scholarly journals In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5009 ◽  
Author(s):  
Stefania Tronci ◽  
Paul Van Neer ◽  
Erwin Giling ◽  
Uilke Stelwagen ◽  
Daniele Piras ◽  
...  
Keyword(s):  
Performance Indicators ◽  
Continuous Process ◽  
Principal Component ◽  
Feedforward Neural Networks ◽  
Data Driven ◽  
Monitoring And Control ◽  
Soft Sensors ◽  
Continuous Processes ◽  
Data Driven Approach ◽  
And Control

The use of continuous processing is replacing batch modes because of their capabilities to address issues of agility, flexibility, cost, and robustness. Continuous processes can be operated at more extreme conditions, resulting in higher speed and efficiency. The issue when using a continuous process is to maintain the satisfaction of quality indices even in the presence of perturbations. For this reason, it is important to evaluate in-line key performance indicators. Rheology is a critical parameter when dealing with the production of complex fluids obtained by mixing and filling. In this work, a tomographic ultrasonic velocity meter is applied to obtain the rheological curve of a non-Newtonian fluid. Raw ultrasound signals are processed using a data-driven approach based on principal component analysis (PCA) and feedforward neural networks (FNN). The obtained sensor has been associated with a data-driven decision support system for conducting the process.

scholarly journals Potential applications of halophilic microorganisms for biological treatment of industrial process brines contaminated with aromatics

2021 ◽  
Author(s):  
Thomas Mainka ◽  
David Weirathmüller ◽  
Christoph Herwig ◽  
Stefan Pflügl
Keyword(s):  
Wastewater Treatment ◽  
Process Monitoring ◽  
Industrial Wastewater ◽  
Continuous Process ◽  
Scale Up ◽  
Monitoring And Control ◽  
Saline Wastewater ◽  
Halophilic Microorganisms ◽  
Industrial Sectors ◽  
And Control

Abstract Saline wastewater contaminated with aromatic compounds can be frequently found in various industrial sectors. Those compounds need to be degraded before reuse of wastewater in other process steps or release to the environment. Halophiles have been reported to efficiently degrade aromatics, but their application to treat industrial wastewater is rare. Halophilic processes for industrial wastewater treatment need to satisfy certain requirements: a continuous process mode, low operational expenditures, suitable reactor systems and a monitoring and control strategy. The aim of this review is to provide an overview of halophilic microorganisms, principles of aromatic biodegradation, and sources of saline wastewater containing aromatics and other contaminants. Finally, process examples for halophilic wastewater treatment and potential process monitoring strategies are discussed. To further illustrate the significant potential of halophiles for saline wastewater treatment and to facilitate development of ready-to-implement processes, future research should focus on scale-up and innovative process monitoring and control strategies.

scholarly journals A data-driven approach to measuring epidemiological susceptibility risk around the world

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alessandro Bitetto ◽  
Paola Cerchiello ◽  
Charilaos Mertzanis
Keyword(s):  
Factor Model ◽  
Principal Component ◽  
Data Driven ◽  
Dynamic Factor ◽  
Country Level ◽  
Epidemiological Risk ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Total Variability ◽  
Pca Method

AbstractEpidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010–2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.

A Data-Driven Methodology for Fault Detection in Electromechanical Actuators

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Anthony J. Chirico ◽  
Jason R. Kolodziej
Keyword(s):  
Feature Extraction ◽  
Failure Modes ◽  
Extraction Process ◽  
Data Driven ◽  
Ball Screw ◽  
Fault Classification ◽  
Electromechanical Actuators ◽  
Rotating Machine ◽  
Data Driven Approach ◽  
And Control

This research investigates a novel data-driven approach to condition monitoring of electromechanical actuators (EMAs) consisting of feature extraction and fault classification. The approach is able to accommodate time-varying loads and speeds since EMAs typically operate under nonsteady conditions. The feature extraction process exposes fault frequencies in signal data that are synchronous with motor position through a series of signal processing techniques. A resulting reduced dimension feature is then used to determine the condition with a trained Bayesian classifier. The approach is based on signal analysis in the frequency domain of inherent EMA signals and accelerometers. For this work, two common failure modes, bearing and ball screw faults, are seeded on a MOOG MaxForce EMA. The EMA is then loaded using active and passive load cells with measurements collected via a dSPACE data acquisition and control system. Typical position commands and loads are utilized to simulate “real-world” inputs and disturbances and laboratory results show that actuator condition can be determined over a range of inputs. Although the process is developed for EMAs, it can be used generically on other rotating machine applications as a Health and Usage Management System (HUMS) tool.

scholarly journals Data-driven approach for monitoring, protection, and control of distribution system assets using micro-PMU technology

2017 ◽  
Vol 2017 (1) ◽  
pp. 1011-1014 ◽  
Author(s):  
Emma Stewart ◽  
Michael Stadler ◽  
Ciaran Roberts ◽  
Jim Reilly ◽  
Dan Arnold ◽  
...  
Keyword(s):  
Distribution System ◽  
Data Driven ◽  
Data Driven Approach ◽  
Protection And Control ◽  
And Control

Application of a grey-box modelling approach for the online monitoring of batch production in the chemical industry

2020 ◽  
Vol 68 (7) ◽  
pp. 582-598
Author(s):  
Ala E. F. Bouaswaig ◽  
Keivan Rahimi-Adli ◽  
Matthias Roth ◽  
Alireza Hosseini ◽  
Hugo Vale ◽  
...  
Keyword(s):  
Batch Processes ◽  
Polymerization Process ◽  
Monitoring And Control ◽  
Continuous Processes ◽  
Model Based ◽  
Modeling Approach ◽  
Industrial Batch ◽  
Laboratory Reactor ◽  
Interest In Research ◽  
And Control

AbstractModel-based solutions for monitoring and control of chemical batch processes have been of interest in research for many decades. However, unlike in continuous processes, in which model-based tools such as Model Predictive Control (MPC) have become a standard in the industry, the reported use of models for batch processes, either for monitoring or control, is rather scarce. This limited use is attributed partly to the inherent complexity of the batch processes (e. g., dynamic, nonlinear, multipurpose) and partly to the lack of appropriate commercial tools in the past. In recent years, algorithms and commercial tools for model-based monitoring and control of batch processes have become more mature and in the era of Industry 4.0 and digitalization they are slowly but steadily gaining more interest in real-word batch applications. This contribution provides a practical example in this application field. Specifically, the use of a grey-box modeling approach, in which a multiway Projection to Latent Structure (PLS) model is combined with a first-principles model, to monitor the evolution of a batch polymerization process and predict in real-time the final batch quality is reported. The modeling approach is described, and the experimental results obtained from an industrial batch laboratory reactor are presented.

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