Development of Water Monitoring and Machine Learning Spectroscopy Platforms Using Plasma in Solution Driven by Pulsed Power

• Main Authors: Ching-Yu Wang, 王靖宇
• Other Authors: Cheng-Che Hsu
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/5dp2k5

碩士 === 國立臺灣大學 === 化學工程學研究所 === 107 === Plasmas in and in contact with liquids are attracting increasing attention for broad applications. In this work, we present the characterization of plasma in aqueos solution driven by bipolar pulsed power source, the development of an online continuous heavy metals monitoring system using optical emission spectroscopy (OES) of plasma in water, and machine learning OES of plasma. The first part of the work presnts characterization of plasma in aqueos solution driven by bipolar pulsed power source. We applied several pulse modes with different voltage waveforms to change the plasma behavior. The results show that using a prepulse is able to generate gases for plasma ignition in the following high-voltage pulse stage, which greatly change the plasma ignition process. In addition, by using the prepulse with positive polarity, the atomic Pb emission intensity increases significantly about 10 times. This enhancement could be attributed to the application of O2 from the electrolytic reactions in the positive prepulse stage, which provides different routes for Pb emanations. These findings provide a better understanding of plasma-liquid interactions and a novel route for environmental monitoring applications. The second part of the work presents the development of an online continuous heavy metals monitoring system using OES of plasma in water. The plasmas were driven by actively modulated pulsed power (AMPP) in order to control the plasma and its emission behavior in solutions with a wide range of electrical conductivity (EC). The AMPP quantified in situ the solutions’ EC and modulated in real time the pulse width based on the EC. We demonstrated the online monitoring of the metallic elements Pb and Zn with a concentration from 0.5 to 250 ppm in solutions with EC ranging from 300 to 1200 μS/cm. The results show that multiple metallic elements, namely Pb and Zn, can be independently and simultaneously detected with less than a 10% variation in the corresponding optical emission lines in solutions with a wide range of EC. We believe the system using plasma spectroscopy with AMPP for online monitoring of metals in water will have a significant impact on the fields of environmental monitoring and protection. The third part of the work presents machine learning OES of plasma. A specially designed platform for efficient acquisition of spectra emanated from plasmas in solutions is developed, and several machine learning algorithms are tested for plasma analysis. We test the OES of plasmas ignited in solutions with designated ECs with pH of 2.2-5.2. A total 40k spectra are collected and tested with principal component analysis (PCA) and artificial neural network (ANN) to predict the solution’s EC. In PCA, the results show that most data points are overlapped in the score plot constructed using principal components 1 and 2, implying that PCA cannot discriminate the EC based on the spectra. In ANN, the results show that the deep ANN significantly improves the accuracy of EC prediction in terms of mean squared error by three orders of magnitude compared with the method of using single emission line.We will discuss the implication and perspectives employing machine learning to plasma spectroscopy as a route for characterizing the plasmas.