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.

Following the above, in this part the discrete element method (DEM) and Plackett–Burman (P–B) design were used to investigate the mixing performance of a double paddle mixer. To this end, several material properties (i.e., particle size ratio, density ratio and composition), operational conditions (i.e., filling pattern, fill level and impeller rotational speed) and geometric parameters (i.e., paddle size, angle and number) were examined. In order to quantitatively analyse their effects on mixing performance, a number of key performance indicators (KPIs) were defined, namely the average steady-state RSD (KPI 1), the mixing time (KPI 2) and the average mixing power (KPI 3). In addition, KPI 4 was formulated as a multiplication of KPI 2 and KPI 3 to examine the mixing time and energy consumption at the same time.

To improve the understanding of the mixing performance of double shaft, batch-type paddle mixers, the discrete element method (DEM) in combination with a Plackett–Burman design of experiments simulation plan is used to identify factor significance on the system’s mixing performance. Effects of several factors, including three material properties (particle size, particle density and composition), three operational conditions (initial filling pattern, fill level and impeller rotational speed) and three geometric parameters (paddle size, paddle angle and paddle number), were quantitatively investigated using the relative standard deviation (RSD). Four key performance indicators (KPIs), namely the mixing quality, mixing time, average mixing power and energy required to reach a steady state, were defined to evaluate the performance of the double paddle mixer.

With the demand of structural adjustment and optimization of China's iron and steel industry, and the requirements of green, low-carbon and high-quality, pellets as high-quality raw materials for blast furnace are more and more favored by the industry, which promotes the rapid development of the pellet industry.

The self-tensioning motor base can avoid these problems perfectly. The design ensures that the belt maintains proper tension throughout the life cycle of the belt, avoiding excessive stretching of the belt and preventing belt relaxation. There is no need to periodically relax or tighten the belt, which guarantees the extension of the belt life and improves the efficiency of the entire transmission system. The self-tensioning motor base reduces downtime, belts are easily installed and removed, and no special training is required to operate the self-tensioning motor base.