Fuel Combination Optimization Model of Thermal Power Plant Based on New Particle Swarm Optimization Algorithm

Abstract

Influenced by the unbalanced state of particle swarm in the process of fuel combustion in thermal power plants, the fuel cost in the thermal power generation stage is relatively high. Therefore, a new particle swarm optimization model for fuel combination in thermal power plants is proposed. Combined with the combustion properties of different fuels, from the point of view of particle swarm optimization, in the process of carrying out specific particle swarm optimization simulation, the original particle swarm optimization algorithm is improved adaptively. A new particle swarm optimization algorithm is constructed by coupling it with a multiphase turbulence model. The fuel combustion performance of the power plant is analyzed by using its coupling. The optimization model of fuel combination in the thermal power plant is constructed by maximizing the energy release of fuel as the core, and the state of the fluid-particle field is taken as the constraint condition. In the test results, the design of the fuel combination optimization model can fully improve the energy release degree of fuel and reduce fuel consumption under the same power generation demand, which has a positive effect on power generation cost control.

Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

“…The successful applications of Particle Swarm Optimization (PSO) in diverse optimization scenarios provide strong support for our choice of PSO in the MUMS framework. These instances, covering energy optimization in thermal power plants to reactive power optimization in distribution networks, demonstrate PSO's versatility and efficacy, thereby reinforcing our decision to employ its enhanced variant, L1_PSO, for achieving optimal energy consumption in the MEC environment [ [23] , [24] , [25] , [26] , [27] , [28] , [29] ].…”