The major uses of radiopharmaceuticals (RP) in clinical areas are diagnosis and/or therapy. The present study aimed to utilize the application of fractional factorial design analysis (FFDA) coupled with particle swarm optimization algorithm (PSO) to assess the optimization of RP production process. In this regard, omeprazole (OMP), which is gastric parietal cell proton pump inhibitor (PPI), was radiolabeled with iodine‐125 (125I) isotope in order to be used as a radiotracer for stomach imaging. Different factors that affect radiolabeling process were studied. According to the proposed design, just 16 experimental runs of radiolabeling process were performed using the extremes of each factor. In addition, one run was executed at the mean point of each factor. Undesirable maximum radiolabeling yield (RY) of radioiodinated omeprazole (125I‐OMP) was deduced from application of FFDA (88.4%). Furthermore, after applying PSO with changing limits of one factor, the maximum RY of 125I‐OMP was found to be 93.78%. Moreover, the practically verification from optimum conditions, which obtained from PSO, was found to give an RY of 93.99%. Overall, the findings of this study confirmed the potential use of that hybrid design for optimization of radiolabeling processes.

The main contribution of this paper is aimed to design and implementation of an intelligent level controller and intelligent 2×2 decentralized PI controller and a lead compensator for the forced circulation evaporator by using PSO strategy. The most important thing to guarantee the safe operation of the forced circulation evaporator, without damaging the installed equipment, is obtaining optimal controllers for the evaporator operating pressure and the level of liquid inside the separator part. Also the percent of the concentration of the non-volatile in the solution must be effectively controlled to required limits. PSO algorithm is implemented in MATLAB and is compared to GA strategy for design and implementation of optimal controllers for the evaporator system by minimizing the summation of the characteristics of unit step response. Also computer simulation results are compared to the different two cost functions methods by analyzing the performance, stability and robustness with respect to variation of the evaporator control system.