Search

[Display omitted] First-principles models of any process usually describe its complex underlying mechanisms using differential and algebraic equations including several unknown parameters, whose values must be normally estimated from experimental data. In this context, assessment of the influence of each parameter on model outputs, also known as sensitivity analysis, is an invaluable tool to, for example, simplify the structure of such model. In this work, variance-based Global Sensitivity Analysis (GSA) using Sobol' main and total effects was carried out on a previously proposed acetification process first-principles model. Three parameters (K SE , K IA and K SO ) showed less influence than the remaining nine considering their stated value ranges; K SE presented no influence in all the analysed experimental conditions, value variation of K IA exhibited a slightly greater effect on experiments with higher mean acetic acid concentrations and K SO showed the strongest impact by varying its value in all the experiments. According to these results, the model was simplified and its simulation compared with the initially proposed model and the experimental data. The analysis performed, by way of example, can be of crucial importance for any other process. [ABSTRACT FROM AUTHOR]