A model for pulse jet fabric filters

A new model for pulse jet fabric filtration is proposed. In contrast to the earlier model of Ravin and Humphries, which was formulated on the steady state assumption, the present study is aimed at developing a predictive capability for both transient and steady state operations, taking into account the compression effect of filter cakes. The model's relative simplicity allows frequent updating of the model parameter values, thus improving the accuracy of predictions. As a result, the model is particularly useful in developing control algorithms and designing controllers of pulse jet fabric filtration systems.     

The role of pressure drop and flow redistribution on modeling mercury control using sorbent injection in baghouse filters

A mathematical model based on simple cake filtration theory was coupled to a previously developed two-stage mathematical model for mercury (Hg) removal using powdered activated carbon injection upstream of a baghouse filter. Values of the average permeability of the filter cake and the filter resistance extracted from the model were 4.4 x 10(-13) m2 and 2.5 x 10(-4) m(-1), respectively. The flow is redistributed during partial cleaning of the filter, with flows higher across the newly cleaned filter section. The calculated average Hg removal efficiency from the baghouse is lower because of the high mass flux of Hg exiting the filter in the newly cleaned section. The model shows that calculated average Hg removal is affected by permeability, filter resistance, fraction of the baghouse cleaned, and cleaning interval.     

活性污泥系统神经网络建模与仿真研究

采用神经网络技术对松江污水厂污水处理活性污泥系统进行建模试验研究，在对实际运行数据按机理准则和范围准则剔除异常数据后，将样本数据随机分成训练样本、检验样本和测试样本。用试凑法确定合理的神经网络隐层节点数，以避免采用过大或过小的网络结构，在训练过程中用检验样本实时监控从而避免“过训练”现象的影响，较好地解决神经网络方法建模的两大难题，从而建立可靠、有效的活性污泥系统神经网络模型。并应用建立的网络模型对活性污泥系统的运行情况进行了仿真研究。建模研究表明，神经网络技术能较好地应用于活性污泥系统的建模，模型具有较好的泛化能力，有很好的实用价值。     

Neuro-fuzzy and neural network systems for air quality control

In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source–receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source–receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source–receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source–receptor models are able to accurately reproduce the simulation of the 3D modelling system.     

Modeling sorbent injection for mercury control in baghouse filters: II--pilot-scale studies and model evaluation

Activated carbon injection for Hg control in a 500-lb/hr pilot-scale coal-fired furnace equipped with a fabric filter for particulate control was evaluated at different operating conditions. The pilot-scale tests showed that Hg removal was improved at lower temperatures and higher C/Hg ratios. The two-stage mathematical model developed to describe Hg removal using powdered activated carbon injection upstream of a baghouse filter was used to obtain Langmuir isotherm parameters as a function of temperature by fitting the model to a subset of experimental data. The predictive capability of the model was then tested by comparing model calculations with additional experimental data from this system obtained using different operating temperatures and sorbent to Hg ratios. Model predictions were in good agreement with experimentally measured Hg removal efficiency. Based on the model predictions, Hg removal in the duct appears to be limited and higher C/Hg ratio, lower operating temperature, and longer cleaning cycle of the baghouse filter should be utilized to achieve higher Hg removal in this system.     

Modeling sorbent injection for mercury control in baghouse filters: I--model development and sensitivity analysis

A two-stage mathematical model for Hg removal using powdered activated carbon injection upstream of a baghouse filter was developed, with the first stage accounting for removal in the ductwork and the second stage accounting for additional removal caused by the retention of carbon particles on the filter. The model shows that removal in the ductwork is minimal, and the additional carbon detention time from the entrapment of the carbon particles in the fabric filter enhances the Hg removal from the gas phase. A sensitivity analysis on the model shows that Hg removal is dependent on the isotherm parameters, the carbon pore radius and tortuosity, the C/Hg ratio, and the carbon particle radius.     

An extended hybrid fuzzy multi-criteria decision model for sustainable and resilient supplier selection

Environmental Science and Pollution Research - The formalization and solution of supplier selection problems (SSPs) based on sustainable (economic, environmental, and social) indicators have become...     

