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Determining biomass chemical exergy using a novel hybrid intelligent approach to promote biomass-based biorefineries

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JOURNAL OF CLEANER PRODUCTION 2020年 277卷 124089-124089页

作者： Aghbashlo, Mortaza Tabatabaei, Meisam Nadian, Mohammad Hossein Soltanian, Salman Ghasemkhani, Hamid Shafizadeh, Alireza Lam, Su Shiung Henan Agr Univ Henan Prov Engn Res Ctr Forest Biomass Value Adde Sch Forestry Zhengzhou 450002 Peoples R China Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Mech Engn Agr Machinery Karaj Iran Univ Malaysia Terengganu Inst Trop Aquaculture & Fisheries AKUATROP Terengganu 21030 Malaysia Biofuel Res Team BRTeam Terengganu Malaysia Agr Res Extens & Educ Org AREEO Microbial Biotechnol Dept Agr Biotechnol Res Inst Iran ABRII Karaj Iran Inst Res Fundamental Sci IPM Brain Engn Res Ctr POB 19395-5531 Tehran Iran Univ Jiroft Fac Agr Dept Mech Engn Biosyst Jiroft Iran

The issue of sustainability has become a strategic imperative for researchers attempting to address energy and environmental concerns using biorefinery approach. Exergy-based methods have shown significant promises in terms of their ability to reliably locate the hotspots of resource degradation in biorefineries. The key step in analyzing biorefineries exegetically is to calculate biomass chemical exergy which is a very computationally-intensive task. Interestingly, proximate and ultimate analysis methods show potential to reflect the chemical exergy content of biomass. Hence, the present study was devoted to introducing a novel hybrid intelligent approach to determine the chemical exergy content of biomass based on both the composition analysis methods. In the developed hybrid models, input score variables in each inner loop of partial least square (PLS) approach were correlated with its output score variables using hybrid adaptive neuro-fuzzy inference system and particle swarm optimization algorithm (ANFIS-PSO). Both the developed modeling systems showed acceptable accuracy in determining the chemical exergy values of biomass materials. The model derived from ultimate analysis was slightly more accurate than that from proximate analysis (mean absolute percentage error of 0.207 vs. 0.506, respectively). Nevertheless, simple and inexpensive character of proximate analysis can facilitate real-world applications of the respective model. Overall, the developed model can pave the way for developing sustainable biorefineries by computing the chemical exergy of biomass more accurately than complex thermodynamic models. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Adaptive neuro-fuzzy inference system Biomass chemical exergy Partial least square particle swarm optimization algorithm Proximate analysis Ultimate analysis

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PSO-BASED PID CONTROLLER DESIGN FOR AN ENERGY CONVERSION SYSTEM USING COMPRESSED AIR

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2017年第3期24卷 671-679页

作者： Atacak, Ismail Kucuk, Bayram Gazi Univ Technol Fac Comp Eng Dept TR-06500 Ankara Turkey Gazi Univ Inst Informat TR-06500 Ankara Turkey

In this study, an optimal control algorithm is proposed to overcome low performance problems arising from the non-linear characteristics of pneumatic motor in compressed air-based energy conversion systems. The effectiveness of the proposed algorithm is tested on an energy conversion system which includes a compressor, a proportional valve, a pneumatic motor (PM), a permanent magnet direct current (PMDC) generator and a control card. The control function of the system is carried out by driving the proportional valve with the control signals which is obtained depending on the PMDC generator output voltage error. In this structure, an optimal proportional-integral-derivative (PID) controller which tunes on-line its own gain parameters by particle swarm optimization (PSO) algorithm according to the operating conditions of the system used. In order to observe the effects of PSO-based PID controller on the system performance, the energy conversion system is also controlled by a discrete time PID controller. The experimental results show that PSO-based PID controller provides more robust control performance than discrete time PID controller under various operating conditions.

