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Multi-scale assessment and multi-objective optimization of a novel solid oxide fuel cell hybrid power system fed by bio-syngas

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JOURNAL OF POWER SOURCES 2022年 524卷 231047-231047页

作者： Zhao, Zhongkai Shi, Xiaomin Zhang, Mingliang Ouyang, Tiancheng Guangxi Univ Sch Mech Engn Nanning 530004 Peoples R China Guangxi Univ Guangxi Key Lab Petrochem Resource Proc & Proc In Nanning 530004 Peoples R China

Nowadays, thermal power generation using fossil fuels is still the main way for electricity supply, resulting in serious environmental pollution and energy consumption. The research on new power generation and alternative energy is urgently needed to conduct. In this paper, a new power supply system combined biomass and fuel cell with waste heat recovery is proposed and analyzed. The system uses agricultural waste as energy source, providing gas fuel for solid oxide fuel cell through the biomass gasification. In addition, the homogeneous charge compression ignition engine can utilize unreacted fuel, and the waste heat energy in flue gas is recovered by the combination of supercritical carbon dioxide cycle and absorption refrigeration cycle. Under selecting working condition, the engine and bottom cycles brings an improvement of 11.10% in thermal efficiency, and the total fuel efficiency can reach 59.35%. Besides, the payback time of this system is nearly 60% shorter than the traditional system, with a minimum of 1.77 years. The whole system realizes the energy cascade utilization, being more efficient and cleaner, and has satisfactory performance.

关键词： Agro-waste Waste heat recovery Homogeneous charge compression ignition engine particle swarm algorithm Multi-perspective evaluation

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Optimal sensor placement methodology of hydraulic control system for fault diagnosis

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年 174卷 109069-109069页

作者： Kong, Xiangdi Cai, Baoping Liu, Yonghong Zhu, Hongmin Liu, Yiqi Shao, Haidong Yang, Chao Li, Haojie Mo, Tianyang China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Shandong Peoples R China Imperial Coll London Dept Aeronaut Exhibit Rd London England South China Univ Technol Sch Automat Sci & Engn Wushang Rd Guangzhou 510640 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China

During the state monitoring and fault diagnosis of hydraulic control system, different kinds of sensors are used to collect fault signals. The arrangement of a limited number of sensors in the most reasonable positions of the hydraulic system, that is, to solve the problem on the optimal placement of sensors, is the key to improving efficiency of fault diagnosis. Aiming at fault diagnosis of hydraulic control system, this paper proposes a sensor placement methodology of hydraulic control system to determine the optimal number and position of sensors based on a discrete particle swarm algorithm. First, the model of fault propagation and sensor response time is evaluated by a simulation model. Second, a discrete optimization model for sensor placement is established. Finally, a discrete particle swarm optimization algorithm is used to calculate the optimal solution for the optimal placement of sensors. In the iterative process, a Monte Carlo simulation-based comparison algorithm is used for the evaluation and comparison of particle. The simulation case of typical multi-circuit hydraulic control systems proves that the proposed method has fast convergence speed and optimization results. A real case of a subsea blowout preventer control system shows that the proposed method reduces the number of sensors and data redundancy effectively. Compared with the traditional method, the robustness of the proposed system under the optimal solution is improved.

关键词： Optimal sensor placement particle swarm algorithm Subsea blowout preventer Fault diagnosis

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Steam-Water Modelling and the Coal-Saving Scheduling Strategy of Combined Heat and Power Systems

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ENERGIES 2022年第1期15卷 141-141页

作者： Guo, Junshan Zheng, Wei Cong, Zhuang Shang, Panfeng Wang, Congyu Song, Jiwei Zhongshi Yitong Grp Jinan 250003 Peoples R China State Grid Shandong Elect Power Res Inst Jinan 250003 Peoples R China Shandong Univ Inst Thermal Sci & Technol Jinan 250061 Peoples R China

China aims to peak carbon emissions by 2030. As a result, small-scale coal-fired combined heat and power (CHP) units and self-provided units are gradually shut down, and large-scale coal-fired CHP units are a solution to undertake the industrial heat loads. From the perspective of the industrial heat load allocation during the non-heating season, the problems regarding the coal-saving scheduling strategy of coal-fired CHP units are addressed. The steam-water equations of CHP units are established to analyze the heat-power coupling characteristics. The energy utilization efficiency, exergy efficiency and the coal consumption are analyzed. The optimization model of saving coal consumption is established and the adaptive mutation particle swarm optimization (AMPSO) is introduced to solve the above model. The 330 MW coal-fired CHP unit is taken as an example, and the results show that for the constant main flow rate, each increase of 1 t/h industrial steam extraction will reduce the power output by about 0.321 MW. The energy utilization efficiency and the exergy are mainly influenced by industrial steam supply and the power load, respectively. For the CHP system with two parallel CHP units, the unequal allocation of industrial heat load between two units saves more coal than equal allocation. The coal consumption can be reduced when the unit with lower power load undertakes more industrial heat load. In the typical day, the total coal consumption after optimization is 3203.92 tons, a decrease of 14.66 tons compared to the optimization before. The two CHP units in the case can benefit about 5,612,700 CHY extra in one year.

