Design Of An Analysis And Control Software For A Portable Instrument For Heavy Metal Detection In Water

In recent years, with the continuous improvement of industrialization, heavy metal pollution in water environment is becoming more and more serious. It is not only harmful to human beings, but also to ecological environment. Therefore, to monitor the concentration of heavy metals in water environment such as lakes and oceans has become an urgent problem to be solved. In this paper, a set of analysis and control software on PC and mobile platform for the portable heavy metal detection system was designed, based on wireless communication technology, electrochemical analysis technology, Qt and Android technology. Firstly, the situation of heavy metal pollution, technology of heavy metal detection and multivariate calibration methods in Chemometrics were introduced. And then electrochemical stripping voltammetry and the hardware system designed in this project were introduced in detail, and the detection results of the system were compared with a professional detection.The innovation points and main work of this thesis are as follows:1. An analysis and control software based on Qt and an application based on Android were designed.The serial and wireless communication between the software and the hardware device and control was realized. The test data was processed and displayed in the form of curves vividly on the software interface, and finally used in quantitative analysis of the concentration of heavy metal ions. In addition, reading and saving of historical data, real-time data display, zoom and translation of curves was also realized.It is demonstrated that the performance and human-computer interaction experience of the software is well. The software is useful and stable in the matter of communication with the instrument wirelessly or through the serial port and real-time data display.2. The processing methods of digital filter and wavelet denoising and their performance are studied.To improve the performance of current denoising method in this project, IIR and FIR digital filters and wavelet denoising technology based on soft-threshold were used to smooth the signal, and the results were compared.It is demonstrated that wavelet denoising technology is a better choice for the detection system. The processed data completely retained the the waveform of the stripping voltammetric curve, with a good smoothing effect, this is very important for concentration analysis.3. An improved algorithm for the extraction of the stripping peak region was proposed, and then used in Partial least squares regression, precision of quantitative detection of heavy metal ion concentration was improved.After the heavy metal stripping voltammetric curve obtained from the instrument was processed, an independent variables data set reduction algorithm based on the extraction of the stripping peak regions was proposed to process the stripping voltammetric curve. Partial least squares regression was used to model the concentration of heavy metals. The test data obtained from the experiment was used to demonstrate the algorithm.It is demonstrated that the quantitative prediction ability of the model derived from the simplified independent variables data set was improved. The algorithm was proved to be effective and useful.