then employed in a deep convolutional neural network (DCNN) to estimate capacity. Using NASA and Oxford datasets, we achieve promising results with a root mean square error (RMSE) of 0.0039 and 0.0153, respectively, under room temperature and constant current conditions. The method's effectiveness is validated using the low temperature and variable current datasets from NASA. Additionally, with fewer DCNN layers and neurons, the method is suitable for deployment on battery management system (BMS) with limited resources.

关键词： Battery capacity estimation Health indicator data-driven method Unit SOC interval capacity

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data-driven State of Health Estimation method of Lithium-ion Batteries for Partial Charging Curves

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2024年第4期39卷 2230-2243页

作者： Tang, Jinrui Li, Yang Wang, Shaojin Xiong, Binyu Li, Xiangjun Pan, Jinxuan Chen, Qihong Wang, Peng Wuhan Univ Technol Hubei Prov NewEnergy Power Battery Engn Res Ctr State Key Lab Maritime Technol & Safety Wuhan 430070 Peoples R China Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China China Elect Power Res Inst Storage Syst Integrat & Configurat Technol Res La Beijing 100192 Peoples R China State Grid Yichun Power Supply Co Yichun 336000 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

State of health (SOH) is one of the most important performance indicators of lithium-ion batteries (LIBs). Accurate estimation of SOH is a prerequisite for the safe and reliable operation of LIBs. Traditional SOH estimation methods predominantly rely on complete charging cycle data acquired through laboratory testing. However, in practical application, the charging behaviors of electric vehicle users are random and unpredictable, making the partial charging curves difficult to utilize the traditional methods. This work introduces a novel data-driven approach to estimating a battery's SOH for partial charging cases. Firstly, a curve fitting method is proposed to extract health indicators (HIs) from partial charging voltage data, where novel HIs based on the energy-voltage curve are extracted. A composite Gaussian process regression-based data-driven method is proposed to achieve highly accurate SOH estimation. The method's adaptability to real-world partial charging habits is evaluated through three representative scenarios derived from extensive charging behavior reports of EV users. The impact of partial charging on HI extraction is analyzed based on the three identified scenarios. The proposed method is verified using a combination of our laboratory testing data and the Oxford open dataset. The results show that the proposed framework demonstrates the ability to estimate SOH accurately and strong robustness to various partial charging behaviors.

关键词： Lithium-ion battery health indicator data-driven method partial charging state of health estimation

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A fast learning method for data-driven design of interval type-2 fuzzy logic system

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第3期32卷 2705-2715页

作者： Li, Chengdong Zhang, Guiqing Yi, Jianqiang Shang, Fang Gao, Junlong Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

In this study, we propose a novel fast learning data-driven method for the design of interval type-2 fuzzy logic system (IT2FLS). In order to accelerate the learning speed, we present a parallel mechanism to generate the antecedents of the IT2FLS and the least square method based learning algorithm to optimize the consequents. Firstly, driven by different sub-data sets, corresponding type-1 fuzzy logic systems (T1FLSs) which have the same initial fuzzy partition (thus the same initial fuzzy rule base) are parallelly obtained through the popular ANFIS method. Then, an ensembling strategy is proposed to form the type-2 fuzzy partition for each input variable through merging corresponding type-1 fuzzy sets (T1FSs) in the type-1 fuzzy partitions of the learned T1FLSs. By this strategy, the antecedents of the IT2FLS are determined and then fixed, however, the consequent parameters still need to be optimized. To achieve both excellent performance and fast training speed, a least square method based learning algorithm is provided for the optimization of the consequent parameters. Finally, three benchmark problems and one real-world application are given, and detailed comparisons with some well performed methods are made. Simulation and comparison results have verified the effectiveness and superiorities of the proposed method.

