In Bioprocess Engineering down streaming process is very important step for extraction and purification of secondary metabolites. Already statistical methods i.e. Response Surface Methodology (RSM) utilized to optimized the different types of extraction parameters such as extraction time, particle size, solvent-solid ratio, solvent composition on maximum extraction of bioactive compounds. But these methods do not deal accurately with non-linearity. Regression-based RSM requires the order of the model to be stated (second, third or fourth order). Intelligent computing methods (ICMs) such as: artificial neural network (ANN) and fuzzy logic (FL) are shown to be important tools to deal with these problems. In this work, we have implemented RSM, ANN and FL for the extraction of oleonolic acid from Ocimum sanctum. The results obtained by the RSM, ANN and FL are compared with experimental results. The correlation coefficient and root mean square error (RMSE) of the experimental result with the ICMs is computed and shown in graphical form. It is observed from the result that the correlation coefficient and RMSE for the ANN was highest when compared to FL and RSM. The objective of work is to optimize and compare various extraction parameters using ICMs which deals with non-linear nature of bioactive compounds irrespective of existing RSM techniques.

在生物过程工程中，下游流过程是次级代谢产物的提取和纯化的重要步骤。已经采用统计方法，即响应面方法（RSM），用于优化生物活性化合物的最大提取量的不同类型的提取参数，例如提取时间，粒径，溶剂固比，溶剂组成。但是这些方法不能准确地处理非线性问题。基于回归的RSM要求说明模型的顺序（第二，第三或第四顺序）。智能计算方法（ICM）诸如：人工神经网络（ANN）和模糊逻辑（FL）被证明是解决这些问题的重要工具。在这项工作中，我们已经实现了RSM，ANN和佛罗里达州的提取oleonolic酸从罗汉果。将RSM，ANN和FL获得的结果与实验结果进行比较。用ICM计算实验结果的相关系数和均方根误差（RMSE），并以图形形式显示。从结果可以看出，与FL和RSM相比，ANN的相关系数和RMSE最高。工作的目的是使用ICM来优化和比较各种提取参数，该方法可处理生物活性化合物的非线性性质，而与现有的RSM技术无关。