Spatial modeling for air pollution monitoring network design: example of residential woodsmoke

The purpose of this paper is to demonstrate how to develop an air pollution monitoring network to characterize small-area spatial contrasts in ambient air pollution concentrations. Using residential woodburning emissions as our case study, this paper reports on the first three stages of a four-stage protocol to measure, estimate, and validate ambient residential woodsmoke emissions in Vancouver, British Columbia. The first step is to develop an initial winter nighttime woodsmoke emissions surface using inverse-distance weighting of emissions information from consumer woodburning surveys and property assessment data. Second, fireplace density and a compound topographic index based on hydrological flow regimes are used to enhance the emissions surface. Third, the spatial variation of the surface is used in a location-allocation algorithm to design a network of samplers for the woodsmoke tracer compound levoglucosan and fine particulate matter. Measurements at these network sites are then used in the fourth stage of the protocol (not presented here): a mobile sampling campaign aimed at developing a high-resolution surface of woodsmoke concentrations for exposure assignment in health effects studies. Overall the results show that relatively simple data inputs and spatial analysis can be effective in capturing the spatial variability of ambient air pollution emissions and concentrations.     

An empirical map of critical loads of acidity for soils in Great Britain

The method used to produce a critical load map of acidity for soils in Great Britain is described. Critical loads were assigned to the dominant soil in each 1 km grid square of the UK national grid. Mineral soils were assigned a critical load based on mineralogy and chemistry, using approaches appropriate to UK conditions. Critical loads for peat soils are based primarily on a maximum acceptable reduction of peat pH, and results from laboratory equilibration studies. The map shows that soils with small critical loads (<0.5 kmol_c ha⁻¹ year⁻¹) i.e. highly sensitive to acidic deposition, dominate in the north and west of Britain; the south and east are dominated by soils with large critical loads, with small areas of more sensitive soils associated with sandy soil-forming materials. A modified critical load map illustrates the potential impact of agricultural liming on soil critical loads.     

基于神经网络的活性污泥系统建模及其控制

针对松江污水厂污水处理活性污泥系统，采用神经网络技术进行建模试验研究，在对实际运行数据剔除异常数据后，将样本数据随机分成训练样本、检验样本和测试样本。用试凑法确定合理的神经网络隐层节点数，用检验样本实时监控训练过程从而避免“过训练”现象，用多次改变网络初始连接权值求得全局极小点，从而建立了泛化能力较好的基于神经网络的活性污泥系统数学模型。利用建立的神经网络模型，对活性污泥系统运行情况的仿真与控制进行了分析研究。示例研究表明：神经网络技术能较好地应用于活性污泥系统的建模与控制，有很好的理论与实践意义。     

Concentrations and specific loads of UV filters in sewage sludge originating from a monitoring network in Switzerland

Many substances related to human activities end up in wastewater and accumulate in sewage sludge. The present study focuses on the analysis of widely used UV filters 3-(4-methylbenzylidene) camphor (4-MBC), octyl-methoxycinnamate (OMC), octocrylene (OC) and octyl-triazone (OT) in sewage sludge originating from a monitoring network in Switzerland. Mean concentrations in stabilised sludge from 14 wastewater treatment plants were 1780, 110, 4840 and 5510 microg/kg dry matter for 4-MBC, OMC, OC and OT, respectively. Specific loads in sewage sludge show that UV filters originate mainly from private households, but surface runoff and industries may be considered as additional sources. This indicates that besides use for sunscreens and cosmetics UV filters might occur in plastics and other materials and be released to the environment by volatilization or leaching. Differences between the modeled per capita loads of UV filters in sewage sludge and the observed specific loads in sewage sludge are probably due to erroneous figures of production volumes, degradation and sorption during wastewater treatment as well as degradation processes during transport in the sewer or sludge treatment. Thus, further research is needed to elucidate the fate of UV filters after application and release into the environment. Other compounds used as UV filters should be included in future studies.     

Natural emissions for regional modeling of background ozone and particulate matter and impacts on emissions control strategies

Natural emissions adopted in current regional air quality modeling are updated to better describe natural background ozone and PM concentrations for North America. The revised natural emissions include organosulfur from the ocean, NO from lightning, sea salt, biogenic secondary organic aerosol (SOA) precursors, and pre-industrial levels of background methane. The model algorithm for SOA formation was also revised. Natural background ozone concentrations increase by up to 4 ppb in annual average over the southeastern US and Gulf of Mexico due to added NO from lightning while the revised biogenic emissions produced less ozone in the central and western US. Natural PM_2.5 concentrations generally increased with the revised natural emissions. Future year (2018) simulations were conducted for several anthropogenic emission reduction scenarios to assess the impact of the revised natural emissions on anthropogenic emission control strategies. Overall, the revised natural emissions did not significantly alter the ozone responses to the emissions reductions in 2018. With revised natural emissions, ozone concentrations were slightly less sensitive to reducing NOx in the southeastern US than with the current natural emissions due to higher NO from lightning. The revised natural emissions have little impact on modeled PM_2.5 responses to anthropogenic emission reductions. However, there are substantial uncertainties in current representations of natural sources in air quality models and we recommend that further study is needed to refine these representations.     