关键词： combined heat and power steam-water equation load optimal allocation particle swarm algorithm

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Correlation analysis of roughness surface height distribution parameters and maximum mises stress

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SURFACE TOPOGRAPHY-METROLOGY AND PROPERTIES 2022年第1期10卷 015046-015046页

作者： Yang Duo Wang Qibo Tang Jinyuan Xia Fujia Zhou Wei Wen Yuqin Cent South Univ State Key Lab High Performance Complex Mfg Changsha Hunan Peoples R China Cent South Univ Coll Mech & Elect Engn Changsha Hunan Peoples R China Hunan Univ Sci & Technol Hunan Prov Key Lab High Efficiency & Precis Machi Xiangtan Peoples R China

Roughness surfaces contact analysis is cutting-edge research in interface design. The 3D rough surface amplitude distribution characterized by height distribution parameters( Sq (root mean square), Ssk (skewness), Sku (kurtosis)) has a great influence on the extreme value and distribution of the interface contact stress. However, the relationship between height distribution parameters and surface maximum mises stress ( sigma ( max )) is still unclear and lacks of in-depth study. Through BP(Back Propagation) neural network, global sensitivity qualitative (Morris) and quantitative (Sobol) analysis methods, the relationship between Sq , Ssk , Sku and sigma ( max ) under different loads is studied. Based on complete polynomial and permutation combination method, the optimal correlation model between height distribution parameters and sigma ( max ) was established, and particle swarm algorithm was introduced to analyze sigma ( max ) extreme values under different Sq . The results show that: (1) Under different loads, the order about height distribution parameters influence on surface contact stress is: Sq > Ssk > Sku , and as the load increases, the influence of Ssk and Sku gradually decreases. (2) In different roughness surfaces, the influence of Ssk and Sku on the contact performance is significantly full of discrepancy. The research results provide reference and technical support for active design of rough surface microstructure to improved contact performance.

关键词： roughness surface height distribution contact stress regression analysis particle swarm algorithm

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Ultrasonic in-situ measurement method and error analysis of wear of PEEK water-lubricated bearing materials

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MEASUREMENT 2023年第1期214卷

作者： Hu, Fei Ning, Changxiong Ouyang, Wu Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan 430063 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Peoples R China State Key Lab Maritime Technol & Safety Wuhan 430063 Peoples R China Natl Engn Res Ctr Water Transport Safety Reliabil Engn Inst Wuhan 430063 Peoples R China

Water-lubricated polymer bearing is the key component to guarantee the function of ship propulsion systems, nuclear main pumps, hydraulic turbines and other important equipment. Abnormal wear of the water-lubricated polymer thrust bearing is a common failure under harsh working conditions such as low speed, heavy load, and sediment. Accurate acquisition of bearing wear is of great value for revealing the bearing wear mechanism and evaluating the bearing service life. Therefore, this paper proposes an in-situ measurement method of bearing wear based on ultrasound and parameter inversion. The objective function is defined as the correlation coeffi-cient of the ultrasonic reflection coefficient amplitude spectrum. Different parameters, such as thickness, acoustic velocity, density and attenuation factor, are inverted by the particle swarm algorithm. Simulations are performed to analyze the influence of single-parameter to multi-parameter inversions on the objective function and the inversion of thickness. Taking the PEEK material as the subject, the experiment shows that the parameter inversion of thickness, density and attenuation factor has the highest accuracy, with errors within 0.15%, 2% and 95%, and the maximum absolute error of wear is 20 mu m. If the amplitude of the reflected signal is small, the intensity of the transmission signal will affect the identification result of thickness.

