An intelligent emissions controller for fuel lean gas reburn in coal-fired power plants

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NOx) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NOx reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach. Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NOx emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system.     

Correction to: Impact of precipitation disparity on groundwater fluctuation in a semi-arid region (Vellore district) of southern India using geospatial techniques

Environmental Science and Pollution Research - A Correction to this paper has been published: https://doi.org/10.1007/s11356-021-13406-7     

Steel mill waste application in soil: dynamics of potentially toxic elements in rice and health risk perspectives

Environmental Science and Pollution Research - Potentially toxic elements (PTEs) are of great concern in steel mill wastes. Therefore, in order to use them as potential fertilizers in soil, risk...     

Plastic waste as a valuable resource: strategy to remove heavy metals from wastewater in bench scale application

Environmental Science and Pollution Research - Single-use plastic waste is gradually considered a potential material for circular economy. Ion exchange resin obtained from polystyrene waste by...     

Solar heterogeneous photocatalytic degradation of phenol on TiO2/quartz and TiO2/calcite: a statistical and kinetic approach on comparative efficiencies towards a TiO2/glass system

Environmental Science and Pollution Research - Phenol is a widely used synthetic organic compound, which according to global estimations, is discharged into the environment at a rate of 10...     

Photocatalysis for arsenic removal from water: considerations for solar photocatalytic reactors

Environmental Science and Pollution Research - The following work provides a perspective on the potential application of solar heterogeneous photocatalysis, which is a nonselective advanced...     

Bound pesticide residues in soils: a review

This article is a review of the current state of knowledge regarding the formation and biological/environmental significance of bound pesticide residues in soils. We begin by defining various terms used in our discussions and identifying the types/classes of pesticides which may be added to soil and interact with it. We then consider various soil properties and aspects of land management which will influence the nature and degree of the soil-pesticide association and discuss the possible physical and chemical binding mechanisms. We then move on to consider the role of microorganisms and other forms of soil biota in bound residue formation and the bioavailability of soil-borne pesticide residues. The review ends with a consideration of the significance of bound pesticide residues.     

Source diagnostic and weathering indicators of tar balls utilizing acyclic, polycyclic and S-heterocyclic components

This study represents a forensic chemical analysis to define the liability for the coastal bitumens polluting the beaches of the Mediterranean city of Alexandria. Six tar balls collected from several locations along the coast of the city were analyzed for their acyclic and polycyclic hydrocarbons as well as sulfur heterocycles using GC/FID, GC/AED and gas chromatography/mass spectrometry techniques. The analysis of one Egyptian crude oil is also included as a possible source oil. The tar ball samples were at early stages of weathering. Based on the GC traces and biomarker signatures, the tar balls could be genetically different. One sample collected from the Eastern Harbor region appears to be a Bunker C type fuel produced from Egyptian crudes. The refining process has removed the low molecular weight components. On the other hand, the wide n-alkane distribution together with the absence of an unresolved complex mixture suggests that crude oils probably from tank washings, ballast discharges or accident spills from tankers could have contributed significantly to the other tar ball samples. The distribution of source specific hopane and sterane markers revealed that the tar samples probably originate from different oil fields.     

Prediction of the vertical profile of ozone based on ground-level ozone observations and cloud cover

A number of statistical techniques have been used to develop models to predict high-elevation ozone (O3) concentrations for each discrete hour of day as a function of elevation based on ground-level O3 observations. The analyses evaluated the effect of exclusion/inclusion of cloud cover as a variable. It was found that a simple model, using the current maximum ground-level O3 concentration and no effect of cloud cover provided a reasonable prediction of the vertical profile of O3, based on data analyzed from O3 sites located in North Carolina and Tennessee. The simple model provided an approach that estimates the concentration of O3 as a function of elevation (up to 1800 m) based on the statistical results with a +/- 13.5 ppb prediction error, an R2 of 0.56, and an index of agreement, d1, of 0.66. The inclusion of cloud cover resulted in a slight improvement in the model over the simple regression model. The developed models, which consist of two matrices of 24 equations (one for each hour of day for clear to partly cloudy conditions and one for cloudy conditions), can be used to estimate the vertical O3 profile based on the inputs of the current day's 1-hr maximum ground-level O3 concentration and the level of cloud cover.