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Boosting biogas production through innovative data-driven modeling and optimization methods at NJWTP

SCIENTIFIC REPORTS 2025年第1期15卷 1-20页

作者： Duan, Jingsong Cao, Guohua Ma, Guoqing Yazdani, Bayram Changchun Univ Sci & Technol Sch Mech & Elect Engn Changchun 130022 Jilin Peoples R China Changchun Univ Sch Mech & Vehicle Engn Changchun 130022 Jilin Peoples R China Changchun Univ Sci & Technol Chongqing Res Inst Chongqing 401133 Peoples R China Harbin Inst Technol Harbin 150001 Heilongjiang Peoples R China Wuhu Hart Robot Ind Technol Res Inst Co Ltd Wuhu 241000 Anhui Peoples R China Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran Islamic Univ Coll Tech Engn Najaf Iraq

This study presents an innovative approach to enhancing biogas production through the anaerobic digestion of Nanjing Jiangnan Wastewater Treatment Plant (NJWTP). Utilizing data-driven modeling and optimization methods, the research focuses on improving the sustainability and cost-effectiveness of waste-to-energy conversion processes. The core of the study involves the comparison of three distinct models: Deep Belief Network (DBN), DBN with Osprey Optimization Algorithm (DBN-OOA), and DBN with Boosted Osprey Optimization Algorithm (DBN-BOOA). In total, 180 data points were gathered from 2016 to 2018 for the purpose of the current study. Among the models evaluated, the Deep Belief Network (DBN) coupled with Boosted Osprey Optimization Algorithm (BOOA) emerged as the superior method, demonstrating high accuracy and optimization capabilities. The DBN-BOOA model achieved remarkable performance metrics, including a correlation coefficient (R) of 0.98, a root mean square error (RMSE) of 0.41 m(3)/min, and an index of agreement (IA) of 0.99, significantly outperforming the standalone DBN and DBN-OOA models. Furthermore, the DBN-BOOA model identified optimal operational parameters that maximized biogas production to 31.35 m(3)/min, surpassing the outputs of the other models. This method's success is attributed to its robust optimization algorithm, which efficiently navigates a diverse search space to locate the global optimum without necessitating input variable pre-processing. Consequently, the DBN-BOOA model offers a practical and user-friendly solution for MWTP operators, enabling real-time adjustments to operational parameters for increased biogas yields and reduced sludge production.

关键词： Biogas production Anaerobic digestion data-driven modeling Optimization methods Waste-to-energy conversion Deep Belief Network (DBN) Boosted Osprey optimization algorithm (BOOA) MWTP operators

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Mass Flow Rate Measurement of Pneumatically Conveyed Solids in a Square-Shaped Pipe Through Multisensor Fusion and data-driven modeling

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Zeng, Xingxing Yan, Yong Qian, Xiangchen Wang, Yongyue Zhang, Jie North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Univ Kent Sch Engn Canterbury CT2 7NT England

Online continuous measurement of the mass flow rate of pneumatically conveyed solids in a square-shaped pipe is desirable for monitoring and optimizing industrial processes. However, existing techniques using a single type of sensor have limitations in measuring the mass flow rate of solids because of the complexity of the dynamics of solids flow due to the four sharp corners of a square-shaped pipe. This article proposes a multisensor fusion and data-driven modeling-based method to tackle this challenge. A multisensor system based on acoustic, capacitive, and electrostatic sensing principles is designed and implemented to obtain the sound pressure level in the flow, volumetric concentration of solids, and solid velocity, respectively. Simultaneously, a range of statistical features is obtained by performing time-domain, frequency-domain, and time-frequency domain analyses on all sensor signals. The statistical features reflecting the variation of the mass flow rate of solids, as well as solid velocity and volume concentration of solids, are then fed into a data-driven model. A data-driven model based on a combined convolutional neural network and long short-term memory (CNN-LSTM) network is established, and its performance is compared with those of the back-propagation artificial neural network (BP-ANN), support vector machine (SVM), CNN, and LSTM models. Experimental tests were conducted on a laboratory-scale rig on both horizontal and vertical pipelines to train and evaluate the CNN-LSTM model with solid velocity ranging from 11 to 23 m/s and the mass flow rate of solids from 8 to 26 kg/h. The CNN-LSTM model outperforms all other models with a relative error within +/- 1% under all test conditions.

