Metaheuristic

Nuclear fuel management includes ways and considerations to decide things to do in order to control flux and reactivity of a reactor core and spatial power distribution and burn-up of nuclear fuels. It has purpose primarily to minimize operation cost of a nuclear power plant but fulfilling the operation requirement.

Generally, nuclear fuel management can be divided into two parts, out-of-core and in-core. Out-of-core fuel management focuses on questions “What to buy?” and “What to be reinserted to the core?” and multi-cycles planning. On contrary, in-core fuel management has an objective to decide loading pattern in a core, e.g. fresh fuels, spent fuels and burnable poisons and control rods location.

In-core fuel management is a problem need to be kept attention in the uses of nuclear energy. Practically, it is done by optimizing fuel loading pattern in a reactor core. An optimum condition in the reactor core can be achieved by looking at its cycle length and power distribution

Optimization in the nuclear reactor core can be quantified by its effective multiplication factor (keff) for certain burnup step. Besides, safety factor must also be considered since optimization may affect the power distribution. For safety reason, PPF or power peaking factor must be used as a constraint in optimizing reactor core. The power peaking factor in the nuclear reactor is defined as the ratio of maximum power to average power in the reactor core. The best PPF is unity, means that all power generation perfectly distributed in the core with exact value.

Practically, fuel loading pattern optimization is difficult to be done because of the excessive numbers of fuel assemblies in a nuclear reactor. In a standard PWR, at least there are more than 1025 combinations to try. The combinatorial problem then will be complicated and need a bunch of time if it is done using conventional algorithms. Metaheuristic optimization methods (for example Genetic Algorithm, Simulated Annealing, Quantum-inspired Evolutionary Algorithm, Bat Algorithm) are thus employed to solve the problem.

List of relevant theses :

  1. Fuel Loading Pattern Optimization using Polar Bear Optimization Algorithm to Extend Reactor Operation Period, Shaffan Haqi, 2021.
  2. Fuel Loading Pattern Optimization of Reactor Based on BEAVRS In Order To Minimize Power Peaking Factor with Reactor Operating Time Restriction using Polar Bear Optimization Algorithm, Amila Amatullah, 2021.
  3. Fuel Loading Pattern Optimization in KSNP-1000 to Extend Reactor Operating Time with Maximum Power Peaking Factor Constraint using Modified Bat Algorihm Method, Yudi Riski Chandratama, 2018.
  4. Fuel Loading Pattern Optimization of KSNP-1000 with Minimum Power Peaking Factor and Reactor Operating Time using Modified Bat Algorithm Method, Kurniawan Adi Saputra, 2018.
  5. Fuel Loading Pattern Optimization with Constrains on Fuel Assembly Inventories using Quantum-inspired Evolutionary Algorithm Method. M. Rizki Oktavian, 2016.
  6. Fuel Loading Pattern Optimization without Constrains on Fuel Assembly Inventories using Quantum-inspired Evolutionary Algorithm Method. Teguh Adi, 2016.
  7. Implementation of Improved Genetic Algorithm Method to the Loading Pattern of PWR. Petrus, 2009.
  8. Implementation of Multi-objective Simulated Annealing Method for PWR Loading Pattern Optimization. Christina Novilla Soewono, 2009.
  9. Optimization of Burnable Poison Placement of PWR Core using Algorithm Genetics. Andin Nugroho, 2009.
  10. Optimization of Burnable Poison Placement of PWR Core using Multi-Objective Simulated Annealing. Damar Canggih Wicaksono, 2009.
  11. Optimization of Loading Pattern of PWR Core using Simulated Annealing Method. Luki Arif Mulyana, 2008.
  12. Optimization of Loading Pattern of PWR Core using Genetic Algorithm Methods. Yos Panagaman Sitompul, 2008.

Tawaran Tugas Akhir

  • Optimasi metaheuristic untuk
    • Loading Pattern di PWR
    • Burnable Poison Placement