Electrocoagulation of Textile Effluent: RSM and ANN Modeling

2009 ◽  
Vol 7 (1) ◽  
Author(s):  
A. Arul Murugan ◽  
Thilakavathi Ramamurthy ◽  
Bala Subramanian ◽  
C. Srinivasa Kannan ◽  
Madhavi Ganesan
Keyword(s):  
Electrochemical Reactor ◽  
Operating Conditions ◽  
Cod Removal ◽  
Textile Effluent ◽  
Statistical Tool ◽  
Wide Range ◽  
Artificial Neural Network Ann ◽  
Electro Coagulation ◽  
Level Of Importance ◽  
Acid Blue 113

This investigation attempts to study electrocoagulation of Acid Blue 113 textile effluent in a batch electrochemical reactor covering wide range in the operating conditions. Experiments were designed using statistical tool of Response Surface Method (RSM) to analyze the combined effect of operating parameters on the efficiency of electro coagulation process. The level of importance of the experimental parameters on the percentage COD removal has been determined by using analysis of variance (ANOVA). Further, the percentage COD removal was also modeled using artificial neural network (ANN).

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

An Intelligent Sensor Validation and Fault Diagnostic Technique for Diesel Engines

1999 ◽  
Vol 123 (1) ◽  
pp. 141-144 ◽  
Author(s):  
Ehsan Mesbahi
Keyword(s):  
Fault Diagnosis ◽  
Confidence Level ◽  
Diagnostic Technique ◽  
Operating Conditions ◽  
Sensor Reading ◽  
Intelligent Sensor ◽  
Sensor Validation ◽  
Wide Range ◽  
On Line ◽  
Artificial Neural Network Ann

Abstract An intelligent sensor validation and on-line fault diagnosis technique for a 6 cylinder turbocharged diesel engine is proposed and studied. A single auto-associative 3-layer Artificial Neural Network (ANN), is trained to examine the accuracy of the measured data and allocate a confidence level to each signal. The same ANN is used to recover the missing or faulty data with a close approximation. For on-line fault detection a feed-forward ANN is trained to classify and consequently recognize faulty and healthy behavior of the engine for a wide range of operating conditions. The proposed technique is also equipped with an on-line learning mechanism, which is activated when the confidence level in predicted fault is poor. It is hoped that a feasible, practical, and reliable sensor reading, as well as highly accurate fault diagnosis system, would be achieved.

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Hadi Salehi ◽  
Mosayyeb Amiri ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Energy Gain ◽  
Operating Conditions ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

scholarly journals Efficiency of the electrochemical treatment of textile effluent using two configurations of sacrificial electrodes

2018 ◽  
Vol 18 (1) ◽  
pp. 13
Author(s):  
Leandro Fleck ◽  
Jeysa Piza Santana Passos ◽  
Andrieli Cristina Helmann ◽  
Eduardo Eyng ◽  
Laércio Mantovani Frare ◽  
...  
Keyword(s):  
Removal Efficiency ◽  
Electrode Array ◽  
Electrochemical Reactor ◽  
Electrochemical Treatment ◽  
Operating Conditions ◽  
Color Removal ◽  
Textile Effluent ◽  
Parallel Plates ◽  
Cylindrical Electrode ◽  
Efficient Treatment

Textile industries have as main characteristic the generation of effluents with high color, and efficient treatment techniques are necessary. In this context, this study compared the efficiency of the electrochemical treatment for color removal from synthetic textile effluent using two configurations of sacrificial electrodes, parallel plates and array of cylindrical electrodes. For application of the electroflocculation technique, an electrochemical reactor was used, in a laboratory scale, operated in a continuous flow. The synthetic textile effluent was prepared with preset concentrations of reactive dye Blue 5G and sodium chloride. Sacrificial iron (Fe) electrodes with different configurations were used: parallel plates and cylindrical electrodes. The Hydraulic Retention Time (HRT) and electric current density (j) were controlled, and their effects on color removal were evaluated using a Central Composite Rotational Design (CCRD) composed of 12 trials. For the electrochemical treatment using parallel plates, the color removal efficiency ranged from 56.13% to 98.95% and for the electrochemical treatment using an array of cylindrical electrode, the color removal efficiency varied from 2.11% to 97.84%. The mathematical models representative of the process explained a high proportion of the total data variability, with a coefficient of variation of 99.49% and 97.21% for parallel plates and arrangement of cylindrical electrodes, respectively. The electrochemical treatment using parallel plates presents advantages over the configuration using a cylindrical electrode array, since the color removal efficiency is superior under the same operating conditions, representing economic and environmental gains.