关键词： Water-lubricated polymer bearing Ultrasonic in-situ wear measurement Multi-parameter inversion particle swarm algorithm

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The design and operation optimization for conductive rubber heated snow melting bridge deck pavement

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NUMERICAL HEAT TRANSFER PART A-APPLICATIONS 2023年

作者： Ma, Zipeng Wei, Haibin Jiang, Boyu Wei, Dongsheng Chen, Jinhao Jilin Univ Sch Transportat Changchun 130022 Peoples R China

In order to solve the problem of ice and snow disasters on roads, this study proposed a new technology for active deicing of bridge decks using conductive rubber heating system (CRHS) as the heat source. The existing measures to improve the operation efficiency of snowmelt pavement often only optimize 1-2 influencing factors, and lack of optimization of multiple factors such as paving design, operation and environment. Therefore, in order to improve the operation efficiency, this study carried out multi-objective optimization on the design and operation of conductive rubber heated snow melting bridge deck pavement. The bridge deck pavement scheme of conductive rubber heating system was proposed, and its two-dimensional numerical model of heat transfer was established. The laying spacing of conductive rubber (s), heating power (p), ambient temperature (t) and wind speed (v) were used as input parameters for system operation optimization. Predictive models for total heating time (THT) and the loss of energy rate (LER) were developed using response surface methodology (RSM). The specific effects of input parameters on THT and LER and their interactions were analyzed by thermal simulation. Aiming at less THT and lost energy (LE), the optimal input parameters of conductive rubber heating system were obtained by the multi-objective particle swarm algorithm. The results indicated that shorter THT and lower LE could be achieved when the laying spacing (s) was 0.25 m, the ambient temperature (t) was -3 degrees C, the wind speed (v) was 2 m/s, and the input power was 550 W/m2. And adjusting s was more effective than adjusting p to obtain low THT and LER. The results and recommendations can effectively guide the operation of conductive rubber heating system. The multi-objective optimization research idea of improving the operation efficiency of snowmelt pavement in this paper can provide reference for similar deicing pavement applications.

关键词： Active deicing conductive rubber heating system particle swarm algorithm response surface methodology thermal simulation

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Colony-Based Search algorithm for numerical optimization

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APPLIED SOFT COMPUTING 2024年 151卷

作者： Civicioglu, Pinar Besdok, Erkan Erciyes Univ Fac Aeronaut & Astronaut Dept Aircraft Elect & Elect Kayseri Turkiye Erciyes Univ Fac Engn Dept Biomed Engn Kayseri Turkiye

The efficiency of an Evolutionary algorithm is highly sensitive to the mutation and crossover operators it possesses, as well as to the strategy used for determining the direction of numerical evolution and the values of evolutionary step sizes. There is no analytical method to efficiently define the direction of numerical evolution and the value of evolutionary step size for EAs. The efficiency of EAs' search processes is also influenced by their ability to maintain numerical diversity within the population. This paper introduces the Colony-Based Search algorithm (CSA). The development of CSA was motivated by the scientific and industrial need for a relatively more efficient EA. CSA possesses relatively more efficient artificial genetic operators and strategies for producing evolutionary direction and step size, and the ability to maintain numerical diversity. CSA generates the Clan Matrix containing the pattern vectors to be evolved in the current iteration by randomly selecting pattern vectors from the Colony Matrix at the beginning of each iteration. This makes it easier for CSA to maintain numerical diversity among pattern vectors for a long time. CSA's mutation method includes three randomly blended components with different properties. The problem-solving performance of CSA is statistically compared with the problem-solving performance of eight popular evolutionary search methods (i.e., SADE, SHADE, LSHADE, COBIDE, JADE, CK, GWO, and SFS) by using benchmark functions of CEC'2017 and CEC'2022. In the experiments, the 3D viewshed analysis was addressed as a real-world problem, employing the CSA. The statistical analyses conducted on the experimental results indicate that CSA performs relatively better than the compared methods to solve numerical problems.

关键词： Differential Evolution algorithm particle swarm algorithm Gray Wolf Optimizer Viewshed analysis

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Multi-objective optimization of solar-driven hollow fiber membrane dehumidification system based on MOPSO

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ENERGY 2024年 304卷

作者： Men, Yukui Dong, Yanfang Zeng, Si Liang, Caihang Tong, Xiaoman Guilin Univ Elect Technol Sch Mechanoelect Engn 1 Jinji Rd Guilin 541004 Peoples R China Guilin Univ Aerosp Technol Sch Energy & Built Environm 2 Jinji Rd Guilin 541004 Peoples R China Guangxi Beitou Environm Protect & Water Grp Co Ltd 153 Minzu Ave Nanning 530025 Peoples R China

