A Graph-Based Dynamic Modeling for Palm Oil Refining Process

Influence of moisture content, temperature, and time on free fatty acid in stored crude palm oil

Consequent to the importance of crude palm oil (CPO) to global food processing industries, and the need for quality assurance of CPO. A kinetic model that describes changes of free fatty acid (FFA) in industrially stored CPO has been developed. CPO FFA is a well-known indicator of the deterioration of CPO. The effect of initial moisture content, storage temperature, and time on CPO FFA have been investigated in this work. Specifically, statistical multi-regression models for changes in FFA and moisture content (MC) were developed at P-value < 0.05 or 95% confidence interval fence. It was found that CPO FFA increases with an increase in moisture content, temperature, and time in their linear term and in respect to decreases in their quadratic term, and interaction between moisture content and temperature. The CPO MC was also found to decrease with an increase in temperature and time and increases in the quadratic term of temperature. Although while the model for CPO FFA, based on Fisher's F-test: [Formula: see text], showed no lack-of-fit; that of CPO MC showed lack-of-fit, [Formula: see text]. Furthermore, based on inference from the statistical model, their kinetic models were also developed. While the CPO FFA kinetic, found to be a half-order kinetic model and its other auxiliary models showed a very good fit (R² {0.9933-0.8614} and RMSE {0.0020-3.6716}); that of CPO MC was a poorly fitted first-order kinetic model (R² {0.9885-0.3935} and RMSE {0.0605-17.8501}).

Palm Oil Processing and Controversies over Its Health Effect: Overview of Positive and Negative Consequences

Palm oil is the world's most commonly used vegetable oil and extracted both from fruit and seed of palm tree. However, its high saturated fatty acid content raised controversies over consumption of the oil. Few scientific findings suggest it as a risk factor for cardiovascular disease and increased consumer's awareness over healthy diet raised claim over it. So that, this article aimed to review literatures on palm oil extraction process and its positive and negative health consequences and besides suggest strategies for healthy diet. Literature search of relevant articles was conducted by using Google scholar, PubMed, Web of science, MEDLINE, World Health Organization library online catalogue, UNICEF library, Open access thesis and dissertations published between 2009 and 2021 explored. Study reports recommend that palmitic acid from vegetable source has less effect on blood total cholesterol and low density lipoprotein cholesterol level as compared to palmitic acid from animal source. In contrary tocotrienols of palm oil lowers blood bad cholesterol level by 7-38%. Moreover, palm oil triacylglycerol arrangement does not have a cardiovascular risk and evidences from available in vitro and in vivo studies are not sufficient enough to conclude palm oil as a causative agent for cardiovascular disease. For healthy diet consumers should avoid trans fatty acids, solid and semi solid oils. Finally, further studies recommended on mitigation strategies to minimize process induced toxicants of palm oil to acceptable level.

IP Analytics and Machine Learning Applied to Create Process Visualization Graphs for Chemical Utility Patents

Researchers must read and understand a large volume of technical papers, including patent documents, to fully grasp the state-of-the-art technological progress in a given domain. Chemical research is particularly challenging with the fast growth of newly registered utility patents (also known as intellectual property or IP) that provide detailed descriptions of the processes used to create a new chemical or a new process to manufacture a known chemical. The researcher must be able to understand the latest patents and literature in order to develop new chemicals and processes that do not infringe on existing claims and processes. This research uses text mining, integrated machine learning, and knowledge visualization techniques to effectively and accurately support the extraction and graphical presentation of chemical processes disclosed in patent documents. The computer framework trains a machine learning model called ALBERT for automatic paragraph text classification. ALBERT separates chemical and non-chemical descriptive paragraphs from a patent for effective chemical term extraction. The ChemDataExtractor is used to classify chemical terms, such as inputs, units, and reactions from the chemical paragraphs. A computer-supported graph-based knowledge representation interface is developed to plot the extracted chemical terms and their chemical process links as a network of nodes with connecting arcs. The computer-supported chemical knowledge visualization approach helps researchers to quickly understand the innovative and unique chemical or processes of any chemical patent of interest.