scholarly journals Regime identification of slurry transport in pipelines: A novel modelling approach using ANN & differential evolution

2010 ◽  
Vol 16 (4) ◽  
pp. 329-343 ◽  
Author(s):  
Sandip Lahiri ◽  
K.C. Ghanta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Differential Evolution ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Slurry Transport ◽  
Pipeline Design

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and differential evolution technique (ANN - DE) for efficient tuning of ANN Meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN - DE method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters. .

Comparison of Deterministic and Linear Cosine Spatial Filtering of Transmission Electron Micrographs

1972 ◽  
Vol 30 ◽  
pp. 596-597
Author(s):  
David A. Ansley
Keyword(s):  
Spherical Aberration ◽  
Focal Length ◽  
Chromatic Aberration ◽  
Spatial Filter ◽  
Operating Conditions ◽  
Objective Lens ◽  
List Type ◽  
Spatial Filters ◽  
Transmission Electron ◽  
Wide Range

The coherence of the electron flux of a transmission electron microscope (TEM) limits the direct application of deconvolution techniques which have been used successfully on unmanned spacecraft programs. The theory assumes noncoherent illumination. Deconvolution of a TEM micrograph will, therefore, in general produce spurious detail rather than improved resolution.A primary goal of our research is to study the performance of several types of linear spatial filters as a function of specimen contrast, phase, and coherence. We have, therefore, developed a one-dimensional analysis and plotting program to simulate a wide 'range of operating conditions of the TEM, including adjustment of the:(1) Specimen amplitude, phase, and separation(2) Illumination wavelength, half-angle, and tilt(3) Objective lens focal length and aperture width(4) Spherical aberration, defocus, and chromatic aberration focus shift(5) Detector gamma, additive, and multiplicative noise constants(6) Type of spatial filter: linear cosine, linear sine, or deterministic

COD REMOVAL OF COAL GASIFIER EFFLUENT BY ELECTRO COAGULATION AND OPTIMIZATION USING RESPONSE SURFACE METHODOLOGY

2017 ◽  
Vol 12 (2) ◽  
pp. 9
Author(s):  
SHANKAR B. UMA ◽  
Lakshmi Chandana M.V.V. ◽  
SRIDEVI V ◽  
LAKSHMI L. NEELIMA CHANDRA ◽  
◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Cod Removal ◽  
Coal Gasifier ◽  
Electro Coagulation

Structure and properties of compact articles from high-alloyed iron powder with adding of boron nitride

2020 ◽  
pp. 39-48
Author(s):  
B. O. Bolshakov ◽  
◽  
R. F. Galiakbarov ◽  
A. M. Smyslov ◽  
◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Boron Nitride ◽  
Grain Boundary ◽  
Bending Strength ◽  
Physical And Mechanical Properties ◽  
Operating Conditions ◽  
Structure And Properties ◽  
Steam Turbines ◽  
Friction Forces ◽  
Wide Range

The results of the research of structure and properties of a composite compact from 13 Cr – 2 Мо and BN powders depending on the concentration of boron nitride are provided. It is shown that adding boron nitride in an amount of more than 2% by weight of the charge mixture leads to the formation of extended grain boundary porosity and finely dispersed BN layers in the structure, which provides a high level of wearing properties of the material. The effect of boron nitride concentration on physical and mechanical properties is determined. It was found that the introduction of a small amount of BN (up to 2 % by weight) into the compacts leads to an increase in plasticity, bending strength, and toughness by reducing the friction forces between the metal powder particles during pressing and a more complete grain boundary diffusion process during sintering. The formation of a regulated structure-phase composition of powder compacts of 13 Cr – 2 Mо – BN when the content of boron nitride changes in them allows us to provide the specified physical and mechanical properties in a wide range. The obtained results of studies of the physical and mechanical characteristics of the developed material allow us to reasonably choose the necessary composition of the powder compact for sealing structures of the flow part of steam turbines, depending on their operating conditions.

