Surrogate based Global Sensitivity Analysis of ADM1-based Anaerobic Digestion Model

In order to calibrate the model parameters, Sensitivity Analysis routines are mandatory to rank the parameters by their relevance and fix to nominal values the least influential factors. Despite the high number of works based on ADM1, very few are related to sensitivity analysis. In this study Global Sensitivity Analysis (GSA) and Uncertainty Quantification (UQ) for an ADM1-based Anaerobic Digestion Model have been performed. The modified version of ADM-based model selected in this study was presented by Esposito and co-authors in 2013. Unlike the first version of ADM1, focused on sewage sludge degradation, the model of Esposito is focused on organic fraction of municipal solid waste digestion. It his recalled that in many applications the hydrolysis is considered the bottleneck of the overall anaerobic digestion process when the input substrate is constituted of complex organic matter. In Esposito's model a surfaced based kinetic approach for the disintegration of complex organic matter is introduced. This approach allows to better model the disintegration step taking into account the effect of particle size distribution on the digestion process. This model needs thus GSA and UQ to pave the way for further improvements and reach a deep understanding of the main processes and leading input factors. Due to the large number of parameters to be analyzed a first preliminary screening analysis, with the Morris' Method, has been conducted. Since two quantities of interest (QoI) have been considered, the initial screening has been performed twice, obtaining two set of parameters containing the most influential factors in determining the value of each QoI. A surrogate of ADM1 model has been defined making use of the two defined quantities of interest. The output results from the surrogate model have been analyzed with Sobol’ indices for the quantitative GSA. Finally, uncertainty quantification has been performed. By adopting kernel smoothing techniques, the Probability Density Functions of each quantity of interest have been defined

Surrogate-based uncertainty and sensitivity analysis for bacterial invasion in multi-species biofilm modeling

In this work, we present a probabilistic analysis of a detailed one-dimensional biofilm model that explicitly accounts for planktonic bacterial invasion in a multi-species biofilm. The objective is (1) to quantify and understand how the uncertainty in the parameters of the invasion submodel impacts the biofilm model predictions (here the microbial species volume fractions); and (2) to spot which parameters are the most important factors enhancing the biofilm model response. An emulator (or “surrogate”) of the biofilm model is trained using a limited experimental design of size N=216 and corresponding to a Halton’s low-discrepancy sequence in order to optimally cover the uncertain space of dimension d=3 (corresponding to the three scalar parameters newly introduced in the invasion submodel). A comparison of different types of emulator (generalized Polynomial Chaos expansion – gPC, Gaussian process model – GP) is carried out; results show that the best performance (measured in terms of the Q2 predictive coefficient) is obtained using a Least-Angle Regression (LAR) gPC-type expansion, where a sparse polynomial basis is constructed to reduce the problem size and where the basis coordinates are computed using a regularized least-square minimization. The resulting LAR gPC-expansion is found to capture the growth in complexity of the biofilm structure due to niche formation. Sobol’ sensitivity indices show the relative prevalence of the maximum colonization rate of autotrophic bacteria on biofilm composition in the invasion submodel. They provide guidelines for orienting future sensitivity analysis including more sources of variability, as well as further biofilm model developments.BERC 2014-2017 (Basque Government); BCAM Severo Ochoa accreditation SEV-2013-0323 (Spanish Ministry of Economy and Competitiveness MINECO); PhD Grant "La Caixa 2014" (La Caixa Foundation)

Free boundary problem for the role of planktonic cells in biofilm formation and development

The dynamics of biofilm lifecycle are deeply influenced by the surrounding environment and the interactions between sessile and planktonic phenotypes. Bacterial biofilms typically develop in three distinct stages: attachment of cells to a surface, growth of cells into colonies, and detachment of cells from the colony into the surrounding medium. The attachment of planktonic cells plays a prominent role in the initial phase of biofilm lifecycle as it initiates the colony formation. During the maturation stage, biofilms harbor numerous microenvironments which lead to metabolic heterogeneity. Such microniches provide conditions suitable for the growth of new species, which are present in the bulk liquid as planktonic cells and can penetrate the porous biofilm matrix. We present a 1D continuum model on the interaction of sessile and planktonic phenotypes in biofilm lifestyle which considers both the initial attachment and colonization phenomena. The model is formulated as a hyperbolic-elliptic free boundary value problem with vanishing initial value. Hyperbolic equations reproduce the transport and growth of sessile species, while elliptic equations model the diffusion and conversion of planktonic cells and dissolved substrates. The attachment is modelled as a continuous, deterministic process which depends on the concentrations of the attaching species. The growth of new species is modelled through a reaction term in the hyperbolic equations which depends on the concentration of planktonic species within the biofilm. Existence and uniqueness of solutions are discussed and proved for the attachment regime. Finally, some numerical examples show that the proposed model correctly reproduces the growth of new species within the biofilm and overcomes the ecological restrictions characterizing the Wanner-Gujer type models.Comment: 17 pages, 9 figures, preprint versio