关键词： data-driven method fast learning fuzzy logic system ANFIS type-2 fuzzy

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Parameter optimization method for the water quality dynamic model based on data-driven theory

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MARINE POLLUTION BULLETIN 2015年第1-2期98卷 137-147页

作者： Liang, Shuxiu Han, Songlin Sun, Zhaochen Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Changjiang River Sci Res Inst Wuhan 430010 Peoples R China

Parameter optimization is important for developing a water quality dynamic model. In this study, we applied data-driven method to select and optimize parameters for a complex three-dimensional water quality model. First, a data-driven model was developed to train the response relationship between phytoplankton and environmental factors based on the measured data. Second, an eight-variable water quality dynamic model was established and coupled to a physical model. Parameter sensitivity analysis was investigated by changing parameter values individually in an assigned range. The above results served as guidelines for the control parameter selection and the simulated result verification. Finally, using the data-driven model to approximate the computational water quality model, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the control parameters. The optimization routines and results were analyzed and discussed based on the establishment of the water quality model in Xiangshan Bay (XSB). (C) 2015 Elsevier Ltd. All rights reserved.

关键词： data-driven method Water quality model Function approximation Parameter optimization

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On the data-driven Generalized Cell Mapping method

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INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS 2019年第14期29卷

作者： Li, Zigang Jiang, Jun Hong, Ling Sun, Jian-Qiao Xian Univ Sci & Technol Dept Mech Xian 710054 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Strength & Vibrat Xian 710049 Shaanxi Peoples R China Univ Calif Merced Dept Mech Engn Sch Engn Merced CA 95343 USA

Global analysis is often necessary for exploiting various applications or understanding the mechanisms of many dynamical phenomena in engineering practice where the underlying system model is too complex to analyze or even unavailable. Without a mathematical model, however, it is very difficult to apply cell mapping for global analysis. This paper for the first time proposes a data-driven generalized cell mapping to investigate the global properties of nonlinear systems from a sequence of measurement data, without prior knowledge of the underlying system. The proposed method includes the estimation of the state dimension of the system and time step for creating a mapping from the data. With the knowledge of the estimated state dimension and proper mapping time step, the one-step transition probability matrix can be computed from a statistical approach. The global properties of the underlying system can be uncovered with the one-step transition probability matrix. Three examples from applications are presented to illustrate a quality global analysis with the proposed data-driven generalized cell mapping method.

关键词： data-driven method generalized cell mapping global analysis nonlinear dynamics

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data-driven modeling and optimization of thermal comfort and energy consumption using type-2 fuzzy method

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SOFT COMPUTING 2013年第11期17卷 2075-2088页

作者： Li, Chengdong Zhang, Guiqing Wang, Ming Yi, Jianqiang Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

In the research domain of intelligent buildings and smart home, modeling and optimization of the thermal comfort and energy consumption are important issues. This paper presents a type-2 fuzzy method based data-driven strategy for the modeling and optimization of thermal comfort words and energy consumption. First, we propose a methodology to convert the interval survey data on thermal comfort words to the interval type-2 fuzzy sets (IT2 FSs) which can reflect the inter-personal and intra-personal uncertainties contained in the intervals. This data-driven strategy includes three steps: survey data collection and pre-processing, ambiguity-preserved conversion of the survey intervals to their representative type-1 fuzzy sets (T1 FSs), IT2 FS modeling. Then, using the IT2 FS models of thermal comfort words as antecedent parts, an evolving type-2 fuzzy model is constructed to reflect the online observed energy consumption data. Finally, a multiobjective optimization model is presented to recommend a reasonable temperature range that can give comfortable feeling while reducing energy consumption. The proposed method can be used to realize comfortable but energy-saving environment in smart home or intelligent buildings.

关键词： Type-2 fuzzy data-driven method Thermal comfort Multiobjective optimization Energy saving

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Discovering differential governing equations of hysteresis dynamic systems by data-driven sparse regression method

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NONLINEAR DYNAMICS 2024年第14期112卷 12137-12157页

作者： Qian, Jiawei Sun, Xiuting Xu, Jian Cheng, Li Tongji Univ Sch Aerosp Engn & Appl Mech Shanghai Peoples R China Tongji Univ Key Lab Ai aided Airworthiness Civil Aircraft Stru Civil Aviat Adm China Shanghai Peoples R China Hong Kong Polytech Univ Dept Mech Engn Kowloon Hong Kong Peoples R China

