A Novel Hybrid Approach for Modeling and Optimisation of Phosphoric Acid Production through the Integration of AspenTech, SciLab Unit Operation, Artificial Neural Networks and Genetic Algorithm

Abstract

The purpose of the study was to identify and predict the optimized parameters for phosphoric acid production. This involved modeling the crystal reactor, UCEGO filter (as a detailed model of the filter is not available in Aspen Plus or other simulation software), and acid separator using Sci-Lab to develop Cape-Open models. The simulation was conducted using Aspen Plus and involved analyzing 10 different phosphates with varying qualities and fractions of P2O5 and other minerals. After a successful simulation, a sensitivity analysis was conducted by varying parameters such as capacity, filter speed, vacuum, particle size, water temperature for washing the filtration cake, flow of recycled acid and strong acid from the separator below the filter, flow of slurry to reactor 1, temperature in reactors, and flow of H2SO4, resulting in nearly one million combinations. To create an algorithm for predicting process parameters and the maximal extent of recovering H3PO4 from slurry, ANN models were developed with a determination coefficient of 99%. Multi-objective optimization was then performed using a genetic algorithm to find the most suitable parameters that would lead to a higher reaction degree (96–97%) and quantity of separated H3PO4 and lower losses of gypsum. The results indicated that it is possible to predict the influence of process parameters on the quality of produced acid and minimize losses during production. The developed model was confirmed to be viable when compared to results found in the literature.