Henry gas solubility optimization for control of a nuclear reactor: A case study
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Meta-heuristic algorithms have found their place in optimization problems. Henry gas solubility optimization(HGSO) is one of the newest population-based algorithms. This algorithm is inspired by Henry'slaw of physics. To evaluate the performance of a new algorithm, it must be used in various problems. Onthe other hand, the optimization of the proportionaleintegralederivative (PID) gains for load-followingof a nuclear power plant (NPP) is a good challenge to assess the performance of HGSO. Accordingly, thepower control of a pressurized water reactor (PWR) is targeted, based on the point kinetics model withsix groups of delayed-neutron precursors. In any optimization problem based on meta-heuristic algorithms,an efficient objective function is required. Therefore, the integral of the time-weighted squareerror (ITSE) performance index is utilized as the objective (cost) function of HGSO, which is constrainedby a stability criterion in steady-state operations. A Lyapunov approach guarantees this stability. Theresults show that this method provides superior results compared to an empirically tuned PID controllerwith the least error. It also achieves good accuracy compared to an established GA-tuned PID controller.
Abstract
Meta-heuristic algorithms have found their place in optimization problems. Henry gas solubility optimization(HGSO) is one of the newest population-based algorithms. This algorithm is inspired by Henry'slaw of physics. To evaluate the performance of a new algorithm, it must be used in various problems. Onthe other hand, the optimization of the proportionaleintegralederivative (PID) gains for load-followingof a nuclear power plant (NPP) is a good challenge to assess the performance of HGSO. Accordingly, thepower control of a pressurized water reactor (PWR) is targeted, based on the point kinetics model withsix groups of delayed-neutron precursors. In any optimization problem based on meta-heuristic algorithms,an efficient objective function is required. Therefore, the integral of the time-weighted squareerror (ITSE) performance index is utilized as the objective (cost) function of HGSO, which is constrainedby a stability criterion in steady-state operations. A Lyapunov approach guarantees this stability. Theresults show that this method provides superior results compared to an empirically tuned PID controllerwith the least error. It also achieves good accuracy compared to an established GA-tuned PID controller.
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