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Statistical analysis of two important chemical features (TDS and (HCO3-) content) in Sudetic therapeutic waters

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Kozłowski J.

Annales Societatis Geologorum Poloniae

1998

tom Vol. 68, No. 4

287--294

Two important chemical features of therapeutic waters from 57 intakes in 13 Sudetic health resorts, i.e. total dissolved solids (TDS) and bicarbonate ion (HCO3-) content, tested with the Kolmogorov test at the significance level α=0.05, during the whole exploitation period, reveal normal distributions. It bears evidence of little variability of these features. Intervals of the most frequent (normal) [HCO3-] content values in individual intakes, determined using the three standard deviations method (3σ) are in most cases concordant with real values. These intervals can be used for verification of monitoring measurements (elimination of incorrect determinations) and/or can indicate sudden changes of chemical composition of exploited therapeutic waters.

Dwie podstawowe własności wód leczniczych w 57 ujęciach z 13 miejscowości uzdrowiskowych Sudetów: suma substancji rozpuszczonych (TDS) i zawartość jonu wodorowęglanowego (HCO3-), przetestowane testem Kołmogorowa na poziomie istotności α=0.05 na przestrzeni całego okresu eksploatacji, wykazują rozkłady normalne, co świadczy o ich dużej stałości. Wyznaczone metodą trzech odchyleń standardowych (3σ) przedziały wartości najczęściej występujących dla zawartości (HCO3-) w poszczególnych ujęciach pokrywają się zwykle z wartościami rzeczywistymi. Uzyskane przedzialy normalnych zawartości anionu wodorowęglanowego mogą być wykorzystywane do weryfikacji prowadzonych obserwacji stacjonarnych (eliminowanie błędnych oznaczeń) i/lub sygnalizacji gwał- townych zmian skladu chemicznego eksploatowanej wody leczniczej.

Fundamentals of a recommendation system for the aluminum extrusion process based on data-driven modeling

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Perzyk M. , Kochański A. , Kozłowski J.

Computer Methods in Materials Science

2022

tom Vol. 22, No. 4

173--188

The aluminum profile extrusion process is briefly characterized in the paper, together with the presentation of historical, automatically recorded data. The initial selection of the important, widely understood, process parameters was made using statistical methods such as correlation analysis for continuous and categorical (discrete) variables and ‘inverse’ ANOVA and Kruskal–Wallis methods. These selected process variables were used as inputs for MLP-type neural models with two main product defects as the numerical outputs with values 0 and 1. A multi-variant development program was applied for the neural networks and the best neural models were utilized for finding the characteristic influence of the process parameters on the product quality. The final result of the research is the basis of a recommendation system for the significant process parameters that uses a combination of information from previous cases and neural models.

Machine learning methods for diagnosing the causes of die-casting defects

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Okuniewska A. , Perzyk M. , Kozłowski J.

Computer Methods in Materials Science

2023

tom Vol. 23, No. 2

45--56

The research was focused on analyzing the causes of high-pressure die-casting defects, more specifically on casting leakage, which is considered perhaps the most important and common defect. The real data used for modelling was obtained from a high-pressure die-casting foundry that manufactures aluminum cylinder blocks for the world’s leading automotive brands. This paper compares and summarizes the results of applying advanced modelling using artificial neural networks, regression trees, and support vector machines methods to select artificial neural networks as the most effective method to perform a multidimensional optimization of process parameters to diagnose the causes of die-casting defects and to indicate the future research scope in this area. The developed system enables the prediction of the level of defects in castings with satisfactory accuracy and is therefore a highly relevant reference for process engineers of high-pressure foundries. This article indicates exactly which process parameters significantly influence the formation of a defect in a casting.