Performance of a two-stage anaerobic digestion system treating fruit pulp waste: The impact of substrate shift and operational conditions

Abstract Upgrading conventional single-stage mesophilic anaerobic digestion to an advanced digestion technology can increase sludge stability, reduce pathogen content, increase biogas production, and also increase ammonia concentrations recycled back to the liquid treatment train. Limited information is available to assess whether the higher ammonia recycle loads from an anaerobic sludge digestion upgrade would lead to higher discharge effluent ammonia concentrations. Biowin, a commercially available wastewater treatment plant simulation package, was used to predict the effects of anaerobic digestion upgrades on the liquid train performance, especially effluent ammonia concentrations. A factorial analysis indicated that the influent total Kjeldahl nitrogen (TKN) and influent alkalinity each had a 50-fold larger influence on the effluent NH3 concentration than either the ambient temperature, liquid train SRT or anaerobic digestion efficiency. Dynamic simulations indicated that the diurnal variation in effluent NH3 concentration was 9 times higher than the increase due to higher digester VSR. Higher recycle NH3 loads caused by upgrades to advanced digestion techniques can likely be adequately managed by scheduling dewatering to coincide with periods of low influent TKN load and ensuring sufficient alkalinity for nitrification.

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Robust Inferences from a Before-and-After Study with Multiple Unaffected Control Groups

Journal of Causal Inference ◽

10.1515/jci-2012-0010 ◽

2013 ◽

Vol 1 (2) ◽

pp. 209-234 ◽

Cited By ~ 1

Author(s):

Pengyuan Wang ◽

Mikhail Traskin ◽

Dylan S. Small

Keyword(s):

Sensitivity Analysis ◽

Control Groups ◽

Two Stage ◽

Time Effects ◽

Unaffected Control ◽

Group Time ◽

Before And After ◽

Group Variation ◽

The Impact ◽

Before And After Study

AbstractThe before-and-after study with multiple unaffected control groups is widely applied to study treatment effects. The current methods usually assume that the control groups’ differences between the before and after periods, i.e. the group time effects, follow a normal distribution. However, there is usually no strong a priori evidence for the normality assumption, and there are not enough control groups to check the assumption. We propose to use a flexible skew-t distribution family to model group time effects, and consider a range of plausible skew-t distributions. Based on the skew-t distribution assumption, we propose a robust-t method to guarantee nominal significance level under a wide range of skew-t distributions, and hence make the inference robust to misspecification of the distribution of group time effects. We also propose a two-stage approach, which has lower power compared to the robust-t method, but provides an opportunity to conduct sensitivity analysis. Hence, the overall method of analysis is to use the robust-t method to test for the overall hypothesized range of shapes of group variation; if the test fails to reject, use the two-stage method to conduct a sensitivity analysis to see if there is a subset of group variation parameters for which we can be confident that there is a treatment effect. We apply the proposed methods to two datasets. One dataset is from the Current Population Survey (CPS) to study the impact of the Mariel Boatlift on Miami unemployment rates between 1979 and 1982.The other dataset contains the student enrollment and grade repeating data in West Germany in the 1960s with which we study the impact of the short school year in 1966–1967 on grade repeating rates.

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Two-Stage anaerobic digestion in agroindustrial waste treatment: A review

Journal of Environmental Management ◽

10.1016/j.jenvman.2020.111854 ◽

2021 ◽

Vol 281 ◽

pp. 111854

Author(s):

Paulo André Cremonez ◽

Joel Gustavo Teleken ◽

Thompson Ricardo Weiser Meier ◽

Helton José Alves

Keyword(s):

Anaerobic Digestion ◽

Waste Treatment ◽

Two Stage ◽

Agroindustrial Waste

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Innovations in anaerobic digestion: a model-based study

Biotechnology for Biofuels ◽

10.1186/s13068-020-01864-z ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Karol Postawa ◽

Jerzy Szczygieł ◽

Marek Kułażyński

Keyword(s):

Mathematical Model ◽

Anaerobic Digestion ◽

Production Efficiency ◽

Biogas Production ◽

Biogas Plant ◽

Methane Content ◽

Pig Manure ◽

Pig Slurry ◽

Production Chain ◽

The Impact

Abstract Background Increasing the efficiency of the biogas production process is possible by modifying the technological installations of the biogas plant. In this study, specific solutions based on a mathematical model that lead to favorable results were proposed. Three configurations were considered: classical anaerobic digestion (AD) and its two modifications, two-phase AD (TPAD) and autogenerative high-pressure digestion (AHPD). The model has been validated based on measurements from a biogas plant located in Poland. Afterward, the TPAD and AHPD concepts were numerically tested for the same volume and feeding conditions. Results The TPAD system increased the overall biogas production from 9.06 to 9.59%, depending on the feedstock composition, while the content of methane was slightly lower in the whole production chain. On the other hand, the AHPD provided the best purity of the produced fuel, in which a methane content value of 82.13% was reached. At the same time, the overpressure leads to a decrease of around 7.5% in the volumetric production efficiency. The study indicated that the dilution of maize silage with pig manure, instead of water, can have significant benefits in the selected configurations. The content of pig slurry strengthens the impact of the selected process modifications—in the first case, by increasing the production efficiency, and in the second, by improving the methane content in the biogas. Conclusions The proposed mathematical model of the AD process proved to be a valuable tool for the description and design of biogas plant. The analysis shows that the overall impact of the presented process modifications is mutually opposite. The feedstock composition has a moderate and unsteady impact on the production profile, in the tested modifications. The dilution with pig manure, instead of water, leads to a slightly better efficiency in the classical configuration. For the TPAD process, the trend is very similar, but the AHPD biogas plant indicates a reverse tendency. Overall, the recommendation from this article is to use the AHPD concept if the composition of the biogas is the most important. In the case in which the performance is the most important factor, it is favorable to use the TPAD configuration.

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A Two-Stage Joint Model for Nonlinear Longitudinal Response and a Time-to-Event with Application in Transplantation Studies

Journal of Probability and Statistics ◽

10.1155/2012/194194 ◽

2012 ◽

Vol 2012 ◽

pp. 1-18 ◽

Cited By ~ 6

Author(s):

Magdalena Murawska ◽

Dimitris Rizopoulos ◽

Emmanuel Lesaffre

Keyword(s):

Empirical Bayes ◽

Cox Model ◽

Data Sets ◽

Two Stage ◽

Nonlinear Mixed Effects Model ◽

Resistance Evolution ◽

Longitudinal Response ◽

Longitudinal Measurements ◽

Subject Specific ◽

The Impact

In transplantation studies, often longitudinal measurements are collected for important markers prior to the actual transplantation. Using only the last available measurement as a baseline covariate in a survival model for the time to graft failure discards the whole longitudinal evolution. We propose a two-stage approach to handle this type of data sets using all available information. At the first stage, we summarize the longitudinal information with nonlinear mixed-effects model, and at the second stage, we include the Empirical Bayes estimates of the subject-specific parameters as predictors in the Cox model for the time to allograft failure. To take into account that the estimated subject-specific parameters are included in the model, we use a Monte Carlo approach and sample from the posterior distribution of the random effects given the observed data. Our proposal is exemplified on a study of the impact of renal resistance evolution on the graft survival.

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