Sensitivity analysis for an elemental sulfur-based two-step denitrification model

A local sensitivity analysis was performed for a chemically synthesized elemental sulfur (S0)-based two-step denitrification model, accounting for nitrite (NO2-) accumulation, biomass growth and S0 hydrolysis. The sensitivity analysis was aimed at verifying the model stability, understanding the model structure and individuating the model parameters to be further optimized. The mass specific area of the sulfur particles (a*) and hydrolysis kinetic constant (k1) were identified as the dominant parameters on the model outputs, i.e. nitrate (NO3-), NO2- and sulfate (SO42-) concentrations, confirming that the microbially catalyzed S0 hydrolysis is the rate-limiting step during S0-driven denitrification. Additionally, the maximum growth rates of the denitrifying biomass on NO3- and NO2- were detected as the most sensitive kinetic parameters

Continuum and discrete approach in modeling biofilm development and structure: a review

The scientific community has recognized that almost 99% of the microbial life on earth is represented by biofilms. Considering the impacts of their sessile lifestyle on both natural and human activities, extensive experimental activity has been carried out to understand how biofilms grow and interact with the environment. Many mathematical models have also been developed to simulate and elucidate the main processes characterizing the biofilm growth. Two main mathematical approaches for biomass representation can be distinguished: continuum and discrete. This review is aimed at exploring the main characteristics of each approach. Continuum models can simulate the biofilm processes in a quantitative and deterministic way. However, they require a multidimensional formulation to take into account the biofilm spatial heterogeneity, which makes the models quite complicated, requiring significant computational effort. Discrete models are more recent and can represent the typical multidimensional structural heterogeneity of biofilm reflecting the experimental expectations, but they generate computational results including elements of randomness and introduce stochastic effects into the solutions

Production of biohythane from food waste via an integrated system of continuously stirred tank and anaerobic fixed bed reactors

The continuous production of biohythane (mixture of biohydrogen and methane) from food waste using an integrated system of a continuously stirred tank reactor (CSTR) and anaerobic fixed bed reactor (AFBR) was carried out in this study. The system performance was evaluated for an operation period of 200 days, by stepwise shortening the hydraulic retention time (HRT). An increasing trend of biohydrogen in the CSTR and methane production rate in the AFBR was observed regardless of the HRT shortening. The highest biohydrogen yield in the CSTR and methane yield in the AFBR were 115.2 (±5.3) L H2/kgVSadded and 334.7 (±18.6) L CH4/kgCODadded, respectively. The AFBR presented a stable operation and excellent performance, indicated by the increased methane production rate at each shortened HRT. Besides, recirculation of the AFBR effluent to the CSTR was effective in providing alkalinity, maintaining the pH in optimal ranges (5.0–5.3) for the hydrogen producing bacteria

Mass loss controlled thermal pretreatment system to assess the effects of pretreatment temperature on organic matter solubilization and methane yield from food waste.

HIGHLIGHTS Direct correlation between substrate composition and TP effect was identified. The new experimental TP set-up minimized organic compound loss during TP of FW. The solubilization of carbohydrate and protein determined the optimal temperature of FW TP. Low temperature (80°C) TP attained the highest carbohydrate solubilization and methane yield. The effects of thermal pretreatment (TP) on the main characteristics of food waste (FW) and its biochemical methane potential (BMP) and distribution of volatile fatty acids (VFAs) under mesophilic condition (35°C) were investigated. The TP experiments were carried out at 80, 100, 120°C for 2 h and 140°C for 1 h. The designed TP set-up was able to minimize the organic matter loss during the course of the pretreatment. Soluble organic fractions evaluated in terms of chemical oxygen demand (COD) and soluble protein increased linearly with pretreatment temperature. In contrast, the carbohydrate solubilization was more enhanced (30% higher solubilization) by the TP at lower temperature (80°C). A slight increment of soluble phenols was found, particularly for temperatures exceeding 100°C. Thermally pretreated FW under all conditions exhibited an improved methane yield compared to the untreated FW, due to the increased organic matter solubilization. The highest cumulative methane yield of 442 (±8.6) mL/gVSadded, corresponding to a 28.1% enhancement compared to the untreated FW, was obtained with a TP at 80°C. No significant variation in the VFAs trends were observed during the BMP tests under all investigated conditions

A sensitivity analysis for sulfur-driven two-step denitrification model

A local sensitivity analysis was performed for a S0-driven two-step denitrification model, accounting for NO2 - accumulation, biomass growth and S0 solubilization. The model sensitivity was aimed at verifying the model stability, understanding the identifiability of the model structure and evaluating the model parameters to be further optimized. The sensitivity analysis identified the mass specific area of the sulfur particles (a*) and hydrolysis kinetic constant (k1) as the dominant parameters. Additionally, the maximum growth rate of the denitrifying biomass on NO3 - (μmax 2,3) and NO2 - (μmax 2,4) were detected as the most sensitive kinetic parameters. Further calibration would be performed for the sensitive model parameters to optimize the quality of the model