Doctoral Dissertations

Keywords and Phrases

Chitosan-grafted-Polyacrylamide; Froth flotation; Machine learning; Organic polymers; Process optimization; Pyrite depressants


“In this work, Chitosan-grafted-Polyacrylamides (Chi-g-PAMs) were studied, for the first time, as selective depressants of pyrite in the flotation of base metal sulfides. Fundamental studies of the adsorption behavior of Chi-g-PAM on model sulfide minerals indicated that Chi-g-PAM was more selective to pyrite’s surfaces as compared to base metal sulfides. Results suggested that the adsorption of Chi-g-PAM at pyrite-water interface was a chemisorption in nature which involved the amine, amide, and hydroxyl groups of Chi-g-PAM. Batch flotation studies of complex sulfide ore of Mississippi Valley Type (MVT) showed that Chi-g-PAM outperformed other pyrite’s depressants at producing less pyrite-diluted concentrates. Statistical optimization of the flotation process of MVT ore in relation to the structural characteristics of Chi-g-PAM (i.e., degree of deacetylation, weight ratio of chitosan: acrylamide, and dosage) showed that concentrates with least iron grade (3.06%) and recovery (15.33%) could be produced at 85g/t of Chi-g-PAM synthesized from chitosan of 95% degree of deacetylation and 1:4.5 chitosan: acrylamide weight ratio.

Moreover, two machine learning models including Artificial Neural Networks (ANN) and Random Forest (RF) were developed for the prediction of the flotation behavior of sulfide minerals from MVT ore in relation to Chi-g-PAM’s and pulp’s characteristics. Results suggested that the RF model is a reliable tool for the prediction of the flotation efficiency in polymetallic sulfide systems.

This research contributes to the advanced efforts to develop “smart and sustainable” processes for the flotation of sulfide minerals in industrial plants”--Abstract, page iii.


Alagha, Lana Z.

Committee Member(s)

Kumar, Aditya
Schuman, Thomas P.
Okoronkwo, Monday Uchenna
Castano Giraldo, Carlos Henry


Mining Engineering

Degree Name

Ph. D. in Mining Engineering


Missouri University of Science and Technology

Publication Date

Spring 2022


xviii, 214 pages

Note about bibliography

Includes bibliographic references (pages 191-213).


© 2022 Keitumetse Cathrine Monyake, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Thesis Number

T 12123