Brown Marine Macroalgae as a Natural Cation Exchanger for Toxic
Metal Ions Separation and Recovery from Water
Thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Environmental Engineering, at the
Faculty of Engineering, University of Porto
Luciana Prazeres Mazur
Supervisor: Doutor Vítor Jorge Pais Vilar
Co-Supervisor: Doutor Rui Alfredo da Rocha Boaventura
Laboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials (LSRE-LCM)
Department of Chemical Engineering Faculty of Engineering
This work was financially supported by: Project POCI-01-0145-FEDER-006984 – Associate Laboratory LSRE-LCM funded by FEDER through COMPETE2020 – Programa Operacional Competitividade e Internacionalização (POCI), by national funds through FCT – Fundação para a Ciência e a Tecnologia, project PTDC/AAG-TEC/2685/2012 (ALGAEVALUE) and by CAPES Foundation, Ministry of Education of Brazil through scholarship BEX-1012/13-4.
FEUP - LSRE - LCM - Universidade do Porto © Luciana Prazeres Mazur, 2013-2017
Acknowledgments
Over the past four years of dedication to this doctoral program I have experienced extreme enriching, both personal and professional, and I went beyond several barriers, which was only possible thanks to the support of certain people, which I would like to express my sincere thanks.
First of all, I would like to deeply acknowledge my supervisors, Dr. Vítor Vilar and Dr. Rui Boaventura, for giving me the opportunity to come to the Associate Laboratory LSRE-LCM and continue my professional formation in environmental engineering. To Dr. Vítor Vilar, I would like to specially thank for the knowledge that he transmitted to me and for the rigor and demand for quality work, he was (and is) a true mentor who helped me immensely in these years and with whom I learned important aspects that I am sure will be useful for me in the future. Next, I would like to express my thanks to my co-supervisor, Dr. Rui Boaventura for providing me the necessary resources for carrying out the work and all support. I am sure that without their help and valuable knowledge it would not have been possible to meet all the objectives proposed for the development of this thesis.
A mention must be made to the following institutions that supported this work: the foundation Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for the doctoral grant; the Associate Laboratory LSRE-LCM (Separation and Reaction Engineering - Catalysis and Materials); the Faculty of Engineering of the University of Porto (FEUP).
I also would like to thank the members of the examining board, for accepting to evaluate this work, thus contributing with their suggestions to the improvement of the thesis.
I am grateful to Eng. Sérgio from the company where the zinc containing wastewater was collected, for his willingness and patience.
I am also very grateful to Dr. Tatiana Pozdniakova for giving me a helping hand when I got in the laboratory, for discussing ideas with me in the first years and partnership. To MSc. Diego Mayer and Dr. Selene de Souza (Federal University of Santa Catarina - UFSC), I would like to thank for helping me in mathematical modelling, and MSc. Luís Carlos Matos (FEUP) for some analyses.
I must thank everyone else at laboratory E404 who somehow helped me, contributing for the accomplishment of my work, shared with me many everyday moments, lab routines, coffees and lunch for the past four years. Especially those who were not only my lab mates but have also become friends, helping me on my bad days, sharing happy moments, giving me encouragement and support. A big thank goes to Maria Alice Cechinel for friendship and for all enriching discussions. A special thanks goes to Belisa Marinho, who became a true friend and was always present and gave me tireless support during these four years away from home.
and Leoni Szostak Prazeres who spared no efforts to invest in my education, for having always encouraged me even if distance has cost them a lot. To my sister, Ana Lúcia Prazeres Sousa, I thank you for being present when I had to be absent and for all help with the elaboration of the images that I used in this thesis.
Finally, to my husband, Paulo Eduardo Mazur, I would like to express my sincere gratitude for his love, for having believed and shared my dream of doing a Ph.D. outside of my country, who ever since being a constant source of support and encouragement during this journey. I am truly grateful to have you in my life.
The highest reward for man's toil is not what he gets for it, but what he becomes by it. John Ruskin
Abstract
The discharge of inadequately treated or untreated industrial wastewaters has greatly contributed to the release of contaminants into the environment, including toxic metals. Toxic metals are persistent and bioaccumulative, being of fundamental importance their removal from wastewaters prior to release into water bodies. The use of marine macroalgae as natural cation exchangers for toxic metal ions removal from aqueous pure solutions under laboratory conditions has been widely studied in the last years and has demonstrated to be an economic and eco-friendly technique, even when applied to diluted solutions. However, the composition of real industrial wastewaters is much more complex than pure metal solutions, which can compromise the biosorption performance. In this context, for industrial applications it is necessary to optimise the biosorption process using real and complex matrices.
The design and optimisation of the biosorption process was developed according to the following steps: i) characterisation of the biosorbents, ii) determination of the equilibrium relationships, iii) determination of the biosorption kinetics, iv) determination of the breakthrough curves in a fixed-bed column, v) determination of the best eluant and regenerant and respective concentrations, vi) scale-up and cost analysis and vii) prediction of the biosorption capacity of raw algae for toxic metal removal in batch and continuous mode. For this purpose, the current thesis was essentially divided into two major stages namely: i) study of the viability of different brown marine macroalgae – Ascophyllum nodosum, Fucus spiralis, Laminaria hyperborea and Pelvetia canaliculata – as biosorbents for the treatment of zinc containing wastewaters generated in the galvanising process and ii) prediction of the biosorption capacity of the raw brown marine macroalgae A. nodosum for copper ions in batch and continuous mode using an ion exchange model including all the ionic species present in liquid and solid phase and considering two types of functional groups responsible for the ionic species binding.
The biosorption capacity of the four brown macroalgae harvested from the north coast of Portugal, A. nodosum, F. spiralis, L. hyperborea and P. canaliculata was similar (2.2-2.4 mEq/g), mainly associated with the presence of weak acidic carboxylic and strong acidic sulphonic groups.
The zinc containing wastewater was characterised by a high conductivity, a low organic content and a zinc concentration ranging from 9 to 22 mg/L. The speciation diagrams obtained taking into account all the inorganic species present in the wastewater matrix showed a molar fraction for Zn2+ species near 80%, ZnCl+ species near 2% and ZnSO4 species corresponding to a molar fraction near 18%.
Zinc uptake on all the biosorbents, at equilibrium, was well described by a linear relationship. Biosorption kinetics was adequately represented by a mass transfer model, considering a linear driving force model for intraparticle diffusion. The mainly identified mechanism of biosorption process by
alkaline earth metals naturally bound to the functional groups on the biomass surface. Therefore the negatively charged carboxylic groups from alginate and sulphonic groups from fucoidan present on the biomass surface act as weakly acidic cation and strongly acidic cation exchangers, respectively.
Among the four macroalgae studied, L. hyperborea showed a higher selectivity for zinc ions present in the galvanisation wastewater, resulting in higher zinc uptake capacities in a lower contact time, being thus chosen as ion exchanger for the next studies in both batch and continuous mode.
The ion exchange capacity for zinc ions is highly affected by the galvanisation wastewater matrix when compared with the performance obtained using a pure zinc solution. Na-loaded L. hyperborea showed a higher affinity for zinc ions when compared with Ca-loaded and raw L. hyperborea. Solutions of HCl, HNO3, H2SO4 and CaCl2 were tested as eluants for zinc displacement from exhausted algae. The batch elution kinetic tests showed that after 20 min, Zn elution was 100% efficient using HCl, HNO3 and H2SO4 at a concentration of 0.1 M. However, HNO3 reached an elution efficiency of 100% in less time than the other acidic eluants tested. After the acid elution, the natural cation exchanger was converted to Na- or Ca-form, using NaCl and CaCl2 solutions. Although the regenerated algae in the form of sodium showed a higher affinity for zinc ions, its physical deterioration after Na loading may affect its application in a full-scale treatment.
From all operational condition tested for the exhaustion step in a fixed-bed column packed with raw L. hyperborea (bed height of 17 and 27 cm, flow rate ranging from 4.5 to 18.2 BV/h, and particle equivalent diameters of 0.8 and 2.0 mm), the highest useful capacity (7.1 mg Zn/g) was obtained for D/dp = 31, L/D =11, 9.1 BV/h, τ = 6.4 min with a service capacity of 124 BV (endpoint of 2 mg Zn/L).
