(200c) Process Simulation and Yield Optimization of Bio-Oil from Hydrothermal Liquefaction of Macroalgae(Kelp) Using Mixtures of Model Compounds.

Hydrothermal liquefaction (HTL) of macroalgae (kelp) has been recently receiving significant attention as an environmentally friendly process for producing renewable fuels. However, Due to the intricacy of the reaction mechanism and variety in the organically rich wet feedstock being evaluated for HTL, it poses challenges to the accurate prediction of product yield. Estimating the quantity of the products is vital to effectively develop and optimize industrial-scale HTL operations. To address this challenge, a batch simulation model was developed to predict the four phases of product formation: aqueous, gas, solid, and oil, using a macro-algae feedstock modeled as mixtures of carbohydrates, protein, and lipids. The model considered the effects of time, temperature, pressure, and feedstock loading on the product yields and was validated using experimental data obtained from the hydrothermal liquefaction of macroalgae. This study used response surface methodology (RSM) to evaluate and optimize bio-crude production using experimental data collected from the literature and rigorous process simulations. We developed a mathematical model using data conducted at numerous temperatures, pressure, residence times, and feedstock loading to predict the statistical correlation between actual predictors and the response variables. A central composite design was employed to generate experimental runs, which are then simulated and statistically analyzed. The Analysis of Variance (ANOVA) of the means showed that the selection of operational parameters could significantly affect the bio-oil yield. The response surface model obtained was statistically significant (p<0.05), indicating that the model explains a large proportion of the variability in the bio-oil yield. The optimal process conditions for maximum bio-oil yield were also identified. The response surface model was validated against the simulated data obtained from the simulator with satisfactory accuracy. This study successfully optimizes the HTL of macroalgae (kelp) for maximum bio-crude yield, using RSM and ANOVA analysis. The results highlight the significance of identifying the key process parameters that significantly affect the response variable, which is essential for optimizing the process. The methodology used in this study can be applied to similar processes in the future.