NN-AirPol: a neural-networks-based method for air pollution evaluation and control

Karaca, Ferhat; Nikov, Alexander; Alagha, Omar

doi:10.1504/ijep.2006.011214

Cited by 24 publications

(9 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Environmental Chemistry of Aerosols Since real-time monitoring is relatively expensive and one of the main reasons for monitoring is to aid in the assessment of human exposure to pollution, numerous papers have been published on methodology to identify idea sites for pollution monitoring. A full understanding of the complex interactions behind the aerosol environmental effects requires a multidisciplinary approach, involving both experimental and theoretical methods [27][28][29][30].…”

Section: Dynamics Of Atmospheric Aerosol Size Distributionsmentioning

confidence: 99%

Thermodynamics of Aqueous Systems

Clark¹

2015

Groundwater Geochemistry and Isotopes

View full text Add to dashboard Cite

Particles in the atmosphere are distinguished by their size, shape and composition. They can be directly emitted from sources and can be formed in the atmosphere by chemical reactions and physical processes. Once particles are formed, their properties can be modulated in space and time by atmospheric physical and chemical processes, such as condensation, evaporation and coagulation. Eventually, the particles are removed from the atmosphere by wet or dry deposition, with such removal occurring minutes to weeks after their release or formation, and after travelling metres to thousands of kilometres. This chapter summarises key physical and chemical properties of atmospheric aerosols. These properties can be described at various levels of aggregation (bottom-up, from the single-particle perspective) or discretisation (topdown, from the aerosol perspective). An accounting of each particle by size and composition would provide the finest resolution. In contrast, the bulk composition over all particles would be the coarsest resolution. Furthermore, given the dynamic nature of atmospheric aerosols, the degree of time integration is important.Particles in the atmosphere are directly emitted by sources (primary emissions) or formed in the atmosphere by physicochemical processes (secondary formation) [1]. Combustion and other high-temperature processes are largely responsible for primary emissions of fine-mode particles, while mechanical processes such as grinding, entrainment of dust and soil and droplet formation by waves generate coarse-mode particles. Condensable gases can homogeneously nucleate to form new particles in the atmosphere if there is insufficient surface area for their uptake. Heterogeneous condensation is the uptake of vapour by pre-existing particles, with the reverse process of evaporation also important.A summary of the atmospheric aerosol properties must necessarily reflect upon the methods used to arrive at the current state of knowledge. As tools to probe the atmosphere continue to advance, refinements will be made to the understanding of atmospheric aerosols. Starting with the dimensions of size, composition, space and time, observational data are collected from measurements that integrate across these dimensions. Most observational data have been collected at fixed locations, although more data are being obtained from satellite and other profiling instruments that provide spatial resolution [2,3]. The spatial zone represented by monitoring at a fixed site will depend on the property of interest. It may be possible to infer

show abstract

Section: Dynamics Of Atmospheric Aerosol Size Distributionsmentioning

confidence: 99%

Thermodynamics of Aqueous Systems

Clark¹

2015

Groundwater Geochemistry and Isotopes

View full text Add to dashboard Cite

show abstract

“…For optimization of NF-GVEIN methodology the ANFIS algorithm with the lowest training error was selected, the optimal neural network structure was constructed and the optimal AV graft angle was determined. The performances of several ANFIS algorithms were determined using different MF types (trimf, trapmf, gbellmf, gaussmf, gauss2mf, pimf, dsigmf, psigmf) and iterating different number of MFs from 2 to 10 (neurons); this selection procedure of a best algorithm for a data set is described by Karaca et al [16]. ANFIS applies two techniques in updating parameters.…”

Section: Optimization Of Nf-gveinmentioning

confidence: 99%

Simulation of Hemodynamics in a Graft-To-Vein Anastomoses by Adaptive Neuro-Fuzzy Based Modeling

Arslan¹,

Karaca

2011

MCA

Self Cite

View full text Add to dashboard Cite

A new methodology for simulating the flow field inside an arteriovenous (AV) graft to vein anastomoses by the adaptive neuro fuzzy inference system (ANFIS) is presented in this study. For determining the optimal AV graft angle, an ANFIS-based model of neuro fuzzy-graft-vein (NF-GVEIN) is proposed. Therefore engineering design of the graft can be supported. The advantage of this neuro-fuzzy hybrid model is that it does not require the model structure to be known a priori, in contrast to most of the modeling techniques. A case study with real experimental data was carried out. NF-GVEIN was optimized by means of selection of the algorithm among 34 ANFIS algorithms by terms of minimal error. The optimal neural network structure was determined. The optimal AV graft angle causing the least turbulence was obtained. The simulation results showed that this model is feasible for forecasting of finding the optimal AV graft angle inside AV graft to vein anastomoses with different flow rates.

show abstract

“…Since real-time monitoring is relatively expensive and one of the main reasons for monitoring is to aid in the assessment of human exposure to pollution, numerous papers have been published on methodology to identify idea sites for pollution monitoring. A full understanding of the complex interactions behind the aerosol environmental effects requires a multidisciplinary approach, involving both experimental and theoretical methods [27][28][29][30].…”

Section: Dynamics Of Atmospheric Aerosol Size Distributionsmentioning

confidence: 99%

Environmental Chemistry of Aerosols

Colbeck¹

2008

View full text Add to dashboard Cite

NN-AirPol: a neural-networks-based method for air pollution evaluation and control

Cited by 24 publications

References 36 publications

Thermodynamics of Aqueous Systems

Thermodynamics of Aqueous Systems

Simulation of Hemodynamics in a Graft-To-Vein Anastomoses by Adaptive Neuro-Fuzzy Based Modeling

Environmental Chemistry of Aerosols

Contact Info

Product

Resources

About