Doctoral Dissertations

Keywords and Phrases

Artificial Intelligence; Biodegradable; Flotation; Froth Stability; Response Surface Methodology; Sulfide Minerals


"Today's major challenges facing the flotation of sulfide minerals involve constant variability in the ore composition; environmental concerns; water scarcity and inefficient plant performance. The present work addresses these challenges faced by the flotation process of complex sulfide ore of Mississippi Valley type with an insight into the froth stability and the flotation performance. The first project in this study was aimed at finding the optimum conditions for the bulk flotation of galena (PbS) and chalcopyrite (CuFeS₂) through Response Surface Methodology (RSM). In the second project, an attempt was made to replace toxic sodium cyanide (NaCN) with the biodegradable chitosan polymer as pyrite depressant. To achieve an optimum flotation performance and froth stability, the third project utilized two types of nanoparticles; silica (SiO₂) and alumina (Al₂O₃) as process aids. The fourth project investigated the impact of water chemistry on the process outcomes in an attempt to replace fresh water with sea water. In the last project, five artificial intelligence (AI) and machine learning (ML) models were employed to model the flotation performance of the ore which will allow the building of intelligent systems that can be used to predict the process outcomes of polymetallic sulfides. It was concluded that chitosan can be successfully used as a biodegradable depressant. Alumina nanoparticles successfully enhanced both froth stability and flotation performance while silica nanoparticles did not. Seawater had a negative effect on both the froth stability and the grade of lead (Pb) and copper (Cu) but it improved the recoveries of both Pb and Cu minerals. Hybrid Neural Fuzzy Interference System (HyFIS) ML model showed the best accuracy to be adopted for automated sulfide ore flotation process in the future"--Abstract, page iii.


Alagha, Lana Z.

Committee Member(s)

Aouad, Nassib
Anderson, Neil L. (Neil Lennart), 1954-
Reidmeyer, Mary R.
Al-Dahhan, Muthanna H.


Mining Engineering

Degree Name

Ph. D. in Mining Engineering


Missouri University of Science and Technology

Publication Date

Summer 2018


xxii, 226 pages

Note about bibliography

Includes bibliographic references (pages 209-225).


© 2018 Muhammad Badar Hayat, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Thesis Number

T 11378

Electronic OCLC #