关键词： compressed air-based energy conversion optimal PID controller particle swarm optimization algorithm pneumatic motor

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VIRTUAL MACHINE DEPLOYMENT STRATEGY BASED ON IMPROVED PSO IN CLOUD COMPUTING

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COMPUTING AND INFORMATICS 2020年第1-2期39卷 83-104页

作者： Pang, Shanchen Dong, Dekun Wang, Shuyu China Univ Petr Coll Comp Sci & Technol Qingdao 266580 Peoples R China

Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance.

关键词： Cloud computing Pareto optimal solution particle swarm optimization algorithm resource reservation virtual machine deployment

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optimization of visible transmission and NIR reflection in multilayer via PSO: Simulation and experimental validation

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RENEWABLE ENERGY 2024年 237卷

作者： Zhang, Kaihua Chen, Zhiying Guo, Jinyang Gao, Shuai Yu, Kun Zhang, Xiansheng Liu, Yufang Wu, Xiaohu Henan Normal Univ Sch Phys Henan Key Lab Infrared Spectrum Measures & Applica Xinxiang 453007 Peoples R China Shandong Inst Adv Technol Jinan 250100 Shandong Peoples R China Qingdao Univ Qingdao 266071 Shandong Peoples R China

Transparent heat reflective windows (THRW) effectively manage solar energy by reflecting near-infrared (NIR) heat while maintaining high visible (VIS) light transmittance, thus improving building energy efficiency. Conventional THRW designs using dielectric/metal/dielectric (D/M/D) structures often increase metal thickness for better NIR reflectance, which compromises VIS transmittance. In this work, we present a dielectric/metal/ dielectric/metal/dielectric (D/M/D/M/D) multilayer structure, utilizing TiO2 as the dielectric and Ag as the metallic component. The film thicknesses were systematically optimized using the particle swarm optimization algorithm to achieve an optimal balance between high VIS transmittance ((TVIS = 87 %) and high NIR reflectance (RNIR = 95 %). Additionally, the structure shows only 15 % UV transmittance due to absorption by TiO2 and reflection by Ag. The multilayer films are fabricated using electron beam evaporator, with the experimental results show that the multilayers maintain good VIS transmittance as well as high NIR reflectance. This highperformance THRW design demonstrates significant potential for energy-saving applications in hot climates, offering a viable solution for enhancing building energy efficiency and occupant comfort in the future.

关键词： Transparent-heat reflective windows Building energy efficiency Spectrally selective regulation particle swarm optimization algorithm

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Engine Idle Speed Control Using Nonlinear Model Predictive Control

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IFAC Proceedings Volumes 2013年第21期46卷 171-176页

作者： F. Xu H. Chen X. Gong Y.F. Hu State Key Laboratory of Automotive Simulation and Control Jilin University Changchun 130025 P.R. China Department of Control Science and Engineering Jilin University Changchun 130025 P.R. China

In this paper, a nonlinear model predictive controller is presented for idle speed control (ISC) problem of spark ignition (SI) engine. The objective is to maintain the engine speed at a prescribed set-point through actuating a electronic throttle, and minimize the effects of load torque disturbances and model uncertainties. The nonlinear programming (NLP) problem formed by nonlinear model predictive control (NMPC) is solved by using particle swarm optimization (PSO) algorithm. Simulation results show that the designed nonlinear model predictive controller can achieve satisfactory performance for ISC.

