... response, and conducting a blind test to ensure the model performance and generality. This proxy is then coupled with the genetic algorithm to find an optimal well configuration in a test case. Verifying the results obtained by our proxy with those of a commercial simulator shows that the objectives...

... for publication in the J ournal of E nergy R esources T echnology . 11 10 2021 16 08 2022 17 08 2022 03 10 2022 subsurface engineering multi-objective optimization genetic algorithm oil recovery petroleum reservoir hydrates/coal bed methane/heavy oil/oil sands/tight gas oil...

... model for BSNOx were combined with a genetic algorithm (GA) to generate the Pareto-optimal front. The results proved the concept of using a hybrid statistical approach (DoE + ANN) with GA as an effective tool to generate a range of compromise design solutions. By extracting designs along the Pareto...

... is carried out using a genetic algorithm to determine the optimum solutions. The Utopian method is used to select the favorable solution from the optimal solutions in the Pareto frontier. Also, the distributions of the values of design variables within their allowable ranges are investigated. It is found...

... gas directly affect the gas recovery. The optimal injection time is determined as the depleted stage, and the corresponding injection rate is optimized by using a genetic algorithm (GA) integrated with TOUGH2. The optimization of CO 2 injection parameters leads to recovery factors (RFs) reaching 62.83...

... fracture design parameters. The results are then compared with the traditionally used optimizers including particle swarm optimization (PSO) and genetic algorithm (GA). The results demonstrated that the multi-CI and TLBO converge at the global best position more often with a success rate of at least 95...

... optimization of the above cycle is carried out using nondominated sorting genetic algorithm-II (NSGA-II). The optimal solutions given by the Pareto frontier are further assessed through widely used decision makers namely LINMAP, TOPSIS, and Bellman–Zadeh techniques. The optimal solutions attained...

... machine learning genetic algorithm super learner active learning hyperparameter tuning In the development of novel design strategies and next-generation combustion technologies that boost fuel economy and comply with stringent emission regulations, one key component will be the optimization...

... are optimized to maximize the electrical efficiency of the PV system. Due to non-convex nature of the objective function, the Genetic algorithm is employed to get the solutions. Simulation results show that the optimized water flowrate for a given interval duration minimizes the consumed power in pumping system...

... depend on its network structure and initial parameters. Due to the weights between the layers of WNN model and the thresholds within the layers are randomly initialized, the network performance will be unstable or even polarized. Therefore, genetic algorithm is used to determine the initial thresholds...

...Shahram Derakhshan; Mohammadreza Khosravian In this paper, a parabolic trough solar collector (PTSC) plant is combined with a liquid air energy storage (LAES) system. The genetic algorithm (GA) is used to optimize the proposed system for different air storage mass flow rates. The roundtrip exergy...

... methods ineffective and improbable. Therefore, the process was optimized by genetic algorithm (GA) method, which is one of the most powerful methods. The optimum values for the number of cooling tubes, feed and cooling water temperatures were 308, 507.6 K, and 522.43 K, respectively. The optimization...

... steam pressure and drum level, in addition to the potential of developing of thermal stresses in the walls of steam risers. A genetic algorithm (GA) based optimization scheme is proposed for tuning the conventional boiler control loops to maximize the ability of the boiler to respond to large steam...

... in an Iranian layered reservoir. In this regard, CRM is coupled with production logging tools (PLT) data to study the effects of layers. A fractional-flow model is also coupled with the developed CRM to estimate oil production. Genetic algorithm (GA) method is used to minimize the error objective function...