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A Repetitive Parameterization and optimization Strategy for the Calibration of Complex and Computationally Expensive Process-Based Models With Application to a 3D Water Quality Model of a Tropical Reservoir

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WATER RESOURCES RESEARCH 2022年第5期58卷 e2021WR031054-e2021WR031054页

作者： Xia, Wei Shoemaker, Christine Ann Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore Campus Res Excellence & Technol Enterprise CREATE Energy & Environm Sustainabil Megac E2S2 Phase II Singapore Singapore Natl Univ Singapore Dept Civil & Environm Engn Singapore Singapore

Parameter calibration is critical for modeling, especially for current process-based models that are complex with many chemical and biological processes and immeasurable model parameters. This analysis quantifies significant disadvantages of the traditional use of local or global sensitivity analysis (SA) for selecting calibration parameters of nonlinear, expensive models when there are a large number of constituents and parameters. We propose a new Repetitive parameterization and optimization (Rep-OPT) strategy that uses multiple optimization steps;and between each optimization step, a modeler picks the parameters to be optimized in the next optimization step. The modeler picks the parameters in each iteration following a suggested set of steps that analyze which processes and parameters are related to the poorly fit constituents with the current parameter set. We successfully applied the Rep-OPT strategy on a complex tropical water quality model with more than 91 parameters using real data. We demonstrate that expert knowledge with assistance of proposed postanalysis techniques (i.e., trade-off analysis, component analysis, and mass-balance analysis) can identify the right calibration parameters and obtain excellent model fit. In contrast, the traditional approach using SA with optimization (SA-OPT) does not find the right calibration parameters for our data. The solution found by Rep-OPT excellently improves manual solution by 32.7% in goodness-of-fit, and all calibrated constituents fit well to observations. The solution found by SA-OPT using global SA improves manual solution by only 13.3%. Local sensitivity by SA-OPT performs very poorly being 49.6% worse than manual solution.

关键词： model calibration optimization algorithm expert knowledge water quality models sensitivity analysis

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Nonlinear Aeroelastic Reduced Order Modeling Based on Nested optimization and Regularization

Nonlinear Aeroelastic Reduced Order Modeling Based on Nested...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Krolick, William C. Wang, Yi Pant, Kapil CFD Res Corp Huntsville AL 35806 USA Univ South Carolina Columbia SC 29208 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

This paper presents methods and algorithms to construct nonlinear aerodynamic reduced order models (AeroROMs) in a computationally efficient framework to significantly improve coupled aeroelastic ROM accuracy. The methods presented are carried out in two steps: (1) linear, state-space ROM system identification (SYSID);and (2) nonlinear correction term identification that is accomplished by minimizing the ROM prediction error with respect to a separate nonlinear aeroelastic training data set. Two different formulations are investigated, the first by adding nonlinear correction terms to the state and output equations in the linear state space ROM, while in the second the nonlinear correction is only added to the output equation. The key novel contributions of this paper are the use of regularization to mitigate overfitting and stabilize nonlinear aeroelastic response for enhanced model generality, and the use of a new nested optimization approach in which the nonlinear correction to the state equation is obtained through iterative optimization and the output equation correction is identified in one-shot using the least square method. This leads to salient computational acceleration over the brute-force method. The use of different nonlinear function inputs is also studied. The proposed methods and nonlinear ROMs are validated and compared against the original linear aeroelastic ROMs at multiple Mach numbers approaching the transonic regime, which demonstrates excellent accuracy and computational efficiency.

关键词： Aerodynamic Force Aeroelastic Analysis System Identification Structural Displacement Finite Element Modeling optimization algorithm CFD Simulation Support Vector Machine Nonlinear Dynamics Neural Networks

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Golden jackal optimization: A novel nature-inspired optimizer for engineering applications

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期198卷 1页

作者： Chopra, Nitish Ansari, Muhammad Mohsin IKGPTU Dept Elect Engn St Soldier Grp Inst Jalandhar India Sind Inst Management & Technol Head Dept Elect Engn Karachi Pakistan St Soldier Grp Inst Dept Elect Engn Jalandhar India

A new nature-inspired optimization method, named the Golden Jackal optimization (GJO) algorithm is proposed, which aims to provide an alternative optimization method for solving real-world engineering problems. GJO is inspired by the collaborative hunting behaviour of the golden jackals (Canis aureus). The three elementary steps of algorithm are prey searching, enclosing, and pouncing, which are mathematically modelled and applied. The ability of proposed algorithm is assessed, by comparing with different state of the art meta heuristics, on benchmark functions. The proposed algorithm is further tested for solving seven different engineering design problems and introduces a real implementation of the proposed method in the field of electrical engineering. The results of the classical engineering design problems and real implementation verify that the proposed algorithm is appropriate for tackling challenging problems with unidentified search spaces.

