Optimisation of Crystallisation Recipe for Varied Cloud Points Characteristics in Palm Oil Fractions

The management of product quality in palm oil crystallisation poses a formidable challenge. Although various model-based optimisation control strategies have been widely applied, their effectiveness hinges on understanding the intricate and highly nonlinear dynamic behavior of crystallisation. Notably, existing research has predominantly focused on diverse applications, such as wastewater treatment, sugar cane crystallisation, and the pharmaceutical industry, leaving a notable research gap in the crystallisation processes specific to the palm oil industry. This research attempts to fill this gap by investigating the impact of an optimisation tool that combines artificial neural network and genetic algorithm (ANN-GA) to optimize the crystallisation recipe, specifically the cooling segments of palm oil, for three different cloud points of palm olein (CP 6, CP 8, and CP 10). The artificial neural network (ANN), which uses the Levenberg-Marquardt algorithm, serves as an internal model for predicting process output, whereas the genetic algorithm (GA) investigates a wide range of recipe combinations to maximise yield. Using MATLAB for optimisation, the ANN-GA approach goes through training, testing, and validation steps with industry-derived datasets. The results show root mean sqaure error (RMSE) of 0.8411 for CP 6, 0.4317 for CP 8, and 0.4105 for CP 10, indicating that ANN is sensitive to dataset volumes. Using GA as an optimisation tool, it generates optimal input variables for industrial validation. Validation results reveal an enhanced yield of 63 % for CP 6 palm olein, 74 % for CP 8 palm olein, which is within industrial range (66-76 %), and 77.26 % for CP 10 palm olein, which is within the range of 76-79 %. Overall, the ANN-GA technique is effective in predicting complicated systems such as palm olein and palm stearin crystallisation processes.