Due to its abundance, calcium carbonate (CaCO3) has high potentials as a source of alkalinity for biotechnological applications. The application of CaCO3 in biological systems as neutralizing agent is, however, limited due to potential difficulties in controlling the pH. The objective of the present study was to determine the dominant processes that control the pH in an acid-forming microbial process in the presence of CaCO3. To achieve that, a mathematical model was made with a minimum set of kinetically controlled and equilibrium reactions that was able to reproduce the experimental data of a batch fermentation experiment using finely powdered CaCO3. In the model, thermodynamic equilibrium was assumed for all speciation, complexation and precipitation reactions whereas, rate limited reactions were included for the biological fatty acid production, the mass transfer of CO2 from the liquid phase to the gas phase and the convective transport of CO2 out of the gas phase. The estimated pH-pattern strongly resembled the measured pH, suggesting that the chosen set of kinetically controlled and equilibrium reactions were establishing the experimental pH. A detailed analysis of the reaction system with the aid of the model revealed that the pH establishment was most sensitive to four factors: the mass transfer rate of CO2 to the gas phase, the biological acid production rate, the partial pressure of CO2 and the Ca(+2) concentration in the solution. Individual influences of these factors on the pH were investigated by extrapolating the model to a continuously stirred-tank reactor (CSTR) case. This case study indicates how the pH of a commonly used continuous biotechnological process could be manipulated and adjusted by altering these four factors. Achieving a better insight of the processes controlling the pH of a biological system using CaCO3 as its neutralizing agent can result in broader applications of CaCO3 in biotechnological industries.

Using a one-dimensional mathematical model that couples tooth demineralisation and remineralisation with metabolic processes occurring in the dental plaque, two mechanisms for subsurface lesion formation were evaluated. It was found that a subsurface lesion can develop only as the result of alternating periods of demineralisation (acid attack during sugar consumption) and remineralisation (resting period) in tooth enamel with uniform mineral composition. It was also shown that a minimum plaque thickness that can induce an enamel lesion exists. The subsurface lesion formation can also be explained by assuming the existence of a fluoride-containing layer at the tooth surface that decreases enamel solubility. A nearly constant thickness of the surface layer was obtained with both proposed mechanisms. Sensitivity analysis showed that surface layer formation is strongly dependent on the length of remineralisation and demineralisation cycles. The restoration period is very important and the numerical simulations support the observation that often consumption of sugars is a key factor in caries formation. The calculated profiles of mineral content in enamel are similar to those observed experimentally. Most probably, both studied mechanisms interact in vivo in the process of caries development, but the simplest explanation for subsurface lesion formation remains the alternation between demineralisation and remineralisation cycles without any pre-imposed gradients.

Long-term emissions of Municipal Solid Waste (MSW) landfills are a burden for future generations because of the required long-term aftercare. To shorten aftercare, treatment methods have to be developed that reduce long-term emissions. A treatment method that reduces emissions at a lysimeter scale is re-circulation of leachate. However, its effectiveness at the field scale still needs to be demonstrated. Field scale design can be improved by theoretical understanding of the processes that control the effectiveness of leachate recirculation treatment. In this study, the simplest and most fundamental sets of processes are distilled that describe the emission data measured during aerobic and anaerobic leachate recirculation in lysimeters. A toolbox is used to select essential processes with objective performance criteria produced by Bayesian statistical analysis. The controlling processes indicate that treatment efficiency is mostly affected by how homogeneously important reactants are spread through the MSW during treatment. A more homogeneous spread of i.e. oxygen or methanogens increases the total amount of carbon degraded. Biodegradable carbon removal is highest under aerobic conditions, however, the hydrolysis rate constant is lower which indicates that hydrolysis is not enhanced intrinsically in aerobic conditions. Controlling processes also indicate that nitrogen removal via sequential nitrification and denitrification is plausible under aerobic conditions as long as sufficient biodegradable carbon is present in the MSW. Major removal pathways for carbon and nitrogen are indicated which are important for monitoring treatment effectiveness at a field scale. Optimization strategies for field scale application of treatments are discussed.

Reliable prediction of the long-term behavior of environmental systems such as Municipal Solid Waste (MSW) landfills is challenging. While many driving forces influence this behavior, characterization of them is limited by measurement techniques. Therefore, a model structure for reliable prediction needs to optimally combine all measured information with suitable mechanistic information from literature. How to get such an optimal model structure? This study presents a toolbox to find and build the model structure that describes an environmental system as close as possible. The toolbox combines environmental frameworks to include all suitable mechanistic information; it fully couples kinetic and equilibrium reactions and contains multiple resources to obtain biogeochemical parameters. Several possible optimal model structures are quickly built and evaluated with objective statistical performance criteria obtained via Bayesian inference. By applying the novel methodology, we select the best model structure for anaerobic digestion of MSW in full scale landfills. Response to Reviewers: There were no comments from the reviewers.

No abstract