关键词： Nature-inspired algorithm Golden Jackal optimization Metaheuristic Constrained problems optimization algorithm GJO

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Fruit flexible collecting trajectory planning based on manual skill imitation for grape harvesting robot

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 225卷

作者： Sun, Jiahui Feng, Qingchun Zhang, Yifan Ru, Mengfei Li, Yajun Li, Tao Zhao, Chunjiang Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Intelligent Equipment Res Ctr Beijing Acad Agr & Forestry Sci Beijing 100097 Peoples R China Beijing Key Lab Intelligent Equipment Technol Agr Beijing 100097 Peoples R China

The flexible collection is vital for the intelligent grape-picking robot. In view of the reliable operation method obtained by long-term training of human prior skill, a fruit flexible collecting trajectory planning method based on manual skill imitation for grape harvesting robot is proposed. The method involves capturing manual teaching trajectory data using a motion capture system, preprocessing the data, extracting features from multiple teaching trajectories, and forming a probability distribution through Gaussian mixture model - Gaussian mixture regression (GMM-GMR). And combined with the key point of manual operation trajectory, the general trajectory generated by GMR is segmented and further imitated by kernelized movement primitives (KMP) to obtain the reference trajectory, respectively. An optimization method for hyperparameter adaptation KMP (O-KMP) was proposed to meet the trajectory fitting effect of multiple key points. Mean square error (MSE) was used to evaluate the deviation of the trajectory from the reference trajectory. The partial optimal trajectory is selected and integrated into a single trajectory. Two experiments were conducted to investigate imitation: For the same starting and ending points task, the MSE of the trajectory generated by O-KMP decreased by 15.274% compared to the original fixed hyperparameter KMP. For different placement tasks, the MSE of the trajectory generated by the O-KMP decreased by 7.296% compared to the original KMP. Finally, the robot arm of the actuator visually presents different task states for the compliant placement of fresh grapes.

关键词： Trajectory planning Manual skill imitation Harvesting robot Kernelized movement primitives optimization algorithm

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Dynamic capacitated facility location problem in mobile renewable energy charging stations under sustainability consideration

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 41卷

作者： Ala, Ali Deveci, Muhammet Bani, Erfan Amani Sadeghi, Amir Hossein Shanghai Jiao Tong Univ Dept Ind Engn & Management Sch Mech Engn Shanghai 200240 Peoples R China Natl Def Univ Turkish Naval Acad Dept Ind Engn TR-34940 Istanbul Turkiye Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon Imperial Coll London Royal Sch Mines South Kensington Campus London SW7 2AZ England Sharif Univ Technol Dept Ind Engn Tehran Iran North Carolina State Univ Dept Ind & Syst Engn Raleigh NC USA

The deployment of mobile renewable energy charging stations plays a crucial role in facilitating the overall adoption of electric vehicles and reducing reliance on fossil fuels. This study addresses the dynamic capacitated facility location problem in mobile charging stations from a sustainability perspective. This paper proposes Twostage stochastic programming with recourse that performs well for this application, and the location of the mobile renewable energy charging station (MRECS) management addresses the complex dynamics of reusable items. To solve this problem, we suggested dealing with differential evolutionary (DE) and DE Q-learning (DEQL) algorithms, as two novel optimization and reinforcement learning approaches, are presented as solution approaches to validate their performance. Evaluation of the outcomes reveals a considerable disparity between the algorithms, and DEQL performs better in solving the presented problem. In addition, DEQL could minimize the total operation cost and carbon emission by 7% and 20%, respectively. In contrast, the DE could decrease carbon emissions and total operation costs by 5% and 2.5%, respectively.

