scholarly journals Real-time monitoring as an adaptive strategy towards green treatment of textile effluent using biosorbent from Acalypha indica

2021 ◽  
Author(s):  
C. Sivapragasam ◽  
V. Aruna Janani ◽  
A. Andappan ◽  
B. Archana ◽  
M. Vasudevan ◽  
...  
Keyword(s):  
Process Control ◽  
Real Time ◽  
Substantial Reduction ◽  
Oxygen Demand ◽  
Physical Parameters ◽  
Close Monitoring ◽  
Order Kinetic ◽  
Real Time Monitoring ◽  
Industry Effluent ◽  
Acalypha Indica

Abstract Performance of green treatment systems such as adsorption to treat textile effluents often suffers lack of longevity and efficiency due to the presence of complex compounds of varying reactivity. There is scope for improving the operational efficiency of such processes using real-time monitoring systems. The present study aimed to evaluate the performance of an activated biosorbent prepared from the leaves of Acalypha indica for treating textile industry effluent by simulating process control with real-time monitoring. Batch experiments were performed with synthetic and real-time dye effluents to identify the optimum conditions (pH = 3.0, dosage = 1.0 g/L; time = 1 h) for the highest adsorption capacity (6 mg g−1 and 2 mg g−1). The evaluation of physical parameters suggested best fit for Freundlich isotherm model and pseudo-second-order kinetic model. The LabVIEW-based simulation control system enabled close monitoring of pH and temperature during the process. Based on the inputs, an alteration of initial pH has resulted in substantial reduction in chemical oxygen demand (COD) (73.91%), turbidity (52.43%) and total dissolved solids (TDS) (19.43%). The average incremental increase was highest for COD (45.80 ± 0.06%) compared to TDS (10.13 ± 0.06%) and turbidity (−1.74 ± 0.03%) for varying dosage (3 g to 11 g). The proposed framework for incorporating a process-control-based monitoring system can help to achieve better performance.

scholarly journals A Process Analytical Concept for In-Line FTIR Monitoring of Polysiloxane Formation

Polymers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 2473
Author(s):  
Julia C. Steinbach ◽  
Markus Schneider ◽  
Otto Hauler ◽  
Günter Lorenz ◽  
Karsten Rebner ◽  
...  
Keyword(s):  
Process Control ◽  
Real Time ◽  
Time Course ◽  
Morphological Changes ◽  
Univariate Analysis ◽  
Principal Component ◽  
Synthesis Process ◽  
Real Time Monitoring ◽  
Wide Range ◽  
Batch Modelling

The chemical synthesis of polysiloxanes from monomeric starting materials involves a series of hydrolysis, condensation and modification reactions with complex monomeric and oligomeric reaction mixtures. Real-time monitoring and precise process control of the synthesis process is of great importance to ensure reproducible intermediates and products and can readily be performed by optical spectroscopy. In chemical reactions involving rapid and simultaneous functional group transformations and complex reaction mixtures, however, the spectroscopic signals are often ambiguous due to overlapping bands, shifting peaks and changing baselines. The univariate analysis of individual absorbance signals is hence often only of limited use. In contrast, batch modelling based on the multivariate analysis of the time course of principal components (PCs) derived from the reaction spectra provides a more efficient tool for real-time monitoring. In batch modelling, not only single absorbance bands are used but information over a broad range of wavelengths is extracted from the evolving spectral fingerprints and used for analysis. Thereby, process control can be based on numerous chemical and morphological changes taking place during synthesis. “Bad” (or abnormal) batches can quickly be distinguished from “normal” ones by comparing the respective reaction trajectories in real time. In this work, FTIR spectroscopy was combined with multivariate data analysis for the in-line process characterization and batch modelling of polysiloxane formation. The synthesis was conducted under different starting conditions using various reactant concentrations. The complex spectral information was evaluated using chemometrics (principal component analysis, PCA). Specific spectral features at different stages of the reaction were assigned to the corresponding reaction steps. Reaction trajectories were derived based on batch modelling using a wide range of wavelengths. Subsequently, complexity was reduced again to the most relevant absorbance signals in order to derive a concept for a low-cost process spectroscopic set-up which could be used for real-time process monitoring and reaction control.

scholarly journals Multi-Organs-on-Chips: Towards Long-Term Biomedical Investigations

