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Luoyang (ancient name——Zhen xun, Xi Bo, Luo Yi, Luo Jing, Jing Luo, Shen Du, Luo Cheng) is also called Yi Luo because of the two rivers of Yi and Luo in its territory. Luoyang, in central China's Henan Province, was named in honor of its situation north of the Luohe River–a tributary of the Yellow River. In ancient China, such a location, or one at the southern foot of a mountain, was deemed to be yang (meaning the sunny side as opposed to yin). Luoyang was thus named. It is one of the first national historical and cultural cities announced by the State Council, one of the four ancient capitals of China, and a world cultural city.

The Dragon Boat Festival,also called Double Fifth Festival, is celebrated on the fifth day of the fifth moon of the lunar calendar.It is one of the most important Chinese festivals the other two being the Autumn Moon Festival and Chinese New Year.

Benevolence. righteousness, propriety, wisdom and faith are the 'five constants' in Chinese traditional Confucianism. These 'five constants' run through the development of Chinese ethics and become the most core elements in Chinese value system. There was no paper in the ancient times, all experience skills were from precept. At that time, pure and simple are reliable, so the integrity means the good faith. No matter for human or business, integrity is the foundation, and nothing would be builded up without it.

Hydrogen-based direct reduction is a promising technology for CO2 lean steelmaking. The electric arc furnace is the most relevant aggregate for processing direct reduced iron (DRI). As DRI is usually added into the arc, the behavior in this area is of great interest. A laboratory-scale hydrogen plasma smelting reduction (HPSR) reactor was used to analyze that under inert conditions.

After validating it with experimental test, this model was utilized to study the mixing performance considering the allowable mass fraction range of every formulation component and a mixer speed of 45 rpm, and the dataset generated from this study was employed along with a machine learning algorithm to obtain a model to predict the mixing index. In this sense, twenty-five different combinations of the defined components were simulated and a mixing index of 0.98–0.99 was obtained in a time of 60 s, revealing that all the combinations were completely mixed. In addition, the developed model was validated with results obtained from the DEM model. The model predicts the mixing index in advance and with accuracy.

The DIY approach promotes small-scale digital manufacturing for the production of customized, fast moving consumer goods, including powder detergent. In this context, a machine was developed to manufacture a customized detergent according to the needs of the clients indicated on a digital platform connected to the machine. The detergent is produced by a mixing process of the formulation components carried out in a 3D mixer. Analysing the mixing performance of the process is essential to obtain a quality product. In this study, the mixing process of the powder detergent was modelled using the discrete element method.

Two mixing indexes are used to evaluate the mixing condition, namely, the Ica Manas-Zlaczower dispersive index and Kramer’s distributive index. Some fluctuations are observed in the predictions of the dispersive mixing index, which could be associated with the free surface of the suspension, thus indicating that this index might not be ideal for partially filled mixers. The Kramer index results are stable and indicate that the particles in the suspension can be well distributed. Interestingly, the results highlight that the speed at which the suspension becomes well distributed is almost independent of applying heat both before and during the process.

This paper presents a new non-isothermal, non-Newtonian CFD model for the sigma blade mixing of an adhesive suspension, where both the free surface and viscous heating are accounted for. The suspension is modeled as a viscoplastic fluid, and model calibration is performed via optical temperature measurements. To evaluate the mixing quality, we have used Zlaczower’s dispersive mixing index as well as Kramer’s distributive mixing index. The model is used to investigate the effect of applying heat both before and during the process on the mixing quality.

In that previous section, we have introduced Plackett–Burman (P–B) design and have defined four key performance indicators (KPIs). This section will discuss the results of the KPIs, summarize the results of P–B design. The results show that the material property effects are not as significant as those of the operational conditions and geometric parameters. In particular, the geometric parameters were observed to significantly influence the energy consumption, while not affecting the mixing quality and mixing time, showing their potential towards designing more sustainable mixers. Furthermore, the analysis of granular temperature revealed that the centre area between the two paddles has a high diffusivity, which can be correlated to the mixing time.