Metaheuristic Optimization of Ammonia Factor as a Eutrophication Pollution Emission Descriptor for Trophic State Stability

Abstract

Aquaponic toxicity relies on the combinations of its pollution parameters that are dissolved in water and emitted in air. Ammonia is considered as an important indicator affecting aquaculture species, water nutrient imbalance and air pollution. Trophic state of aquatic body is measured by ammonia. In this study, the suitability of metaheuristic models, namely, genetic algorithm, simulated annealing, water cycle algorithm, enhanced vibrating particles system and particle swarm optimization, in determining the optimum condition of ammonia factor for providing minimal toxicity and oligotrophication was determined by varying its corresponding hyperparameters. The parameters that were optimized are water temperature and pH level. These parameters significantly affect ammonia factor that is an essential contributor to eutrophication. The optimized genetic algorithm yielded the practical-ideal fitness function value for ammonia factor as to compare with other optimized metaheuristics based on optimizing time. It selected the 50 fittest individuals based on their fitness score with the rate of 0.2 and proceeds to recombination process to extract characteristics from parent chromosomes with crossover rate of 0.8. The mutation rate of 0.01 was injected to form diversity and to test if the global solution was attained. The tournament size is 4 and the reproduction elite count is 2.5. The best condition of the ammonia factor was extracted when the number of generations has been reached. The GA results showed that the optimum condition for ammonia factor that will prevent eutrophication and provide ecological balance in aquaponic system needs a temperature of 29.254 °C and pH of 7.614.