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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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data-driven modeling for rapid prediction of aerodynamic noise directivity of flow over a cylinder

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APPLIED MATHEMATICAL MODELLING 2025年 141卷

作者： Jin, Yao Liao, Fei Cai, Jinsheng Xian Univ Architecture & Technol Sch Bldg Serv Sci & Engn Xian 710055 Peoples R China Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Natl Key Lab Aircraft Configurat Design Xian 710072 Peoples R China

Conventional high-fidelity simulations are prohibitively expensive for aerodynamic noise optimization and control applications. Developing surrogate models of minimal computational costs provide efficient alternatives in noise estimation for various numerical conditions. In the present work, three data-driven acoustic directivity modeling strategies are proposed, including a purely data-driven POD model and two semi-analytical data-driven models: force-model and dipole- model. All the three models significantly accelerate the prediction of noise directivity, which is attributed to their independence of conventional numerical approaches, despite with minor discrepancies in accuracy among each other. The POD-model is constructed based on the singular vector decomposition and an interpolation method, enabling high accuracy but requiring a large amount of training data. A sensitivity study is conducted on four parametric aspects, such as mode number, interpolation method, inherent flow nonlinearity in wake region, and the distribution of interpolation states in training datasets. The force-model, derived from the Ffowcs Williams-Hawkings equation, requires force fluctuations on the body surfaces. Also the dipole-model, leveraging the analytical formulation of dipole velocity potential, requires a careful specification of potential coefficient. These required information in force-model and dipole-model are directly approximated by data regression methods. Compared to POD-model, the notable merit of force-model and dipole-model is that rapid predictions can be implemented with reduced data requirements.

关键词： Aerodynamic noise prediction data-driven modeling POD model Force model Dipole model Efficiency enhancement Cylinder flow

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Optimized System Identification (SI) of Brushless DC (BLDC) motor using data-driven modeling Methods

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SCIENTIFIC REPORTS 2025年第1期15卷 1-20页

作者： Khan, Muhammad Aseer Baig, Dur-e-Zehra Ali, Husan Albogamy, Fahad R. Air Univ Dept Elect Engn Aerosp & Aviat Campus Kamra 43570 Pakistan Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Elect Engn Topi 23640 Swabi Pakistan Taif Univ Turabah Univ Coll Math Dept Comp Sci Program Box 11099 Taif 21944 Saudi Arabia

This study investigates the nonlinear dynamics of Brushless DC (BLDC) motors using the MATLAB/Simulink platform, emphasizing system identification through the Least Squares (LS) method and Nonlinear Autoregressive Network With Exogenous Inputs (NARX) models with variable regressors. Accurate data-driven models derived from these techniques are essential for designing efficient feedback control systems, enabling precise motor dynamics representation and facilitating early fault detection by identifying deviations from normal operation. A detailed simulation of the BLDC motor under no-load conditions is performed to analyze the speed response and ripple effects in torque and speed, underscoring the need for effective modeling and control. Comprehensive datasets are generated to develop LS and NARX models across varying operational conditions. A variable step input voltage signal with both ascending and descending steps is employed for training, while a distinct validation signal of similar trends but different magnitudes is used for performance evaluation. All techniques are benchmarked using identical training and validation signals. Among the models, the NARX model with customized regressors demonstrated superior performance, achieving 99.1% training accuracy and 98.01% validation accuracy in predicting motor dynamics. All the models are further tested under real-time signal conditions like ramp-up acceleration, deceleration, & turning and noisy signal conditions to evaluate the robustness and accuracy of these models in real-world conditions. The findings highlight the NARX model's potential to enhance control strategies and improve BLDC motor stability, with statistical analysis confirming the robustness and effectiveness of the proposed approach.

