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OPTIMIZATION AND SENSITIVITY ANALYSIS OF AN EXTENDED DISTRIBUTED DYNAMIC MODEL OF SUPERCRITICAL CARBON DIOXIDE EXTRACTION OF NIMBIN FROM NEEM SEEDS
Author(s) -
ZAHEDI G.,
ELKAMEL A.,
BIGLARI M.
Publication year - 2011
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/j.1745-4530.2010.00576.x
Subject(s) - supercritical carbon dioxide , computer science , process engineering , supercritical fluid extraction , matlab , sensitivity (control systems) , context (archaeology) , extraction (chemistry) , mathematical optimization , mathematics , chemistry , engineering , chromatography , paleontology , electronic engineering , operating system , biology
In this article, supercritical extraction of nimbin from neem seeds has been studied. In order to investigate the effect of parameters on nimbin extraction yield, a partial differential equation model based on mass conservation principles. The model was solved using MATLAB software. The results were successfully validated with available laboratory experimental data. The optimum values of the operating parameters were obtained using gradient search strategy. Optimization routine was employed to maximize process profit. The optimum value of temperature, pressure, CO 2 flow rate and particle diameter were found to be 305K, 177.339 bar, 0.9660 cm 3 /min and 0.0575 cm, respectively. Finally, a sensitivity analysis was carried out on the different model parameters, and found that process profit is mostly sensitive to neem price.PRACTICAL APPLICATIONS This work uses mathematical optimization as a computational engine to arrive at the best solution for neem extraction in a systematic and efficient way. In the context of neem supercritical fluid extraction (SFE) systems, coupling optimization with suitable simulation modules opens a new avenue of possibilities. It saves money and provides economical benefits. In neem SFE process, measuring parameters and understanding the process are difficult. In this case, modeling can provide virtual environmental for operator practice.