Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks

Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network （ML-CNN） is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.     

Polycarbonate biodegradation by isolated molds using clear-zone and atomic force microscopic methods

The accumulation of dry waste containing synthetic polymers due to their resistance to microorganisms and other environmental factors has posed some serious problems to the environment in recent years. On the other hand, plastics constitute the foundations of economy as they are widely used in agriculture, constructions, packaging, health care and also medicine. The aim of this research was to investigate the role of different isolated fungi in the degradation of polycarbonate polymers. For this purpose, sampling was done using the garden soil and waste leachate from Isfahan Waste Management Organization. Samples were enriched in the liquid mineral salt medium supplemented with polycarbonate and then were transferred to the same medium solidified with agar to isolate and identify different fungi. Finally, their biodegradation activity was investigated with the help of clear-zone and atomic force microscopic (AFM) techniques, and also lipase and amylase production was tested. Among 15 isolated genera of mold fungi, Fusarium, Ulocladium, Chrysosporium and Penicillium showed biodegradation activity. According to the diameter of clear zone around the fungal colonies and also AFM results, the highest rate of degradation was related to Fusarium. Lipase activity of all isolated fungi was positive, but amylase activity of Ulocladium was negative. It can be concluded that some fungal strains such as Fusarium can be used for the biodegradation of plastic materials as it leads to a very eco-friendly biodegradation process.     

Triethylamine removal using biotrickling filter (BTF): effect of height and recirculation liquid rate on BTFs performance

This study investigated the removal of triethylamine using a biotrickling filter. The influence of affecting parameters, such as height and recirculation liquid rate (V _L) on contaminant removal efficiency, was examined in detail. The results demonstrated that in the constant empty bed residence time (EBRT), when V _L was increased, the removal efficiency (RE) increased. Also, for a specific V _L, increasing EBRT could also increase RE values. However, it seems that an increasing V _L is a more cost-effective way to enhance RE as compared to an increasing EBRT. The obtained outcomes represented that for a constant EBRT, an increase in inlet loading (IL) could decrease RE. For lower ILs, the removal of the contaminant could be carried out faster in height. The first part of the bed contributed the most to contaminant removal, and for the lower ILs, the contribution was even further. For the first section of bed in a constant IL, increasing EBRT could increase RE. In a constant IL and EBRT, increasing V _L could increase RE, as well as the removed mass loading by at least 20–25 g/m³/h. Also, the effect of the V _L increase on removal amount in the second and third sections of the bed was negligible.     

Revisited rainfall network design: evaluation of heuristic versus entropy theory methods

Rain gauges are installed to measure pointwise precipitation and provide a comprehensive perspective of its spatiotemporal variations. Selection of an efficient and reliable rainfall monitoring network is a key role to reduce its maintenance and handling cost. The main purpose of the current paper is to compare efficiencies of various network design methods. The applied methods are entropy theory (as probabilistic multi-criteria decision-making) and genetic algorithm (as one of the heuristic methods) with three objective functions. Also, two classical (ordinary kriging; OK) and modern (Bayesian maximum entropy; BME) spatial simulation methods were undertaken to provide a comprehensive spatial simulation of precipitation. The proposed assessment was applied on spatial mean annual precipitation variability in the Namak Lake watershed located in the central part of Iran. The final efficiency of developed network design methods is evaluated in terms of three criteria known as mass estimation error, total error, and spatial bias of estimated rainfall. Based on the results, different network distributions have been proposed by the methods. Despite the reliability of the heuristic approach in nonlinear optimization due to its mathematical principle, the results indicated that the network design based on entropy theory can be used to estimate long-term mean annual precipitation more reliably and accurately. Results of the mass estimation error have shown 78 and 83% superiority of the entropy theory approach from the worst approach obtained from the OK and BME methods, respectively.     

The effect of different intensity measures and earthquake directions on the seismic assessment of skewed highway bridges

In this study the probable seismic behavior of skewed bridges with continuous decks under earthquake excitations from different directions is investigated.A 45° skewed bridge is studied.A suite of 20 records is used to perform an Incremental Dynamic Analysis(IDA) for fragility curves.Four different earthquake directions have been considered:-45°, 0°, 22.5°, 45°.A sensitivity analysis on different spectral intensity measures is presented; efficiency and practicality of different intensity measures have been studied.The fragility curves obtained indicate that the critical direction for skewed bridges is the skew direction as well as the longitudinal direction.The study shows the importance of finding the most critical earthquake in understanding and predicting the behavior of skewed bridges.     

Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine

Natural Resources Research - It is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted...     

Effect of empty bed residence time on biotrickling filter performance: case study—triethylamine

In this study, a laboratory-scale biotrickling filter (BTF) is used to remove Triethylamine (TEA) from gaseous wastes. The BTF is made of stainless steel with a height of 210 cm and an internal diameter of 21 cm packed with lava rocks. TEA elimination pattern was evaluated by changing empty bed residence times (EBRTs). The maximum elimination capacity (EC) has been determined to be 87 g/m³/h. At all EBRTs 52, 31, 20, and 10 s, contaminant transferring from gas phase to liquid was more than the EC. Also, the removal efficiency was 100 % for a mass loading of 100 g/m³/h. While the liquid recirculation velocity of 3.466 m³/m²/h was maintained, the flow rate was adjusted to 60, 100, 156, and 312 L/min. The results show that due to the high solubility of TEA in water for all the EBRTs, TEA can be solved in the circulated liquid and then be degraded gradually by microorganisms. Therefore, the least EBRT of 10 s is more appropriate.