Automation Research and Design Institute of Metallurgical Industry (China)

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises.

Large-scale industrial processes are characterized by complex reaction mechanisms and strong correlation among operational units, which makes it more difficult to extract the temporal correlation of process data, thereby affecting the process monitoring performance. Thus, a novel temporal variational autoencoder network based process monitoring method for large-scale industrial processes is proposed. First, the large-scale industrial processes are divided into multiple sub-blocks based on the spatial distribution of operational units. Second, the consistent trend feature analysis method is introduced to quantify the degree of consistency between adjacent trend features by calculating the differences among trend vectors. Then, the differences between trend features are integrated into the variational autoencoder to extract temporal features from each sub-block. Finally, Bayesian fusion strategy is employed to fuse the temporal features from all sub-blocks, constructing a comprehensive global monitoring indicators for process monitoring. Experimental results from an actual hot strip mill process demonstrate that the proposed method outperforms traditional approaches in monitoring performance.

Dynamic Vehicle Route Planning (DVRP) severely challenges logistics system optimization as it must respond to dynamic events like unexpected orders and traffic changes in real time. Traditional methods suffer from high computational overhead and poor adaptability to dynamic environments, while existing deep learning methods struggle to balance solution quality and computational efficiency. To address this, this paper proposes the lightweight graph capsule network (LGCN) algorithm. It encodes dynamic path states into spatio-temporal graph structures, uses a hierarchical capsule mechanism instead of traditional graph convolution, and applies lightweight processing to the hierarchical capsule network. This effectively resolves the conflict between fast response to dynamic changes and high-quality computational results. The method's effectiveness is evaluated by comparison with transformer-based algorithms, OR-Tool algorithms, and Clarke Wright's algorithm. Experiments show that LGCN not only significantly improves real-time dynamic response efficiency but also performs excellently in multiple key metrics such as average routing cost and average vehicles.

To overcome the limitations of single-objective optimization in permanent magnet synchronous motor (PMSM) performance enhancement, this study proposes an adaptive moving least squares (AMLS) for a 12-pole/36-slot built-in PMSM. Through comprehensive exploration of the design space, a systematic approach is established for holistic motor performance improvement. The Gaussian weight function is modified to improve the model’s fitting accuracy, and the decay rate of the control weight is optimized. The optimal adaptive meta-model for the built-in PMSM is selected based on the coefficient of determination. Subsequently, sensitivity analysis is conducted to identify the parameters that most significantly influence key performance indicators, including torque ripple, stator core loss, electromagnetic force amplitude, and average output torque. These parameters are then chosen as the optimal design variables. A multi-objective optimization framework, built upon the optimal adaptive meta-model, is developed to address the multi-objective optimization problem. The results demonstrate increased output torque, along with reductions in stator core loss, torque ripple, and radial electromagnetic force, thereby significantly improving the overall performance of the motor.

Satisfactory progress has been achieved recently in universal segmentation of CT images. Following the success of vision-language methods, there is a growing trend towards utilizing text prompts and contrastive learning to develop universal segmentation models. However, there exists a significant imbalance in information density between 3D images and text prompts. Moreover, the standard fully connected layer segmentation approach faces significant challenges with handling multiple classes and exhibits poor generalizability. To address these challenges, we propose VOxel Interacting with LAnguage method (VOILA) for universal CT image segmentation. Initially, we align voxels and language into a shared representation space and classify voxels based on cosine similarity. Subsequently, we develop the Voxel-Language Interaction framework to mitigate the impact of class imbalance caused by foreground-background discrepancies and variations in target volumes. Furthermore, a Complexity-Aware Sampling method is proposed to focus on region hard to segment, achieved by generating pseudo heatmaps from a trainable Gaussian mixture distribution. Our results indicate the proposed VOILA is capable to achieve improved performance with reduced parameters and computational cost during training. Furthermore, it demonstrates significant generalizability across diverse datasets without additional fine-tuning.

Abstract After the thin slab continuous casting and rolling production line of a domestic steel plant was put into operation, with the vigorous development of hot-dip galvanized products, the strip blackening defect on the surface became more prominent. After hot-dip galvanizing, the coil had a certain spacing of strip black spot defects on the surface, which could not meet the needs of high-end users and restricted the improvement of the manufacturing capacity of the headless production line.Therefore, this article uses scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), three-dimensional profilometer, and optical microscope (OM) to study the microscopic characteristics and causes of black spot defects on the surface of hot-dip galvanized steel. There are a large number of oxidized black spots on the surface of the stripe black area. The zinc layer thickness of the cross-section at the blackened area is smaller than that of the normal area, and the secondary dendrites inside the zinc layer at the blackened area are not well-developed in the normal area. The surface roughness Ra and Rt of the blackened position in the three-dimensional morphology display show significant differences from the normal position. Electron microscopy observation shows that the grain size of the substrate at the blackened area is different from that at the normal area after removing the zinc layer, which is the direct cause of the final surface showing stripe black spot defects. By optimizing the heating temperature uniformity of the IH on the thin slab continuous casting and rolling production line, the stripe black spot defects were ultimately eliminated.

Maximum Torque Per Ampere (MTPA) control strategy effectively enhances the efficiency of permanent magnet synchronous motors (PMSMs), widely applied to extend the driving range of electric vehicles. However, as the battery discharges, the DC bus voltage of the inverter decreases gradually, limiting the switching frequency of the MTPA control strategy due to the voltage limit circle constraint. This reduction affects the acceleration performance and uphill climbing capability of electric vehicles. To address this issue, a novel control strategy is proposed. It introduces a Quasi-Z source network into the PMSM inverter and utilizes the rate of change of phase current amplitudes of the PMSM as a compensating factor in the voltage loop control of the Quasi-Z source network. This forms a positive feedback voltage loop control structure, enhancing the DC bus voltage and its dynamic response performance, thereby improving the switching frequency and extending the operating range of the PMSM. MATLAB/Simulink simulations and experimental validations demonstrate the effectiveness and feasibility of the proposed control strategy.