Hysteresis phenomenon widely exists in various metamaterials and smart actuators. Governing equations can describe and predict the static/dynamic behavior of the systems with hysteresis property. However, the hysteresis force cannot be measured nor explicitly expressed by state variables, which brings great challenge for the model reconstruction of the hysteresis systems, especially when the nonlinear restoring and damping forces also exist. In this paper, a data-driven method is proposed to reconstruct the model of the systems with both hysteresis and nonlinearity properties from dynamic information. From the proposed method, the linear, nonlinear, and hysteresis forces, can be separately reconstructed based on the data generation with incremental generation of dynamics signals under supervision. Facing to the challenge for the functional representation of hysteresis, based on an agent model, the function library is successfully constructed. Next, for the sparsity and accuracy of the reconstruction model, the sparse regression method is generalized to identify all the nonlinear terms and coefficients. Once linear, nonlinear and hysteresis terms are figured out, the discovery of differential governing equations of hysteresis dynamic systems is completed. Three numerical examples are carried out to demonstrate the effectiveness and capability of the proposed data-driven method in the dynamic systems with different nonlinearities, dimensions and hysteresis;and the model reconstruction for Tachi-Miura polyhedron (TMP) origami structure, which possesses both hysteresis nonlinearity and geometric nonlinearity, is shown in experiments. The proposed model reconstruction method realizes the reconstruction of constitutive relation and governing equations of nonlinear hysteresis systems based on compressive sensing from dynamics, which demonstrates that great benefit of dynamic data. The model reconstruction method also provides an accurate estimation method for constitutive

关键词： Model reconstruction Hysteresis systems Sparse regression data-driven method

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Model-free data-driven methods for response-only system identification

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MEASUREMENT 2025年 246卷

作者： Lin, Chang-Sheng Wu, Yi-Xiu Natl Kaohsiung Univ Sci & Technol Dept Mech Engn Kaohsiung Taiwan China Motor Corp Vehicle Testing Div Noise & Vibrat Dept Taoyuan Taiwan

Traditional modal testing uses the impulse response and excitation of a structure to evaluate the frequency response function to estimate the modal parameters of a system. In practice, it is difficult and expensive to conduct modal tests on large-scale structures, so it is necessary to develop output-only modal estimation methods from the ambient response only. However, when we use the singular spectrum to determine the minimum order of the system under consideration, the poor parametric estimation could be caused due to incomplete excitation of structural modes or improper selection of the system order. In this study, the covariance matrix calculation is extended to the nonstationary response, and the covariance data correlation matrix is introduced into the modal identification process of the Ibraham time domain method for the nonstationary excitation with slowly varying in the form of a product model. We use the relationship between the data matrices corresponding to different time series in the data correlation matrix, and use the stabilization diagrams of different orders to improve the effectiveness of modal parameter estimation. Finally, numerical simulation and experimental verification are used to confirm the applicability of the proposed method.

关键词： Response-only parametric identification Modal estimation System identification data-driven method

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A GAN-Based data Injection Attack method on data-driven Strategies in Power Systems

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IEEE TRANSACTIONS ON SMART GRID 2022年第4期13卷 3203-3213页

作者： Liu, Zengji Wang, Qi Ye, Yujian Tang, Yi Southeast Univ Sch Cyber Sci & Engn Nanjing 210000 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210000 Peoples R China

With the expansion of system scale and data size in power systems, data-driven methods are gradually becoming widely used. However, compared with traditional methods, data-driven methods face more threats in data security and algorithm security. This paper proposes a black box data injection attack method against data-driven strategies in power systems. To obtain a stealthy attack scheme with the greatest impact, an attack vector generator based on deep convolutional neural networks (DCNN) is designed. The generator includes a node selector, a filter, an encoder, and a decoder. The node selector is used to select the most likely successful attack scheme for the filter, the encoder is used to extract sample features, and the decoder is used to generate disturbances based on the features. The proposed generator is trained with an improved generative adversarial network (GAN) and can generate minimal disturbances in real time based on measurement data from the power grid. The effectiveness of the proposed method is demonstrated with an attack experiment on an online transient stability application.

关键词： Power system stability Security Training Generators Real-time systems Power systems Generative adversarial networks Cyber security data-driven method cyber-attack generative adversarial networks

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