https://jopr.mpob.gov.my Wed, 13 Aug 2025 05:44:50 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 https://jopr.mpob.gov.my/prediction-of-oil-palm-bunches-production-using-artificial-neural-network/ Wed, 13 Aug 2025 05:44:50 +0000 https://jopr.mpob.gov.my/?p=14717 The study aimed to evaluate the ability of Artificial Neural Networks (ANN) to estimate the current monthly production of oil palm bunches using variables from the forest inventory, climatic elements, water deficit, soil and those related to the registration and management of plantations. The ANN estimated current production with a correlation above 0.6 and an average percentage relative error of around 13.0%, with the variables that gave the greatest contribution to modelling being those related to management, soil, genetic material and the accounting of mature bunches. Climatic variables were not as important, however, due to the influence of the climatic element on oil palm productivity, it is necessary to keep them in the modelling. The ANN demonstrated that it is capable of modelling oil palm production, characterised by high variability, opening opportunities for future studies, combining and using new variables to improve the accuracy of estimates using this tool.

]]> https://jopr.mpob.gov.my/comparison-of-artificial-intelligence-models-to-predict-oil-palm-biomass-pyrolysis-and-kinetics-using-thermogravimetric-analysis/ Thu, 11 Aug 2022 04:36:29 +0000 https://jopr.mpob.gov.my/?p=12469 Kinetic modeling is a challenging aspect of biomass conversion due to its inherent complex reactions. Further, it is difficult to achieve the accuracy of predicting the biomass pyrolysis process at varying experimental conditions, particularly for more complex samples based on kinetic modeling alone. Therefore, this study aims to use artificial intelligence (AI) models [artificial neural network (ANN), support vector machine (SVM), and decision tree (DT)] to predict biomass thermogravimetric (TG) behaviour, derivative TG (DTG), product (volatile and char) yield, and kinetic triplets. Two oil palm biomass types-empty fruit bunches (EFB) and oil palm shells (OPS) – are used to generate a 72-experiment dataset from a thermogravimetric analyser (TGA) at different heating rates (5, 10, 15 and 20°C min^-1). The results reveal that each AI model can accurately predict the DTG profile with high coefficients of determination (R2) in the range of 0.94 and 0.99 and low mean square errors (MSE) between 0.09 and 5.12. The product yield prediction results are not as promising, as indicated by higher MSE values (4.27, 2.79, 6.67). However, the ANN models most capably predicted the activation energies of oil palm biomass pyrolysis (~260°C – 360°C at 20°C min^-1) for both model-free and model-fitting methods, followed by the DT and SVM models.

]]>