Elution efficiency using 5 BV of HCl and HNO3 0.1 M as eluants was 20 and 26% (71 and 100% after 45 BV) respectively, at a specific flow rate of 6.1 BV/h. These results revealed that the use of HCl needs more time than HNO3 to achieve identical elution efficiency. Nevertheless, in real applications for process safety, the use of nitric acid is not recommended, thus, HCl was chosen for elution of zinc from L. hyperborea in a fixed-bed column. Elution was faster and near to 100% effective using 10 BV of HCl 1.0 M for flow rates higher than 4.5 BV/h. Calcium chloride solution (0.1 M) was selected as the best regenerant, allowing the reuse of the natural cation exchanger for more than 3 continuous exhaustion/elution/regeneration cycles.
A mass transfer model, including the mass balance to the packed bed column and thin plate algal particle, and complementary equations, fitted well the experimental data. The best operation conditions were scaled-up and tested in a pre-pilot plant prototype. The scale-up design, using short-cut techniques, of the cation exchange process was proposed for the treatment of 2.4 m3/day of the galvanising wastewater,
Since a large amount of eluant and regenerant was necessary to promote the use of the natural cation exchanger in the next exhaustion cycle, the elution and regeneration steps were performed in co-flow and counter-flow direction by using 1.0 M HCl (3.0%) and 0.1 M CaCl2 (pH = 8.0) as eluant and regenerant, respectively. These experiments were carried out in a fixed-bed column packed with the natural cation exchanger A. nodosum loaded with copper ions. Copper was chosen due to its higher affinity for the natural cation exchanger than zinc ions, i.e. are more difficult to be eluted. The algae A. nodosum was chosen because has the same ion exchange capacity as L. hyperborea but did not present very different amounts of light metals in its composition, i.e. it did not present an exaggerated selectivity for calcium ions (making the regeneration step more difficult). 90% of elution using the counter-flow direction was obtained in 22 min spending 97 g HCl/L cation exchanger, whereas the elution at co-flow direction took 28 min and spent 164 g HCl/L cation exchanger. The regeneration step after elution with 1.0 M HCl in co-flow direction needed more volume/time to reach a solution pH of 4.0 (t/τ = 190; 84 BV) than in counter-flow direction (t/τ = 173; 77 BV).
After the complete optimisation of the exhaustion, elution and regeneration cycles, the ion exchange modelling was performed. For this purpose, the cation exchanger properties of raw and different ionic forms of A. nodosum for copper separation from aqueous solutions were studied in batch and continuous mode. The total amount of light metals present on the surface of raw and modified biomass was 2.4 mEq/g, proving that the conversion of seaweeds to different ionic forms consists in an ion exchange process. Carboxylic (≈1.3 mEq/g) and sulphonic (≈1.1 mEq/g) groups were identified as the main functional groups responsible for cations binding.
The mechanistic model based on the mass action law was successfully developed in order to predict the ion exchange equilibrium in batch mode. The model took into account the complex composition of raw algae, the competitive binding of metals and protons and the presence of two functional groups responsible by the overall metal binding. The selectivity series obtained by this model were in the order H+ > Cu2+ >> Ca2+ > Mg2+ > K+ > Na+ for carboxylic groups and Cu2+ >> Ca2+ > Mg2+ > K+ > Na+ > H+ for sulphonic groups.
In the fixed-bed column, for four cycles of exhaustion/elution/regeneration, the operating capacity ranged between 0.6 – 0.8 mEq Cu2+/g, treating 27 – 33 L of influent until the breakthrough point of 0.02 mEq Cu2+/L, corresponding to a service capacity of 301 – 367 BV. Higher elution efficiency was observed for 3.0% HCl in counter-flow mode and no biomass damage was observed after four elution cycles. The regeneration step was successfully performed with CaCl2 0.1 M at pH = 8.0 making possible the reuse of the biomass in multiple cycles.
A phenomenological model based on the mass transfer resistance, considering equilibrium given by the mass action law, and a linear driving force model for intraparticle diffusion, was able to predict well the ion exchange process using the raw and ionic forms of algae A. nodosum in batch and continuous systems for all cationic species in liquid and solid phase.
Resumo
A descarga de águas residuais industriais inadequadamente tratadas ou não tratadas contribuiu fortemente para a libertação de contaminantes para o ambiente, incluindo os metais tóxicos. Os metais tóxicos são substâncias persistentes e bioacumuláveis, sendo de fundamental importância a sua remoção das águas residuais antes da libertação no meio hídrico. O uso de macroalgas marinhas como permutadores catiónicos naturais para remoção de iões metálicos tóxicos de soluções aquosas puras à escala laboratorial tem sido amplamente estudado nos últimos anos e tem-se mostrado uma técnica económica e amiga do ambiente, mesmo quando aplicada a soluções muito diluídas. No entanto, a composição de águas residuais industriais reais é muito mais complexa do que as soluções de metal puro, o que pode comprometer o desempenho da biossorção. Neste contexto, é necessário otimizar o processo de biossorção usando matrizes reais e complexas visando a aplicação em escala industrial.
A conceção e otimização do processo de biossorção foram desenvolvidas de acordo com as seguintes etapas: i) caracterização dos biossorventes, ii) determinação das relações de equilíbrio, iii) determinação de cinéticas de biossorção, iv) determinação das curvas de rutura em coluna de escoamento contínuo, v) determinação do melhor eluente e regenerante e respetivas concentrações, vi) dimensionamento do processo e análise de custos e vii) avaliação da capacidade de biossorção de algas brutas para remoção de metais tóxicos em modo descontínuo e contínuo. Desta forma, a presente tese foi essencialmente dividida em duas etapas principais: i) estudar a viabilidade de diferentes macroalgas marinhas – Ascophyllum nodosum, Fucus spiralis, Laminaria hyperborea and Pelvetia canaliculata – como biossorventes para o tratamento de águas residuais carregadas com zinco geradas no processo de galvanização e ii) prever a capacidade de biossorção da macroalga marinha A. nodosum bruta para iões metálicos tóxicos em modo descontínuo e contínuo usando um modelo de permuta iónica incluindo todas as espécies iónicas presentes em fase líquida e sólida e considerando dois tipos de grupos funcionais responsáveis pela ligação das espécies iónicas.
A capacidade de biossorção das quatro macroalgas castanhas colhidas na costa norte de Portugal, A. nodosum, F. spiralis, L. hyperborea e P. canaliculata foi semelhante (2,2 - 2,4 mEq/g), principalmente associada à presença de grupos carboxílicos fracamente ácidos e grupos sulfónicos fortemente ácidos.
As águas residuais contendo zinco foram devidamente caracterizadas apresentando uma alta condutividade, um baixo conteúdo orgânico e uma concentração de zinco variando entre 9 e 22 mg/L. Os diagramas de especiação obtidos tendo em consideração todas as espécies inorgânicas presente nas águas residuais mostraram uma fração molar para espécies Zn2+ de aproximadamente 80%, para espécies
ZnCl+ de aproximadamente 2% e o ZnSO4 correspondeu a uma fração molar de aproximadamente de 18%.
O equilíbrio de biossorção de zinco, para todos os biossorventes, foi bem descrito por uma relação linear. A cinética de biossorção foi adequadamente representada por um modelo de transferência de massa, considerando o modelo de força diretriz linear para a difusão intraparticular. A permuta iónica foi o principal mecanismo responsável pela capacidade de biossorção das macroalgas marinhas, na qual os iões de zinco foram substituídos pelos metais alcalinos e alcalinos-terrosos naturalmente ligados aos grupos funcionais presentes na superfície da biomassa. Por conseguinte, os grupos carboxílicos carregados negativamente do alginato e grupos sulfónicos do fucoidan presentes na superfície da biomassa atuam como permutadores catiónicos fracamente e fortemente ácidos, respetivamente.
Entre as quatro macroalgas estudadas, a alga L. hyperborea apresentou uma maior seletividade para iões zinco presentes nas águas residuais de galvanização, resultando em maiores capacidades de biossorção de zinco em menor tempo de contato, sendo assim escolhido como trocador de iões para os próximos estudos em sistema fechado e contínuo.
