Using particle swarm optimization algorithm to search for a power ascension path of boiling water reactors

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

“…The individual extreme is the optimal solution of particle, and the global extreme is the optimal solution of groups. The velocity and location of particle can be calculated by the following expressions: where, x denotes the current location of particle, v denotes the current movement velocity of particle, p i denotes the extreme of particle, p d denotes the global extreme, c a and c b are the weighted factors of individual and global extremes, respectively, c a ∈ (0, 1), c b ∈ (0, 1), rand is the random number between 0 and 1, and τ is the inertia weight factor, which can expand the optimal space of particle. According to the global searching requirement, the particle should have better performance in the initial stage of optimization and have better development ability at the end of optimization, then the convergence efficiency of algorithm can be improved; therefore, the inertia weighted factor can be regulated by the following equation: Where, iter denotes the iteration number of algorithm, iter max denotes maximum iteration number of optimization, τ max denotes the maximum inertia weighted factor, and τ min denotes the maximum inertia weighted factor, where the change interval of τ max is {1.…”

“…The individual extreme is the optimal solution of particle, and the global extreme is the optimal solution of groups. The velocity and location of particle can be calculated by the following expressions 29 :…”

Switching Control Method for Optimal State Feedback Controller of Nuclear Reactor Power System

“…In terms of particle swarm optimization algorithm, Reference [8] uses genetic algorithm and particle swarm optimization algorithm to adjust the PID gain of PWR load following, and compares the workload of the two algorithms, the results show that the particle swarm optimization algorithm achieves the optimal solution under the condition of fewer function evaluation times, in order to improve the load tracking capacity of nuclear reactors, Reference [9] uses particle swarm optimization algorithm to optimize the PID controller gain of typical pressurized water reactor power control, and the simulation results show that the closed-loop PID controller optimized by particle swarm optimization algorithm has good stability and power response ability. Reference [10] controls the mo-tion of the control rod based on the particle swarm optimization algorithm, and designs an automatic search program for the power improvement path of the boiling water reactor. Reference [11] designs a closed-loop fuzzy controller based on particle swarm optimization algorithm for power level control of nuclear research reactors, and this control system can operate satisfactorily under most operating conditions, even at small initial power levels.…”

Small modular reactor full scope core optimization using Cuckoo Optimization Algorithm

“…Mahmoudi and Aghaie (2019) have used burn-up cycle length, K eff and PPF. Lin et al (2017) have used particle swarm algorithm to search for a power ascension path of boiling water reactors. In the present study besides the neutronic parameter such as K eff , PPFs and cycle burn-up length; thermal-hydraulic parameters including Minimum Departure from Nucleate Boiling (MDNBR), fuel rod centerline temperature and clad temperature have been considered simultaneously as the optimization objectives.…”