关键词： Engine idle speed control nonlinear model predictive control nonlinear programming problem uncertainty particle swarm optimization algorithm

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Online Detecting of Inter-Turn Short-Circuit in Generator Rotor Winding Relying on ν-SVR Machine

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2021年第9期35卷 2150026-2150026页

作者： Pan, Feng Guo, Xiansheng Pan, Shengwang Chengdu Univ Sch Architecture & Civil Engn 2025 Chengluo Ave Chengdu 610106 Peoples R China Univ Elect Sci & Technol China Dept Informat & Commun Engn 2006 Xiyuan AveHi Tech Zone West Zone Chengdu 611731 Peoples R China

To probe an accurate diagnosing approach for synchronous generator (SG) with rotor winding inter-turn short-circuit, a novel online monitoring and detecting method relying on the nu-support vector regression (nu-SVR) machine was proposed, and its effectiveness was further verified by the micro-synchronous generator dynamic simulation. Terminal voltage, active and reactive power of SG were selected as input variables for a novel prediction model based on the nu-SVR, and field current was selected as an output variable of the prediction model. The structures and parameters of the field current prediction model were optimized with the particle swarm optimization (PSO) algorithm and training samples, then the prediction model was established and the field current prediction got under way. By comparing the predicted field current with the corresponding online measured field current, inter-turn short-circuit of rotor winding in SG could be detected sensitively once its absolute value of the prediction relative error exceeded a specific threshold. The micro-synchronous generator dynamic simulation indicated that the proposed online detecting approach based on the nu-SVR machine overcame the shortage of the back-propagation (BP) diagnosis method for misdiagnosis, and its accuracy, sensitivity and threshold setting range of the diagnosis method was the most prominent among these diagnosis methods such as the BP diagnosis method, the Bayesian regularization back-propagation (BRBP) diagnosis method and the epsilon-support vector regression (epsilon-SVR) diagnosis method.

关键词： Online diagnosis synchronous generator inter-turn short-circuit field current nu-support vector regression machine particle swarm optimization algorithm

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A Novel Measuring Method of Interfacial Tension of Transformer Oil Combined PSO Optimized SVM and Multi Frequency Ultrasonic Technology

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IEEE ACCESS 2019年 7卷 182624-182631页

作者： Yang, Zhuang Zhou, Qu Wu, Xiaodong Zhao, Zhongyong Southwest Univ Coll Engn & Technol Chongqing 400715 Peoples R China

Transformer oil is an important insulating material in power transformers. The detection of transformer oil is an important means to ensure the normal operation of the power system, which is recognized by the public. Interfacial tension (IFT) is an important parameter to characterize the flow properties of liquids. Too low interfacial tension of transformer oil can cause major accidents in transformers. Therefore, it is of great practical signiflcance to achieve effective detection of interfacial tension. This work proposes the measurement of the IFT of transformer oil using multi frequency ultrasonic and a support vector machine that was optimized by particle swarm optimization algorithm (PSO-SVM). 210 samples, which were detected by multi frequency ultrasonic and the Ring Method, were divided into training sets (200 samples) and test sets (10 samples). Then multi-frequency ultrasonic data is subjected to MDS dimensionality reduction to obtain 11 dimensional low-dimensional data which as the input of SVM, and the IFT, which was detected by the Ring Method, as the output of SVM. After that, a particle swarm optimization algorithm (PSO) was incorporated to optimize the parameters (C, g) for a support vector machine (SVM).Then, the SVM model with the optimized parameters (g q=1.356 and C=2.831) was trained with the training sets, and the model was verified with the test sets. Results show that the model, which proposed in this article, that describes the nonlinear relationship between multi frequency ultrasonic data and the interfacial tension of transformer oil shows higher accuracy.

关键词： Interfacial tension multi frequency ultrasonic particle swarm optimization algorithm support vector machine transformer oil

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Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network

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ENERGY 2021年 227卷 120478-120478页

作者： Li, Jinchao Wu, Qianqian Tian, Yu Fan, Liguo North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China North China Elect Power Univ Dept Econ & Management 689 Huadian Rd Baoding 071000 Peoples R China