A capacidade de permuta iónica para iões de zinco é fortemente afetada pela matriz de águas residuais de galvanização quando comparada com o desempenho usando uma solução pura de zinco. A alga L. hyperborea saturada com sódio apresentou uma maior afinidade pelos iões de zinco do que a alga L. hyperborea bruta e saturada com cálcio. Soluções de HCl, HNO3, H2SO4 e CaCl2 foram testadas como eluentes para a remoção dos iões zinco da superfície da alga. Os ensaios cinéticos de eluição em sistema fechado mostraram que após 20 min a eficiência de eluição de Zn era de 100% utilizando HCl, HNO3 e H2SO4 a uma concentração de 0,1 M. No entanto, usando HNO3 a eficiência de eluição atingiu 100% num menor tempo de contato do que os outros eluentes ácidos testados. Após a eluição ácida, o permutador natural de catiões foi convertido na forma de Na ou Ca utilizando soluções de NaCl e CaCl2, respetivamente. Embora as algas regeneradas sob a forma de sódio tenham uma maior afinidade pelos iões de zinco, a deterioração física observada pode afetar a sua aplicação em grande escala.
De todas as condições operacionais testadas para a etapa de exaustão numa coluna de leito fixo empacotada com L. hyperborea bruta (altura do leito de 17 e 27 cm, caudal de 4,5 a 18,2 BV/h e diâmetros equivalentes de 0,8 e 2,0 mm), a capacidade útil mais elevada (7,1 mg de Zn/g) foi obtida utilizando as seguintes condições: D/dp = 31, L/D = 11, 9,1 BV/h, τ = 6,4 min com uma capacidade de serviço de 124 BV (ponto de rutura de 2 mg de Zn/L).
A eficiência de eluição utilizando 5 BV de HCl e HNO3 0,1 M como eluentes foi de 20 e 26% (71 e 100% após 45 BV), respetivamente, a um caudal específico de 6,1 BV/h. Estes resultados revelaram que
entanto, em aplicações reais para a segurança do processo, o uso de ácido nítrico não é recomendado. Desta forma, HCl foi escolhido para a eluição de zinco das algas L. hyperborea numa coluna de leito fixo. Uma etapa de eluição mais rápida e 100% eficaz foi obtida utilizando 10 BV de HCl 1,0 M, para caudais superiores a 4,5 BV/h. A solução de cloreto de cálcio (0,1 M) foi selecionada como o melhor regenerante, permitindo a reutilização do permutador natural de catiões durante mais de 4 ciclos contínuos de exaustão/eluição/regeneração.
Um modelo de transferência de massa, incluindo o balanço de massa à coluna de leito fixo e as partículas de algas como placas finas, e suas equações complementares, ajustou bem os dados experimentais. A melhor condição de operação foi testada num protótipo à escala pré-piloto. O dimensionamento, utilizando técnicas de atalho, do processo de permuta catiónica foi proposto para o tratamento de 2,4 m3/dia das águas residuais de galvanização, resultando em um custo estimado de 2,44 €/m3, considerando 10 ciclos consecutivos de operação.
Devido ao facto de ser necessária uma grande quantidade de eluente e regenerante para promover a utilização do permutador natural de catiões no próximo ciclo de exaustão, as etapas de eluição e regeneração foram realizadas em co-fluxo e contra-fluxo, utilizando 1,0 M HCl (3,0%) e 0,1 M CaCl2 (pH = 8,0) como eluente e regenerante, respetivamente. Estas experiências foram realizadas numa coluna de leito fixo empacotada com o permutador catiónico natural A. nodosum carregado com iões cobre. O cobre foi escolhido pela sua maior afinidade para os grupos funcionais da biomassa do que os iões zinco, isto é, são mais difíceis de serem eluídos. As algas A. nodosum foram escolhidas por apresentarem a mesma capacidade de permuta iónica que as algas L. hyperborea, mas não apresentarem quantidades muito diferentes de metais leves na sua composição, ou seja, não apresentarem uma seletividade exagerada pelos iões de cálcio (tornando mais difícil o passo de regeneração). Obteve-se 90% de eluição em contra-fluxo levando 22 min e gastando 97 g HCl/L de permutador catiónico, enquanto que a eluição em co-fluxo levou 28 min e gastou 164 g HCl/L de permutador catiónico. O passo de regeneração após eluição com HCl 1,0 M em co-fluxo necessitou de mais volume para atingir uma solução a pH de 4,0 (t/τ = 190; 84 BV) do que em contra-fluxo (t/τ 173; 77 BV).
Após a completa otimização dos ciclos de exaustão, eluição e regeneração, procedeu-se à modelação do processo de permuta iónica. Para este efeito foram estudadas as propriedades do permutador de catiões A. nodosum, na sua forma bruta e em diferentes formas iónicas, na separação de cobre de soluções aquosas, em modo descontínuo e contínuo. A quantidade total de metais leves presentes na superfície da biomassa na sua forma bruta e modificada foi de 2,4 mEq/g, comprovando que a conversão de algas marinhas em diferentes formas iónicas se baseia num processo de permuta iónica. Grupos carboxílicos (≈1,3 mEq/g) e sulfónicos (≈1,1 mEq/g) foram identificados como os principais grupos funcionais
Um modelo mecanicista baseado na lei de ação de massa foi desenvolvido com sucesso para prever o equilíbrio de permuta iónica em sistema fechado. O modelo teve em conta a composição complexa das algas brutas, a ligação competitiva entre os iões metálicos e os protões e a presença de dois grupos funcionais responsáveis pela ligação das espécies catiónicas. As séries de seletividade obtidas por este modelo foram na ordem: H+ > Cu2+ >> Ca2+ > Mg2+ > K+ > Na+ para os grupos carboxílicos e Cu2+ >> Ca2+ > Mg2+ > K+ > Na+ > H+ para os grupos sulfónicos.
Na coluna de leito fixo, durante quatro ciclos de exaustão/eluição/regeneração, a capacidade operacional variou entre 0,6 - 0,8 mEq Cu2+/g, tratando 27 - 33 L de influente até o ponto de rutura de 0,02 mEq Cu2+/L, correspondente a uma capacidade de serviço de 301 - 367 BV. Foi observada uma maior eficiência de eluição usando HCl 3,0% em contra fluxo e não foi observado nenhum dano à biomassa após quatro ciclos de eluição. O passo de regeneração foi realizado com sucesso com CaCl2 0,1 M a pH = 8,0 tornando possível a reutilização da biomassa em múltiplos ciclos.
Um modelo fenomenológico de transferência de massa, considerando o equilíbrio dado pela lei de ação de massa, e o modelo de força diretriz linear para a difusão intraparticular, foi capaz de prever bem o processo de permuta iónica usando a alga A. nodosum bruta e em diferentes formas iónicas em sistemas descontínuo e contínuo para todas as espécies catiónicas na fase líquida e sólida.
