基于粒子群算法的提取过程优化

Madhuri Arya, Kusum Deep
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摘要

本文应用粒子群优化方法对栀子果实提取物中3种有效活性化合物的产率进行了优化。其中两种化合物用作食品和药品中的天然着色剂,而第三种化合物具有很高的抗氧化能力,用于治疗许多疾病的药物。栀子花提取物中这些化合物的得率取决于提取温度、提取时间和乙醇浓度三个工艺变量,确定了提取条件。在本研究中,PSO用于确定最佳提取条件,即三个过程变量的值,将产生三种生物活性化合物的最佳产率。在这个方向上的大部分工作都使用了响应面方法。但我们的模拟结果表明,粒子群算法更适合手头的问题。
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Extraction process optimisation using particle swarm algorithm
In this paper, particle swarm optimisation PSO is applied for optimising the yields of three useful bioactive compounds in the extract of the fruits of Gardenia, a Chinese herb. Two of these compounds are used as natural colouring agents in food and medicine whereas the third, having high anti-oxidant capacity, is used in drugs for the cure of many diseases. The yields of these compounds in Gardenia extract are dependent on three process variables, namely, extraction temperature, extraction time and ethanol concentration, defining the extraction conditions. In this study, PSO is used to determine the optimum extraction conditions, i.e., the values of the three process variables that will produce optimum yields of the three bioactive compounds. Most of the work in this direction has used response surface methodology. But the results of our simulations show that PSO is better suited for the problem at hand.
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