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Modelling and analysis of supercritical fluid extraction using soft computing based approaches.

机译:使用基于软计算的方法对超临界流体萃取进行建模和分析。

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摘要

Supercritical fluid extraction is an environmental friendly separation technique which exploits the solvent properties of fluid near the critical point, and is used in food science, chemical industry and petroleum industry. Modelling of the solubility of biomaterials is an essential issue in supercritical fluid extraction, and can help to develop a simulation and auto control system for quality control in industrial production to combine technical, economic and environmental aspects.; In this thesis, soft computing approaches are used to develop the simple and proper models for supercritical fluid extraction. Firstly, a radial basis function networks model and a hybrid model that combines the radial basis function networks and Peng-Robinson equation of state model are proposed. Secondly, a hybrid dynamic genetic algorithms and Peng-Robinson equation of state model for supercritical fluid extraction is designed to recognize the change of temperature during the extraction process. Thirdly, a novel neuro-fuzzy model for supercritical fluid extraction is proposed as an extension to the neural networks models. This neuro-fuzzy model can provide accurate prediction of the extraction process under a wide range in pressure and temperature. Simulation studies show that results using the proposed models are generally better than those using the conventional Peng-Robinson equation of state method. Finally, based on the studies above, a novel neuro-fuzzy approach is proposed to model an industrial process, extraction of the beta-carotene and lycopene by supercritical CO2 extraction from tomato waste, which can help to achieve predictive control and improve productivity.
机译:超临界流体萃取是一种环境友好的分离技术,可在临界点附近利用流体的溶剂特性,并用于食品科学,化学工业和石油工业。对生物材料的溶解度进行建模是超临界流体萃取中必不可少的问题,它可以帮助开发用于工业生产中质量控制的模拟和自动控制系统,以结合技术,经济和环境方面。本文采用软计算的方法为超临界流体萃取建立简单而适当的模型。首先,提出了径向基函数网络模型和将径向基函数网络与状态模型的Peng-Robinson方程相结合的混合模型。其次,设计了一种混合动力遗传算法和Peng-Robinson状态模型方程,用于超临界流体萃取,以识别萃取过程中的温度变化。第三,提出了一种用于超临界流体萃取的新型神经模糊模型,作为对神经网络模型的扩展。这种神经模糊模型可以在很大的压力和温度范围内提供准确的提取过程预测。仿真研究表明,使用所提出模型的结果通常优于使用常规Peng-Robinson状态方程方法的结果。最后,基于上述研究,提出了一种新型的神经模糊方法来模拟工业过程,即通过超临界CO2从番茄废料中提取β-胡萝卜素和番茄红素,从而有助于实现预测性控制并提高生产率。

著录项

  • 作者

    Zeng, Jin.;

  • 作者单位

    University of Guelph (Canada).;

  • 授予单位 University of Guelph (Canada).;
  • 学科 Computer Science.
  • 学位 M.Sc.
  • 年度 2005
  • 页码 121 p.
  • 总页数 121
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

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