The efficient and environmentally sound treatment of oily sludge is a critical challenge for the petroleum industry's green transformation. This article systematically reviews the principles and characteristics of mainstream treatment methods, from traditional landfilling and incineration to modern biological and pyrolysis technologies, aiming to provide a clear reference for technology selection within the industry.

In 2025, with the deepened "dual carbon" policy and upgraded environmental standards, resource utilization of industrial solid waste and harmless treatment of high-viscosity materials have become industry focuses. Per the 2024 White Paper on the Renewable Resources Industry, 32% of SMEs see over 15% raw material loss due to poor sorting accuracy of solid waste equipment, driving urgent demand for high-efficiency, eco-friendly equipment². Against this backdrop, Luoyang Kaizheng Environmental Protection Technology & Equipment Co., Ltd. (hereinafter "Luoyang Kaizheng") offers customized industrial clean production solutions via its three core business segments.

Our company has been recognized as a "SRDI Enterprise". We have been focusing on the development of integrated solutions for raw material preparation, batching, solid waste, residual material and recycled material processing. Our goal is to help clients process all kinds of materials more effectively and produce value from them.

Section 6 discussed what this implies when comparing emulsification efficiencies between the two modes of operation. Several different theories have been suggested, but there is of yet no clear consensus in the literature for how continuous mode RSMs should be operated in order to give the same emulsion as in a batch RSM.

This review summarizes and critically compares the current understanding of differences between these two operating modes, focusing on shaft power draw, pumping power, efficiency in producing a narrow region of high intensity turbulence, and implications for product quality differences when transitioning from batch to continuous rotor-stator mixers.

Although continuous production processes are often desired, many processing industries still work in batch mode due to technical limitations. Transitioning to continuous production requires an in-depth understanding of how each unit operation is affected by the shift. This contribution reviews the scientific understanding of similarities and differences between emulsification in turbulent rotor-stator mixers (also known as high-speed mixers) operated in batch and continuous mode.

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.

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.