Refined Palm Oil Product Quality Predictor for Supporting Palm Oil Refinery Energy Management System

Abstract

As a part of the smart palm oil refining purported to be the factory for the future achieving Industry 4.0 targets, smart quality prediction tools have been developed to minimise current hourly manual sampling practice. The quick and excellent forecasted quality of the refined, bleached and deodorized palm oil (RBDPO) on daily basis will reduce the rework of the off-spec products. The study aims to develop RBDPO quality forecasting model. It began with data collection, followed by a pre-processing stage to acquire the optimum sampling time and the processing time of the refining process using statistical tools such as boxplots, histograms, autocorrelation and cross-correlation plots. Using the pre-processed data, the predictor coefficients are then developed using various multivariate statistical analysis methods such as Partial Correlation Analysis (PCorrA), Principal Components Analysis (PCA), Partial Least Square (PLS), and Principal Components Regression (PCR) algorithms with the help of MATLAB programming software, and the forecasted data are being plotted together with the actual real time data in control charts to assess the refining process performance of Lahad Datu Edible Oils Sdn. Bhd. (LDEO). For the 327 sample size data, the sampling frequency is reduced by 75 % as product sampling time carry out at every 4 h. The residence time selected at 8 h. Through mean squared error (MSE) computations, PCorrA showed consistently low MSE readings of 0.0000386, 0.000014, 0.0036 and 0.04531 for FFA, MC, IV and COL. With proper energy management system, energy saving of 9 %, 9.5 %, 10 % and 10 % were registered for steam, LNG gas, electricity and water with the implementation of PCorrA predicting model. PCorrA is selected as the best forecasting algorithm which enables a systematic refining process monitoring and raw materials planning as well as supporting the palm oil refinery energy management system.