Molecules ◽  
2019 ◽  
Vol 24 (4) ◽  
pp. 675 ◽  
Author(s):  
Yi Zhao ◽  
Ranjith Kankala ◽  
Shi-Bin Wang ◽  
Ai-Zheng Chen
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Drug Efficacy ◽  
Dynamic Responses ◽  
Physical Parameters ◽  
Real Time Monitoring ◽  
Multiple Organs ◽  
On Chip

With advantageous features such as minimizing the cost, time, and sample size requirements, organ-on-a-chip (OOC) systems have garnered enormous interest from researchers for their ability for real-time monitoring of physical parameters by mimicking the in vivo microenvironment and the precise responses of xenobiotics, i.e., drug efficacy and toxicity over conventional two-dimensional (2D) and three-dimensional (3D) cell cultures, as well as animal models. Recent advancements of OOC systems have evidenced the fabrication of ‘multi-organ-on-chip’ (MOC) models, which connect separated organ chambers together to resemble an ideal pharmacokinetic and pharmacodynamic (PK-PD) model for monitoring the complex interactions between multiple organs and the resultant dynamic responses of multiple organs to pharmaceutical compounds. Numerous varieties of MOC systems have been proposed, mainly focusing on the construction of these multi-organ models, while there are only few studies on how to realize continual, automated, and stable testing, which still remains a significant challenge in the development process of MOCs. Herein, this review emphasizes the recent advancements in realizing long-term testing of MOCs to promote their capability for real-time monitoring of multi-organ interactions and chronic cellular reactions more accurately and steadily over the available chip models. Efforts in this field are still ongoing for better performance in the assessment of preclinical attributes for a new chemical entity. Further, we give a brief overview on the various biomedical applications of long-term testing in MOCs, including several proposed applications and their potential utilization in the future. Finally, we summarize with perspectives.

scholarly journals The Estimation of Chemical Oxygen Demand of Erhai Lake Basin and Its Links with DOM Fluorescent Components Using Machine Learning

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3629
Author(s):  
Yuquan Zhao ◽  
Jian Shen ◽  
Jimeng Feng ◽  
Zhitong Sun ◽  
Tianyang Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chemical Oxygen Demand ◽  
Real Time ◽  
Oxygen Demand ◽  
Organic Pollution ◽  
Lake Basin ◽  
Erhai Lake ◽  
Real Time Monitoring ◽  
The Relationship

Water quality estimation tools based on real-time monitoring are essential for the effective management of organic pollution in watersheds. This study aims to monitor changes in the levels of chemical oxygen demand (COD, CODMn) and dissolved organic matter (DOM) in Erhai Lake Basin, exploring their relationships and the ability of DOM to estimate COD and CODMn. Excitation emission matrix–parallel factor analysis (EEM–PARAFAC) of DOM identified protein-like component (C1) and humic-like components (C2, C3, C4). Combined with random forest (RF), maximum fluorescence intensity (Fmax) values of components were selected as estimation parameters to establish models. Results proved that the COD of rivers was more sensitive to the reduction in C1 and C2, while CODMn was more sensitive to C4. The DOM of Erhai Lake thrived by internal sources, and the relationship between COD, CODMn, and DOM of Erhai Lake was more complicated than rivers (inflow rivers of Erhai Lake). Models for rivers achieved good estimations, and by adding dissolved oxygen and water temperature, the estimation ability of COD models for Erhai Lake was significantly improved. This study demonstrates that DOM-based machine learning can be used as an alternative tool for real-time monitoring of organic pollution and deepening the understanding of the relationship between COD, CODMn, and DOM, and provide a scientific basis for water quality management.

Towards the real-time monitoring of industrial wastewater treatment processes via photoelectrochemical oxygen demand measurements

2018 ◽  
Vol 4 (3) ◽  
pp. 394-402 ◽  
Author(s):  
Kimia Aghasadeghi ◽  
Melissa J. Larocque ◽  
David R. Latulippe
Keyword(s):  
Wastewater Treatment ◽  
Real Time ◽  
Industrial Wastewater ◽  
Oxygen Demand ◽  
Real Time Monitoring ◽  
Industrial Wastewater Treatment ◽  
The Real ◽  
Treatment Processes ◽  
Photoelectrochemical Oxidation