关键词： data-driven modeling gas-solid two-phase flow mass flow rate measurement multisensor fusion square-shaped pipe

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An application of data-driven modeling for hydroelasticity of an elastically supported semi-circular pipe conveying fluid

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ENGINEERING COMPUTATIONS 2023年第5期40卷 1195-1227页

作者： Xu, Jianhang Li, Peng Yang, Yiren Southwest Jiaotong Univ Sch Mech & Aerosp Engn Chengdu Peoples R China

PurposeThe paper aims to develop an efficient data-driven modeling approach for the hydroelastic analysis of a semi-circular pipe conveying fluid with elastic end supports. Besides the structural displacement-dependent unsteady fluid force, the steady one related to structural initial configuration and the variable structural parameters (i.e. the variable support stiffness) are considered in the ***/methodology/approachThe steady fluid force is treated as a pipe preload, and the elastically supported pipe-fluid model is dealt with as a prestressed hydroelastic system with variable parameters. To avoid repeated numerical simulations caused by parameter variation, structural and hydrodynamic reduced-order models (ROMs) instead of conventional computational structural dynamics (CSD) and computational fluid dynamics (CFD) solvers are utilized to produce data for the update of the structural, hydrodynamic and hydroelastic state-space equations. Radial basis function neural network (RBFNN), autoregressive with exogenous input (ARX) model as well as proper orthogonal decomposition (POD) algorithm are applied to modeling these two ROMs, and a hybrid framework is proposed to incorporate *** proposed approach is validated by comparing its predictions with theoretical solutions. When the steady fluid force is absent, the predictions agree well with the "inextensible theory". The pipe always loses its stability via out-of-plane divergence first, regardless of the support stiffness. However, when steady fluid force is considered, the pipe remains stable throughout as flow speed increases, consistent with the "extensible theory". These results not only verify the accuracy of the present modeling method but also indicate that the steady fluid force, rather than the extensibility of the pipe, is the leading factor for the differences between the in- and extensible ***/valueThe steady fluid force and the variable structural parameters are co

关键词： data-driven modeling Reduced-order methods Semi-circular pipe conveying fluid Hydroelasticity Steady fluid force Variable parameters

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Maximum Likelihood Estimation in data-driven modeling and Control

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第1期68卷 317-328页

作者： Yin, Mingzhou Iannelli, Andrea Smith, Roy S. S. Swiss Fed Inst Technol Zurich Automatic Control Lab CH-8092 Zurich Switzerland

Recently, various algorithms for data-driven simulation and control have been proposed based on the Willems' fundamental lemma. However, when collected data are noisy, these methods lead to ill-conditioned data-driven model structures. In this article, we present a maximum likelihood framework to obtain an optimal data-driven model, the signal matrix model, in the presence of output noise. data compression and noise-level estimation schemes are also proposed to apply the algorithm efficiently to large datasets and unknown noise-level scenarios. Two approaches in system identification and receding horizon control are developed based on the derived optimal estimator. The first one identifies a finite impulse response model. This approach improves the least-squares estimator with less restrictive assumptions. The second one applies the signal matrix model as the predictor in predictive control. The control performance is shown to be better than existing data-driven predictive control algorithms, especially under high noise levels. Both approaches demonstrate that the derived estimator provides a promising framework to apply data-driven algorithms to noisy data.

关键词： data-driven modeling maximum likeli-hood estimation (MLE) model-predictive control (MPC) system identification

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Full Feedback Dynamic Neural Network with Exogenous Inputs for Dynamic data-driven modeling in Nonlinear Dynamic Power Systems

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2023年第6期18卷 876-890页

作者： Zhang, Zhenhui Zhang, Zhengjiang Zhao, Sheng Hong, Zhihui Huang, Shipei Li, Quanfang Wenzhou Univ Natl & Local Joint Engn Lab Elect Digital Design T Wenzhou 325000 Zhejiang Peoples R China Wenzhou Univ Key Lab Low Voltage Apparat Intellectual Technol Z Wenzhou 325035 Peoples R China Zhejiang Juchuang Smart Technol Co Res & Dev Dept Wenzhou 325035 Peoples R China

