Application of Hybrid Predictive Tools for Prediction and Simulation in Supercritical Fluid Extraction - An Overview

Sitinoor Adeib Idris, Masturah Markom

Research output: Contribution to journalConference article

Abstract

Supercritical fluid technology (SFT) has been applied in many areas, such as in pharmaceutical and food sectors, due to its outstanding features. SFT is an efficient technology that performs extraction and leaves no or less organic residues compared to conventional processes. Recently, the simulation and prediction of the process output from supercritical fluid extraction was determined using intelligent system predictive tools. The prediction of the set of results from supercritical fluid extraction for designing and scale up purposes is crucial because it can not only reduce the usage of extraction solvent and the energy and time of the process but it can also solve the problem that the complex mathematical model cannot solve. A neural network is considered as one of the artificial intelligent systems and is a key technology in industry 4.0. The use of hybrid predictive tools is also a developing area in the prediction and simulation of supercritical fluid extraction and therefore will be further discussed in this paper.

Original languageEnglish
Article number012051
JournalIOP Conference Series: Materials Science and Engineering
Volume551
Issue number1
DOIs
Publication statusPublished - 14 Aug 2019
EventInternational Conference on Green Engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019 - Bangkok, Thailand
Duration: 4 Feb 20195 Feb 2019

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Supercritical fluids
Intelligent systems
Solvent extraction
Drug products
Supercritical Fluid Chromatography
Mathematical models
Neural networks
Pharmaceutical Preparations
Industry

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

Cite this

Application of Hybrid Predictive Tools for Prediction and Simulation in Supercritical Fluid Extraction - An Overview. / Adeib Idris, Sitinoor; Markom, Masturah.

In: IOP Conference Series: Materials Science and Engineering, Vol. 551, No. 1, 012051, 14.08.2019.

Research output: Contribution to journalConference article

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