关键词： Renewable energy Stochastic programming optimization algorithm Reinforcement learning Facility location Sustainability

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A modified modeling approach of virtual power plant via improved federated learning

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 158卷

作者： Taheri, Seyed Iman Davoodi, Mohammadreza Ali, Mohd. Hasan Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

The Virtual Power Plant (VPP) is a promising solution to power systems' challenges. However, its energy management system (EMS) faces challenges due to centralized Big-Data analysis, i.e., complexity and high computational cost. Decentralized strategies, e.g., federated learning (FL), have been proposed to mitigate these challenges. Although conventional FL can be implemented in VPPs, its effectiveness is limited by its typical structure, which relies on single-pattern modeling. Indeed, this particular structure leads to delays and inaccuracies in optimizing the heterogeneous generator dispatch patterns, particularly renewables. Hence, addressing this critical hindrance is essential for FL's efficient and effective deployment in VPPs. To overcome this drawback, this paper proposes novel VPP modeling utilizing multi-task FL modernized by multi-pattern modeling to decentralize the EMS processing. The proposed FL defines electricity generators' agents as clients collaborating to train the decentralized local data coordinated by a central server. The innovative idea is to create a novel multi-pattern-multi-task FL for electricity generation prediction so that the generator's agents learn to provide an optimized dispatch. Thus, the conventional multi-task FL is improved by proposing an optimized neural-network clustering technique. Simulation results demonstrate that multi-pattern-multi-task FL is 39%41% faster than the best-reported methods, i.e., teaching-learning-based and honey-bee-mating optimization algorithms. (c) 2017 Elsevier Inc. All rights reserved.

关键词： Virtual power plant Federated learning optimization algorithm Energy management system Distributed generation

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MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer

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SENSORS 2024年第5期24卷 1502页

作者： Liu, Jing Liu, Zhentian Wu, Yang Li, Keqin Inner Mongolia Univ Hohhot Coll Comp Sci Hohhot 010021 Peoples R China SUNY Coll New Paltz Dept Comp Sci New York NY 12561 USA

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.

关键词： Boltzmann selection multi-objective grey wolf optimizer optimization algorithm web service composition

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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm

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JOURNAL OF ENERGY STORAGE 2024年 98卷

作者： Reza, M. S. Hannan, M. A. Mansor, M. Ker, Pin Jern Rahman, S. A. Jang, Gilsoo Mahlia, T. M. Indra Univ Tenaga Nas Dept Elect & Elect Engn Kajang 43000 Malaysia Sunway Univ Sch Engn & Technol Bandar Sunway 47500 Malaysia Korea Univ Sch Elect Engn Seoul 136701 South Korea Univ Technol Sydney Sch Civil & Environm Eng Ultimo NSW 2007 Australia

An essential component of assessing lithium-ion battery (LIB) performance, reliability, and administration in the application of battery health monitoring and management is determining the battery's Remaining Useful Life (RUL). However, existing RUL prediction approaches have difficulties with variability and nonlinearity that occur during battery degradation, data extraction, feature extraction, hyperparameters optimization, and prediction model uncertainty. To address these problems, this paper introduces a novel hybrid approach for RUL prediction, combining a Lightning Search algorithm (LSA) with a Long-Short Term Memory (LSTM) deep learning model. At first, the hybrid LSA + LSTM model is trained using a comprehensive framework comprising 31 data features, utilizing a mathematical systematic sampling (SS) approach. This sampling technique enables the identification of 10 related data features including temperature, voltage, and current, recorded during each charging cycle from the LIB parameters. Moreover, the LSA optimization technique is introduced to optimally determine the LSTM deep neural model hyperparameters including the number of hidden neurons, learn rate, epoch, learn rate drop factor, learn rate drop period, and gradient decay factor. The effectiveness of the proposed LSA + LSTM model is assessed using battery aging data from the NASA dataset. In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. The robustness of the proposed method is further validated with existing literature as well as with another battery dataset obtained from the MIT Stanford dataset. The RUL prediction results with uncertainty quan

关键词： Lithium-ion battery Remaining useful life prediction Systematic sampling Uncertainty parameters optimization algorithm Deep learning

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Optimizing of working conditions of vanadium redox flow battery based on artificial neural network and genetic algorithms

