Simulation of sludge blanket height in clarifiers

Abstract

Sludge blanket height (SBH) is an important parameter in the clarifier design, operation and control. Based on an overview and classification of SBH algorithms, a modified SBH algorithm is proposed by incorporating a threshold concentration limit into a relative concentration sharp change algorithm to eliminate the disturbance of compression interfaces on the correct simulation of SBH. Pilot-scale test data are adopted to compare reliability of three SBH algorithms reported in literature and the modified SBH algorithm developed in this paper. Calculated results demonstrate that the three SBH algorithms give results with large deviation (> 50%) from measured SBH, especially under low solid flux conditions. The modified algorithm is computationally efficient and reliable in matching the measured data. It is incorporated into a onedimensional clarifier model for stable simulation of pilot-scale experimental clarifier data and into dynamic simulation of a full-scale wastewater treatment plant (WWTP) clarifier data.

Forecast of Transportation CO2 Emissions in Shanghai under Multiple Scenarios

“…From 2015 to 2019, the growth rate continued to increase. This is consistent with the conclusion of Zhou [43]. The results are slightly different from those of Chen [42] and Wu [41], which may be due to the different study periods and the different choices of energy types for emissions.…”

“…Furthermore, our study finds that oil consumption is the main source of transport carbon emissions in Shanghai, which is consistent with the findings of other scholars. Zhou [43] found that the steady growth of fuel and kerosene was the main factor contributing to the growth of transportation emissions. Wu [41] found that petroleum consumption contributed the most to Shanghai's transportation CO 2 emissions, for example, reaching 91.80% in 2010.…”

“…In other studies, because of the partial differences in factor selection, the ranking of the degree of influence is not the same. Zhou [43] found that, according to the degree of impact, the order of factors is population scale, freight turnover, passenger turnover, the proportion of secondary industries, the proportion of tertiary industries, and per capita GDP. Chen [42] found that the factors ranked as urbanization rate, per capita GDP, and per capita consumption expenditure.…”

“…Therefore, the development of transportation CO 2 emissions is crucial to the construction of a low-carbon city and the achievement of peak carbon in Shanghai. Yet previous studies have only focused on the calculation of and influential factors in Shanghai's transportation CO 2 emissions between 2000-2010 [41], 1998-2012 [42], and 2001-2015 [43], without making predictions.…”

Causal analysis of container loss accidents based on AHP-CV and 24 model

“…The indirect cause includes four aspects: safety knowledge, safety awareness, safety psychology, and safety physiology, The immediate causes include unsafe human behavior and unsafe state of objects; The process is shown in Figure 1. In recent years, 24 model has been widely used in accident analysis in the field of maritime safety, such as Jiao et al [9] conducted research in 2022 on the safety culture of shipping enterprises by combining the 24 model and 5why methods; Scholars such as Huang et al [10] combined 24 models and decision trees in 2022, and used C5.0 algorithm to quantitatively analyze the causes of ship accidents.…”