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Determination of biocoagulant dosage for water clarification using developed neuro-fuzzy network integrated with user-interface-based calculator

机译:集成了基于用户界面的计算器的发达神经模糊网络确定用于澄清水的生物凝结剂剂量

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This study is aimed at developing a neuro-fuzzy model with the Matlab Graphical User Interface (GUI) for calculating the biocoagulant quantity needed for turbid water clarification. A neuro-fuzzy network (NFN) was developed for three different levels (low, medium and high) of turbid water. Experimental turbid water bioclarification data were used, in the Matlab environment through a sub-clustering neuro-fuzzy function, for modelling NFN. The network consisted of four inputs (untreated water turbidity, untreated water pH, settling time as well as treated water turbidity) and Mango Kernel Coagulant (MKC) dosage as the output variable. The best NFN architectures that produced minimum percentage error were considered for biocoagulant dosage calculator GUI development and implementation. The experimental data and results obtained from the NFN-GUI calculator were compared; and the prediction of the dosage has Root Mean Square Error (RMSE) as well as correlation coefficient ranges of 0.01-0.10 and 0.93-0.99 respectively. The high correlation coefficient found in this study indicates that the NFN-GUI calculator is a perfect match with the traditional jar-test calculator. Therefore, the Matlab-based calculator template is able to predict the biocoagulant quantity needed in a community water bioclarification treatment unit.
机译:这项研究旨在利用Matlab图形用户界面(GUI)开发神经模糊模型,以计算澄清水所需的生物凝结剂数量。针对三种不同水平(低,中和高)的浑浊水开发了神经模糊网络(NFN)。在Matlab环境中,通过亚簇神经模糊功能,使用了实验性浑浊水的生物澄清数据来对NFN进行建模。该网络由四个输入(未经处理的水浊度,未经处理的水pH值,沉降时间以及经处理的水浊度)和芒果粒凝结剂(MKC)剂量作为输出变量组成。对于生物凝血剂量计算器GUI的开发和实施,考虑了产生最小百分比误差的最佳NFN架构。比较了从NFN-GUI计算器获得的实验数据和结果;剂量预测具有均方根误差(RMSE)以及相关系数范围分别为0.01-0.10和0.93-0.99。在这项研究中发现的高相关系数表明,NFN-GUI计算器与传统的jar测试计算器是完美匹配。因此,基于Matlab的计算器模板能够预测社区水生物净化处理单元中所需的生物凝结剂数量。

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