Photoelectrochemical oxidation of different macromolecules was studied to investigate the potential of peCOD for use in industrial wastewater treatment.

scholarly journals Industrial Parameters Monitoring System based on Embedded Web Server

2019 ◽  
Vol 8 (9) ◽  
pp. 2450-2453
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Electronic Device ◽  
Physical Parameters ◽  
Real Time Monitoring ◽  
Time Data ◽  
The Real ◽  
Industrial Sectors ◽  
Free Data ◽  
Real Time Data

The main aim of this paper is to deal with remote monitoring of various physical parameters of an electrical device via web-based application. This system facilitate user to monitor the real-time data from across the globe as the whole data is made available through pre-designed website. Real-time monitoring of electrical parameters is needed beside the high performance and precision of measurements with the development of modern industry towards networking. The main objectives of paper are to access the real-time data on global scale, to reduce the cost of visit & maintenance and finally to improve quality as well as throughput of production. All the physical parameters of an electronic device such as temperature, current, gas flow, viscosity etc. will be monitor independently. Microcontroller is used for the interconnection of all sensors and all collected information will be send to the web page using GSM facility. This real-time monitoring system definitely offers user for hassle free data accession. For high precision, repeatability of real-time data monitoring system has been done. This concept is helpful in industrial sectors for real time monitoring.

A Parallel Detecting, Spectroscopic Ellipsometer for Intelligent Process Control of Continuously Deposited CIGS Films

2000 ◽  
Vol 616 ◽  
Author(s):  
L. J. Simpson ◽  
B. S. Joshi ◽  
L. A. Gonzales ◽  
J. Verley ◽  
T. E. Furtak
Keyword(s):  
Process Control ◽  
Real Time ◽  
Flexible Substrate ◽  
Polarization State ◽  
Processing System ◽  
Real Time Monitoring ◽  
Spectroscopic Ellipsometer ◽  
Intelligent Process ◽  
Intelligent Process Control

AbstractAdvanced materials processing involves active control of fabrication and real-time monitoring of the final product. Sensors must be an integral part of the overall material processing system. ITN Energy Systems, Inc. and the Colorado School of Mines have developed a Parallel Detection, Spectroscopic Ellipsometer (PDSE) sensor for in-situ, real-time characterization and process control of multi-layered vapor deposited films. By measuring changes in the polarization state of reflecting light as a function of wavelength (250 to 5000 nm), the PDSE sensor determines the complex reflectance and/or the ellipsometric amplitude and phase. The PDSE provides cost-effective in-line sensing for film process control through detection of critical product variables that directly relate to film performance including: film thickness, optical excitation states, impurity concentrations, conductivity/resistance, intermixing at interfaces, microstructure, surface roughness, void fraction, defects, and grain size. The PDSE sensor is an optical probe with no moving parts that can measure the optical properties of thin films in as little as 3 msec with sensitivity to films less than a monolayer in thickness. We will use this PDSE system to provide real time process control of vapor deposited CuInGaSe2 (CIGS) films on a continuous flexible substrate. Initial results from CIGS films indicate that the PDSE has the sensitivity and accuracy to provide intelligent process control. The remaining challenge is to develop interpretive algorithms; the amount and quality of information required will determine their complexity. In addition, with the inception of in-situ, real-time monitoring, we hope to enable minimal data analysis approaches1 that provide extremely useful information with minimum interpretive algorithm development.

Ultrasonic Real-Time Monitoring of Slip Cast Cake Thickness

1988 ◽  
Vol 142 ◽  
Author(s):  
David Jarman ◽  
Adam J. Gesing ◽  
Bahram Farahbakhsh ◽  
Gene Burger ◽  
David Huchins ◽  
...  
Keyword(s):  
Process Control ◽  
Production Process ◽  
Real Time ◽  
Ultrasonic Method ◽  
Slip Casting ◽  
Casting Process ◽  
Real Time Monitoring ◽  
Cake Thickness ◽  
Slip Cast ◽  
Pulse Echo

AbstractAn ultrasonic method was developed for real-time monitoring of cake thickness during the casting of an alumina slip. The technique uses pulse-echo ultrasound to measure the time of flight and hence the thickness of the cast layer. Variations on the method are applicable to both production process control and to the fundamental studies of slip casting process kinetics.

Sign in / Sign up

Export Citation Format

Share Document