Dynamic neural networks (DNNs) are widely used in data-driven modeling of nonlinear control systems. Due to the complexity of the actual operating nonlinear power systems, rigorous dynamic models are always unknown. DNNs can focus on methods that only use input and output information to establish accurate dynamic models and reduce noise in measured data, which is called data-driven modeling. The core of the DNN is the feedback with memory function. This paper analyzes the traditional Elman neural network (ENN) and nonlinear auto-regression with exogenous input (NARX) neural network with different structure feedback structures, and proposes a full feedback dynamic neural network (FF-DNN) with exogenous input. Eight different kinds of neural networks (including ENN, NARX, etc.) are compared and analyzed. The eight kinds of neural networks are applied on the experimental data of the DC-AC inverter and the power system of Zhejiang Juchuang Smart Technology Company Park in Wenzhou. The experimental results are used to compare the performance of the data-driven models established by eight different kinds of neural networks under different noise conditions, verify the robustness and generalization performance of dynamic data-driven modeling based on FF-DNN, and demonstrate the feasibility and effectiveness of FF-DNN in actual power systems. (C) 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： dynamic neural network data-driven modeling feedback structure power system

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Development of data-driven modeling method for nonlinear coupling components

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Ryu, Taesan Baek, Seunghun Pusan Natl Univ Sch Mech Engn 30 Jangjeon Dong Busan 46241 South Korea

This research introduces a methodology for data-driven regression modeling of components exhibiting nonlinear characteristics, utilizing the sparse identification of nonlinear dynamics (SINDy) method. The SINDy method is extended to formulate regression models for interconnecting components with nonlinear traits, yielding governing equations with physically interpretable solutions. The proposed methodology focuses on extracting a model that balances accuracy and sparsity among various regression models. In this process, a comprehensive model was generated using linear term weights and an error histogram. The applicability of the proposed approach is demonstrated through a case study involving a sponge gasket with nonlinear characteristics. By contrasting the predictive model with experimental responses, the reliability of the methodology is verified. The results highlight that the regression model, based on the proposed technique, can effectively establish an accurate dynamical system model, accounting for realistic conditions.

关键词： Spars identification data-driven modeling Nonlinear coupling components Nonlinear vibration Sponge gasket

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data-driven modeling of Nonlinear Joints in Space Structures 41

Data-driven Modeling of Nonlinear Joints in Space Structures

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第41届中国控制会议

作者： Yonglei Zhang Xiaoyu Wang Xinyuan Li Hao Wen Shidong Xu State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The existence of nonlinear joints has a great influence on the dynamics of large flexible sapce *** a prerequisite of attitude control and vibration suppression of flexible spacecraft,it is of great importance to establish highconfidence models of joints to predict its dynamic characteristics ***,a single-degree-of-freedom model of the nonlinear joint is established based on the describing function method and harmonic balance method(HBM).In particular,the dynamic funtions of the joints with cubic stiffness and freeplay nonlinearity are derived,and the response and external excitation data are used to train surrogate models by a multi-step DNN *** validity of the data-driven modeling approach is further verified by response prediction under a different excition ***,the characteristic of an unknown nonlinearjoint is revealed by the data-driven modeling approach based on the experimental data.

关键词： Nonlinear joints space structures data-driven modeling response prediction

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Experimental demonstration of SnO_(2) nanofiber-based memristors and their data-driven modeling for nanoelectronic applications

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Chip 2023年第4期2卷 142-153页

作者： Soumi Saha Madadi Chetan Kodand Reddy Tati Sai Nikhil Kaushik Burugupally Sanghamitra DebRoy Akshay Salimath Venkat Mattela Surya Shankar Dan Parikshit Sahatiya Department of Electrical&Electronics Engineering Birla Institute of Technology and Science PilaniHyderabad CampusHyderabad 500078India Department of Computer Science and Information Systems Birla Institute of Technology and Science PilaniHyderabad CampusHyderabad 500078India Nanomagnetic Division Ceremorphic India Pvt LtdIndia Ceremorphic Inc.San JoseCA 95131USA

