Dynamic modelling of trace metal speciation in anaerobic digestion: An extended ADM1-based mechanistic model framework
- Autores: Susan George
- Directores de la Tesis: Fernando González Fermoso (dir. tes.)
- Lectura: En la Universidad Pablo de Olavide ( España ) en 2024
- Idioma: inglés
- Programa de doctorado: Programa de Doctorado en Biotecnología, Ingeniería y Tecnología Química por la Universidad Pablo de Olavide
- Materias:
- Enlaces
- Resumen
In attempts to improve methane yield from anaerobic digestors, the influence of trace metals is extensively studied and recognized. Trace metals (TM) like Fe, Co and Ni are components of enzymes and co-factors involved in different anaerobic digestion pathways. Lack of trace metals can cause hinderance to related enzymatic reactions and relevant digestion pathways.
Moreover, excess of TMs can result in toxicity. Hence, optimal lability of metals in the media is significant and it depends on their chemical speciation. Metal speciation is driven by operational conditions like pH, redox potential, hydraulic retention time and is influenced by thermodynamics and kinetics of chemical processes. Due to analytical limitations in quantifying bioavailable fraction of trace metals in an anaerobic digestion system, a mathematical model-based approach will facilitate better understanding of the system, and optimising a reliable and precise trace metal dosing strategy for practical applications. This thesis aims to develop a dynamic mechanistic model for trace metal speciation in anaerobic digestion.
Major processes and factors influencing speciation of trace metals in anaerobic digestion have been identified and mathematically defined. Significance of considering ionic strength and ion pairing while modelling trace metal speciation has been extensively studied. It has been observed that anaerobic digestion performance increases with increase in ionic strength and ion pairing due to decrease in precipitation and increase in metal labile fractions. In addition to precipitation process, ionic strength also influences TM adsorption and hence ionic strength is an important operational parameter controlling the system.
The thesis presents the complete model framework where major processes are defined. The model includes metal uptake, precipitation, adsorption, inorganic complexation, organic complexation with metabolites and strong chelating agents. Influence of reactor conditions such as pH, temperature and ionic strength on these processes are included in the model along with other significant mechanisms such as metal release and dose response. Numerical simulations have been performed under different scenarios to check adaptability and capability of the model. Experiments have been conducted for calibration and application of the model in a lab scale reactor. The model reasonably predicted trace metal effects like metal deprivation in an anaerobic digestor as being observed during the experimental study.
The thesis further discussed the application of the model to optimise trace metal dosing strategies such as mode of dosing, dosing frequency, concentration of dosing, dosing form and time of dosing. Model results have been verified with existing observations from experimental studies and the dose response function has been modified to explicitly capture differential response of microbes to different trace metals. Best ways to dose trace metals to anaerobic digesters for improving methane yield have been analysed. Model results showed that repeated pulse dosing is the best mode to dose TM in comparison with other modes of dosing such as continuous, single pulse, preloading and in-situ loading. Low TM concentration at high dosing frequency is preferable over high dosage at low frequency. Trace metals should be supplemented at the earliest time possible after metal deficits at optimum concentration levels. An excess of metal supplementation can result in toxicity. Preferred dosing form depends on reactor configuration. Easily dissociable metal forms like metal chlorides are ideal in continuous reactors whereas strong metal chelates like ethylenediamine tetraaceticacid (EDTA) complexes are advisable for reactors with high retention times like batch or semi-continuous reactors.
Applicability of the model in a full-scale reactor context has been studied using real full-scale reactor data and potential applications of the model including optimising metal dosing and co-digestion to improve methane yield have been tested. In the case of iron as an additive to reduce H2S concentration, high dosage of iron will not only result in reduction of methane yield due to inhibition of Fe but will also add to extra costs due to adding excess of FeCl3 for H2S removal. The model has suggested that ideal dosage of iron for removal of H2S from biogas is to maintain iron to sulphide ratio around 1. Results from trace metal speciation model for co-digestion indicate that substrate characteristics such as sulphide content and trace metal concentration influence co-digestion. In case of metal deprivation, co-digestion can be performed using co-substrates rich in trace metals to overcome metal limitation.
However, optimum mixing ratio should be maintained to ensure sufficient metal bioavailability.
The mechanistic model framework presented in the thesis for defining trace metal speciation during anaerobic digestion has been tested and verified under various application domains such as predicting metal deprivation, optimising dosing strategies and full-scale application during co-digestion. Due to experimental limitations in quantifying bioavailable metal concentrations, the model framework after additional calibration and validation can be used as a predictive tool to understand trace metal bioavailability and develop a reliable dosing strategy applicable on each individual case.
