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Abstract Esterification of adipic acid and oleyl alcohol in a solvent-free system featuring a stirred tank reactor containing commercially immobilized Candida antarcticalipase B was performed. The process was carried out using an artificial neural network (ANN) trained by the Levenberg-Marquardt (LM) algorithm. The effects of four operative variables, temperature, time, amount of enzyme,and impeller speed, on the reaction yield were studied. By examining different ANN configurations, the best network was found to consist of seven hidden nodes using a hyperbolic tangent sigmoid transfer function. The values of the coefficient of determination (R ) and root mean squared error (RMSE) between the actual and predicted responses were determined to be 1 and 0.0058178 for training and 0.99467 and 0.622540 for the testing datasets,respectively. These results imply that the developed model was capable of predicting the esterification yield. 23602
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The operative variables affected the yield, and their order of contribution was as follows: time > amount of enzyme >temperature > impeller speed. A high percentage of yield
(95.7%) was obtained using a low level of enzyme (2.5%
w/w), and the temperature, time, and impeller speed were
66.5°C, 354 min (about 6 h), and 500 rpm, respectively. A
simple protocol for efficient substrate conversion in a
solvent-free system evidenced by high enzyme stability is
indicative of successful ester synthesis.
Keywords: lipase, adipate ester, artificial neural network,
stirred tank reactor, optimization
1. Introduction
Esters of adipic acid are a broad and perse family of
synthetic lubricants that can be used in automotive and
industrial oil applications either as sole basestocks or as
blendstocks with other functional fluids. Excellent tribo-
logical properties, low volatility, high flash point, shear
stability, and high viscosity index are some of the advant-
ages offered by these high performance lubricants [1].
While stable against oxidative and thermal breakdown,
adipate esters are highly biodegradable and thus environ-
mentally friendly. Adipates are also widely used in other
applications such as plasticizers, food packaging, paint
strippers, perfumes, cosmetics, and coatings [1,2]. The
simplest and most direct route for the synthesis of adipate
esters is reaction of adipic acid and a monohydric alcohol
in the presence of an acid catalyst [3].
Chemical synthesis of adipate esters poses some dis-
advantages such as high temperature, prolonged reaction
period, corrosion of equipment, formation of byproducts,and high pollution [4].
Use of enzymes as biocatalysts for
preparation of these high-value-added esters can overcome
such drawbacks. Enzymatic synthesis of adipate ester in
the presence of organic solvent has been previously report-
ed [2,5]. However, solvent toxicity, high cost due to low
productivity, and costly downstream processing including
evaporation and recycling of the solvent are some of the
problems inherent in solvent-based systems [6]. So far,
there have been few studies on the enzymatic synthesis of
adipate esters in a solvent-free system.
One of the most important stages in any biotechno-
logical process is optimization of the reaction conditions
without increasing the cost [7]. Due to the nonlinear beha-
vior and complicated structure of biochemical processes, it
is always difficult to predict the effects of independent
variables on the reaction yield. The sensitivity of enzymes
in response to variables may potentially increase the com-
plexity of models. Most classic statistical methods require
screening for effective parameters and a unimodel objec-
tive function [8].
In recent years, application of artificial neural networks