Hongmei Yin

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.

In the V-cycle of distributed electric wheel loaders (DEWLs), transport accounts for about 70% of the cycle, making energy saving urgent, while shovel-stage slip limits traction stability. This paper proposes a two-module control framework: (i) a preferred-motor transport strategy that reduces parasitic losses and concentrates operation in high-efficiency regions; and (ii) a load-ratio-based front–rear torque distribution for shoveling that allocates tractive effort according to instantaneous axle vertical loads so that each axle’s torque respects its available adhesion. For observability, we deploy a pre-calibrated lookup-table (LUT) mapping from bucket cylinder pressure to the front-axle load ratio, derived offline from a back-propagation neural network (BP-NN) fit. Tests on a newly developed DEWL show that, compared with dual-motor fixed-ratio control, transport-stage mechanical and electrical power drop by 18–37%, and drive-system efficiency rises by 6–13%. During shoveling, the strategy reduces the peak inter-axle slip from 22–35% to 13–15% and lowers the mean slip to 2.6–5.9%, suppressing sawtooth-like wheel-speed oscillations without sacrificing peak capacity. The method reduces parasitic energy flow, improves traction utilization, and is readily deployable.