Green Fuels Production: Parameter Estimation of Fatty Acid Hydrodeoxygenation from Batch Data

Replacing fossil fuels with renewable alternatives has become increasingly critical. The hydrodeoxygenation of fatty acids presents a viable route for green fuel production, and understanding the kinetics of these reactions is crucial for evaluating their economic viability. The present paper focuses on estimating the kinetic parameters for the hydrodeoxygenation of stearic and palmitic acids to produce <i>n</i>-alkanes, considering two reaction schemes: RS1, a detailed theoretical pathway model, and RS2, a simplified kinetic representation and categorization of similar reactions into families with linear free energy relationships (LFERs). Aspen Plus v14 was used as the process simulation environment to fit the batch data, employing the generalized Langmuir-Hinshelwood-Hougen-Watson kinetic model for RS1, while RS2 was described using a power-law kinetic expression, both using Soave-Redlich-Kwong as the thermodynamic model. To minimize the error between simulated and experimental data, the sum of squared errors was used as the objective function with an algorithm that integrates particle swarm optimization and golden search. RS2 demonstrated superior performance in global error minimization (<i>R</i> ² of 0.985) and parameter reliability compared to RS1 (<i>R</i> ² of 0.981). Grouping reactions into families using the LFER method successfully achieved reliable results. This approach shows promise for estimating kinetic parameters of systems with complex feedstocks while reducing the number of variables. Using kinetic-based models instead of conversion-based models enhanced the optimization of green fuel production units.