An integrated numerical and physical modeling system for an enhanced in situ bioremediation process

Groundwater contamination due to releases of petroleum products is a major environmental concern in many urban districts and industrial zones. Over the past years, a few studies were undertaken to address in situ bioremediation processes coupled with contaminant transport in two- or three-dimensional domains. However, they were concentrated on natural attenuation processes for petroleum contaminants or enhanced in situ bioremediation processes in laboratory columns. In this study, an integrated numerical and physical modeling system is developed for simulating an enhanced in situ biodegradation (EISB) process coupled with three-dimensional multiphase multicomponent flow and transport simulation in a multi-dimensional pilot-scale physical model. The designed pilot-scale physical model is effective in tackling natural attenuation and EISB processes for site remediation. The simulation results demonstrate that the developed system is effective in modeling the EISB process, and can thus be used for investigating the effects of various uncertainties.     

An analytical model for the interpretation of pulse injection experiments performed for testing the spatial variability of clay formations

This paper presents an analytical model to describe pulse injection experiments. This model solves the advection-diffusion equation while taking into account back diffusion from the clay core to the inlet and from the outlet to the clay core. In most analytical models, back diffusion is neglected. For sufficiently high Péclet numbers, this is a good approximation. However, in experiments where the Péclet number is low, back diffusion is important and must be taken into account. An additional advantage of the present model is that both concentration and flux are conserved at the inlet and at the outlet of the clay core. This model is used to fit pulse injection experiments with iodide and tritiated water (HTO) in clay cores. The (new) model is required for fitting the experimental results since in clay layers advection is very slow leading to a low Péclet number. The experiments are performed on clay cores taken from different depths from the Boom Clay and the Ypres Clay layer under the site of the nuclear power plant of Doel (Belgium). The quality of all fits is excellent and the obtained parameter values are coherent. For HTO, the fitted value for the diffusion accessible porosity is consistent with measurements of the water content in Ypres Clay cores. In both types of clays, the apparent diffusion coefficient at zero flow is between 10(-10) and 2 x 10(-10) m(2)/s for iodide and between 2 x 10(-10) and 3 x 10(-10) m(2)/s for HTO. The dispersion length is in the order of 10(-3) m. The average value for the diffusion accessible porosity is between 0.35 and 0.4 for HTO and between 0.2 and 0.25 for iodide.     

An improved graph convolutional network with feature and temporal attention for multivariate water quality prediction

Environmental Science and Pollution Research - The analysis and prediction of water quality are of great significance to water quality management and pollution control. In general, current water...     

Loss of particle nitrate from teflon sampling filters: effects on measured gravimetric mass in California and in the IMPROVE network

The extent of mass loss on Teflon filters caused by ammonium nitrate volatilization can be a substantial fraction of the measured particulate matter with an aerodynamic diameter less than 2.5 microm (PM2.5) or 10 microm (PM10) mass and depends on where and when it was collected. There is no straightforward method to correct for the mass loss using routine monitoring data. In southern California during the California Acid Deposition Monitoring Program, 30-40% of the gravimetric PM2.5 mass was lost during summer daytime. Lower mass losses occurred at more remote locations. The estimated potential mass loss in the Interagency Monitoring of Protected Visual Environments network was consistent with the measured loss observed in California. The biased mass measurement implies that use of Federal Reference Method data for fine particles may lead to control strategies that are biased toward sources of fugitive dust, other primary particle emission sources, and stable secondary particles (e.g., sulfates). This analysis clearly supports the need for speciated analysis of samples collected in a manner that preserves volatile species. Finally, although there is loss of volatile nitrate (NO3-) from Teflon filters during sampling, the NO3- remaining after collection is quite stable. We found little loss of NO3- from Teflon filters after 2 hr under vacuum and 1 min of heating by a cyclotron proton beam.     