Table of Contents
1. Introduction ... 1
1.1. Overview of toxic metals sources, chemistry and toxicity ... 3
1.1.1. Sources of toxic metal pollution ... 3
1.1.1.1. Metal finishing industries ... 7
1.1.2. Chemistry and behaviour of toxic metals in aqueous systems ... 11
1.1.3. Metals toxicity ... 14
1.2. Treatment techniques for toxic metals removal from wastewater ... 15
1.2.1. Chemical precipitation ... 17 1.2.2. Coagulation/flocculation ... 17 1.2.3. Membrane filtration ... 18 1.2.4. Electrochemical processes ... 18 1.2.5. Ion exchange ... 19 1.2.6. Adsorption ... 20 1.2.6.1. Biosorption ... 21
1.3. Biosorption by brown marine macroalgae ... 23
1.3.1. A comparison with other algae ... 23
1.3.2. Cell wall of brown marine macroalgae ... 24
1.3.3. Biosorption mechanism of brown marine macroalgae... 25
1.3.4. Design of the biosorption process at lab-scale ... 28
1.3.4.1. Biomass characterisation ... 28
1.3.4.1.1. Biomass digestion ... 29
1.3.4.1.2. Potentiometric titration ... 29
1.3.4.1.3. Fourier transform infrared spectroscopy analysis ... 30
1.3.4.1.4. Scanning electron microscopy analysis coupled to energy dispersive spectroscopy X-ray microanalysis ... 30
1.3.4.1.5. Esterification studies ... 31
1.3.4.2. Batch experiments ... 31
1.3.4.2.1. Equilibrium studies ... 33
1.3.4.2.2. Kinetic studies ... 36
1.3.4.2.3. Influence of operational parameters ... 37
1.3.4.3. Packed bed column experiments ... 39
1.3.4.3.1. Exhaustion cycle ... 39
1.3.4.3.1.1. Dynamic modelling of ion exchange in a fixed-bed column ... 47
1.3.4.3.1.1.1. Linear driving force (LDF) model... 47
1.3.4.3.1.1.2. Thomas model ... 49 1.3.4.3.1.1.3. Yan model ... 49 1.3.4.3.1.2. Design modes ... 50 1.3.4.3.1.2.1. Scale-up approach ... 50 1.3.4.3.1.2.2. Capacity at breakpoint ... 50 1.3.4.3.1.2.3. Thomas model ... 51 1.3.4.3.1.2.4. Yan model ... 51
1.3.4.3.2. Elution and regeneration cycles ... 51
1.3.5. Behaviour of brown marine macroalgae as natural cation exchangers in real wastewaters 59 1.4. Objectives ... 63
1.5. Thesis outline ... 64
1.6. References ... 66
2.1.1. Brown marine macroalgae ... 81 2.1.2. Chemicals ... 82 2.2. Experimental units ... 85 2.2.1. Batch system ... 85 2.2.2. Lab-scale continuous system ... 87 2.2.3. Pre-pilot scale continuous system ... 89 2.3. Analytical methods ... 93 2.3.1. Natural cation exchanger ... 94 2.3.1.1. Biomass preparation ... 94 2.3.1.2. Biomass ionic form ... 94 2.3.1.3. Biomass protonation... 95 2.3.1.4. Chemical treatment of functional groups present on the biomass surface ... 96 2.3.1.5. Biomass characterisation ... 96 2.3.2. Galvanic wastewater ... 101 2.3.2.1. Galvanic wastewater characterisation ... 102 2.3.3. Batch experiments ... 103 2.3.3.1. Cation exchange kinetic studies ... 103 2.3.3.2. Cation exchange equilibrium studies ... 104 2.3.3.3. Matrix influence on cation exchange performance for zinc removal ... 105 2.3.3.4. Zinc elution studies ... 107 2.3.3.5. Regeneration studies ... 107 2.3.4. Continuous experiments ... 107 2.3.4.1. Ion exchange breakthrough curves in the exhaustion cycle ... 107 2.3.4.2. Ion exchange breakthrough curves in the elution cycle ... 109 2.3.4.3. Ion exchange breakthrough curves in the regeneration cycle ... 110 2.3.4.4. Pre-pilot plant experiments ... 111 2.4. References ... 112 3. Brown macroalgae as natural cation exchangers for the treatment of zinc containing wastewaters generated in the galvanising process ... 113 3.1. Introduction ... 115 3.2. Materials and methods ... 116 3.3. Results and discussion... 116 3.3.1. Biomass characterisation ... 116 3.3.2. Galvanisation wastewater ... 128 3.3.2.1. Wastewater characteristics ... 128 3.3.3. Batch studies ... 129 3.3.3.1. Natural cation exchanger selection for zinc removal ... 129 3.3.3.2. Matrix influence on cation exchange performance for zinc removal ... 133 3.3.3.3. Cation exchange capacity of modified L. hyperborea ... 134 3.3.3.4. Eluant selection ... 137 3.3.3.5. Cation exchange capacity of modified L. hyperborea after the regeneration step ... 139 3.4. Conclusions ... 142 3.5. References ... 143 4. Design of a fixed-bed ion exchange process for the treatment of wastewater generated
in the galvanisation process using Laminaria hyperborea as natural cation exchanger ... 147 4.1. Introduction ... 149 4.2. Materials and methods ... 150 4.3. Results and discussion... 150
4.3.2.1. Exhaustion cycle ... 153 4.3.2.1.1. Effect of bed depth (L) ... 153 4.3.2.1.2. Effect of linear flow rate (u) ... 155 4.3.2.1.3. Effect of particle diameter (dp) ... 159 4.3.2.1.4. Effect of co-ions present in the wastewater ... 160 4.3.2.2. Elution cycle ... 162 4.3.2.2.1. Effect of eluant type ... 163 4.3.2.2.2. Effect of eluant concentration ... 164 4.3.2.2.3. Effect of elution flow rate ... 166 4.3.2.3. Regeneration cycle ... 168 4.3.3. Pre-pilot experiments ... 171 4.3.4. Ion exchange plant design and operation ... 175 4.3.4.1. Fixed-bed column considerations and sizing ... 175 4.3.4.2. General process and equipment for real application ... 177 4.3.5. Cost estimation ... 178 4.4. Conclusions ... 180 4.5. References ... 181 5. Cation exchange prediction model for copper binding onto raw brown marine macroalgae Ascophyllum nodosum: batch and fixed-bed studies ... 183 5.1. Introduction ... 185 5.2. Materials and methods ... 187 5.3. Results and discussion... 187 5.3.1. Characterisation of raw and ionic form algal biomass ... 187 5.3.2. Biomass characterisation and ion exchange equilibrium with algae after chemical
treatment ... 191 5.3.3. Ion exchange equilibrium using raw and ionic form biomass ... 194 5.3.4. Ion exchange kinetic studies using raw and ionic form biomass ... 212 5.3.5. Packed bed column studies ... 218 5.3.5.1. 1st cycle of exhaustion/elution/regeneration ... 218 5.3.5.2. Reuse of algae in consecutive exhaustion cycles ... 223 5.3.5.3. Effect of eluant concentration and flow direction on ion exchange elution and
regeneration ... 228 5.3.5.4. Physical/chemical behaviour of the natural ion exchanger after reuse ... 230 5.4. Conclusions ... 233 5.5. References ... 234 6. Final remarks ... 237 6.1. Conclusions ... 239 6.1.1. Biomass characterisation ... 239 6.1.2. Chemical treatment of functional groups present in the A. nodosum surface ... 240 6.1.3. Galvanising wastewater characterisation ... 240 6.1.4. Natural cation exchanger selection for zinc removal ... 240 6.1.5. Matrix influence on cation exchange performance for zinc removal ... 241 6.1.6. Exhaustion cycle ... 241 6.1.7. Elution cycle ... 241 6.1.8. Regeneration cycle ... 242 6.1.9. Reuse of algae in consecutive exhaustion cycles ... 243 6.1.10. Pre-pilot experiments, ion exchange plant design and cost estimation ... 243 6.1.11. Ion exchange modelling ... 244 6.2. Suggestions for future work ... 245
List of Figures
