T. Jerič, Darinka Brodnjak Vončina, Alenka Majcen Le Maréchal, Darja Kavšek
{"title":"Chemometric characterization of textile waste waters from different processes","authors":"T. Jerič, Darinka Brodnjak Vončina, Alenka Majcen Le Maréchal, Darja Kavšek","doi":"10.36547/nbc.1272","DOIUrl":null,"url":null,"abstract":"The aim of this work is focused on water quality classification of the textile waste water streams and evaluation of pollution. Data from the chemical characterization of the effluents were elaborated to identify a useful separation in potentially treatment for reuse. This was done with the aim of realizing a full scale characterization of effluents. In the two textile companies analyzed, machineries are used to carry out different production processes such as sizing and desizing, weaving, scouring, bleaching, mercerizing, carbonizing, fulling, dying and finishing. Different process effluents from the same machinery were found to be very diverse in pollution level. 25 and 49 samples of textile waste waters from two different textile companies were analysed and physical chemical measurements were performed. The following physicochemical and chemical water quality parameters were controlled: absorbance measured at three different wavelengths, pH, conductivity, turbidity, total suspended solids, volatile suspended solids, chemical oxygen demand, metals content (Ba, Ca, Cu, Mn, K, Sr, Fe, Al, Na) and total nitrogen content. For \nhandling the results, basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, were performed. Different chemometric methods, namely, principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) were used to find hidden information about textile waste water quality.","PeriodicalId":19210,"journal":{"name":"Nova Biotechnologica et Chimica","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nova Biotechnologica et Chimica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36547/nbc.1272","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
The aim of this work is focused on water quality classification of the textile waste water streams and evaluation of pollution. Data from the chemical characterization of the effluents were elaborated to identify a useful separation in potentially treatment for reuse. This was done with the aim of realizing a full scale characterization of effluents. In the two textile companies analyzed, machineries are used to carry out different production processes such as sizing and desizing, weaving, scouring, bleaching, mercerizing, carbonizing, fulling, dying and finishing. Different process effluents from the same machinery were found to be very diverse in pollution level. 25 and 49 samples of textile waste waters from two different textile companies were analysed and physical chemical measurements were performed. The following physicochemical and chemical water quality parameters were controlled: absorbance measured at three different wavelengths, pH, conductivity, turbidity, total suspended solids, volatile suspended solids, chemical oxygen demand, metals content (Ba, Ca, Cu, Mn, K, Sr, Fe, Al, Na) and total nitrogen content. For
handling the results, basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, were performed. Different chemometric methods, namely, principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) were used to find hidden information about textile waste water quality.