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undergraduate thesis
Algoritmi strojnog učenja u ekoinženjerstvu
Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology, 2021. urn:nbn:hr:149:814604
Viljevac, Matej
University of Zagreb
Faculty of Chemical Engineering and Technology

Institutional repository: Repository of Faculty of Chemical Engineering and Technology University of Zagreb

Cite this document

Viljevac, M. (2021). Algoritmi strojnog učenja u ekoinženjerstvu (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology. Retrieved from https://urn.nsk.hr/urn:nbn:hr:149:814604

Viljevac, Matej. "Algoritmi strojnog učenja u ekoinženjerstvu." Undergraduate thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, 2021. https://urn.nsk.hr/urn:nbn:hr:149:814604

Viljevac, Matej. "Algoritmi strojnog učenja u ekoinženjerstvu." Undergraduate thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, 2021. https://urn.nsk.hr/urn:nbn:hr:149:814604

Viljevac, M. (2021). 'Algoritmi strojnog učenja u ekoinženjerstvu', Undergraduate thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, accessed 23 November 2024, https://urn.nsk.hr/urn:nbn:hr:149:814604

Viljevac M. Algoritmi strojnog učenja u ekoinženjerstvu [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology; 2021 [cited 2024 November 23] Available at: https://urn.nsk.hr/urn:nbn:hr:149:814604

M. Viljevac, "Algoritmi strojnog učenja u ekoinženjerstvu", Undergraduate thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:149:814604

Metadata
TitleAlgoritmi strojnog učenja u ekoinženjerstvu
Title (english)Machine learning algorithms in environmental engineering
AuthorMatej Viljevac
MentorMiroslav Jerković (mentor)
Committee memberMiroslav Jerković (predsjednik povjerenstva)
Committee memberMarija Vuković Domanovac (član povjerenstva)
Committee memberErna Begović Kovač (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Chemical Engineering and Technology
Zagreb
Defense date and country2021-09-22, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
Otpadne vode jedan su od glavnih problema kojima se bave ekoinženjeri. One su često kontaminirane raznim tvarima (opasnim ili neopasnim) nastalim iz različitih izvora, a najčešće ljudskim aktivnostima. Ispuštanje otpadnih voda koje prethodno nisu pročišćene, odnosno čiji parametri nisu zadovoljavajući, značajno doprinose onečišćenju okoliša. Pokazatelji kakvoće otpadnih voda kao što su biokemijska potrošnja kisika (BPK5), kemijska potrošnja kisika (KPK), ukupne suspendirane čestice, nitrati, nitriti, ukupni dušik, amonij i ukupne otopljene tvari daju mjeru glavnih onečišćujućih tvari, a određivanje tih parametara zahtijeva skupe postupke analize. Kako bi se smanjili troškovi, a zadovoljili kriteriji, mogu se koristiti algoritmi strojnog učenja koji će nam s određenom točnošću procijeniti vrijednost nepoznatih parametara. U ovome radu koristit će se programski jezik R koji je jedan od pogodnijih za implementaciju raznih algoritama. Cilj je ovoga istraživanja unaprijediti tehnologije u analizi otpadnih voda i smanjiti uporabu opasnih otapala koja se koriste u postupcima određivanja parametara kakvoće otpadnih voda, zamjenjujući ih približno točnim procjenama parametara na osnovi primjene algoritama strojnog učenja.
Abstract (english)
One of the main issues environmental engineers are dealing with are waste waters. They are often contaminated with different substances (hazardous and non hazardous), emerged from different sources, most frequently from people. Discharge of waste water that is not previously purified, in other words, whose parameters are not satisfactory, significantly contributes to environmental pollution. Indicators of the waste water quality such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended particles and total dissolved solids measure main pollutants and the determination of those parameters requires expensive anaylsis procedures. In order to reduce costs and satisfy criteria, it is possible to use machine learning algorithms that will, with certain accuracy, estimate the value of unknown parameters. A programming language R, which is one of the most adequate for implementation of different algorithms, will be used in this work. The aim of this research is to improve technologies in the waste water analysis and to reduce the usage of hazardous solvents that are used within procedures of determining parameters of waste water quality by replacing them with approximate assessments of parameters on the basis of machine learning algorithms.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:149:814604
Study programmeTitle: Environmental Engineering - Undergraduate study
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka ekoinženjerstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka ekoinženjerstva)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2023-04-26 09:14:17