Construction and application of a digital twin model for multi-objectiveoptimization of intelligent tape conveyor system

The performance inconsistency in belt cleaning post-discharge presents significant challenges in long-term usage of mine belt conveyors. Issues include material adhesion, dust dispersion, and material spills, posing a direct impact on transportation safety and the effective utilization of energy. Responding to this problem, we propose an effective and environmentally friendly intelligent conveyor system leveraging Digital Twin (DT) technology. In this conceptual model, an Online Sequential Extreme Learning Machine (OS-ELM) is applied within the virtual component to construct a near real-time model predicting sweeping force, using physical feedback data as input parameters. Within the physical counterpart, the sweep force in practice is computed by integrating mechanical models, intellectual and experiential knowledge from the engineering field, as well as operational data. Moreover, forecast values of sweep force at the current moment help formulate the dispatch plan for the cleaning mechanism in the next moment, thus ensuring precise control of the physical model. The Improved Whale Optimization Algorithm (IWOA) is employed to optimize parameters in this blueprint, facilitating real-time scheduling by rapidly converging to the global optimum. In evaluating system performance, we establish key performance indicators: the amount of spillage, wear of crucial components, total power used by the cleaning mechanism, and accuracy in predicting the sweeping force. We compare our system’s performance under various operational scenarios against an array of common optimization algorithms. Simultaneously, we use Sobol’s method and the Fast sensitivity analysis to verify our model’s input reasonability. Results show that the DT-based intelligent conveyor system reduces coal spillage to less than 100 g/min, decreases blade wear rate by 8.99%, cuts actual power consumption by 8.61%, with Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) in sweep force prediction being 3.2748% and 0.017 respectively. The innovative solution establishes dynamic informational mapping between the virtual and real worlds. This process helps gain precise real-time readings of sweeping forces in conveyor systems and enables the reasonable dispatch of multiple levels of sweeping mechanisms. Consequently, it significantly lightens maintenance workloads, mitigates environmental pollution, and greatly enhances the safety and stability of conveyor equipment.