Figure 1.1. Sources of toxic metal pollution. Adapted from: FilterWater.com (2017) [9]. ... 4 Figure 1.2. Emissions of toxic metals in Europe. Based on the information provided in E-PRTR (2016) [10]. ... 6 Figure 1.3. Galvanising process flowsheet. The arrows turned down indicate the wastewater production source. Adapted from: NPi (1999) [15]. ... 8 Figure 1.4. Emissions of toxic metals in Europe by surface treatment of metals and plastics facilities. Based on the information provided in E-PRTR (2016) [10]. ... 10 Figure 1.5. Distribution of (a) cadmium, (b) hexavalent chromium, (c) copper, (d) nickel, (e) lead and (f) zinc species present in aqueous medium as a function of the solution pH ([Cd] = 56 mg/L, [Cr(VI)] = 26 mg/L; [Cu] = 32 mg/L; [Ni] = 29 mg/L; [Pb] = 104 mg/L; [Zn] = 33 mg/L; T = 25 °C; ionic strength = 0.1 M). ... 12 Figure 1.6. (a) Hypothetical model of the biochemical organization of cell walls of brown algae, and structures of the main polysaccharides typical of brown algae: (b) alginate and (c) fucoidan. Adapted from: Davis et al. (2003) [67] and Michel et al. (2010) [141]. ... 26 Figure 1.7. Schematic drawing of the egg-box model for ionotropic gelation of alginate. Adapted from: Grant et al. (1973) [150]. ... 28 Figure 1.8. Mass transfer by ion exchange in a stirred batch reactor. Adapted from Zagorodni (2007) [79]. ... 33 Figure 1.9. Division of an ion exchange column in three zones when the influent solution is moved through the column (a) upflow and (b) down flow: exhausted resin (a) bottom and (b) top, the zone of the ion exchange reaction (middle), and regenerated resin (a) top and (b) bottom. Adapted from Zagorodni (2007) [79]. ... 40 Figure 1.10. Evolution of the fixed-bed concentration front in the exhaustion operation cycle (a) real conditions (S-shaped profile), (b) ideal conditions (stoichiometric front) and (c) overlapping of the real and ideal concentration fronts. Adapted from Anderson (1977) [199]. ... 41 Figure 1.11. Schematic representation of a complete service cycle (exhaustion, elution, regeneration and rinse) of an ion exchange unit and their respective reactions. Adapted from Zagorodni (2007) [79]. ... 53 Figure 1.12. Schematic representation of ion exchange operation cycle: (a) counter-flow and (b) co-flow mode. ... 54 Figure 1.13. Evolution of the concentration front in the elution operation cycle in a fixed-bed (a) co-flow and (b) counter-flow mode. Adapted from Zagorodni (2007) [79] and [80] . ... 56 Figure 2.1. Photographs of the four brown algae used in this study: (a) Ascophyllum nodosum, (b) Fucus spiralis, (c) Laminaria hyperborea and (d) Pelvetia canaliculata. ... 81 Figure 2.2. Geographic location of brown algae harvesting place obtained from Google Earth (41°41'49"N, 8°51'06"W). ... 82 Figure 2.3. Set of Erlenmeyers flasks on an orbital shaker (VWR advanced digital system) inside a thermostatic cabinet (Lovibond). ... 85 Figure 2.4. Schematic representation of the experimental setup of ion exchange kinetic study in batch mode. ... 86 Figure 2.5. Schematic representation of the experimental setup of ion exchange equilibrium in batch mode. ... 86 Figure 2.6. Picture of the lab-scale ion exchange column. ... 88 Figure 2.7. Schematic representation of the experimental setup of the lab-scale ion exchange column. ... 88 Figure 2.8. Picture of the ion exchange pre-pilot plant. ... 89 Figure 2.9. Schemes of the (a) pre-pilot plant and the (b) program in LabView environment. ... 91 Figure 2.10 . Picture of the (a) AAS with flame (GBC, 932 Plus) and (b) burner. ... 93 Figure 2.11. Flowchart of the main steps of the raw biomass preparation. ... 95
(c) FTIR IRAffinity spectrometer (Shimadzu) with diffuse reflectance accessory for solid samples (Pike
Technologies Inc., TM EasiDiff) and (d) SEM/EDS (Quanta 400FEG ESEM/EDAX Genesis X4M). ... 98
Figure 2.13. Picture of algae raw A. nodosum after vigorous milling in coffee grinder. ... 99 Figure 2.14. Flow diagram of Cyanide + Zn wastewater treatment. ... 102 Figure 3.1. Photographs of the four brown algae used in this study: (a) A. nodosum, (b) F. spiralis,
(c) L. hyperborea and (d) P. canaliculata: (1) in their native form and (2) after biomass preparation. ... 117
Figure 3.2. SEM images of raw (a) A. nodosum, (b) F. spiralis, (c) L. hyperborea and (d) P. canaliculata: (1-3) algae surface and (4-5) algae fracture surface (Magnifications: (1) = 60 x, (2) = 500 x, (3) = 4000 x, (4a) = 500 x, (4b) = 1000 x, (4c) = 1100 x, (4d) = 850 x, and (5a) = 200 x, (5b) = 500 x, (5c) = 500 x, (5d) = 140 x). ... 121 Figure 3.3. EDS microanalysis results obtained during the SEM analysis of raw (a) A. nodosum, (b) F. spiralis, (c) L. hyperborea and (d) P. canaliculata: (1) algae surface and (2) algae fracture surface. ... 122 Figure 3.4. (a) FTIR spectra of raw algae: — A. nodosum, — F. spiralis, — L. hyperborea and
— P. canaliculata and (b) FTIR spectra zoomed for wavenumber lower than 1800 cm-1. ... 123 Figure 3.5. Experimental data and model curves for the raw macroalgae, potentiometric titrations and
affinity distribution function for hydrogen ions: experimental data (ionic strength = 0.1 M); — continuous model; --- sips distribution: (a) A. nodosum, (b) F. spiralis, (c) L. hyperborea and
(d) P. canaliculata... 126 Figure 3.6. Distribution of zinc species present in (a) galvanising wastewater and (b) pure metal solution as a function of the solution pH (T = 25 ºC; ionic strength calculated by MINEQL+). ... 129 Figure 3.7. (a) Equilibrium and (b) kinetic data points and model lines for zinc uptake by the raw algae
A. nodosum, F. spiralis, L. hyperborea and P. canaliculata, at pH = 6.0 and T = 25 ºC. . 130 Figure 3.8. Removal of algae L. hyperborea accumulated in the sand of beach of the northern coast of Portugal. ... 133 Figure 3.9. (a) Equilibrium and (b) kinetic data points and model lines for zinc uptake present in the wastewater and in ZnCl2 pure solution by the raw L. hyperborea, at pH = 6.0 and T = 25 ºC. .. 134
Figure 3.10. (a) Equilibrium and (b) kinetic data points and model lines for zinc uptake by the raw,
Na- and Ca-loaded L. hyperborea and Na- and Ca-loaded L. hyperborea after protonation, at pH = 6.0 and T = 25 ºC. ... 136 Figure 3.11. Ion exchange kinetics using Na- (a, b) and Ca-loaded (c, d) L. hyperborea for the treatment of zinc containing wastewater: (a, c) concentration profile of all ionic species; (b, d) the sum of concentration of species loaded and released: CZn, CNa, CK, CMg, CCa, CH and Csum, at pH = 6.0 and T = 25 ºC. For calculations in mEq the ionic valences (Zn2+, Ca2+, Mg2+, Na+, K+ and H+) were considered. ... 137
Figure 3.12. Elution efficiency using different eluant solutions at different concentrations. ... 138 Figure 3.13. Elution efficiency data points and model lines as a function of time using acid eluants:
HCl, HNO3 and H2SO4 0.1 M; and CaCl2·2H2O 0.2 M. ... 139
Figure 3.14. Ion exchange kinetics using Na- (a, b) and Ca-loaded (c, d) L. hyperborea after protonation for the treatment of zinc containing wastewater: (a, c) concentration profile of all ionic species; (b, d) the sum of concentration of species loaded and released, at pH = 6.0 and T = 25 ºC: CZn, CNa, CK, CMg, CCa, CH and Csum. For calculations in mEq the ionic valences (Zn2+, Ca2+, Mg2+, Na+, K+ and H+) were considered. ... 141 Figure 4.1. Equilibrium data points and model lines for zinc uptake by the raw algae L. hyperborea during: batch experiments ([Zn]0 = 10 mg/L and pH = 6.0), column experiments using [Zn]0 = 11.2 mg/L and pH = 6.7 and column experiments using [Zn]0 = 17.8 mg/L and pH = 6.6, at T = 25 ºC. ... 151
Figure 4.3. Breakthrough curves for zinc removal from wastewater at bed length of 17 cm (L/D = 4) and 27 cm (L/D = 6) (CE ≈ 12 mg/L, D/dp = 24 and u = 0.6 cm/min): (a) as function of t/τ and (b) as function of volume treated. ... 154 Figure 4.4. Experimental data (points) and model fittings (lines) for zinc breakthrough curves at different linear flow rates 1.0, 2.0 and 4.1 cm/min (CE ≈ 18 mg/L; m = 20.0 g; L/D = 11;
D/dp = 13): (a) as function of t/τ and (b) as function of volume treated. ... 159 Figure 4.5. Experimental data (points) and model fittings (lines) for zinc breakthrough curves at different equivalent diameter of algae particles: D/dp = 13 and D/dp = 31. (CE ≈ 18 mg/L; m = 20.0 g; u = 2.0 cm/min; L/D = 11). ... 160 Figure 4.6. (a) Concentration profile of all ionic species and (b) the sum of metals loaded and released: Zn, Na, K, Ca, Mg, sum of metals loaded and sum of metals released. For calculations in mEq the ionic valences (Zn2+, Ca2+, Mg2+, Na+, K+ and H+) were considered. ... 161
Figure 4.7. Breakthrough curves for zinc removal from wastewater and from synthetic zinc wastewater at the following operational conditions: CE ≈ 17.6 mg/L, L = 26.5 cm, L/D = 11, D/dp = 13,