The global trade scale of natural gas is expanding, and its price forecasting has become one of the most critical issues in the planning and operation of public utilities. In this paper, a hybrid forecasting model of monthly Henry Hub natural gas prices based on variational mode decomposition (VMD), particle swarm optimization (PSO) and deep belief network (DBN) is proposed. In addition, influencing factors of the long-term natural gas price variation are investigated and considered on the natural gas price forecasting. Empirical forecasting results validate that the newly proposed hybrid forecasting model has better forecasting performance than the traditional models. The results also show that natural gas consumption, natural gas gross withdrawals, monthly West Texas Intermediate (WTI) crude oil spot prices, the proportion of extreme high temperature weather, and the proportion of extreme low temperature weather all contribute to long-term Henry Hub natural gas spot prices forecasting to varying degrees. By comparing the accuracy of forecasting models with different combinations of influencing factors, it is found that the hybrid model with natural gas consumption and WTI crude oil spot prices has the best forecasting performance. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Natural gas prices forecasting Hybrid model Variational mode decomposition particle swarm optimization algorithm Deep belief network

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Analysis of Charging Load Acceptance Capacity of Electric Vehicles in the Residential Distribution Network

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第11期13卷 214页

作者： Hua, Yuan-Peng Wang, Shi-Qian Han, Ding Bai, Hong-Kun Wang, Yuan-Yuan Li, Qiu-Yan State Grid Henan Elect Power Co Econ & Technol Res Inst Zhengzhou 450015 Peoples R China

After large-scale electric vehicles (EVs) are connected to the residential distribution network, community charging has become one of the main bottlenecks at present, especially in old residential areas. Therefore, the current residential distribution network's ability to accept charging load and when and how the distribution network needs to be transformed have become meaningful research points. Based on the characteristics of the EVs' charging load of residential areas on a typical day and the size of the target annual charging load, this paper analyzes the acceptance capacity of the charging load of the distribution network on typical weekdays and weekends. By taking the charging load characteristics, the charging time of EVs, the voltage of each node of the distribution network, the line capacity, the transformation capacity of the distribution station as constraints, and the maximum capacity of the residential distribution network to accept the charging load as the objective function, the charging load capacity of the residential distribution network is analyzed. The particle swarm optimization algorithm is employed to solve the optimized mathematical model. The simulation uses an actual residential distribution network as an analysis example, and the partition optimization results prove the correctness and feasibility of this proposed method.

关键词： residential distribution network electric vehicle acceptance capacity charging load particle swarm optimization algorithm

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Design and analysis of energy efficient urban buildings based on BIM model

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第6期24卷 3785-3798页

作者： Wu, Yang Chongqing Business Vocat Coll Dept Publishing Media Chongqing 401331 Peoples R China

Traditional buildings face problems such as low construction efficiency and high costs during the construction process, which cannot meet the requirements of green and sustainable development of buildings. To address this, a design model is constructed using BIM technology as a framework, which optimizes the energy efficiency of multiple building indicators and constructs a building energy efficiency optimization model based on decomposition of multiple objectives. Considering the cost and convergence issues of the goal solving model, an agent-assisted multi-objective particle swarm optimization model is introduced to construct an energy-saving design optimization model. The fitness of the particle swarm is optimized through an agent optimization strategy, thereby adjusting the speed and position of particles and improving the model optimization effect. Performed performance testing on the proposed model, and the proposed model has the best optimization performance under the Ackley function. After 100 iterations, it tends to converge. At this point, the best fitness value of the model is 0.452. Applying the proposed technology to specific cases for target optimization of urban single-room office buildings, the proposed optimization model has a shorter search time, a minimum of 2408 uncomfortable hours, and a minimum total energy consumption of 47.5, which are superior to the other three models. Finally, by comparing the comprehensive application effects of the model, the proposed model has the best performance in terms of super volume index, reaching 26,473 in urban office buildings. Compared to the comparison model, the proposed model takes 1.3 h, and the overall optimization time is the shortest. In traditional residential building testing, the proposed model has a super volume index of 50,132, which is also the best and has the shortest training time. It can be seen that the proposed optimization model has excellent optimization ability, and compared to other tec

关键词： BIM energy saving architecture particle swarm optimization algorithm multiple objectives simulation

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