关键词： Brushless direct current (BLDC) motor data-driven modeling Electric vehicles (EVs) modeling and simulation approaches Nonlinear Autoregressive Network With Exogenous Inputs (NARX) System identification (SI)

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A data-driven Koopman modeling Framework With Application to Soft Robots

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2025年第1期23卷 249-261页

作者： Han, Lvpeng Peng, Kerui Chen, Wangxing Liu, Zhaobing Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China

This paper presents a data-driven Koopman modeling framework for globally linearizing highly nonlinear dynamical systems in lifted infinite-dimensional state space. In this framework, three data-driven models are proposed and identified to approximate the infinite-dimensional linear Koopman operator through a method called extended dynamic mode decomposition (EDMD). The implementation of EDMD requires a data set of snap pairs and a dictionary of scalar observables, which affects the accuracy of data-driven modeling. Five basis functions are compared and discussed to illustrate their suitable application scenarios. To verify the Koopman data-driven modeling framework, we apply it to the modeling of soft robotic systems, which is thought to be an extremely difficult task due to the large and continuous deformation of soft materials. Results demonstrate that Koopman linear, bilinear, and nonlinear models for both two-dimensional (2D) and three-dimensional (3D) soft robots are superior to the existing state-space modeling approach by achieving less normalized root mean square error (NRMSE). Among the three Koopman models, the constructed nonlinear model has higher performance than the bilinear model, followed by the linear one. Furthermore, the Monomial and Hermite basis functions are the optimal choices for constructing the Koopman linear, bilinear, and nonlinear models for the investigated 2D and 3D soft robots as they have the same structure when the degree of basis function is chosen less than four. Although the Fourier basis function is expected to outperform the Hermite and Gaussian basis functions in modeling systems with oscillatory motion, it is not a preferable selection in the high dimensional soft robotic modeling in our case due to its computational complexity and tendency to be non-convergent. The Sparse Fourier basis function cannot be regarded as a good choice for modeling soft robots, as it is only suitable for generating models with sparse data. It is

关键词： data-driven modeling identification method Koopman operator nonlinear systems soft robots

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Applications of data-driven Dynamic modeling of Power Converters in Power Systems: An Overview

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 2434-2456页

作者： Subedi, Sunil Gui, Yonghao Xue, Yaosuo Oak Ridge Natl Lab Electrificat & Energy Infrastruct Div Oak Ridge TN 37831 USA

Power electronic converter (PEC)-based resources are growing ubiquitously in power systems and there is a vital necessity for precise dynamic models to comprehend their dynamics to different events and control strategies. Inaccurate modeling can lead to instability, higher costs, and reliability issues. Anticipating the increase in PECs in the near future, detailed modeling becomes computationally and mathematically complex, requiring extensive computing power and knowledge of vendor-specific PECs. To overcome these challenges, data-driven machine learning/artificial intelligence (ML/AI) approaches are widely used, tracking the dynamic responses of PECs operating in various modes with limited knowledge. These models find applications in protection, stability, fault diagnosis, optimization, control and monitoring, and power quality. While the literature on power systems often emphasizes the advantages of data-driven modeling, an in-depth look at the limitations, challenges, and opportunities related to converter-dominated grids is still lacking. The purpose of this survey is to conduct a comprehensive review of ML/AI methodologies in PECs and investigate their applications in power systems. The article introduces various PEC types, their roles, and modeling approaches. It then provides an in-depth overview of how ML/AI can be applied to PECs in power systems. Finally, the survey highlights gaps in the field's knowledge and suggests potential directions for future research.

关键词： Power system stability Power system dynamics Computational modeling Reviews Hidden Markov models Vehicle dynamics Topology Load modeling Matrix converters Dynamic scheduling Artificial intelligence converter-dominated data-driven modeling dynamics fault diagnosis power electronic converter power quality protection stability

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data-driven Energy modeling of Machining Centers Through Automata Learning

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 5769-5780页

作者： Lestingi, Livia Frigerio, Nicla Bersani, Marcello M. Matta, Andrea Rossi, Matteo Politecn Milan Dept Elect Informat & Bioengn I-20133 Milan Italy Politecn Milan Dept Mech Engn I-20156 Milan Italy

The paper addresses the problem of estimating the energy consumed by production resources in manufacturing so that alternative process designs can be compared in terms of energy expenditure. In particular, the proposed methodology focuses on Computer Numerical Controlled (CNC) machining centers. Classical approaches to energy modeling require high expertise and large development effort since, for example, data acquisition is resource-specific and must be repeated frequently to avoid obsolescence. An automated and flexible data-driven methodology is designed in this work. A data-driven method is employed to learn a hybrid and stochastic model of a CNC machining center's energetic behavior. The learned model is used to provide offline energy consumption estimates of simulated part-programs before the actual execution of the cutting. Numerical results show the performance of the proposed method on a set of case studies. The methodology is also applied to a real industrial application, including data collected during machine production.

