Modelling the removal of volatile pollutants under transient conditions in a two-stage bioreactor using artificial neural networks

López, M. Estefanía; Rene, Eldon R.; Boger, Zvi; Veiga, María C.; Kennes, Christian

doi:10.1016/j.jhazmat.2016.03.018

Cited by 70 publications

(33 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, López et al. ). In this study, rock outcrop, altitude, slope gradient, slope position, microhabitat, land use, and soil type were defined as factors, and

{SOC}_{{normalm}^{2}}

level was defined as a dependent variable.…”

Section: Discussionmentioning

confidence: 97%

Spatial heterogeneity of soil organic carbon in a karst region under different land use patterns

2020

View full text Add to dashboard Cite

Citation: Zhang, Z., X. Huang, and Y. Zhou. 2020. Spatial heterogeneity of soil organic carbon in a karst region under different land use patterns. Ecosphere 11(3):Abstract. Using the grid-based sampling method, 2755 soil profiles consisting of 22,057 soil samples were sampled and analyzed to study the spatial distribution and storage of soil organic carbon (SOC) in the Houzhai River Basin in Guizhou Province, a typical karst region in southwestern China. The results suggest that the total levels of SOC stored in the 0-10, 0-20, 0-30, and 0-100 cm soil layers of the Houzhai River Basin are approximately 1.48 9 10 8 , 2.65 9 10 8 , 3.44 9 10 8 , and 5.39 9 10 8 kg C, respectively. SOC content and soil bulk density levels were found to vary depending on the land use. In the 0-50 cm soil layer, forest SOC content levels (including arbor forests, arbor-shrub mixed forests, shrubland, and shrub grassland) are generally higher than those of uncultivated land, followed by grassland and cropland (including paddy fields, arid land, sloping cropland, and abandoned cropland), gardens, and orchard. It is believed that land use is a vital factor directly affecting carbon storage patterns in the study area. Soil thickness, rock outcrop, and slope gradients are the main factors in arrangement of land use by local governments and peasants. This study has established a feasible method for assessment of SOC stock in karst mountainous areas. The findings of this study are applicable to other karst mountainous regions globally.

show abstract

“…, López et al. ). In this study, rock outcrop, altitude, slope gradient, slope position, microhabitat, land use, and soil type were defined as factors, and

{SOC}_{{normalm}^{2}}

level was defined as a dependent variable.…”

Section: Discussionmentioning

confidence: 97%

Spatial heterogeneity of soil organic carbon in a karst region under different land use patterns

2020

View full text Add to dashboard Cite

show abstract

“…In the present study, a three layer (input layer, hidden layer and output layer) feed-forward artificial neural network was selected to develop the predictive model ( Figure 2). The output in each layer of BP-ANN was produced by summing all the input weight through the activation function [34]. A tangent sigmoid transfer function (tansig) at input-hidden layer was adopted (Equation (7)), while a linear transfer function (purelin) was used in hidden-output layer (Equation (8) It is an important step to determine the number of neurons in hidden layer for training the network.…”

Section: Back Propagation Artificial Neural Network (Bp-ann)mentioning

confidence: 99%

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Ruan

Shi

et al. 2017

Applied Sciences

View full text Add to dashboard Cite

Abstract:The commercially available nanoscale zerovalent zinc (nZVZ) was used as an adsorbent for the removal of malachite green (MG) from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration) and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO) and artificial neural network coupled with genetic algorithm (ANN-GA) were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12%) was compatible with the experimental value (90.72%). Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (∆G 0 , ∆H 0 and ∆S 0 ) were calculated from the Van't Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG.

show abstract

“…Biological treatment of waste-gases and odours has proven to be an effective and economical option for the treatment of gasphase pollutants at low concentrations and high gas-flow rates [1][2][3][4][5][6][7][8][9]. Biotechnologies for waste-gas treatment exploits the ability of a suitable biocatalyst to mineralize the contaminants present in air.…”

mentioning

confidence: 99%

“…hydrogen sulphide) can be effectively removed using biological wastegas treatment systems. The most widely used bioreactor configurations are the biofilter, the biotrickling filter and the bioscrubber [1][2][3][4][5][6][7][8][9].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Conventional bioprocesses for the removal of gas-phase contaminants

Wambugu¹,

Marvins²,

Das³

et al. 2017

Res Rev Insights

View full text Add to dashboard Cite

Modelling the removal of volatile pollutants under transient conditions in a two-stage bioreactor using artificial neural networks

Cited by 70 publications

References 47 publications

Spatial heterogeneity of soil organic carbon in a karst region under different land use patterns

Spatial heterogeneity of soil organic carbon in a karst region under different land use patterns

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Conventional bioprocesses for the removal of gas-phase contaminants

Contact Info

Product

Resources

About