The solar-driven hollow fiber membrane dehumidification (SHFMD) system is a renewable, energy-saving, and highly effective dehumidification system. It provides an effective solution to the problem of air-entrained liquid droplets during the traditional solution dehumidification process. The system's mathematical model was established and the reliability of the mathematical model was verified by experimental data. To enhance the comprehensive performance of the system, the multi-objective particle swarm optimization (MOPSO) algorithm is used in this paper to optimize the multi-objective parameters of the system. The objective function is the total exergy destruction of the system and the dehumidification capacity of the system. The optimization variables are cold water temperature, cold water flow rate, solution flow rate, air flow rate, and hot water flow rate. The optimization is performed using the multi-objective particle swarm optimization (MOPSO) algorithm to obtain the Pareto front, and the points of the Pareto front are normalized. Finally, the optimal trade-off point of the Pareto frontier is obtained. Each operating parameter of the optimal point of the Pareto front is also analyzed. The results show that the air flow rate and cold water flow rate have the most influence on the conflict between the total exergy destruction of the system and the dehumidification capacity of the system.

关键词： Solar energy Liquid dehumidification Hollow fiber membrane particle swarm algorithm Multi -objective optimization

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Optimization control strategy for diesel urea-selective catalytic reduction (SCR) urea injection based on the interior point method (IP)

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2024年第6期12卷

作者： Liu, Wenlong Gao, Ying You, Yuelin Jiang, Changwen Hua, Taoyi Xia, Bocong Jilin Univ State Key Lab Automot Simulat & Control Changchun 130025 Peoples R China Jilin Univ Coll Automot Engn Changchun 130025 Jilin Peoples R China

In order to ensure that the NOx and NH3 emissions of diesel engines comply with the Euro VII emission standards, this paper optimizes the urea injection using a SCR model combined with the interior point method. First, the equations of the higher-order SCR model were simplified by the variable substitution method and the line method and solved by the backward difference formula to improve computational accuracy. The LevenbergMarquardt algorithm was used to calibrate the chemical reaction rate parameters to optimize model performance. Second, an optimization cost function was designed to optimize the upstream NH3 concentration in combination with the interior point method and the higher-order SCR model to meet the requirements of Euro VII emission standards. Third, the model effect was verified by performing WHTC hot-start testing on an engine complying with the National VI emission standard. The results show that the average error between the measured NOx concentration and the calculated is 1.49 ppm, which verifies the high accuracy of the model. Finally, the effects of the interior point method, NSGA-II, and MOPSO algorithms in optimizing the upstream NH3 concentration were compared. The results showed that the interior point method outperformed the other algorithms in terms of optimization time and effect, with an optimization time of 149 s, a NOx conversion rate of 97.36%, and an average downstream NH3 concentration of 4.65 ppm. This achieved a high NOx conversion rate and low NH3 slip in accordance with the requirements of the Euro VII regulation.

关键词： Selective catalytic reduction Optimization Interior point method Undominated sorting genetic algorithm II particle swarm algorithm

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Interfacial Friction Prediction in a Vertical Annular Two-Phase Flow Based on Support Vector Regression Machine

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WATER 2021年第24期13卷 3609页

作者： Liu, Qiang Feng, Xingya Chen, Junru Southern Univ Sci & Technol Dept Ocean Sci & Engn Shenzhen 518055 Peoples R China Southern Marine Sci & Engn Guangdong Lab Guangzho Guangzhou 511458 Peoples R China

Accurate prediction of interfacial friction factor is critical for calculation of pressure drop and investigation of flow mechanism of vertical annular two-phase flows. Theoretical models of interfacial friction factor based on physical insight have been developed;however, these are inconvenient in engineering practice as too many parameters need to be measured. Although many researchers have proposed various empirical correlations to improve computation efficiency, there is no generally accepted simple formula. In this study, an efficient prediction model based on support vector regression machine (SVR) is proposed. Through sensitivity analysis, five factors are determined as the input parameters to train the SVR model, relative liquid film thickness, liquid Reynolds number, gas Reynolds number, liquid Froude number and gas Froude number. The interfacial friction factor is chosen as the output parameter to check the overall performance of the model. With the help of particle swarm algorithm, the optimization process is accelerated considerably, and the optimal model is obtained through iterations. Compared with other correlations, the optimal model shows the lowest average absolute error (AAE of 0.0004), lowest maximum absolute error (MAE of 0.006), lowest root mean square error (RMSE of 0.00076) and highest correlation factor (r of 0.995). The analysis using various data in the literature demonstrates its accuracy and stability in interfacial friction prediction. In summary, the proposed machine learning model is effective and can be applied to a wider range of conditions for vertical annular two-phase flows.

关键词： vertical annular two-phase flow interfacial friction factor support vector regression machine particle swarm algorithm

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