关键词： Machining Energy consumption Task analysis Biological system modeling Predictive models Numerical models Training Energy modeling machining automata learning data-driven modeling

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Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine

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COMPUTER PHYSICS COMMUNICATIONS 2025年 313卷

作者： Farcas, Ionut-Gabriel Gundevia, Rayomand P. Munipalli, Ramakanth Willcox, Karen E. Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX 78712 USA Virginia Tech Dept Math Blacksburg VA USA Amentum Edwards AFB CA USA Air Force Res Lab Edwards AFB CA USA

High-performance computing (HPC) has revolutionized our ability to perform detailed simulations of complex real-world processes. A prominent contemporary example is from aerospace propulsion, where HPC is used for rotating detonation rocket engine (RDRE) simulations in support of the design of next-generation rocket engines;however, these simulations take millions of core hours even on powerful supercomputers, which makes them impractical for engineering tasks like design exploration and risk assessment. data-driven reduced-order models (ROMs) aim to address this limitation by constructing computationally cheap yet sufficiently accurate approximations that serve as surrogates for the high-fidelity model. This paper contributes a distributed memory algorithm that achieves fast and scalable construction of predictive physics-based ROMs trained from sparse datasets of extremely large state dimension. The algorithm learns structured physics-based ROMs that approximate the dynamical systems underlying those datasets. This enables model reduction for problems at a scale and complexity that exceeds the capabilities of standard, serial approaches. We demonstrate our algorithm's scalability using up to 2,048 cores on the Frontera supercomputer at the Texas Advanced Computing Center. We focus on a real-world three-dimensional RDRE for which one millisecond of simulated physical time requires one million core hours on a supercomputer. Using a training dataset of 2,536 snapshots each of state dimension 76 million, our distributed algorithm enables the construction of a predictive data-driven reduced model in just 13 seconds on 2,048 cores on Frontera.

关键词： High-performance computing data-driven modeling Scientific machine learning Large-scale simulations Rocket combustion

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data driven modeling advancements for air temperature predictions in data centers

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NUMERICAL HEAT TRANSFER PART B-FUNDAMENTALS 2025年第2期86卷 244-261页

作者： Patel, Dhaval Joshi, Yogendra Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA USA Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA

While data center cooling energy usage optimization studies have been performed through computational fluid dynamics/heat transfer (CFD/HT), and heuristic methods, data driven modeling techniques are now also being used for these applications. This paper investigates the air temperature prediction capabilities of static artificial neural network (ANN), Gaussian progress regression (GPR), support vector regression (SVR), relevance vector machine (RVM), linear regression, and regression trees;and transient long-short term memory (LSTM), and nonlinear autoregressive neural network with external input (NARX)) data driven modeling frameworks. The static study compared various models and found that GPR provided the best results (average error of 0.56 degrees C), closely followed by the ANN and SVR (average error of 0.60 degrees C and 0.68 degrees C respectively) methods. The transient study compared models based on an experimental data set and found that NARX outperforms LSTM for normal operations (0.83 degrees C and 1.07 degrees C average error respectively), and that data driven models are able to provide relatively good predictions, even if the input variables are slightly outside the training domain.

关键词： data center data-driven modeling LSTM NARX thermal modeling

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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 of a four-dimensional stochastic projectile system

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Chinese Physics B 2022年第7期31卷 157-162页

作者： Yong Huang Yang Li School of Energy and Power Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Automation Nanjing University of Science and TechnologyNanjing 210094China

The dynamical modeling of projectile systems with sufficient accuracy is of great difficulty due to high-dimensional space and various *** the rapid development of data science and scientific tools of measurement recently,there are numerous data-driven methods devoted to discovering governing laws from *** this work,a data-driven method is employed to perform the modeling of the projectile based on the Kramers–Moyal *** specifically,the four-dimensional projectile system is assumed as an It?stochastic differential *** the least square method and sparse learning are applied to identify the drift coefficient and diffusion matrix from sample path data,which agree well with the real *** effectiveness of the data-driven method demonstrates that it will become a powerful tool in extracting governing equations and predicting complex dynamical behaviors of the projectile.

关键词： data-driven modeling machine learning projectile systems Kramers–Moyal formulas

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