The behavior of detergent–dispersant additives in lubricating oils depends upon
their chemical composition and synthesis, particularly for lubricants used in
rotary equipment under harsh conditions, in the upstream and downstream oil and
gas industries. Alkylphenolate-based additives are extensively used, among
others, due to their high alkalinity and good dispersion stability. However,
their performance is extremely sensitive to synthesis parameters, making
experimental optimization time-consuming and resource-intensive.
In this study, an alkylphenolate-based additive (AKI-152) was synthesized and
optimized systematically via a response surface methodology (RSM), derived from
a central composite design (CCD). The influence of three important process
variables, namely the alkylphenol/amino mass ratio, reaction temperature, and
Ca(OH)2/CO2 mass ratio, was assessed with respect to
the total base number (TBN), kinematic viscosity, and corrosivity of the final
product.
A quadratic regression model was developed and validated, showing good agreement
between predicted and experimental results. Analysis of variance (ANOVA)
indicated that the alkylphenol/amino ratio and Ca(OH)2/CO2
ratio exert the strongest influence on TBN, while temperature also contributes
significantly within the investigated operating range. Numerical optimization
identified a stable optimum region corresponding to TBN values of approximately
160 mgKOH/g, viscosity values close to 54 mm2/s, and corrosivity
values below 0.76 g/m2 under the selected optimization criteria. No
separate experimental run was performed to verify the selected numerical
optimum, as the optimization was conducted within the experimentally
investigated CCD space using the developed and statistically evaluated response
surface models.
These results demonstrate that the applied modeling approach can be effectively
used to define suitable synthesis conditions for high-performance lubricant
additives.