This paper demonstrated the fabrication,characterization,datadriven modeling,and practical application of a 1D SnO_(2)nanofiber-based memristor,in which a 1D SnO_(2)active layer wassandwiched between silver(Ag)and aluminum(Al)*** yielded a very high ROFF:RON of~104(ION:IOFF of~105)with an excellent activation slope of 10 mV/dec,low set voltage ofVSET~1.14 V and good *** paper physically explained the conduction mechanism in the layered SnO_(2)*** conductive network was composed of nanofibersthat play a vital role in the memristive action,since more conductive paths could facilitate the hopping of electron *** structures experimentally extracted with the adoption of ultraviolet photoelectron spectroscopy strongly support the claimsreported in this *** machine learning(ML)–assisted,datadriven model of the fabricated memristor was also developedemploying different popular algorithms such as polynomialregression,support vector regression,k nearest neighbors,andartificial neural network(ANN)to model the data of the *** have proposed two types of ANN models(type I andtype II)algorithms,illustrated with a detailed flowchart,to modelthe fabricated *** with standard ML techniques shows that the type II ANN algorithm provides the bestmean absolute percentage error of 0.0175 with a 98%R^(2)*** proposed data-driven model was further validated with the characterization results of similar new memristors fabricated adoptingthe same fabrication recipe,which gave satisfactory ***,the ANN type II model was applied to design and implementsimple AND&OR logic functionalities adopting the fabricatedmemristors with expected,near-ideal characteristics.

关键词： Nanofiber-based memristors data-driven modeling Artificial neural network(ANN) SnO_(2)

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Online quantitative detection for rotor misalignment based on data-driven modeling under nonstationary harmonic excitations

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年

作者： Liang, Haiying Cui, Ningyuan Zhao, Yulai Liu, Yang Giglio, Marco Sbarufatti, Claudio Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China Politecn Milan Dept Mech Engn Milan Italy Northeastern Univ Key Lab Vibrat & Control Aeroprop Syst Minist Educ Shenyang Peoples R China

Misalignment is a commonly encountered malfunction that can generate reaction forces and moments in the coupling leading to excessive vibration in the rotor. Generally, the vibration responses of a rotor with shaft misalignment comprised of nonlinear components. Some widely focused linear detection methods are incapable of extracting the key nonlinear information. Nonlinear output frequency response functions weighted contribution rate (WNOFRFs), as a convenient nonlinear detection method, fails in online fault detection due to the requirement for more than one-time tests. Although the nonlinear autoregressive with eXogenous inputs (NARX) model can address this challenge, it encounters difficulties associated with its instability and inaccuracy when applied to nonlinear systems under nonstationary harmonic excitations. This research proposed an innovative online quantitative detection method based on data-driven modeling under nonstationary harmonic excitations. Additionally, a novel index related to WNOFRFs for representing nonlinear features is put forward. Simulation and experiments conducted on rotor shaft misalignment validate the efficacy of this method. This approach is not only proficient in the online detection of rotor misalignment but also in quantitatively representing detailed angles of rotor shaft misalignment.

关键词： Nonlinear output frequency response functions weighted contribution rate data-driven modeling nonlinear auto-regressive with eXogenous inputs online fault detection rotor misalignment

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data-driven modeling for complex contacting phenomena via improved neural networks considering link switches

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MECHANISM AND MACHINE THEORY 2024年 191卷

作者： Ma, Jia Wang, Jie Peng, Jing Yin, Lairong Dong, Shuai Tang, Jinsong Changsha Univ Sci & Technol Sch Civil Engn Changsha 410114 Peoples R China Saarland Univ Dept Mat Sci & Engn D-66123 Saarbrucken Germany Changsha Univ Sci & Technol Sch Automot & Mech Engn Changsha 410114 Peoples R China Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China

Recent years saw tremendous developments of data-driven modeling in various engineering fields. As for the contact modeling between complex surfaces, the utilization of neural networks successfully eliminates the limitations encountered by the traditional physics-based contact modeling strategy. However, contrary to its increasingly extensive applications, very little attention has been paid to the role of network hyper-parameters in reducing the model redundancy and improving its training efficiency. In this work, a novel neural network considering link switches has been presented for the data-driven modeling of complex con-tact phenomena. In order to further boost its prediction performance, genetic algorithm (GA) is employed for the optimal settings of relevant hyper-parameters. An indoor exper-imental setup is utilized to demonstrate the effectiveness of the presented methodology. Comprehensive comparisons with the base models indicate the superiorities of the established locally-connected-neural-network-based contact force model for complex geometries.

关键词： data-driven modeling Complex contacting phenomena Neural networks Link switches

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