New Analytical Approaches for Pharmaceutical Wastewater Treatment Using Graphene Based Materials

2018 ◽  
pp. 397-411
Author(s):  
P. Senthil Kumar ◽  
A. Saravanan
Keyword(s):  
Wastewater Treatment ◽  
Pharmaceutical Wastewater ◽  
Analytical Approaches

scholarly journals Detection of untreated sewage discharges to watercourses using machine learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter Hammond ◽  
Michael Suttie ◽  
Vaughan T. Lewis ◽  
Ashley P. Smith ◽  
Andrew C. Singer
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Training Data ◽  
Regulatory Oversight ◽  
Untreated Wastewater ◽  
Untreated Sewage ◽  
Water Companies ◽  
Analytical Approaches ◽  
Sewage Discharges

AbstractMonitoring and regulating discharges of wastewater pollution in water bodies in England is the duty of the Environment Agency. Identification and reporting of pollution events from wastewater treatment plants is the duty of operators. Nevertheless, in 2018, over 400 sewage pollution incidents in England were reported by the public. We present novel pollution event reporting methodologies to identify likely untreated sewage spills from wastewater treatment plants. Daily effluent flow patterns at two wastewater treatment plants were supplemented by operator-reported incidents of untreated sewage discharges. Using machine learning, known spill events served as training data. The probability of correctly classifying a randomly selected pair of ‘spill’ and ‘no-spill’ effluent patterns was above 96%. Of 7160 days without operator-reported spills, 926 were classified as involving a ‘spill’. The analysis also suggests that both wastewater treatment plants made non-compliant discharges of untreated sewage between 2009 and 2020. This proof-of-principle use of machine learning to detect untreated wastewater discharges can help water companies identify malfunctioning treatment plants and inform agencies of unsatisfactory regulatory oversight. Real-time, open access flow and alarm data and analytical approaches will empower professional and citizen scientific scrutiny of the frequency and impact of untreated wastewater discharges, particularly those unreported by operators.

Gallic acid magnetic nanoparticles for photocatalytic degradation of meloxicam: synthesis, characterization and application to pharmaceutical wastewater treatment

RSC Advances ◽  
2015 ◽  
Vol 5 (127) ◽  
pp. 104981-104990 ◽  
Author(s):  
Ahmed H. Nadim ◽  
Medhat A. Al-Ghobashy ◽  
Marianne Nebsen ◽  
Mostafa A. Shehata
Keyword(s):  
Wastewater Treatment ◽  
Magnetic Nanoparticles ◽  
Photocatalytic Degradation ◽  
Gallic Acid ◽  
Pharmaceutical Wastewater

Gallic acid magnetic nanoparticles for photocatalytic degradation of meloxicam in pharmaceutical wastewater.

PVDF membranes embedded with PVP functionalized nanodiamond for pharmaceutical wastewater treatment

2018 ◽  
Vol 140 ◽  
pp. 241-250 ◽  
Author(s):  
M. Javadi ◽  
Y. Jafarzadeh ◽  
R. Yegani ◽  
S. Kazemi
Keyword(s):  
Wastewater Treatment ◽  
Pharmaceutical Wastewater

scholarly journals OPTIMIZATION OF PHARMACEUTICAL WASTEWATER TREATMENT BY BIOSORPTION USING GENETIC ALGORITHM

2021 ◽  
Vol 6 (2) ◽  
pp. 31-38
Author(s):  
Ola Belal Hasan Abdallah ◽  
Priy Brat Dwivedi
Keyword(s):  
Genetic Algorithm ◽  
Wastewater Treatment ◽  
Model Equation ◽  
Diclofenac Sodium ◽  
Pharmaceutical Wastewater ◽  
Design Expert ◽  
Pharmaceutical Industries ◽  
Percentage Removal ◽  
Optimized Conditions

Purpose of the study: Optimizing the process of pharmaceutical wastewater treatment by biosorption using a genetic algorithm. Methodology: The main steps followed were, determination of the wavelength at maximum absorbance (λmax), drawing the calibration curve between the absorbance and the concentration of diclofenac sodium, designing the experiment using Design-Expert software, finding the percentage removal of diclofenac sodium for each run, obtaining the model equation of the analysis, finding the optimized condition using genetic algorithm in MATLAB software, running the experiment at the optimized conditions and analyzing the results. Main Findings: The technique used in the optimizing process was effective, in which the percentage removal was obtained as 8.73% at the optimized conditions. It was equivalent to 3.43 mg removal / g of activated carbon. Applications of this study: This technique can be applied in different industries especially the chemical and pharmaceutical industries. Novelty/Originality of this study: Using genetic algorithm in order to find the optimized condition of removing diclofenac sodium based on a set of data.

Bioremediation of Pharmaceutical Wastes

2017 ◽  
pp. 364-393 ◽  
Author(s):  
Alka Bali
Keyword(s):  
Public Health ◽  
Wastewater Treatment ◽  
Plant Species ◽  
General Public ◽  
Pharmaceutical Wastewater ◽  
Remediation Measures ◽  
Pharmaceutical Contaminants ◽  
Pharmaceutical Pollutants

The astounding increase in the use of pharmaceuticals in the last decade has raised concerns over their occurrence in the soils and wastewaters posing potential dangers to the general public health and environment. Considering the limitations of chemical remediation measures in treatment of recalcitrant xenobiotics, several bioremediation measures are being currently investigated and proposed for removal of pharmaceutical contaminants from the environment. Several bacterial, fungal and plant species have shown promising bioremediation potential with regard to the removal of pharmaceuticals. Varying configurations of anaerobic and aerobic reactors have been utilized for pharmaceutical wastewater treatment. This chapter is intended to give a compilation and overview of the various types of bioremediation measures currently being employed or investigated to remove the pharmaceutical pollutants.

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