A 3-D model to predict the temperature of liquid manure within storage tanks

Rennie, Timothy J.; Baldé, Hambaliou; Smith, Ward; VanderZaag, Andrew

doi:10.1016/j.biosystemseng.2017.08.014

Cited by 20 publications

(13 citation statements)

References 21 publications

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“…The reverse of the temperature differential process between the top and bottom layers of stored manure happens during the cool storage period. These modeled results are consistent with results studies that have reported temperature variability by depth in manure pits [15,21] and lagoons [44]. The compartmental PBM predicts differentiated manure TAN concentration at different depths (layers) during the storage period, while the non-compartmental PBM predicts uniform TAN concentration in the manure pit ( Figs.…”

Section: Ammonia Emission Ratessupporting

confidence: 91%

“…(1), discretized by the finite difference method. Using the 1-D approach for temperature simulation has been reported as adequate by Rennie et al [21]. The finite difference method used to discretize the heat transfer equation presents adequate computational tractability and ease of application to the geometry resulting from accumulating manure during the storage period.…”

Section: Heat Transfer and Temperature Profile In Stored Manurementioning

confidence: 99%

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Compartmental process-based model for estimating ammonia emissions from stored liquid dairy manure

2020

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This study developed a compartmental process-based model (PBM) as an alternative approach to estimate ammonia emission from liquid dairy manure during storage to improve the accuracy of quantification tools for nitrogen cycling at the farm level compared to the currently used non-compartmental PBM. The compartmental PBM developed partitions stored manure into several layers in the vertical domain to facilitate the spatial temperature and substrate concentration calculations. In contrast, the non-compartmental PBMs currently used consider the bulk stored manure as a material with homogenous properties. The models used similar equations and processes from the well-established principles of heat and mass transfer and pertinent known biogeochemical processes for the production and emission of ammonia. The models were calibrated, and their performance assessed using literature-based experimentally derived ammonia emission rates. Additionally, a scenario analysis was conducted to compare ammonia emission rate estimates by the models from stored liquid manure at a dairy farm during two (cold and warm) storage periods. Model outputs were sensitive to ambient air temperature, manure pH, wind speed, manure total ammoniacal nitrogen concentration, and the two-way interactions of ambient air temperature, pH, and wind speed. Ammonia emission rates by the models and literature-based experimentally derived ammonia emission rates were similar (p > 0.05). In general, the compartmental PBM predicted lower ammonia emission rates compared to the non-compartmental PBM. Also, the compartmental PBM had a relatively better performance compared to the non-compartmental PBM. Compared to the non-compartmental PBM, the compartmental model estimated lower ammonia emission rates, i.e., 28% and 34%, for the cold and warm periods, respectively.

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Section: Ammonia Emission Ratessupporting

confidence: 91%

Section: Heat Transfer and Temperature Profile In Stored Manurementioning

confidence: 99%

Compartmental process-based model for estimating ammonia emissions from stored liquid dairy manure

2020

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“…The diurnal temperature fluctuations over short periods of time are shown in figure 5. The large fluctuations in daily temperature are due to the small size of the buckets used in this experiment and would not occur in full-scale liquid manure storage (Baldé et al, 2016;Rennie et al, 2017). A greater daily internal temperature fluctuation occurred in the water treatments as compared to the manure treatments.…”

Section: Surface and Internal Temperaturesmentioning

confidence: 89%

“…Manure temperature greatly affects emissions of methane, other greenhouse gasses, and odors (IGES, 2006;Muck and Steenhuis, 1982;Dewes et al, 1990). Recently, Rennie et al (2017) developed a model to predict liquid manure temperature based on the energy budget; however, due to lack of data, it was necessary to assume that evaporation from liquid manure was equivalent to water. This assumption presents an area of uncertainty that needs to be addressed.…”

