Prévision du NO2 en utilisant la méthode du réseau de neurones

Cet article décrit une procédure de modélisation du phénomène de dispersion de la concentration du dioxyde de nitrogène (NO2) en utilisant la technique du perceptron multicouche (MLP). Notre objectif est de prouver que la concentration du NO2 est une variable autorégressive et expliquée par des variables météorologiques. Pour ce faire, nous avons procédé par trois étapes : dans la première étape nous avons utilisé la variable concentration NO2 uniquement, dans la seconde étape nous avons utilisé les variables météorologiques uniquement et dans la troisième étape nous avons utilisé la concentration du NO2 combinée avec les variables météorologiques. Les résultats ont montré que le troisième cas est plus performant que les deux autres ce qui prouve notre hypothèse

AN ANFIS – BASED AIR QUALITY MODEL FOR PREDICTION OF SO2 CONCENTRATION IN URBAN AREA

This paper presents the results of attempt to perform modeling of SO2concentration in urban area in vicinity of copper smelter in Bor (Serbia), using ANFIS methodological approach. The aim of obtained model was to develop a prediction tool that will be used to calculate potential SO2 concentration, above prescribed limitation, based on input parameters. As predictors, both technogenic and meteorological input parameters were considered. Accordingly, the dependence of SO2concentration was modeled as the function of wind speed, wind direction, air temperature, humidity and amount sulfur emitted from the pyrometallurgical process of sulfidic copper concentration treatment

Price Formation of Dry Bulk Carriers in the Chinese Shipbuilding Industry

In this paper we present, for the first time, the price formation of China’s dry bulk carrier using vessel prices quoted by major Chinese shipyards in actual shipbuilding orders. This allows us to investigate the relationship of price and determinants in the Chinese shipbuilding industry by including generic market factors as well as Chinese elements. The analysis, employing Principal Component Regression (PCR) approach, indicates that the time charter rate has the most significantly positive impact. While increases in other four factors, namely shipbuilding cost, price cost margin, shipbuilding capacity utilization and credit rate, have descending order of positive influences. Different from traditional perception, we assert that the most important role of time charter rate plays mainly attributes to the ‘China Factor’ in bulk carrier sector. In addition, simulations are performed to investigate what would happen to the Chinese dry bulk carrier prices under changes of time charter rate and shipbuilding cost. This paper has implications for the Chinese shipyards, shipbuilding industry customers and industry policy makers. Acknowledgment - This research is partly funded by the Chinese Scholarship Council and TORM Foundation.Price Formation, Dry Bulk Carrier, Chinese Shipbuilding Industry

Hybrid artificial intelligence model for prediction of heating energy use

Currently, in the building sector there is an increase in energy use due to the increased demand for indoor thermal comfort. Proper energy planning based on a real measurement data is a necessity. In this study we developed and evaluated hybrid artificial intelligence models for the prediction of the daily heating energy use. Building energy use is defined by significant number of influencing factors, while many of them are difficult to adequately quantify. For heating energy use modelling, the complex relationship between the input and output variables is hard to define. The main idea of this paper was to divide the heat demand prediction problem into the linear and the non-linear part (residuals) by using (Afferent statistical methods for the prediction. The expectations were that the joint hybrid model, could outperform the individual predictors. Multiple linear regression was selected for the linear modelling, while the non-linear part was predicted using feedforward and radial basis neural networks. The hybrid model prediction consisted of the sum of the outputs of the linear and the non-linear model. The results showed that both hybrid models achieved better results than each of the individual feedforward and radial basis neural networks and multiple linear regression on the same dataset. It was shown that this hybrid approach improved the accuracy of artificial intelligence models

The Different Roles of Water in Photocatalytic DeNOx Mechanisms on TiO2 : A Basis for Engineering Nitrate Selectivity?

