Optimization of Ethanol Isolation From Molasses Fermentation Mesh Using Batch Distillation: Simulation With Secondary Data Verification

Abstract

This study focuses on the optimization of ethanol isolation from molasses fermentation mash through batch distillation, employing a simulation approach with secondary data verification. Molasses, a byproduct of sugar production, serves as an economical feedstock for ethanol production. The aim is to enhance the efficiency of the distillation process, maximizing ethanol yield while minimizing energy consumption. The research employs process simulation software to model and analyze the batch distillation of ethanol from molasses fermentation mash. The simulation incorporates secondary data verification, ensuring the accuracy and reliability of the model against experimental results from previous studies. This iterative process allows for the refinement of simulation parameters, ultimately improving the predictive capability of the model. Key factors such as reflux ratio, distillation time, and temperature profile are systematically varied to identify optimal conditions for ethanol isolation. The study also considers the impact of impurities present in the fermentation mash on the distillation process. Through sensitivity analysis, the influence of different variables on ethanol yield and purity is assessed, aiding in the identification of critical parameters for process optimization. Results obtained from the simulation and verified against experimental data reveal insights into the optimal operational conditions for ethanol isolation. The findings contribute to the development of more efficient and sustainable practices in ethanol production from molasses, offering potential economic and environmental benefits. In conclusion, this research bridges the gap between theoretical simulations and practical applications by optimizing the batch distillation process for ethanol isolation from molasses fermentation mash. The incorporation of secondary data verification enhances the reliability of the simulation, providing valuable insights for process engineers and researchers working towards the advancement of ethanol production technologies