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Laboratory-scale data on a component level are frequently used for prognostics because acquiring them is time and cost efficient. However, they do not reflect actual field conditions. As prognostics is for an in-service system, the developed prognostic methods must be validated using real operational data obtained from an actual system. Because obtaining real operational data is much more expensive than obtaining test-level data, studies employing field data are scarce. In this study, a prognostic method for screws was presented by employing multi-source real operational data obtained from a micro-extrusion system. The analysis of real operational data is more challenging than that of test-level data because the mutual effect of each component in the system is chaotically reflected in the former. This paper presents a degradation feature extraction method for interpreting complex signals for a real extrusion system based on the physical and mechanical properties of the system as well as operational data. The data were analyzed based on general physical properties and the inferred interpretation was verified using the data. The extracted feature exhibits valid degradation behavior and is used to predict the remaining useful life of the screw in a real extrusion system.

Environmental pollution, in particular, the wastewater from the textile and dyeing production process is causing serious issues for the ecosystem and humans. It has been well-demonstrated that zero-valent iron nanoparticles (nZVI) are one of the most promising materials in the treatment of environmental pollutants. In this work, the Cleistocalyx operculatus leaf extract was employed as a green reductant for the synthesis of the nZVIs nanoparticles from iron ions precursor. Notably, a microfluidic device was also used as micro-reactor for the synthesis process of the nanoparticles, making it easy to control and optimise the morphologies and sizes of nZVI. The morphology and crystallinity of nZVI were characterised by scanning electron microscopy (SEM) and x-ray diffraction (XRD), respectively. The nZVIs prepared in a microfluidic reactor using Cleistocalyx operculatus leaf extract as reductant were in plate-like structure with the thickness of around 5 nm and the diameter in range of hundred nanometers. The removal efficiency of Rhodamin B (RhB) dye by the prepared nZVI was determined by ultraviolet-visible (UV–vis) spectroscopy. The results showed that the nZVI revealed high removal performance toward RhB with removal efficiency of higher than 90% after 30 min of treatment. The effects of dye’s concentration, time, and pH solution on the treatment performance of RhB were also studied.

This paper describes an investigation on the dynamic behavior of droplet formation in a microfluidic flow-focusing device (MFFD) under the effect of surfactant using a phase-field method and the Koterweg stress applied in Navier–Stokes equations. The effects of variously important parameters, such as capillary number (Ca0), water fraction ( wf), the viscosity ratio ( α), and particularly surfactant concentration ( cb), were examined. Consequently, the numerical results match the experimental ones. Additionally, the droplet formation is significantly affected by the surfactant, and the droplet size decreases with increasing cb for the whole range of both wf and Ca0. Based on the extensive study, the phase diagrams with two main modes, namely, dropping and threading, are provided. A mountain shape of the dropping mode was found, and this mode can be extended for higher wf with the presence of surfactant. In particular, new generalized correlations as a function of wf, Ca0, and cb are first proposed for predicting quickly and effectively the droplet size. Furthermore, the droplet formation depends significantly on α. With the presence of surfactant, smaller size of the droplet forms and the threading mode occurs at very high α. The results obtained in this study are very useful for understanding the dynamic behavior of droplet formation in MFFDs, which can be used in potential applications such as biomedical and drug delivery systems.

A deep learning-based method for optimizing a membraneless microfluidic fuel cell (MMFC)performance by combining the artificial neural network (ANN) and genetic algorithm (GA) was for the first time introduced. A three-dimensional multiphysics model that had an accuracy equivalent to experimental results (R