The emergence of soft robots provides new opportunities for developing phosphorite processing equipment. In this article, a soft pneumatic gripper (SPG) for gripping phosphorites is designed. On this basis, the dynamic modeling method and hysteresis inverse compensation control method for the SPG are proposed. First, an SPG for gripping phosphorites is designed based on pneumatic actuation technology. Meanwhile, the gripping ability of the designed SPG is experimentally examined. Next, a dynamic model of the SPG is established by combining the Bouc–Wen model and a linear dynamic model. The output of the established dynamic model can fit the experimental data well, which shows that the established dynamic model of the SPG can describe its motion characteristics. Then, by constructing the inverse expression of the established dynamic model, the hysteresis inverse compensation control method for the SPG is presented to realize its motion control. Finally, the result of the control system simulation illustrates that the presented control method is effective.

The grabbing of phosphorite rocks is an important process in the mining industry. Traditional grabbing technology based on rigid robots faces challenges such as heavy weight, low flexibility, and insufficient safety. This study presents the structural design, characteristic analysis, and modeling of a novel tailored soft robot for phosphorite grabbing (TSRPG). The TSRPG is designed with soft, flexible materials, providing flexible movement and high safety in complex environments. The design inspiration of the robot comes from humans using their thumb and index finger to hold things, and the structural design mainly focuses on the flexibility and grabbing function of the robot. The grabbing function of the TSRPG is exhibited by several actual grabbing experiments. In addition, through characteristic analysis, we explore the robot’s motion properties under various input air pressure conditions. A mathematical model of the TSRPG is developed to depict its characteristics based on the nonlinear ARX model. The developed mathematical model provides a base for promoting the practical application of the TSRPG.

Accidents in process industries continue to cause severe casualties, and a small number of events account for a large share of fatalities. This study proposes a Topic–Hierarchy Coincidence Analysis (T-H CNA) framework to identify condition combinations associated with high-fatality outcomes by integrating BERTopic, Human Factors Analysis and Classification System (HFACS), and Coincidence Analysis (CNA). The framework is applied to 121 Chinese investigation reports of serious-or-above chemical accidents from 2015 to 2025. BERTopic is used to extract 25 causal semantic themes from accident-cause texts, which are then mapped by expert classification onto eight second-level HFACS categories (Fleiss’κ = 0.7118). On this basis, CNA identifies minimally sufficient configurations (MSCs) and traces their cross-level transmission pathways. Six three-condition MSCs are obtained, with consistency values ranging from 0.750 to 0.923; the broadest pathway covers 28.3% of high-fatality cases. Deficient organizational climate and failure to correct known problems recur as the main upstream endpoints and remain stable under stricter fatality thresholds. Although operational errors appear in 88.43% of cases, they do not enter any sufficient configuration. The results indicate that high-fatality outcomes are more closely associated with coupled upstream organizational and supervisory failures than with terminal errors alone, supporting upstream-oriented process safety governance.