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Bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed-film bioreactor
Thesis (PhD)--University of Pretoria, 2017.
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University of Pretoria
2017
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| _version_ | 1867613602161098752 |
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| access_status_str | Open Access |
| author2 | Chirwa, Evans M.N. |
| author_browse | Chirwa, Evans M.N. |
| author_facet | Chirwa, Evans M.N. |
| collection | Thesis |
| dc_rights_str_mv | © 2017 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Thesis (PhD)--University of Pretoria, 2017. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/62789 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:38:45.051Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/62789 Bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed-film bioreactor Chirwa, Evans M.N. u24018164@tuks.co.za Rava, Eleonora Maria Elizabeth Artificial neural network (ANN) Chemical oxygen demand (COD) Microbial diversity Next generation sequencing (NGS) UCTD Engineering, built environment and information technology theses SDG-06 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-12 Thesis (PhD)--University of Pretoria, 2017. Coal gasification stripped gas liquor (CGSGL) wastewater contains large quantities of complex organic and inorganic pollutants which include phenols, ammonia, hydantoins, furans, indoles, pyridines, phthalates and other monocyclic and polycyclic nitrogen-containing aromatics, as well as oxygen- and sulphur-containing heterocyclic compounds. The performance of most conventional aerobic systems for CGSGL wastewater is inadequate in reducing pollutants contributing to chemical oxygen demand (COD), phenols and ammonia due to the presence of toxic and inhibitory organic compounds. There is an ever-increasing scarcity of freshwater in South Africa, thus reclamation of wastewater for recycling is growing rapidly and the demand for higher effluent quality before being discharged or reused is also increasing. The selection of hybrid fixed-film bioreactor (HFFBR) systems in the detoxification of a complex mixture of compounds such as those found in CGSGL has not been investigated. Thus, the objective of this study was to investigate the detoxification of the CGSGL in a H-FFBR bioaugmented with a mixed-culture inoculum containing Pseudomonas putida, Pseudomonas plecoglossicida, Rhodococcus erythropolis, Rhodococcus qingshengii, Enterobacter cloacae, Enterobacter asburiae strains of bacteria, as well as the seaweed (Silvetia siliquosa) and diatoms. The results indicated a 45% and 79% reduction in COD and phenols, respectively, without bioaugmentation. The reduction in COD increased by 8% with inoculum PA1, 13% with inoculum PA2 and 7% with inoculum PA3. Inoculum PA1 was a blend of Pseudomonas, Enterobacter and Rhodococcus strains, inoculum PA2 was a blend of Pseudomonas putida iistrains and inoculum PA3 was a blend of Pseudomonas putida and Pseudomonas plecoglossicida strains. The results also indicated that a 70% carrier fill formed a dense biofilm, a 50% carrier fill formed a rippling biofilm and a 30% carrier fill formed a porous biofilm. The autotrophic nitrifying bacteria were out-competed by the heterotrophic bacteria of the genera Thauera, Pseudaminobacter, Pseudomonas and Diaphorobacter. Metagenomic sequencing data also indicated significant dissimilarities between the biofilm, suspended biomass, effluent and feed microbial populations. A large population (20% to 30%) of unclassified bacteria were also present, indicating the presence of novel bacteria that may play an important role in the treatment of the CGSGL wastewater. The artificial neural network (ANN) model developed in this study is a novel virtual tool for the prediction of COD and phenol removal from CGSGL wastewater treated in a bioaugmented H-FFBR. Knowledge extraction from the trained ANN model showed that significant nonlinearities exist between the H-FFBR operational parameters and the removal of COD and phenol. The predictive model thus increases knowledge of the process inputs and outputs and thus facilitates process control and optimisation to meet more stringent effluent discharge requirements. mi2026 Chemical Engineering PhD Unrestricted SDG-06: Clean water and sanitation SDG-09: Industry, innovation and infrastructure SDG-12: Responsible consumption and production 2017-10-13T13:41:22Z 2017-10-13T13:41:22Z 2017-09-08 2017 Thesis Rava, EME 2017, Bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed-film bioreactor, PhD Thesis, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/62789> S2017 http://hdl.handle.net/2263/62789 © 2017 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Artificial neural network (ANN) Chemical oxygen demand (COD) Microbial diversity Next generation sequencing (NGS) UCTD Engineering, built environment and information technology theses SDG-06 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-12 Bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed-film bioreactor |
| title | Bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed-film bioreactor |
| title_full | Bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed-film bioreactor |
| title_fullStr | Bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed-film bioreactor |
| title_full_unstemmed | Bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed-film bioreactor |
| title_short | Bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed-film bioreactor |
| title_sort | bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed film bioreactor |
| topic | Artificial neural network (ANN) Chemical oxygen demand (COD) Microbial diversity Next generation sequencing (NGS) UCTD Engineering, built environment and information technology theses SDG-06 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-12 |
| url | http://hdl.handle.net/2263/62789 |