The authors gratefully acknowledge funding from the UK Engineering and Physical Sciences Research Council (Grant Ref: EP/M003299/1) and the Natural Science Foundation of China (No. 51461135005) International Joint Research Project (EPSRC-NSFC).Peer reviewedPostprin

MEASUREMENT OF GROUND LEVEL OZONE IN SELECTIVE LOCATIONS IN BAGHDAD CITY

The ground level ozone concentration at different locations in Baghdad city was identified. Five different sites have been chosen to identify the ground level ozone concentration. Al- Dora and Al- Za'afarania were chosen as areas contained point source ( power plant station ) in addition to high traffic load , while Al –Uma park, Aden square and Al-Mawal square were chosen as area contained heavy traffic only (line source). The measurement focuses on spring and fall because these periods display favorable meteorology to ozone formation. During the research period the maximum values (peaks) for ground level ozone concentration were observed at fall: at Al-Za'afarania area 101ppb as an average, at Al-Dora 87 ppb as an average and at line source areas 48 ppb as an average. Among the line sources area Al-Mawal square represent the highest peak value at fall 68 ppb. At spring the peaks of ozone concentration observed to be at the same height, 50 ppb for all sites. The downwind sites from the power plant stations at Al-Dora and Al-Za'afarania areas record higher ozone peaks compared with up wind sites. It can be concluded that the effect of power plant stations in forming ozone is larger than traffic load. The comparison between the ground level ozone concentrations that measured during the research period in spring and fall, and the ambient air quality standards (AAQS) shows that: • No exceeded levels were observed in spring for all sites. • In fall the AAQS for ozone was exceeded in Al-Za'afarania area at 12: PM, 1: PM, 2: PM and 3: PM, and in Al-Dora at 2: PM

Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model

Models to predict seasonal hydrogen sulfide (H2S) concentrations were constructed using neural networks. To this end, two types of generalized regression neural networks and radial basis function networks are considered and optimized. The input data for H2S were collected from August 2005 to Fall 2006 from a huge industrial complex located in Ansan City, Korea. Three types of seasonal groupings were prepared and one optimized model is built for each dataset. These optimized models were then used for the analysis of the sensitivity and main effect of the parameters. H2S was noted to be very sensitive to rainfall during the spring and summer. In the autumn, its sensitivity showed a strong dependency on wind speed and pressure. Pressure was identified as the most influential parameter during the spring and summer. In the autumn, relative humidity overwhelmingly affected H2S. It was noted that H2S maintained an inverse relationship with a number of parameters (e.g., radiation, wind speed, or dew-point temperature). In contrast, it exhibited a declining trend with a decrease in pressure. An increase in radiation was likely to decrease during spring and summer, but the opposite trend was predicted for the autumn. The overall results of this study thus suggest that the behavior of H2S can be accounted for by a diverse combination of meteorological parameters across seasons

Agro-ecological evaluation of sustainable area for citrus crop production in Ramsar District, Iran

Citrus growing is regarded as an important cash crop in Ramsar, Iran. Ramsar District has a temperate climate zone, while citrus is a sub-tropical fruit. Few studies on citrus crop in terms of negative environmental factors have been carried out by researchers around the world. This study aims to integrate Geographical Information System (GIS) and Analytical Network Process (ANP) model for determination of citrus suitability zones. This study evaluates the agro-ecological suitability, determine potentials and constraints of the region based on effective criteria using ANP model. ANP model was used to determine suitable, moderate and unsuitable areas based on (i) socio-economic, morphometry and hydro-climate factors using 15 layers based on experts’ opinion; (ii) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite image of the year 2003 with 98.45% overall accuracy, and (iii) developed Multiple Linear Regression (MLR) model for citrus prediction. Thereby, weighted overlay of 15 factors was obtained using GIS. In this study, the citrus orchards map of 2003 and the new map of the citrus areas of 2014 namely Citrus State Development Program (CSDP) of the study area were compared. The results of this study demonstrated: (i) suitable areas (free risk areas) based on negative environmental factors and areas which are susceptible to citrus plantation; (ii) high-risk areas which are unsuitable for citrus plantation, and (iii) the high weights derived by ANP model were assigned to altitude, frost and minimum temperature. The MLR model was successfully developed to predict citrus yield in Ramsar District by 10% error. The MLR model would propose optimum citrus crop production areas. As conclusion, the main outcome of this study could help growers and decision makers to enhance the current citrus management activities for current and future citrus planning