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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]

The predictive modelling for refined, bleached, and deodorised palm oil (RBDPO) quality was performed with an aim to shape a smart and economically sustainable palm oil refining industry. The aim of this study is to improve the RBDPO predictive modelling framework via the novel use of moving average and moving windows form of prediction. Weighted Moving Average (WMA) data smoothing technique was introduced to investigate the effect of the moving average algorithm by comparing the outcomes with the unsmoothed dataset. The Multiple Least-Squares Regression (MLSR) technique was used for model training and prediction of the RBDPO quality. The models were performed using dynamic windows in an expanding and rolling windows form as well as the conventional static window form of prediction. The prediction improvement was consistently observed in all the methods used and was compared in terms of the Mean Squared Error of Prediction (MSEP). It was revealed that the data smoothing result with WMA reduced 33.73 % of the error compared to the unsmoothed data. By comparing the prediction accuracy of the order of the model, the process fitted the first-order model and gave 16 times error less than the second-order model. The dynamic windows prediction form reflected better precision with an average of 37 % error reduced dynamic form that fulfilled the transient nature of the palm oil refining process, in which the data distribution shifts over time [ABSTRACT FROM AUTHOR]