m = 20.0 g, Q = 10 mL/min (9.1 BV/h), u = 2.0 cm/min, = 0.49 and = 6.4 min. ... 162 Figure 4.8. Ion exchange column elution peaks for HCl and HNO3 0.1 M at the following operational conditions: L = 17.3 cm, L/D = 4, D/dp = 24, m = 30.0 g, Q = 10 mL/min, u = 0.6 cm/min, ɛ = 0.68 and τ = 21.4 min. ... 164 Figure 4.9. (a) 1st and 2nd cycles of zinc uptake after (b) 2nd and 3rd cycles of elution with 1.0 M HCl and regeneration with tap water until pH = 4.0 (—— pH profile). ... 165 Figure 4.10. SEM images of surface of Laminaria hyperborea: (a) after one cycle of elution (H-algae) and (b) after one cycle of exhaustion (Zn-algae). Magnifications (1): 500 x and (2) 4000 x. ... 166 Figure 4.11. Elution peaks for different linear flow rates: 0.1, 1.0, 2.0 and 3.1 cm/min ([HCl]0 = 1.0 M): (a) time until 100 min and (b) time until 700 min. ... 168
Figure 4.12. SEM images of surface of Laminaria hyperborea: (a) after one cycle of exhaustion, elution and regeneration with tap water with pH adjusted to 8.0 with NaOH solution and (b) after three cycles
of exhaustion, elution and regeneration with 0.1 M CaCl2. Magnifications: (1) 500 x and (2) 4000 x. ... 171
Figure 4.13. (a) Breakthrough curves for zinc from wastewater until breakpoint and (b) elution curves during 3 cycles of exhaustion/elution/regeneration at pre-pilot scale plant with bed characteristics of D = 6.0 cm and L = 41.0 cm: 1st, 2nd and 3rd cycles. ... 173
Figure 4.14. Schematic flowchart of the plant designed for wastewater purification from a galvanising company using L. hyperborea as ion exchanger. ... 178 Figure 5.1. SEM images of surface of raw algae after copper loading. Magnifications of (a) 500 x and (b) 4000 x. ... 188 Figure 5.2. EDS microanalysis results obtained during the SEM analysis of surface of raw algae after copper loading. ... 189 Figure 5.3. (a) FTIR spectra of different forms of macroalgae A. nodosum: — raw algae, — Na-loaded, — K-loaded, — Mg-loaded, — Ca-loaded and — Cu-loaded; and (b) FTIR spectra zoomed for wavenumber lower than 1800 cm-1. ... 189 Figure 5.4. Potentiometric titration for different forms of macroalgae A. nodosum: experimental data (ionic strength = 0.1 M); — continuous model; --- sips distribution: (a) Na-loaded, (b) K-loaded, (c) Mg-loaded and (d) Ca-loaded. ... 190 Figure 5.5. (a) FTIR spectra of the algae A. nodosum — after chemical treatment of carboxylic groups and — after chemical treatment of sulphonic groups; and (b) FTIR spectra zoomed for wavenumber lower than 1800 cm-1... 191
Figure 5.6. Potentiometric titration of different forms of macroalgae A. nodosum: experimental data (ionic strength = 0.1 M); — continuous model; --- sips distribution: (a) after chemical treatment of carboxylic groups and (b) after chemical treatment of sulphonic groups. ... 192 Figure 5.7. Cu2+ uptake on raw and raw algae after chemical treatment of functional groups (Cj, = 300 mg/L (9.4 mEq/L) and algae dose = 1.0 g/L). ... 193
Figure 5.8. Ligth metals ion exchange experimental data points (empty symbols) and model prediction points (solid symbols) using different forms of macroalgae A. nodosum: (a) Na-loaded, (b) K-loaded, (c) Mg-loaded, (d) Ca-loaded and (e) raw algae. ... 204 Figure 5.9. Cu2+ equilibrium sorption experimental data points (empty symbols) and model prediction points (solid symbols) for different pH values ( pH = 2.0, pH = 2.5, pH = 3.0 and pH = 4.0) using different forms of macroalgae A. nodosum: (a) Na-loaded, (b) K-loaded, (c) Mg-loaded, (d) Ca-loaded and (e) raw algae.. ... 205 Figure 5.10. Ligth metals ion exchange experimental data points (empty symbols) and model prediction points (solid symbols) using different forms of macroalgae A. nodosum: (a) Na-loaded, (b) K-loaded, (c) Mg-loaded, (d) Ca-loaded and (e) raw algae. ... 207 Figure 5.11. Cu2+ equilibrium sorption experimental data points (empty symbols) and model prediction points (solid symbols) for different pH values ( pH = 2.0, pH = 2.5, pH = 3.0 and pH = 4.0) using different forms of macroalgae A. nodosum: (a) Na-loaded, (b) K-loaded, (c) Mg-loaded, (d) Ca-loaded and (e) raw algae.. ... 208 Figure 5.12. Kinetic experimental data (points) and model prediction (lines) for H+ ion exchange at pH 2.0 on: (a) Na-loaded, (b) K-loaded, (c) Mg-loaded, (d) Ca-loaded and (e) raw algae. CH+, qH+, pH, CNa+, CK+, CMg2+ and CCa2+. ... 214
Figure 5.13. Kinetic experimental data (points) and model prediction (lines) for Cu2+ ion exchange (C0 = 6.3 mEq/L and pH = 4.0) on ionic form biomass: (a) Na-loaded, (b) K-loaded, (c) Mg-loaded, (d) Ca-loaded; and on raw algae (e) C0 = 3.2 mEq/L at pH 2.0 and (f) C0 = 6.3 mEq/L at pH 4.0. CCu2+, qCu2+, pH, CNa+, CK+, CMg2+ and CCa2+. ... 215
Figure 5.14. Ion exchange column (a) breakthrough curves and (b) elution peaks for 1st exhaustion/elution/regeneration/rinse cycle: Cu2+ and pH. ... 220 Figure 5.15. Schematic representation of ion exchange operation cycle: (a) counter-flow and (b) co-flow mode. ... 223 Figure 5.16. Ion exchange column (a) breakthrough curves, and (b) elution peaks for four consecutive exhaustion/elution/regeneration/rinse cycles: 1st, 2nd, 3rd and 4th exhaustion cycle; and elution using HCl: 1.0 M, 0.5 M and 0.1 M in counter-flow mode and 1.0 M in co-flow mode. .. 224 Figure 5.17. Ion exchange column breakthrough curves for copper removal from aqueous solution using raw A. nodosum (1st cycle): Cu2+ experimental data, ---- Cu2+ model data model 1 and —— Cu2+ model data model 2. ... 225 Figure 5.18. Ion exchange column breakthrough curves for copper removal from aqueous solution for (a) 1st cycle, (b) 2nd cycle, (c) 3rd cycle and (d) 4th cycle: experimental data of Cu2+, pH and Ca2+; and model data of —— Cu2+, —— pH, —— Na+, —— K+,—— Mg2+ and —— Ca2+. ... 227 Figure 5.19. Ion exchange column elution peaks for different HCl concentrations and flow directions: (a) 1.0 M at counter-flow mode, (b) 0.5 M at counter-flow mode, (c) 0.1 M at counter-flow mode and
(d) 1.0 M at co-flow mode: experimental data of Cu2+ and pH; and model data of —— Cu2+, —— pH, —— Na+, —— K+, —— Mg2+ and —— Ca2+. ... 230
Figure 5.20. SEM images of surface of algae after four cycles of exhaustion/elution/regeneration in a fixed-bed column in two different magnifications: (a) 500 x and (b) 4.000 x; and (c) EDS microanalysis results obtained during the SEM analysis of surface of algae after four cycles of exhaustion/elution/regeneration in a fixed-bed column. ... 232
List of Tables
Table 1.1. Physicochemical characterisation of industrial wastewater from metal finishing units in different countries. ... 9 Table 1.2. Limits for toxic metals present in metal finishing wastewater for direct discharge to surface waters. ... 10 Table 1.3. Anthropogenic contamination sources and adverse health effects of some metals. ... 15 Table 1.4. Advantages and disadvantages of the main techniques to remove toxic metal ions from wastewaters. ... 16 Table 1.5. Reported data on toxic metal removal by low cost adsorbents. General conditions: pure solution, single component and batch system. ... 22 Table 1.6. Mechanistic models based on ion exchange reactions applied to the biosorption of metal ions by algae. ... 35 Table 1.7. Reported data on toxic metal removal using a fixed-bed packed with different brown marine macroalgae. ... 48 Table 1.8. Reported data on the application of brown marine macroalgae in consecutive exhaustion, elution and regeneration cycles. ... 58 Table 1.9. Reported data on toxic metal removal from wastewater by brown marine macroalgae. ... 61 Table 2.1. Chemicals description. ... 83 Table 2.2. Operating conditions used to analyse the different metals by AAS. ... 94 Table 2.3. Experimental conditions used in cation exchange equilibrium studies. ... 106 Table 2.4. Operational conditions used in the exhaustion experiments. ... 109 Table 2.5. Operational conditions used in the elution experiments. ... 110 Table 3.1. Amount of alkali and alkaline earth ions present on the surface of different brown macroalgae (mean value of 4 samples ± standard deviation). ... 117 Table 3.2. Common stretching frequencies in FTIR spectra associated with the main functional groups present on brown marine macroalgae. ... 123 Table 3.3. Parameters of the continuous distribution model for the raw macroalgae (ionic strength = 0.1 M). ... 127 Table 3.4. Characteristics of the zinc-bearing wastewater. ... 128 Table 3.5. Equilibrium parameters for zinc ions uptake from the zinc containing wastewater, pH = 6.0 and T = 25 ºC. ... 130 Table 3.6. Estimated parameters for the mass transfer model for zinc ions uptake from the zinc containing wastewater, pH = 6.0 and T = 25 ºC. ... 132 Table 4.1. Seasonal variation of zinc wastewater characteristics. ... 152 Table 4.2. Results from breakthrough curves for zinc separation from wastewater at different operational conditions. ... 156 Table 4.3. Experimental conditions and results for elution step. ... 168 Table 4.4. Experimental conditions and results for regeneration step. ... 170 Table 4.5. Results from exhaustion step using the following operational conditions in a pre-pilot plant:
L = 41 cm, L/D = 7, D/dp = 30, m = 267 g, Qs = 2.9 BV/h, u = 1.5 cm/min, ɛ = 0.24, τ = 6.6 min and
CE = 21.5 mg/L. ... 173 Table 4.6. Zinc wastewater characteristics before and after ion exchange treatment in a fixed-bed column packed with L. hyperborea protonated and pre-treated with CaCl2 in a pre-pilot plant. ... 174
Table 4.7. Results from the elution step using the following operational conditions in the pre-pilot plant:
L = 41 cm, L/D = 7, D/dp = 30, m = 267 g and ɛ = 0.24. ... 174 Table 4.8. Results from regeneration step using the following operational conditions in a pre-pilot plant:
L = 41 cm, L/D = 7, D/dp = 30, m = 267 g and ɛ = 0.24. ... 174 Table 4.9. Characteristics of the industrial ion exchange plant taking in account the safety factor design (SFD1) and L/D = 6.84 for the four design methods proposed. Volumetric flow rate = 2.4 m3/day. .. 176
Table 4.11. Treatment costs by ion exchange at industrial scale. ... 179 Table 5.1. Amount of alkali and alkaline earth ions present on the surface of different forms of macroalgae A. nodosum (mean value of 4 samples ± standard deviation). ... 188 Table 5.2. Parameters of the continuous distribution model for different forms of macroalgae
A. nodosum (ionic strength = 0.1 M; value ± standard deviation). ... 191
Table 5.3. Parameters of the continuous distribution model for different forms of macroalgae
A. nodosum (ionic strength = 0.1 M; value ± standard deviation). ... 192
Table 5.4. Mass action law equations applied for each specific scenario. ... 198 Table 5.5. Binding strength characterisation parameters. ... 203 Table 5.6. Ion exchange equilibrium model parameters. ... 206 Table 5.7. Ion exchange equilibrium model parameters. ... 209
Table 5.8. Ion exchange capacities of the ionic form biomass in different senary and ternary systems. ... 210
Table 5.9. Cu2+ sorption capacities using different brown marine macroalgae. ... 211 Table 5.10. Estimated parameters for the mass transfer model (T = 25 ºC). ... 217 Table 5.11. Results from breakthrough curves for copper separation from aqueous solution at the following operational conditions: L = 26.5 cm, L/D = 11, D/dp = 31, m = 24 g, Q = 10 mL/min (6.7 BV/h), u = 2 cm/min, = 0.31 and = 4 min. ... 220 Table 5.12. Results from column elution and regeneration step at the following operational conditions:
L = 26.5 cm, L/D = 11, D/dp = 31, m = 24 g, Q = 10 mL/min (6.7 BV/h), u = 2 cm/min, ɛ = 0.31 and
τ = 4 min. ... 221
Notation
a empirical parameter of Yan model Ac column surface area (cm2)
Ai geometric surface area of the particle (mm2)
Ap mean geometrical specific surface area of the particle (mm2)
ap specific area of the thin plate particles (1/cm)
Cbp breakthrough concentration (mg/L)
CE feed concentration of species j (mg/L or mmol/L or mEq/L)
CH concentration of protons in solution (mol/L)
Ci metal concentration in the influent (mg/L or mmol/L or mEq/L)
Cj concentration of species j in the liquid phase at time t (mg/L or mEq/L)
Cj,0 initial concentration of species j in the liquid phase (mg/L or mEq/L)
Cout concentration of species j at the column outlet (mEq/L)
Csum sum of concentrations of released species (mEq/L)
*
j
C equilibrium concentration of species j in the liquid phase (mg/L or mEq/L) exp
'
m
C experimental concentration at the point m’ num
m
C ' numerical concentration at the point m’ D column inner diameter (cm)
Daz axial dispersion coefficient (cm2/s)
Dh coefficient of homogeneous diffusion of species j inside the particle (cm2/s)
Dm molecular diffusion coefficient (cm2/s)
dp equivalent particle diameter (mm)
f swelling factor
fLUB fraction of unused bed (%)
fLUSE fraction of used bed (%)
hi particle thickness (mm)
K equilibrium constant (L/g)
K1 Thomas model rate constant (LEq/day)
kp,j mass transfer coefficient for intraparticle diffusion of species j (cm/s)
H i D
K
, selectivity coefficient between divalent ions (D = Mg2+, Ca2+ or Cu2+) in the particle and H+ ion in solutionCu i D
K
, selectivity coefficient between divalent ions (D = Mg2+, Ca2+ or Cu2+) in the particle and Cu2+ ion in solutionH i M
K
, selectivity coefficient between monovalent ions (M = Na+ or K+) in the particle and H+ ion in solutionCu i M
K
, selectivity coefficient between monovalent ions (M = Na+ or K+) in the particle and Cu2+ ion in solution´ ,H i
K average value of the affinity distribution for the protons (L/mol) int
,H i
K intrinsic proton affinity constant at each binding site i (L/mol) L bed height (cm)
LMTZ length of the MTZ (cm)
LUB length of the unused bed (cm)
LUSE length of the used bed (cm)
m algae dry weight (g)
mH,i width of binding groups in the Sips distribution
mn algae dry weight of n particles (g)
ms sampling point
n
number of experimental points Pi particle perimeter (mm)pKa dissociation constant
Q feed flow rate (mL/min)
qb operating capacity until achieving the breakthrough point (mg/g or mEq/g algae)
qE total capacity (mg/g or mEq/g algae)
qE,j concentration of species j in the solid phase in equilibrium with CE (mg/g)
QH biomass surface charge (mmol/g)
qj concentration of species j in the solid phase (mg/g or mEq/g)
qj,0 concentration of species j at the solid phase in equilibrium with Cj,0 (mg/g or mEq/g)
qmax total capacity from batch experiments (mg/g or mEq/g algae)
Qmax,i maximum concentration of each functional group (i = 1 for acidic groups; i = 2 for basic groups) (mmol/g)
Qs specific flow rate (BV/h) j
r wastewater treatment ratio (m3/kg) Rp half of the thickness of the particle (cm)
t time (h)
tbp time at breakthrough point (min or h)
te exhaustion time (min)
tMTZ time of MTZ (min)
tst stoichiometric time (min)
u superficial fluid velocity or linear flow rate (cm/s) ui interstitial fluid velocity (cm/s)
ush shock wave velocity (cm/min)
V volume of the solution (L) Vc column volume (mL or L)
Vdry volume of dry resin (mL or L)
Vr resin volume (mL or L)
Vsizing volume of the column at scale-up (L or m3)
Vt throughput volume (L)
Vwet volume of wet resin (mL or L)
W mass of resin for the scale-up (kg) z bed axial position (cm)
Greek letters
ɛ bed porosity
ζ total binding strength ζm mass batch capacity factor
ξ mass column capacity factor ρb bulk density of the pilot-plant bed
ρp particle density (g/L, dry basis) τ residence time (min)
τd,j time constant for diffusion of species j into the particle (s)
H T ,
1. Introduction
This first chapter presents the background and motivation of this thesis. A general overview of the impact of the discharge of industrial wastewater laden with toxic metals on the environment is provided. The galvanising process is also described. Additionally, a review of some of the most common and potential treatment methods for metal removal is presented. The mechanism responsible for the metal binding ability of brown marine macroalgae and the biosorption process design are also included. Lastly, objectives and thesis outline are provided.
