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.     

Modeling relationships between indoor and outdoor air quality

Information about the ratio between indoor and outdoor concentrations (IO ratios) of air pollutants is a crucial component in human exposure assessment. The present study examines the relationship between indoor and outdoor concentrations as influenced by the combined effect of time patterns in outdoor concentrations, ventilation rate, and indoor emissions. Two different mathematical approaches are used to evaluate IO ratios. The first approach involves a dynamic mass balance model that calculates distributions of transient IO ratios. The second approach assumes a linear relationship between indoor and outdoor concentrations. We use ozone and benzene as examples in various modeling exercises. The modeled IO ratio distributions are compared with the results obtained from linear fits through plots of indoor versus outdoor concentrations.     

Hepatotoxicity of graphene oxide in Wistar rats

Environmental Science and Pollution Research - Graphene oxide (GO) has a multitude of applications in areas of nanomedicine, electronics, textile, water purification, and catalysis among others. GO...     

Inflammation biomarkers associated with arsenic exposure by drinking water and respiratory outcomes in indigenous children from three Yaqui villages in southern Sonora,México

Environmental Science and Pollution Research - Environmental arsenic exposure in adults and children has been associated with a reduction in the expression of club cell secretory protein (CC16) and...     

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...     

Glyphosate concentrations in global freshwaters: are aquatic organisms at risk?

Environmental Science and Pollution Research - Glyphosate is the most used herbicide worldwide. Many studies have reported glyphosate risks to aquatic organisms of different trophic levels....     

Innovative aspects of environmental chemistry and technology regarding air,water, and soil pollution

Environmental Science and Pollution Research -     

Physiological responses of beet and cabbage plants exposed to copper and their potential insertion in human food chain

Environmental Science and Pollution Research - Copper (Cu) can be toxic to vegetables when it is absorbed and accumulated at large concentrations, a fact that increases the risk of excessive...     

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.