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Industry 4.0 Technologies for Sustainable Asset Life Cycle Management

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Asset life cycle management is not a new concept for industries. Life cycle thinking means that people have a life cycle model in mind that affects the scope of their activities. A life cycle perspective for manufacturing assets is often mentioned in the literature regarding sustainability. This approach aims to understand and analyze individual stages of the asset life cycle, identify potential economic, social, and environmental risk factors and opportunities at each stage, and create possibilities to take advantage of these opportunities and reduce potential risks. In the Industry 4.0 era, manufacturers can monitor assets and make smart decisions in each phase of their life cycle through real-time communication and cooperation with humans, machines, sensors, etc. These technologies can support all stages of ALC through various emergent communication, information, and intelligence technologies. Technologies such as Digital Twin (DT), Internet of Things (IoT), Cyber-Physical Systems (CPS) and their respective specialization to industry, Industrial Internet of Things (IIoT), and Cyber-Physical Production System (CPPS), are considered in this Special Issue to increase the effectiveness of asset life cycle management (ALCM).

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Investigation on the Rheological Properties of Polydimethylsiloxane

Author(s): Javanbakht T.

Affiliation(s): Department of Chemistry and Biochemistry, Department of Physics, Concordia University, Richard J. Renaud Science Complex, 714,1 Sherbrooke St. West, H4B 1R6, Montreal, Quebec, Canada

*Corresponding Author’s Address: [email protected]

Issue: Volume 9, Issue 1 (2022)

Submitted: January 20, 2022
Accepted for publication: March 18, 2022
Available online: March 23, 2022

Javanbakht T. (2022). Investigation on the rheological properties of polydimethylsiloxane. Journal of Engineering Sciences, Vol. 9(1), pp. C1-C7, doi: 10.21272/jes.2022.9(1).c1

DOI: 10.21272/jes.2022.9(1).c1

Research Area:  MANUFACTURING ENGINEERING: Materials Science

Abstract. This paper focuses on studying the rheological properties of polydimethylsiloxane (PDMS). This polymer has been used to fabricate membranes and filters in engineering. The analysis of the rheological properties of this polymer is required for a further investigation of its mechanical behavior. In this study, the rheological behavior of PDMS is reported at different temperatures. This polymer showed steady shear viscosity during a short duration. However, this behavior changed with time and increased more with increasing temperature. The impact of the temperature increase was also observed when the shear viscosity of PDMS increased with shear strain. The increase of torque with shear strain and time was observed at different temperatures. Shear stress increased linearly with the shear rate at 20 °C and 40 °C. As expected, the deformation of the polymer required less shear stress with the increase of temperature. However, the change of shear stress with the shear rate at 60 °C was not linear, and the slope of the curve increased more at high shear rates. The results of this investigation can provide the required information for a better fabrication of membranes and filters with this polymer.

Keywords: rheology, polymer, mechanical properties, materials science, industrial growth.


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Modelling, Simulation and Control in Combustion Processes of Renewable Fuels

The Special Issue of MDPI Processes (Impact Factor 2.753) “Modelling, Simulation and Control in Combustion Processes of Renewable Fuels” invites researchers to make a submission.

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The modeling and simulation of combustion processes is still a challenging field. In principle, it requires the integration of heat and mass transfer, flow conditions, and reaction chemistry. Available tools for such modeling are very different and are usually problem-specific. One special field of interest is fluidized bed combustion of solid fuels, which additionally encounter the fluidized bed hydrodynamics and particle interactions. Recent trends in the field are focusing on the more detailed description and understanding of the burn-out mechanism of solid fuel particles, which is essential for modeling to have reasonable outputs. From a control point of view, dynamic models of combustion processes are very important, for example, in model-based control algorithms. Due to their complexity, dynamic modeling based on partial differential equations and parameter identification for the corresponding transient models is a topical problem, which can be solved using a comprehensive approach based on experimental data, numerical simulation, regression modeling, and also artificial intelligence methods. One common challenge is a validation of the models in the real process, which requires in-depth and precise measurements that are typically complicated by limited access into the combustion process zone. This information is important also for the control and monitoring of the combustion processes.

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