The Optimization Of Boiler Operation Based On Intelligent Algorithm

The coal as fired, with unidentified characteristics of the coal gangue, was burned on a 300MW circulating fluidized bed unit. The equipment of the coal conveying system was damaged and the boiler operation was unstable. In response to the problems, the coal quality data and storage conditions of the coal were examined and the site was spot-checked to evaluate the coal quality characteristics. At the same time, the typical representative parameters of the coal handling system and boiler operation were selected. According to the analysis of coal quality and coal storage, the coal quality fluctuates greatly and the uniformity of particle size distribution is poor. There is actually the coal gangue with hard texture and hard to grind in the coal pile. The coal gangue will have adverse effects on the fine screening machine, fine crusher and other equipment. After burned this type of coal, the fluidized quality of the boiler bed is degraded to make an impact on the safe and stable operation of the boiler. It is recommended that the coal should be screened and then burned into the furnace to ensure safe and stable operation of the boiler.

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Research on intelligent clustering of male upper body

Textile Research Journal ◽

10.1177/00405175211000125 ◽

2021 ◽

pp. 004051752110001

Author(s):

Pengpeng Cheng ◽

Xianyi Zeng ◽

Pascal Bruniaux ◽

Jianping Wang ◽

Daoling Chen

Keyword(s):

Artificial Bee Colony ◽

Young Men ◽

Cuckoo Search ◽

The Body ◽

Upper Body ◽

Intelligent Algorithm ◽

Density Peak ◽

Swarm Optimization ◽

Bee Colony ◽

Length Width

To study the upper body characteristics of young men, the body circumference, length, width, thickness, and angle of young men aged 18–25 and 26–35 years were collected to comprehensively characterize the concave and convex features of the front, back, and side of the human body. The Cuckoo Search-Density Peak intelligent algorithm was used to extract the feature factors of the upper body of men, and to cluster them. To verify the effectiveness of the intelligent algorithm, the clustering results of Cuckoo Search-Density Peak, Density Peak, Particle Swarm Optimization-Density Peak algorithm, Ant Colony Optimization-Density Peak algorithm, Genetic Algorithm-Density Peak algorithm, and Artificial Bee Colony-Density Peak algorithm were evaluated by Silouette and F-measures, respectively. The results show that the Cuckoo Search-Density Peak algorithm has the best clustering results and is superior to other algorithms. There are some differences in somatotype characteristics and somatotype indexes between young men aged 18–25 and 26–35 years.

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Intelligent optimization of EV comfort based on a cooperative braking system

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211004461 ◽

2021 ◽

pp. 095440702110044

Author(s):

Lingying Zhao ◽

Min Ye ◽

Xinxin Xu

Keyword(s):

System Model ◽

Regenerative Braking ◽

Coupling Mechanism ◽

Intelligent Algorithm ◽

Distribution Strategy ◽

Braking System ◽

Logic Diagram ◽

Braking Torque ◽

Braking Force ◽

The Time Domain

To address the comfort of an electric vehicle, a coupling mechanism between mechanical friction braking and electric regenerative braking was studied. A cooperative braking system model was established, and comprehensive simulations and system optimizations were carried out. The performance of the cooperative braking system was analyzed. The distribution of the braking force was optimized by an intelligent method, and the distribution of a braking force logic diagram based on comfort was proposed. Using an intelligent algorithm, the braking force was distributed between the two braking systems and between the driving and driven axles. The experiment based on comfort was carried out. The results show that comfort after optimization is improved by 76.29% compared with that before optimization by comparing RMS value in the time domain. The reason is that the braking force distribution strategy based on the optimization takes into account the driver’s braking demand, the maximum braking torque of the motor, and the requirements of vehicle comfort, and makes full use of the braking torque of the motor. The error between simulation results and experimental results is 5.13%, which indicates that the braking force’s distribution strategy is feasible.

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