Adsorption and Kinetic study of priority organic pollutant phenol from aqueous waste using Activated carbon

Many organic compounds present in industrial and domestic wastewater are carcinogenic in nature. Removal of these organic compounds from waste waters has become a great challenge to waste water treatment technologies as many of them are non-biodegradable in nature. Adsorption on activated carbon has emerged as an efficient and economically viable technology for the removal of a broad spectrum of toxic organic compounds from domestic and industrial waste water. The adsorption of hazardous organic compounds on activated carbon has been the subject of research for the past three decades. In the present study, kinetic study for adsorption of some priority organic pollutants like phenol on activated carbon, was studied at laboratory scale. Series of experiments were carried out to determine kinetics for adsorbate, when present in aqueous solution as a single component. The commercially available bituminous coal based activated carbon Filtrasorb-400 was used as an adsorbent. A simplified interpretation of the kinetic data based on Langmuir theory has been used. The kinetics performed gives the adsorption equilibrium time. The adsorption and desorption rate constants were evaluated from the graph

COMPARATIVE ANALYSIS OF STANDARD ERROR USING IMPUTATION METHOD

Presence of missing values in the dataset remains great challenge in the process of knowledge extracting. Also this leads to difficulty for performance analysis in data mining task. In this research work, student dataset is taken that contains marks of four different subjects of engineering college. Mean Imputation, Mode Imputation, Median Imputation and Standard Deviation Imputation were used to deal with challenges of incomplete data. By implementing imputation methods for example Mean Imputation, Mode Imputation, Median Imputation and Standard Deviation Imputation on the student dataset and find out standard errors for each imputation method then analyze obtained result. Mean Imputation with standard error is less as compare with other imputation method with standard error. Hence Mean Imputation Method with standard error is more suitable to handling the missing values in the dataset

COMPARISION OF PERCENTAGE ERROR BY USING IMPUTATION METHOD ON MID TERM EXAMINATION DATA

The issue of incomplete data exists across the entire field of data mining. In this paper, Mean Imputation, Median Imputation and Standard Deviation Imputation are used to deal with challenges of incomplete data on classification problems. By using different imputation methods converts incomplete dataset in to the complete dataset. On complete dataset by applying the suitable Imputation Method and comparing the percentage error of Imputation Method and comparing the resul

International Nosocomial Infection Control Consortiu (INICC) report, data summary of 43 countries for 2007-2012. Device-associated module

We report the results of an International Nosocomial Infection Control Consortium (INICC) surveillance study from January 2007-December 2012 in 503 intensive care units (ICUs) in Latin America, Asia, Africa, and Europe. During the 6-year study using the Centers for Disease Control and Prevention's (CDC) U.S. National Healthcare Safety Network (NHSN) definitions for device-associated health care–associated infection (DA-HAI), we collected prospective data from 605,310 patients hospitalized in the INICC's ICUs for an aggregate of 3,338,396 days. Although device utilization in the INICC's ICUs was similar to that reported from ICUs in the U.S. in the CDC's NHSN, rates of device-associated nosocomial infection were higher in the ICUs of the INICC hospitals: the pooled rate of central line–associated bloodstream infection in the INICC's ICUs, 4.9 per 1,000 central line days, is nearly 5-fold higher than the 0.9 per 1,000 central line days reported from comparable U.S. ICUs. The overall rate of ventilator-associated pneumonia was also higher (16.8 vs 1.1 per 1,000 ventilator days) as was the rate of catheter-associated urinary tract infection (5.5 vs 1.3 per 1,000 catheter days). Frequencies of resistance of Pseudomonas isolates to amikacin (42.8% vs 10%) and imipenem (42.4% vs 26.1%) and Klebsiella pneumoniae isolates to ceftazidime (71.2% vs 28.8%) and imipenem (19.6% vs 12.8%) were also higher in the INICC's ICUs compared with the ICUs of the CDC's NHSN