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The purpose of this paper is to present the predictor improvement for the refined palm oil quality based on the adaptive multivariate statistical process control. The time-varying behaviour of the palm oil refinery process has made it difficult for the industrial personnel to monitor and sustain the production of high quality refined palm oil. It will be very costly for the palm oil industries to repeat the refining process for the low quality refined palm oil, to meet the customer’s preference in the market. Alternatively, the quality of the refined palm oil can be measured before the process through a systematic quality monitoring, where the information from the quality analysis and process condition is integrated to develop an efficient quality prediction tool. The hybrid of timeseries expansion methods namely recursive window (RW) analysis and exponentially weighted recursive window (EWRW) analysis along with the nonlinear principal component regression based on the nonlinear iterative partial least squares algorithm (NIPALS-PCR) are proposed to develop the adaptive prediction model. The predictor coefficient is then used to predict the refined palm oil quality based on the input quality and process variables. Through the validation with the online data, both NIPALS-PCR RW and NIPALS-PCR EWRW perform better than the NIPALS-PCR static, where the prediction is improved by 95 %. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the goal of shaping a smart, economically-sustainable palm oil refining industry, this paper aims to improve the prediction modelling of Refined, Bleached and Deodorized Palm Oil (RBDPO) quality for early quality fault detection and remediation via the novel use of a moving windows form of prediction in addition to the conventional static window form. In this study, both Multiple Least Squares Regression (MLSR) and Partial Least Squares Regression (PLSR) model training techniques and prediction computations are carried out in the form of two moving data windows types, namely the expanding and rolling fixed-size data windows, together with the conventional static window form of prediction as control. Prediction improvement is observed consistently across both regression techniques when carried out in moving windows form, where both types of moving windows predictions, i.e. expanding and rolling windows, have fared significantly better than the static window form, with an average prediction error reduction of 20.6 % for Free Fatty Acid prediction, 55.9 % for Moisture Content prediction, 32.6 % for Iodine Value prediction and 34.2 % for Colour Value prediction in RBDPO. Among the moving windows themselves, the superiority of expanding windows over rolling windows and vice versa is insignificant. On the whole, this study paves the way for a revamped RBDPO quality prediction form which better reflects the dynamic, transient nature of the palm oil refining process, thus further consolidating its reliability for widespread practical use. [ABSTRACT FROM AUTHOR]