1.1. Overview of toxic metals sources, chemistry and toxicity
1.1.1. Sources of toxic metal pollution
From the technological point of view, metals are defined on the basis of their common physical properties in the solid state: as a solid with a crystal structure that has magnetic properties, high electrical conductivity, high thermal conductivity, metallic lustre due to their high reflectivity, and mechanical properties of strength and ductility. However, these physical properties are not so significant in metal toxicology. A more useful definition of metals to make it possible to explain the toxic effects is based on their properties in aqueous solutions[1]. In the environmental context, the more correct term to be used is toxic metals, that can be defined as stable metals or metalloids that under biological conditions may react by losing one or more electrons to form cations, which are potentially toxic to biota and are prone to bioaccumulation and biomagnification through the food chain [1-3]. Some metalloids are included in this definition because they produce adverse health effects in humans, either by themselves or by interaction with other elements [1].
Some metals such as Fe, Cu, Zn, Mn and Co play an integral role in the life processes of living organisms serving as micronutrients and are essential up to a maximum concentration limit [4]. Many other metals, such as Cd, Pb and Hg have no biological role and are non-essential to living organisms. However, at high concentration levels, both essential and non-essential metals are potentially toxic to human and other biological life [5].
The emission of toxic metals into the environment can occur both by natural and anthropogenic routes (Figure 1.1). The natural sources are dominated by geochemical weathering of soil and rocks, erosion of surface deposits of metal minerals and volcanic activity. The main sources stemming from human activities are the battery manufacturing, fertiliser production, mining wastes, ores refining, tannery wastes, landfill leachates, municipal wastewater, urban runoff and industrial wastewaters, particularly from the electronic, metal finishing industries, and most recently from nanotechnology developments [6-8].
Figure 1.1. Sources of toxic metal pollution. Adapted from: FilterWater.com (2017) [9].
Figure 1.2 shows the European emissions of toxic metals released to water available at the European Pollutant Release and Transfer Register (E-PRTR) database. The E-PRTR contains environmental data reported annually by industrial facilities in European Union Member States and in Iceland, Liechtenstein, Norway, Serbia and Switzerland. The register contains data reported by more than 30,000 industrial facilities covering 65 economic activities within 9 industrial sectors: energy, production and processing of metals, mineral industry, chemical industry, waste and wastewater management, paper and wood production and processing, intensive livestock production and aquaculture, animal and vegetable products from the food and beverage sector, and other activities. Pollutant releases and transfers from E-PRTR countries must be reported according to the E-PRTR Regulation EC Nº166/2006 of the European Parliament Council Directives 91/689/EEC and 96/61/EC. The facilities that undertake one or
1.2(a)) it is possible to see the European emissions of toxic metals released into the water in 2014 from different industrial sectors, excluding waste and wastewater management, since this segment just receive the pollutants released from other industrial sectors. The industrial sectors that produce less than 1% of the total of toxic metals released were added to the other activities category. It is possible to observe that the production and processing of metals (~30%) and mineral industry (~26%) together released more than half of the total of toxic metals released in 2014 by the E-PRTR countries, followed by intensive livestock production and aquaculture (~14%), chemical industry (~10%), energy (10%), wood production and processing (~7%) and other activities (~2%). The toxic metals with larger amounts released in 2014 were zinc (1080 t), chromium (401 t) and copper (270 t). Production and processing of metals are responsible for most emissions of chromium (278 t) and nickel (96 t) while mineral industry is responsible for most emissions of copper (115 t), cadmium (3 t), lead (43 t) and zinc (326 t).
The column chart (Figure 1.2 (b)) shows that the toxic metals (Cu, Cd, Cr, Ni, Pb and Zn) released by the 9 industrial sectors of E-PRTR database added to the emissions that occurred accidentally into the water between 2008 and 2014 amounted on average approximately 4000 t. These wastewaters charged with toxic metals were produced by an average of 8,216 facilities around 22 different E-PRTR countries.
As mentioned above the production and processing of metals, including the metal finishing process, was the most responsible for the release of toxic metals into the water in 2014, releasing approximately 620 t, mainly of chromium (278 t) zinc (188 t) and nickel (96 t). Besides that, the wastewaters produced during the metal finishing process are one of the most worrying and complicated wastewaters to handle, mainly due to of their high content of economically valuable toxic metals and complex composition [11].
1.1.1.1. Metal finishing industries
Metal finishing comprises a broad range of technological and industrial processes, including electroplating and galvanising, which change the surface of a product or enhance its appearance, increase its corrosion resistance, or produce surface characteristics essential for subsequent operations [12]. For this reason, it is present in construction, transport, agriculture, aviation and everywhere to ensure a good corrosion protection and long life. In Europe, there are more than 650 galvanising plants – each making a significant impact on the local manufacturing economy and employment [13]. A common industrial process of metal finishing includes three steps, namely: i) surface cleaning or preparation, ii) surface treatment and iii) post treatment [14]. Figure 1.3 presents a basic galvanising flow diagram and some expected wastewater emission points [15]. The typical sources of generation of metal finishing wastewater are rinsing, bath dumps, and spills/leaks in the plant operation. Before galvanisation, metal goods can be treated by an alkaline or acid degreasing solution, an acid pickling solution (hydrochloric acid or sulphuric acid) to remove rust and scale, rinsing water and a preflux solution. The preflux solution, usually prepared with zinc ammonium chloride (ZnCl2.3NH4Cl), is used to promote the zinc-metal bond. After pre-treatment, the metal is immersed in molten zinc (batch hot-dip galvanising) or in an alkaline or acid electrolyte (electro-galvanising), followed by a quench bath (sodium dichromate) [15]. In the electro-galvanising method, zinc is applied as an expendable electrode in a cyanide, alkaline non-cyanide, or acid chloride salt solution. Cyanide baths are the most operationally efficient but they can potentially create pollution and are hazardous [16]. Waste liquids may be generated from spent pre-treatment solutions and sometimes from quenching activities after galvanising, contaminated principally with zinc, iron, hexavalent chromium and cyanide. Due to high metal pollution potential, rinse waters and exhausted acid pickling baths constitute the main environmental problems from metal finishing industry [17]. The metal finishing wastewaters are complex and their composition vary according to the chemicals used in the industrial process and other activities conducted in the plant. However, the main contaminants usually found in this type of wastewater are organic substances, suspended solids, phosphate, fluoride, ammonia and toxic metals, such as cadmium, cyanide, chromium, copper, iron, lead, nickel, silver, tin and zinc [18,19]. Table 1.1 shows the physicochemical characterisation of industrial wastewater generated from metal finishing units in different countries, indicating that the major contaminants found in this type of wastewater are suspended solids, chloride, phosphate, sulphate, nickel, chromium, zinc and copper. In accordance with this, the main toxic metals released by the surface treatment of metals and plastics using electrolytic or chemical processes, a subclass of the industrial sector of production and processing of metals, into the water by E-PRTR countries, are zinc, chromium, nickel and copper (Figure 1.4) [10].
Figure 1.3. Galvanising processflowsheet. The arrows turned down indicate the wastewater production source. Adapted from: NPi (1999) [15].
Degreasing Water Rinse Pickling Water Rinse
Neutralisation Zinc Galvanising Bath Zinc Recovery Bath Water Rinse Activation Bath Passivation Water Rinse Product NaOH HCl and H2SO4 NaOH NaOH, NaCN, Zn, Brightening Agents HNO3 Cr(VI) Cr(VI) Wastewater Zn Wastewater Acid/Base Wastewater Base Wastewater