Activated soil filters for removal of biocides from contaminated run-off and waste-waters

Building facades can be equipped with biocides to prevent formation of algal, fungal and bacterial films. Thus run-off waters may contain these highly active compounds. In this study, the removal of several groups of biocides from contaminated waters by means of an activated soil filter was studied.A technical scale activated vertical soil filter (biofilter) with different layers (peat, sand and gravel), was planted with reed (Phragmites australis) and used to study the removal rates and fate of hydrophilic to moderate hydrophobic (log K_ow 1.8-4.4) biocides and biocide metabolites such as: Terbutryn, Cybutryn (Irgarol® 1051), Descyclopropyl-Cybutryn (Cybutryn and Terbutryn metabolite), Isoproturon, Diuron, and its metabolite Diuron-desmonomethyl, Benzo-isothiazolinone, n-Octyl-isothiazolinone, Dichloro-n-octylisothiazolinone and Iodocarbamate (Iodocarb). Three experiments were performed: the first one (36 d) under low flow conditions (61 L m⁻² d⁻¹) reached removal rates between 82% and 100%. The second one was performed to study high flow conditions: During this experiment, water was added as a pulse to the filter system with a hydraulic load of 255 L m⁻² within 5 min (retention time <1 h). During this experiment the removal rates of the compounds decreased drastically. For five compounds (Cybutryn, Descyclopropyl-Cybutryn, Diuron, Isoproturon, and Iodocarb) the removal dropped temporarily below 60%, while it was always above 70% for the others (Terbutryn, Benzo-isothiazolinone, n-Octyl-isothiazolinone, Dichloro-n-octylisothiazolinone). However, this removal is a considerable improvement compared to direct discharge into surface waters or infiltration into soil without appropriate removal. In the last experiment the removal efficiencies of the different layers were studied. Though the peat layer was responsible for most of the removal, the sand and gravel layers also contributed significantly for some compounds. All compounds are rather removed by degradation than by sorption.     

Optimizing stabilization of waste-activated sludge using Fered-Fenton process and artificial neural network modeling (KSOFM,MLP)

Sludge management is a fundamental activity in accordance with wastewater treatment aims. Sludge stabilization is always considered as a significant step of wastewater sludge handling. There has been a progressive development observed in the approach to the novel solutions in this regard. In this research, based on own initially experimental results in lab-scale regarding Fered-Fenton processes in view of organic loading (volatile-suspended solids, VSS) removal efficiency, a combination of both methods towards proper improving of excess biological sludge stabilization was investigated. Firstly, VSS removal efficiency has been experimentally studied in lab-scale under different operational conditions taking into consideration pH [Fe²⁺]/[H₂O₂], detention time [H₂O₂], and current density parameters. Therefore, the correlations of the same parameters have been determined by utilizing Kohonen self-organizing feature maps (KSOFM). In addition, multi-layer perceptron (MLP) has been employed afterwards for a comprehensive evaluation of investigating parameters correlation and prediction aims. The findings indicated that the best proportion of iron to hydrogen peroxide and the optimum pH were 0.58 and 3.1, respectively. Furthermore, maximum retention time about 6 h with a hydrogen peroxide concentration of 1,568 mg/l and a current density of 650–750 mA results to the optimum VSS removal (efficiency equals to 81 %). The performance of KSOFM and MLP models is found to be magnificent, with correlation ranging (R) from 0.873 to 0.998 for the process simulation and prediction. Finally, it can be concluded that the Fered-Fenton reactor is a suitable efficient process to reduce considerably sludge organic load and mathematical modeling tools as artificial neural networks are impressive methods of process simulation and prediction accordingly.     

A statistical approach for estimating uncertainty in dispersion modeling: An example of application in southwestern USA

A method based on a statistical approach of estimating uncertainty in simulating the transport and dispersion of atmospheric pollutants is developed using observations and modeling results from a tracer experiment in the complex terrain of the southwestern USA. The method takes into account the compensating nature of the error components by representing all terms, except dispersion error and variance of stochastic processes. Dispersion error and the variance of the stochastic error are estimated using the maximum likelihood estimation technique applied to the equation for the fractional error. Mesoscale Model 5 (MM5) and a Lagrangian random particle dispersion model with three optional turbulence parameterizations were used as a test bed for method application. Modeled concentrations compared well with the measurements (correlation coefficients on the order of 0.8). The effects of changing two structural components (the turbulence parameterization and the model grid vertical resolution) on the magnitude of the dispersion error also were examined. The expected normalized dispersion error appears to be quite large (up to a factor of three) among model runs with various turbulence schemes. Tests with increased vertical resolution of the atmospheric model (MM5) improved most of the dispersion model statistical performance measures, but to a lesser extent compared to selection of a turbulence parameterization. Method results confirm that structural components of the dispersion model, namely turbulence parameterizations, have the most influence on the expected dispersion error.