Nitrogen Removal from Anaerobically-Treated Leachate

1984 ◽  
Vol 19 (1) ◽  
pp. 87-100
Author(s):  
D. Prasad ◽  
J.G. Henry ◽  
P. Elefsiniotis
Keyword(s):  
Nitrogen Removal ◽  
Air Flow ◽  
Operating Conditions ◽  
Air Flow Rate ◽  
Flow Rates ◽  
Anaerobic Filter ◽  
Ph Values ◽  
Wide Range ◽  
Ammonia Desorption ◽  
Effects Of Ph

Abstract Laboratory studies were conducted to demonstrate the effectiveness of diffused aeration for the removal of ammonia from the effluent of an anaerobic filter treating leachate. The effects of pH, temperature and air flow on the process were studied. The coefficient of desorption of ammonia, KD for the anaerobic filter effluent (TKN 75 mg/L with NH3-N 88%) was determined at pH values of 9, 10 and 11, temperatures of 10, 15, 20, 30 and 35°C, and air flow rates of 50, 120, and 190 cm3/sec/L. Results indicated that nitrogen removal from the effluent of anaerobic filters by ammonia desorption was feasible. Removals exceeding 90% were obtained with 8 hours aeration at pH of 10, a temperature of 20°C, and an air flow rate of 190 cm3/sec/L. Ammonia desorption coefficients, KD, determined at other temperatures and air flow rates can be used to predict ammonia removals under a wide range of operating conditions.

scholarly journals Applications of Biocatalysts for Sustainable Oxidation of Phenolic Pollutants: A Review

2021 ◽  
Vol 13 (15) ◽  
pp. 8620
Author(s):  
Sanaz Salehi ◽  
Kourosh Abdollahi ◽  
Reza Panahi ◽  
Nejat Rahmanian ◽  
Mozaffar Shakeri ◽  
...  
Keyword(s):  
Phenolic Compounds ◽  
Operating Conditions ◽  
Phenolic Pollutants ◽  
Petroleum Refinery Wastewater ◽  
Start Up ◽  
Low Concentrations ◽  
Effective Removal ◽  
Wide Range ◽  
Loading Effects ◽  
Spent Caustic

Phenol and its derivatives are hazardous, teratogenic and mutagenic, and have gained significant attention in recent years due to their high toxicity even at low concentrations. Phenolic compounds appear in petroleum refinery wastewater from several sources, such as the neutralized spent caustic waste streams, the tank water drain, the desalter effluent and the production unit. Therefore, effective treatments of such wastewaters are crucial. Conventional techniques used to treat these wastewaters pose several drawbacks, such as incomplete or low efficient removal of phenols. Recently, biocatalysts have attracted much attention for the sustainable and effective removal of toxic chemicals like phenols from wastewaters. The advantages of biocatalytic processes over the conventional treatment methods are their ability to operate over a wide range of operating conditions, low consumption of oxidants, simpler process control, and no delays or shock loading effects associated with the start-up/shutdown of the plant. Among different biocatalysts, oxidoreductases (i.e., tyrosinase, laccase and horseradish peroxidase) are known as green catalysts with massive potentialities to sustainably tackle phenolic contaminants of high concerns. Such enzymes mainly catalyze the o-hydroxylation of a broad spectrum of environmentally related contaminants into their corresponding o-diphenols. This review covers the latest advancement regarding the exploitation of these enzymes for sustainable oxidation of phenolic compounds in wastewater, and suggests a way forward.

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