This paper presents a new mathematical model and a two-layer neural network approach to predict the single droplet collection efficiency (SDCE), h d , of countercurrent spray towers. SDCE values were calculated using MATLAB \ algorithm for 205 different artificial scenarios given in a large range of operating conditions. Theoretical results were compared with outputs obtained from a two-layer neural network and DataFit \ scientific software. The predicted model developed from linear-nonlinear regression analysis and network outputs agreed with the theoretical data, and all predictions proved to be satisfactory with a correlation coefficient of about 0.921 and 0.99, respectively. By using the proposed model, iterations between Reynolds number (Re), drag coefficient (C D ) and terminal velocity values (v T ) were neglected for a large range of operating conditions. SDCE values were also obtained speedily and practically for five main operating inputs used in the model.

Coronavirus disease 2019 (COVID-19) is caused by the SARS-CoV-2 virus and has been affecting the world since the end of 2019. The disease led to significant mortality and morbidity in Turkey, since the first case was reported on March 11 th , 2020. Studies suggest a positive association between air pollution and SARS-CoV-2 infection. The aim of the present study was to investigate the role of ambient particulate matters (PM), as potential carriers for SARS-CoV-2. Ambient PM samples in various size ranges were collected from 13 sites including urban and urban-background locations and hospital gardens in 10 cities across Turkey between 13th of May and 14th of June 2020 to investigate the possible presence of SARS-CoV-2 RNA on ambient PM. A total of 203 daily samples (TSP, n=80; PM 2.5 n=33; PM 2.5-10 , n=23; PM 10 μm, n=19; and 6 size segregated PM, n=48) were collected using various samplers. The N1 gene and RdRP gene expressions were analyzed for the presence of SARS-CoV-2, as suggested by the Centers for Disease Control and Prevention (CDC). According to real time (RT)-PCR and three-dimensional digital (3D-d) PCR analysis, dual RdRP and N1 gene positivity were detected in 20 (9.8 %) samples. Ambient PM-bound SARS-CoV-2 was analyzed quantitatively and the air concentrations of the virus ranged from 0.1 copies/m 3 to 23 copies/m 3 . The highest percentages of virus detection on PM samples were from hospital gardens in Tekirdağ, Zonguldak, and Istanbul, especially in PM 2.5 mode. Findings of this study have suggested that SARS-CoV-2 may be transported by ambient particles, especially at sites close to the infection hot-spots. However, whether this has an impact on the spread of the virus infection remains to be determined.

No abstract

Biogas production rate was modeled and estimated in a thermophilic upflow anaerobic sludge blanket digester. Data set covers a time period of both steady-state conditions and an abnormal operation condition, i.e., organic loading shocks. Multilayer neural networks topology was used as the modeling tool. Half of the experimental data were used for the training of the model and the remaining half were used for the testing stage. Model results were evaluated from the point of view of both steady conditions and abnormal conditions. It was seen from the time series trends of the estimated data that biogas production rates at steady state operation conditions were closely estimated by the model while the results for organic loading shocks were sufficiently followed. Artificial neural network models gave encouraging estimation results for the online control of thermophilic reactors.

In this study, a clinoptilolite bed column system was used to remove ammonium from municipal landfill leachate. Laboratory-scale column experiments were conducted in upflow fixed-bed and fluidized-bed modes with different ammonium concentrations. Ammonium removal was managed mainly by Ca2+ and K+ cations of clinoptilolite, and higher treatment performances were obtained at lower flow rates. On the other hand, higher effluent volumes and removal rates were produced by lower ammonium concentrations, and increased expansion ratios in the fluidized-bed column reduced the treatment efficiency. The sum of normalized errors (SNE) procedure was applied using five different error functions to model the experimental data. The Clark and Yoon−Nelson kinetic models were applied to column data to predict the breakthrough data, and the results indicated that the Clark model is better for modeling the experimental data. Ammonia stripping was investigated as a pretreatment step to obtain lower ammonium concentrations in the effluent. With a 12-h aeration time at pH 12, the ammonium concentration of leachate was decreased from about 3000 to below 400 mg/L. These results indicate that combining ammonia stripping and an upflow fixed-bed system is feasible in reducing higher ammonium concentrations to satisfactory levels.