vegetable oils deacidification by short path molecular distillation: modeling and simulation

Physical refining of vegetable oils suggests achieving healthier oils, avoiding excessive oil loss, and reducing production waste. deacidification or removal of free fatty acids (ffa) is a vital step in the physical refining of vegetable oils. this study aimed to develop a mathematical model that shows the deacidification behavior of cold-pressed camelina oil and lampante olive oil using short-path molecular distillation (spmd) as a solventless and green technology. mass conservation balance along with the langmuir equation were used to describe the evaporation and separation mechanisms. for the oil samples, evaluation of the model prediction capabilities was assessed at different feed flow rates (q) and evaporation temperatures (et) while other factors included. feed temperature, vacuum pressure, condensation temperature, and wiper speed were kept constant. to predict vapor pressure, the group contribution method was applied. the model predictions were in reasonable agreement with the experimental data, and showed that higher et and lower q led to higher deacidification efficiency. additionally, the proposed model can be used for determining ffa concentration at different longitudinal parts of the spmd column.

vegetable oils deacidification by short path molecular distillation: modeling and simulation

physical refining of vegetable oils suggests achieving healthier oils, avoiding excessive oil loss, and reducing production waste. deacidification or removal of free fatty acids (ffa) is a vital step in the physical refining of vegetable oils. this study aimed to develop a mathematical model that shows the deacidification behavior of cold-pressed camelina oil and lampante olive oil using short-path molecular distillation (spmd) as a solventless and green technology. mass conservation balance along with the langmuir equation were used to describe the evaporation and separation mechanisms. for the oil samples, evaluation of the model prediction capabilities was assessed at different feed flow rates (q) and evaporation temperatures (et) while other factors included. feed temperature, vacuum pressure, condensation temperature, and wiper speed were kept constant. to predict vapor pressure, the group contribution method was applied. the model predictions were in reasonable agreement with the experimental data, and showed that higher et and lower q led to higher deacidification efficiency. additionally, the proposed model can be used for determining ffa concentration at different longitudinal parts of the spmd column.