Surrogate modeling and optimization of wet phosphoric acid production process based on mechanism and data hybrid driven
Based on Aspen Plus platform and combined with the dynamic subroutine of acid decomposition reaction and crystallization kinetics of phosphate rock written by Fortran,the rigorous mechanism modeling of the whole hemi-dihydrate wet phosphoric acid process was completed,and the model was calibrated with industrial data.Quasi-Monte Carlo stochastic simulation was then used to generate a high-quality sample data set,and a machine learning algorithm was used to establish an agent model of the phosphoric acid production process.The results show that the surrogate model obtained by this method can accurately predict the key parameters of phosphoric acid production process.For example,the prediction accuracy of random forest agent model is the best in this case,most of the relative errors are controlled within 2.5%,and the maximum is not more than 10%.Based on this surrogate model,the operating parameters of the production process were optimized.The results showed that under the condition that the lower limit of P2 O5 concentration in the finished phosphoric acid was 37%,and the upper limit of SO2-4 concentration was 5%,the maximum phosphorus yield could be obtained by 98%,and the optimization effect was obvious,and the technical production index was met.It can provide solutions and data support for real-time optimization operation,design and transformation of production.
wet phosphoric acidAspen Plus process simulationuser dynamics modelmachine learningsurrogate modeloptimization