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首页> 外文期刊>Arabian Journal for Science and Engineering >Chlorine Soft Sensor Based on Extreme Learning Machine for Water Quality Monitoring
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Chlorine Soft Sensor Based on Extreme Learning Machine for Water Quality Monitoring

机译：基于极限学习机的氯软传感器水质监测

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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的测量硬件和与水的成分有关的某些变量的实验室分析，在线测量面临持续的困难。实际上，由于一些原因，例如某些传感器的高成本，它们的数量，检查传感器的专用时间，清洁操作，校准例程和传感器更换，使得它们的正确操作难以确保高质量的水成分。此外，在水质监测中，存在大量的异构传感器，这些异构传感器在测量和处理阶段可能很耗时。然而，对于任何传感器故障情况，软传感器方法都可以提供一种有效且经济的方式来解决此问题。这项工作为软氯传感器的研究和开发做出了贡献，该软传感器用于氯气水质监测。支持向量机（SVM）和极限学习机（ELM）技术在学习时间和其他用于回归和分类的参数方面的比较研究。主要目标是建立一个基于软传感器的水质系统架构，以便针对水质问题的控制和监控做出适当的决策。由于ELM具有比SVM技术更好的特性，因此显示它是解决上述问题的最合适的技术。提供了一个应用示例，重点放在使用氯软传感器的兴趣上，因为它是一种准确，有效且成本较低的工具。

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