mentioning

confidence: 99%

Surface Covers Affect Liquid Manure Temperature, Albedo, and Evaporation

Cluett¹,

VanderZaag²,

Rennie³

et al. 2020

Transactions of the ASABE

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HIGHLIGHTS Evaporation from clear water, manure, and separated liquid manure was 4.6 mm d -1 on average.  Straw, foam, geotextile, and roof covers decreased evaporation by 54%, 53%, 31%, and 21%, respectively.  Albedo was highest for floating foam covers and lowest for metal roof covers.  Straw, foam, and geotextile increased manure temperature compared to uncovered manure.ABSTRACT. Evaporation is a key component of the surface energy budget of liquid manure. Models rely on accurate energy budgets to predict manure temperature, which in turn is used to model temperature-dependent greenhouse gas emissions from liquid manure storages. Due to lack of data, it has been assumed that liquid manure has similar evaporative properties to water; however, this assumption may be inaccurate. Many factors, including manure crusting, covers, and turbidity, are all likely to affect the surface energy budget and the evaporation rate. This experiment investigated the differences in evaporation between eight treatments, including water, dyed water, raw and separated liquid manure, and four commonly used covers (straw, geotextile, foam, and roof), by measuring weekly evaporation. Albedo, surface temperatures, and internal temperatures were also measured to determine treatment effects. Over the 10-week study, no significant difference was found between the evaporation rates of water, raw manure, and separated liquid manure, with an average rate of 4.6 mm d -1 . Notably, the raw manure did not form a consistent surface crust, which may explain the similarities in evaporation rates in this study and is unlikely to represent manure with a crust. Overall, covers significantly decreased evaporative losses by between 21% and 54% compared to uncovered raw manure. Average evaporation rates of the covered treatments were 1.9 mm d -1 for straw cover, 2.0 mm d -1 for foam cover, 2.9 mm d -1 for geotextile cover, and 3.4 mm d -1 under a roof cover. Similarities between each treatment and water as well as between the four covered treatments and the uncovered raw manure were found using linear regression on weekly evaporation. Generally, the uncovered treatments were more similar and could be predicted (high R 2 ) by multiple linear regression with environmental variables, while the covered treatments differed more and were not as well predicted (lower R 2 ). Results from this study can help adjust evaporation rates in biophysical models to improve estimates of manure temperature, tank holding capacity, and emission predictions.

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“…Along with the large differences between liquid and dry systems, temperature plays a major role in regulating manure emissions, and model misrepresentation of this effect (which can occur, for example, when using surface skin temperatures to approximate manure lagoon temperatures, as in the GEPA inventory) can lead to significant bottom-up errors in both the magnitude and seasonality of predicted fluxes (Park et al, 2006). Local factors such as solar absorptivity, wind, manure depth, pH, and humidity can also influence emissions (Rennie et al, 2017;VanderZaag et al, 2013) but are not generally accounted for in inventories. Further, use of lagoon covers and anaerobic digestion systems can reduce methane emissions by up to 90%, and inadequate information on such factors will lead to inventory errors.…”

Section: Livestock Methane: Enteric Emissions Well-represented But Lamentioning

confidence: 99%

Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions

Yu¹,

Millet²,

Wells³

et al. 2020

Preprint

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Abstract. We apply airborne measurements across three seasons (summer, winter, spring 2017–2018) in a multi-inversion framework to quantify methane emissions from the US Corn Belt and Upper Midwest, a key agricultural and wetland source region. Combing our seasonal results with prior fall values we find that wetlands are the largest regional methane source (32 %, 20 [16–23] Gg/d), while livestock (enteric/manure; 25 %, 15 [14–17] Gg/d) are the largest anthropogenic source. Natural gas/petroleum, waste/landfills, and coal mines collectively make up the remainder. Optimized fluxes improve model agreement with independent datasets within and beyond the study timeframe. Inversions reveal coherent and seasonally dependent spatial errors in the WetCHARTs ensemble mean wetland emissions, with an underestimate for the Prairie Pothole region but an overestimate for Great Lakes coastal wetlands. Wetland extent and emission temperature dependence have the largest influence on prediction accuracy; better representation of coupled soil temperature-hydrology effects is therefore needed. Our optimized regional livestock emissions agree well with Gridded EPA estimates during spring (to within 7 %), but are ~ 25 % higher during summer/winter. Spatial analysis further shows good top-down/bottom-up agreement for beef facilities, but larger (~ 30 %) seasonal discrepancies for dairies and hog farms. Findings thus support bottom-up enteric emission estimates but suggest errors for manure; we propose that the latter reflects inadequate treatment of management factors including field application. Overall, our results confirm the importance of intensive animal agriculture for regional methane emissions, implying substantial mitigation opportunities through improved management.

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A 3-D model to predict the temperature of liquid manure within storage tanks

Cited by 20 publications

References 21 publications

Compartmental process-based model for estimating ammonia emissions from stored liquid dairy manure

Compartmental process-based model for estimating ammonia emissions from stored liquid dairy manure

Surface Covers Affect Liquid Manure Temperature, Albedo, and Evaporation

Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions

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