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Chlorine Soft Sensor Based on Extreme Learning Machine for Water Quality Monitoring

机译：基于极端学习机的水质监测氯软传感器

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摘要

A major problem in water treatment plants is the continuous difficulty faced in online measurement by means of dedicated measuring hardware and laboratory analysis of certain variables related to the composition of water. Actually, for several reasons, such as the high cost of some sensors, their number, the dedicated time to check out the sensors, cleaning operation, calibration routines and sensor replacement, make their proper operation hard to ensure high-quality composition of water. Furthermore, in water quality monitoring, there is a huge number of heterogeneous sensors which may be time-consuming in the measurement and processing stages. Nevertheless, soft sensor approach can provide an effective and economic way to solve this problem for any cases of sensor failure. This work presents a contribution to the study and development of a soft sensor used in water quality monitoring using chlorine. A comparative study between support vector machine (SVM) and extreme learning machine (ELM) techniques in terms of learning time and other parameters for regression and classification is presented. The main objective is to set up a system architecture based on a soft sensor for water quality in order to make an adapted decision to the control and monitoring of water quality issues. ELM is shown to be the most suitable technique to address the previously mentioned problems as it has better characteristics than those of the SVM technique. An example of application is provided to focus on the interest of using a chlorine soft sensor as it is accurate, efficient and less cost-effective tool.
机译：水处理厂的主要问题是通过专用测量硬件和与水组成的某些变量的专用测量硬件和实验室分析进行在线测量的连续困难。实际上，由于几种原因，例如一些传感器的高成本，他们的号码，专用的时间来检查传感器，清洁操作，校准程序和传感器更换，使其适当的操作难以确保高质量的水组成。此外，在水质监测中，存在大量的异构传感器，其在测量和处理阶段可能是耗时的。尽管如此，软传感器方法可以为任何传感器故障的情况提供有效和经济的方式来解决这个问题。这项工作提出了使用氯的水质监测中使用的软传感器的研究和开发的贡献。提出了支持向量机（SVM）和极端学习机（ELM）技术之间的比较研究，用于学习时间和其他参数进行回归和分类。主要目的是基于软型传感器来建立系统架构，用于水质，以便对控制和监测水质问题进行改进的决定。 ELM被证明是解决前面提到的问题的最合适的技术，因为它具有比SVM技术更好的特性。提供了一个应用程序的示例，以专注于使用氯软传感器，因为它是准确，有效且较差的工具。

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