Efficient machine learning model to predict dynamic viscosity in phosphoric acid production

The rheological behavior of the phosphoric acid slurry, during the production process, strongly depends on its dynamic viscosity. Controlling this property limits P2O5 losses, minimizes energy consumption and ensures optimal flow conditions. Thus, reliable simulation tools predicting the viscosity property are needed for analysis and process optimization. To this end, three machine learning (ML) methods: single-layer artificial neural network (ANN), gradient boosting (GB) and random forest (RF), were tested using 456 data of dynamic viscosity at different solid content, shear rate and temperature, obtained from industry. The performance of these models was evaluated and compared using diverse precision metrics. The GB has shown to be the outperforming model with determination coefficient greater than 99%, and Root Mean Squared Error lower than 0.750, on both training and validation datasets. Based on the importance of the explanatory variables, all models agree on the large effect of solid content on dynamic viscosity, followed by shear rate, then temperature. The GB relative partial dependence diagram made it possible to deduce operating intervals for the solid content of the pulp to be fed to the phosphoric acid reactor, leading to optimal flow of the suspension at the level of the attack and maturation units

Dynamic Modeling and Simulation of the Sulfur Combustion Furnace in Industrial Smelter

In industrial smelters, sulfuric acid is manufactured using the elemental sulfur in a series of three-unit operations: elemental sulfur oxidation, sulfur dioxide catalytic conversion, and sulfur trioxide absorption. The sulfur oxidation, which is the basic step in this process, is generally performed under a sulfur combustion furnace that ensures the production of the process gas stream, which will be the main supply stream to the other unit operations. In this paper, a dynamic model is developed based on the fundamental mass and energy balance, including the sulfur oxidation and the dynamic flow behavior aspects within the furnace. The obtained model is simulated in the Matlab/Simulink environment and data from an industrial plant were used to validate the model. The simulation results and the plant measurement comparison showed an accuracy of 96%, with a mean absolute error of 16.12 °C and a root mean square error of 23.27 °C. Afterwards, the effect of different operating conditions and disturbance parameters on the sulfur combustion furnace performance were studied. Finally, the relationship and a correlation between the temperature and sulfur dioxide molar fraction at the outlet of the furnace were investigated for industrial use