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JOURNAL OF ENERGY STORAGE 2024年 100卷

作者： Wang, Qiong Yan, Ruijie Ren, Longhui Qu, Zhiguo Jiang, Zhiyuan Wang, Zhengdong Zhang, Cheng Wang, Juan Xian Univ Architecture & Technol Sch Mech & Elect Engn 13 Yanta Rd Xian 710055 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Chem Engn & Technol 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Coventry Univ Inst Clean Growth & Future Mobil CALPS Cheetah Rd Coventry CV12TL England Xian Univ Architecture & Technol Sch Mech & Elect Engn Shanxi Key Lab Nanomat & Nanotechnol 13 Yanta Rd Xian 710055 Shaanxi Peoples R China

The integration of electrode compression in a vanadium redox flow battery (VRFB) with optimized operating conditions is essential for achieving the maximum net discharge power. This study presents the development of a 3D steady-state model for VRFB aimed at maximizing net discharge power (Pnet) by optimizing key working conditions, including electrode compression ratio (CR), applied current density (Iapp), and electrolyte inflow rate (Qin). The comprehensive impacts of CR, Iapp, and Qin on VRFB charging and discharging curves, energy efficiency (EE), and system efficiency (SE), and Pnet were investigated. Subsequently, the optimization of CR and Qin under a fixed applied current density range of 20-400 mA/cm2 was carried out employing a hybrid methodology integrating artificial neural network (ANN) and genetic algorithms (GA). Results reveals that increasing CR does not linearly improve system efficiency and net discharge power, with an optimal CR identified to maximize net discharge power. The optimal CR varies with operating conditions and is influenced by factors such as Qin and Iapp. Notably, both CR and Qin negatively impact net discharge power due to increased pump losses, while Iapp and state of charge (SOC) positively affect net discharge power through enhanced electron transfer and higher active substance concentration, respectively. The optimal CR is identified around 65 % for Iapp exceeding 60 mA/ cm2. Furthermore, the optimal Qin correlates directly with increasing Iapp, as higher Iapp rates necessitate greater Qin to sustain active species supply. This study offers an optimization approach for addressing coupled operating conditions and provides insights for achieving high net discharge power in VRFB operation.

关键词： Vanadium redox flow battery Net discharge power Compression ratio Flow rate Current density optimization algorithm

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Abatement of Sewer Overflow Pollution Based on Distributed Optimal Control Approach

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ACS ES&T WATER 2022年第3期4卷 859-868页

作者： Zhao, Zhichao Zhang, Huijin Yu, Ziwen Yin, Hailong Xu, Zuxin Changjiang Inst Survey Planning Design & Res Wuhan 430010 Hubei Peoples R China Tongji Univ Key Lab Yangtze River Water Environm Minist Educ Shanghai 200092 Peoples R China Tongji Univ Shanghai Inst Pollut Control & Ecol Safety Shanghai 200092 Peoples R China Univ Florida Gainesville FL 32603 USA

Optimal control is promising to mitigate the pollution from combined sewer overflows during wet-weather days. In this study, a distributed optimal control approach to regulating multipoint controlling units of the urban sewer system was developed. By introducing a differential evolution algorithm with ranking-based mutation operators, good simulation results with lower overflow mass loadings can obtain higher evolutionary probabilities. Thus, the optimal solutions to minimize the overflow mass from a large sewer network can be efficiently obtained. The developed approach was demonstrated in a catchment (total coverage area of 22.4 km2) served by one wastewater treatment plant in Chaohu City, Anhui Province of China, with five pumping stations and 18 associated sewage or stormwater pumps as the distributed controllers. After a global optimization among various controlling units, performance of the sewer network in reducing its overflow was improved obviously through fully utilizing in-sewer storage and adjusting the sewer flow transportation harmoniously (i.e., an alternative mode of full storing and fast transportation in sequence). Accordingly, the overflow volume and the overflow mass loading of total nitrogen was reduced by 32.3% and 37.6%, respectively, without retrofitting the current sewer network. On the basis of the presented approach, decision-makers can develop a database for optimal regulation of sewer network under various rainfall scenarios and respond to sewer overflow control flexibly and cost-effectiveness.

关键词： urban sewers optimization algorithm sewer overflow integrated regulation mathematical model

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