In this study, real-scale wastewater treatment plant (Hurma WWTP) sludge anaerobic digestion process was modeled by Anaerobic Digestion Model (ADM1) with the purpose of generating the data to understand the process better by contributing to the prediction of the process operational conditions and process performance, which will be a base for future anaerobic sludge stabilization process investments.
Real-scale anaerobic sludge digestion process data was evaluated in terms of known process and state variables and also process yields. Average VS removal yield, methane production yield, and methane production rate values of the anaerobic sludge digestion unit were calculated as 46.4%, 0.49 m3CH4/kg VSremoved, and 0.33 m3 CH4/m3day, respectively. In this study, ADM1 was intended to predict the behavior of real-scale anaerobic digester processing sewage sludge under dynamic conditions. To estimate the variables of real-scale sludge anaerobic digestion process with high accuracy and to provide high model prediction performance, values of the four parameters (disintegration rate constant, carbohydrate hydrolysis rate constant, protein hydrolysis rate constant, and lipid hydrolysis rate constant) that have strong effects on structured ADM1 were estimated by using the parameter estimation module in Aquasim program and their values were found as 0.101, 10, 10, and 9.99, respectively. When the numbers of kinetic parameters with the processes included in ADM1 along with the dynamic and non-linear structure of the real scale anaerobic digestion were taken into consideration, model simulations were in good agreement with measured results of the biogas flow rate, methane flow rate, pH, total alkalinity, and volatile fatty acids. 相似文献
The uncertainty of reported greenhouse gases emission inventories obtained by the aggregation of partial emissions from all
sources and estimated to date for several countries is very high in comparison with the countries’ emissions limitation and
reduction commitments under the Kyoto Protocol. Independent calculation of the estimates could confirm or question the undertainty
estimates values obtained thus far. One of the aims of this paper is to propose statistical signal processing methods to enable
calculation of the inventory variances. The annual reported emissions are used and temporal smoothness of the emissions curve
is assumed. The methods considered are: a spline-function-smoothing procedure; a time-varying parameter model; and the geometric
Brownian motion model. These are validated on historical observations of the CO2 emissions from fossil fuel combustion. The estimates of variances obtained are in a similar range to those obtained from
national inventories using TIER1 or TIER2. Additionally, some regularities in the observed curves were noticed. 相似文献