Formation mechanisms and supervisory prediction of scaling in water supply pipelines: A review.

The scaling problem in the water supply pipeline will increase the resistance coefficient of the pipeline and the pressure of the water supply pipeline, which will not only affect the operation safety of the water supply pipeline, but also cause energy waste. The scale in the pipeline will also enrich heavy metal ions and pathogenic microorganisms, affecting the safety of water supply water quality and causing secondary pollution of water quality. At present, a lot of research has been done on the composition structure and crystallization process of the scale. The study found that calcite is the main component of the scale; the scale process is a heterogeneous nucleation process induced by heavy metal particles and their corrosion products in the pipeline. The introduction of electrochemical detection technology, density functional theory and molecular dynamics simulation has greatly improved the accuracy and timeliness of water scaling conditions detection and realized the visualization of scaling mechanism. In this paper, the measurement methods of the scale in the water supply pipeline and the corresponding material composition and crystal structure characteristics are reviewed, and the mechanism of the scale and the water quality conditions are summarized. At the end of this paper, based on summarizing the existing water quality scaling tendency evaluation methods, it is proposed to establish a water quality potential scaling risk assessment framework based on Puckorius scaling index (PSI) and electrochemical impedance spectroscopy (EIS) in the future.

A Characteristic Interval Modeling Method for Simultaneous Detection of Multiple Metal Ions.

Aiming at the problems of low accuracy and large prediction errors caused by the serious overlap of multi-metal spectral signals in zinc smelting industrial wastewater, a characteristic interval modeling method is proposed. First, according to the absorption spectra of mixed solution, the characteristic intervals of copper and nickel are preliminarily screened by using different partition lengths. Second, take the smallest root mean squares error of cross validation and the largest correlation coefficient as the evaluation indicators, compare the full-spectral model and each local model, and select the optimal feature sub-intervals of copper and nickel. Last, the partial least squares method is used to model the combined wavelengths of the optimal sub-intervals to realize the simultaneous detection of copper and nickel. The linear determination ranges are 0.3-3.0 mg/L for copper and nickel. the correlation coefficients of copper and nickel are 0.9974 and 0.9966, respectively. The results show that the method reduces the complexity of the wavelength variable screening process, improves the accuracy of the model, and lays the foundation for the accurate analysis of polymetallic ions in zinc smelting industrial wastewater.

A Nonlinear Integrated Modeling Method of Extended Kalman Filter Based on Adaboost Algorithm.

In the zinc hydrometallurgical purification process, the concentration ratio of zinc ion to trace nickel ion is as high as 105, so that the nickel spectral signal is completely covered by high concentration zinc signal, resulting in low sensitivity and nonlinear characteristics of nickel spectral signal. Aiming at the problem that it is difficult to detect nickel in zinc sulfate solution, this paper proposes a nonlinear integrated modeling method of extended Kalman filter based on Adaboost algorithm. First, a non-linear nickel model is established based on nickel standard solution. Second, an extended Kalman filter wavelength optimization method based on correlation coefficient is proposed to select wavelength variables with high signal sensitivity, large amount of information and strong nonlinear correlation. Finally, a nonlinear integrated modeling method based on Adaboost algorithm is proposed, which uses extended Kalman filter as a basic submodel, and realizes the stable detection of trace nickel through the weighted combination of multiple basic models. The results show that the average relative error of this method for detecting nickel is 4.56%, which achieves accurate detection of trace nickel in zinc sulfate solution.