SummaryThis paper presents a simulation study of municipal solid waste (MSW) combustion using a multi‐fuel combustion model, which was an extended version from the one previously developed by the authors. The model was a steady‐state model with partial transient implementation as user‐defined functions (UDF) developed in ANSYS Fluent platform to investigate the combustion behavior of fuel interacting in the solid and gas phases. The solid phase was simulated using discrete particle model (DPM). The extended simulation model took into consideration the difference of properties and combustion characteristics of different MSW components. The main improvement was the application of a newly developed algorithm that allows the influence of the surrounding particles and environment on the simulating particle and the possible synergy between them. The combustion and furnace zone of an MSW power plant in Hatyai, Thailand was the simulated case study and the measured temperatures from various sensor locations at the plant exhaust were used for model validation. The inputs for volatile combustion were obtained from pyrolysis experiments. For compatibility with GRI‐Mech 3.0, CH3CHO and C2H2 were used to represent the tar component. In the fuel bed zone, the simulated temperatures were higher than the measured temperatures up to 36%, which were the results of the complete combustion of the simulated fuel bed at too early location on the grate. For the fluid phase, the simulated mean temperature at the locations that were not affected by fuel bed combustion was approximately 6% different from the measured values. Synergy between particles was observed and attributed to the effect of nearby particles' properties on the combustion of simulating particle.
Objectives
Face masks are low-cost, but effective in preventing transmission of COVID-19. To visualize public’s practice of protection during the outbreak, we reported the rate of face mask wearing using artificial intelligence-assisted face mask detector, AiMASK.
Methods
After validation, AiMASK collected data from 32 districts in Bangkok. We analyzed the association between factors affecting the unprotected group (incorrect or non-mask wearing) using univariate logistic regression analysis.
Results
AiMASK was validated before data collection with accuracy of 97.83% and 91% during internal and external validation, respectively. AiMASK detected a total of 1,124,524 people. The unprotected group consisted of 2.06% of incorrect mask-wearing group and 1.96% of non-mask wearing group. Moderate negative correlation was found between the number of COVID-19 patients and the proportion of unprotected people (r = -0.507, p<0.001). People were 1.15 times more likely to be unprotected during the holidays and in the evening, than on working days and in the morning (OR = 1.15, 95% CI 1.13–1.17, p<0.001).
Conclusions
AiMASK was as effective as human graders in detecting face mask wearing. The prevailing number of COVID-19 infections affected people’s mask-wearing behavior. Higher tendencies towards no protection were found in the evenings, during holidays, and in city centers.
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