AC and DC study of the temperature effect on mild steel corrosion in acid media in the presence of benzimidazole derivatives

Adsorption and inhibitive properties of some new Mannich bases of Isatin derivatives on corrosion of mild steel in acidic media

Thermodynamic, electrochemical and quantum chemical investigation of some Schiff bases as corrosion inhibitors for mild steel in hydrochloric acid solutions

Experimental and theoretical study for corrosion inhibition of mild steel in normal hydrochloric acid solution by some new macrocyclic polyether compounds

Experimental and theoretical investigation of the adsorption behaviour of new triazole derivatives as inhibitors for mild steel corrosion in acid media

Inhibitor adsorption is specific and leads to decreased surface heterogeneity; this is demonstrated in tests with the acetylenic alcohol 1-octyn-3-ol. Finally, corrosion of steel in 28% HCl (8.7N) is fundamentally different than in 15% HCl, and the inhibition mechanisms in the two media differ as well

Time‐Resolved Impedance Spectroscopy of Mild Steel in Concentrated Hydrochloric Acid

The biggest problem facing air protection in Poland is the high levels of suspended particular matter concentrations. Air monitoring reports show that air quality standards, related to PM10 and PM2.5 concentrations, are exceeded every year in many Polish cities. The PM2.5 aerosol fraction is particularly dangerous to human and animal health. Therefore, monitoring the level of PM2.5 concentration should be considered particularly important. Unfortunately, most monitoring stations in Poland do not measure this dust fraction. However, almost all stations are equipped with analyzers measuring PM10 concentrations. PM2.5 is a fine fraction of PM10, and there is a strong correlation between the concentrations of these two types of suspended dust. This relationship can be used to determine the concentration of PM2.5. The main purpose of this analysis was to assess the accuracy of PM2.5 concentration prediction using PM10 concentrations. The analysis was carried out on the basis of long-term hourly data recorded at several monitoring stations in Poland. Artificial neural networks in the form of a multilayer perceptron were used to model PM2.5 concentrations.

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The Use of Multilayer Perceptrons to Model PM2.5 Concentrations at Air Monitoring Stations in Poland

Air quality is assessed on the basis of air monitoring data. Monitoring data are often not complete enough to carry out an air quality assessment. To fill the measurement gaps, predictive models can be used, which enable the approximation of missing data. Prediction models use historical data and relationships between measured variables, including air pollutant concentrations and meteorological factors. The known predictive air quality models are not accurate, so it is important to look for models that give a lower approximation error. The use of artificial neural networks reduces the prediction error compared to classical regression methods. In previous studies, a single regression model over the entire concentration range was used to approximate the concentrations of a selected pollutant. In this study, it was assumed that not a single model, but a group of models, could be used for the prediction. In this approach, each model from the group was dedicated to a different sub-range of the concentration of the modeled pollutant. The aim of the analysis was to check whether this approach would improve the quality of modeling. A long-term data set recorded at two air monitoring stations in Poland was used in the examination. Hourly data of basic air pollutants and meteorological parameters were used to create predictive regression models. The prediction errors for the sub-range models were compared with the corresponding errors calculated for one full-range regression model. It was found that the application of sub-range models reduced the modeling error of basic air pollutants.

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R. J. Jasinski

Papers

Time‐Resolved Impedance Spectroscopy of Mild Steel in Concentrated Hydrochloric Acid

The Use of Multilayer Perceptrons to Model PM2.5 Concentrations at Air Monitoring Stations in Poland

Air Quality Modeling with the Use of Regression Neural Networks