This review comprehensively focuses on catalyst dense-phase packing technology. It provides a systematic review of its principles, characteristics, and applications across diverse fields. A detailed comparison between dense-phase packing and traditional packing methods is presented, along with an in-depth analysis of its application status in petrochemical, fine-chemical, and other industries. The innovative trends of this technology in packing equipment and processes, as well as the challenges and countermeasures in its application, are thoroughly discussed. The aim is to offer a comprehensive and profound reference for relevant research and industrial practices, guiding future development in this crucial area of chemical engineering.

Emissions of airborne particulate matter are a major cause of air pollution. A numerical approach was used to establish a single-filter-bag cleaning model based on the discrete phase particle deposition distribution law to study the effect of pulse backflushing operation parameters on the cleaning performance of the specific structure of the dust collector. It was found that increasing the pulse blowing pressure (0.2 MPa to 0.6 MPa) and extending the pulse width (0.02 s to 0.1 s) resulted in an increase in the side wall pressure of the filter bag and, thus, improved its cleaning effect. The influence of the injection pressure on the side wall pressure peak of the filter bag was more obvious than that of the pulse width. However, it cannot be concluded that the greater the injection pressure and pulse width, the better the cleaning effect. Therefore, in the actual production application, the operating cost of the equipment needs to be taken into account, as well as the amount of dust on the surface of the filter bag.

Fixed-bed reactors are pivotal in chemical industries, where catalyst loading critically determines reactor performance and economy. This critical review delineates and analyzes a three-stage evolution of loading technology: from empirical manual methods, through scenario-adaptive innovations, to closed-loop intelligent systems. It aims to decode the underlying scientific principles, assess the performance enhancements and inherent limitations of each stage, and critically examine the architectural framework and constraints of intelligent loading systems. Industrial validation data, such as from a 2.4 Mt/a hydrocracker, demonstrate potential improvements (e.g., 20–22% catalyst life extension, 1.8% bed pressure-drop fluctuation). However, the progression presents complex trade-offs in terms of scalability, cost, and standardization. The future direction is discussed, pointing toward addressing challenges in multi-physics modeling, digital twin integration, and fundamental research gaps. This work provides a balanced framework for evaluating loading technology evolution, acknowledging its context-dependent applicability.