Air Pollution Reduction Based on Intelligent Nonlinear Control Methodology

On Nonlinear Processing of Air Pollution Data

Three methods { DVS plots, attractor reconstruction, and variance analysis of delay vectors { for detecting nonlinearities in time series are compared on an air pollution dataset. For rigour each method is also used on a surrogate dataset, based on a highorder linear t to the original data. Finally, a comparison of a standard linear analysis to a neural network model analysis of the air polluti...

Intelligent Rescheduling Trains for Air Pollution Management

Optimization of timetable is the need of the day for the rescheduling and routing of trains in real time. Trains are scheduled in parallel with the road transport vehicles to the same destination. As the number of trains is restricted due to single track, customers usually opt for road transport to use frequently. The air pollution increases as the density of vehicles on road transport is incre...

Optimization the Efficiency of Gas Turbines for Air Pollution Reduction

Increasing concerns about energy and emissions from fuel consumption in gas turbines has attracted many researchers to protect the environment and reduce pollutants in the world. The main objective of this paper is to investigate the increasing efficiency of three-stroke gas turbine operation based on the technical analysis of the operation of three-axis gas turbine cycles with non-design condi...

Nonlinear Adaptive Filtering to Forecast Air Pollution

Nonlinear modelling of air pollution time series

An analysis of predictability of a nonlinear and nonstationary ozone time series is provided. For rigour, the DVS analysis is first undertaken to detect and measure inherent nonlinearity of the data. Based upon this, neural and linear adaptive predictors are compared on this time series for various filter orders, hence indicating the embedding dimension. Simulation results confirm the analysis ...