Concentrate grade and tailings grade are two vital parameter indexes in a flotation process. To detect the grade succinctly and continuously, a soft sensor based on case-based reasoning (CBR) is proposed. Historic production data is first switched into the form of a case. The case problem includes feed grade, raw ore grade, raw ore ferrous oxide content, raw ore magnetic iron content, target concentrate grade, target tailings grade, dosage of four kinds of reagents; the case solution includes concentrate grade and tailings grade. Simulation result shows that the CBR soft sensor has a higher accuracy and speed in forecasting both concentrate grade and tailings grade when compared with soft sensors supported by other algorithms. The application result in a Chinese iron core dressing mill indicates that the soft sensor presented by this paper causes no damage to people and it can forecast product quality in real-time.
The grinding product particle size is the most crucial operational index of mineral grinding processes. The size and consistency of the product directly affects the subsequent dressing and sintering. In this paper, a novel expert system is proposed for guiding the operating variables to keep the product stable with the wildly varying ore properties. First, case-based reasoning (CBR) is introduced to describe the whole grinding process with the historical data and expert experience. Second, the generative adversarial network (GAN) is employed to extend the raw data to enhance the flexibility of CBR. Moreover, the weights of different features in CBR is optimized by improved non-dominated sorting genetic algorithm II (NSGA-II). Finally, the proposed method is validated by a set of actual data collected from a Chinese dressing plant. The experimental result demonstrates the effectiveness of the proposed method.
Motor power curves (MPCs) have received great attention for use in diagnosing the working conditions of sucker rod pumping systems (SRPSs) because of their advantages in accessibility and real-time performance. However, existing MPC-based approaches mostly need a rigorous assumption that the MPC instances of different working conditions are sufficient, which does not hold in industrial scenarios. To this end, this paper proposes an unsupervised fault diagnosis methodology to leverage readily available dynamometer cards (DCs) to diagnose collected unlabeled MPCs. Firstly, a mathematical model of the SRPS is presented to convert actual DCs to MPCs. Secondly, a novel diagnostic methodology based on adversarial domain adaptation is proposed for the problem of data distribution discrepancy across the collected and converted MPCs. Specifically, the collected unlabeled MPCs may only cover a subset of the working conditions of the abundant DCs, which will easily cause negative transfer and lead to dramatic performance degradation. This proposed methodology employs class-level and distribution-level weighting strategies so as to guide the network to focus on the instances from shared categories and down-weight the outlier ones. Validation experiments are performed to evaluate the mathematical model and the diagnostic methodology with a set of actual MPCs collected by a self-developed device. The experimental result indicates that the accuracy of the proposed algorithm can reach 99.3% in diagnosing actual MPCs when only labeled DCs and unlabeled actual MPCs are used.
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