With the proliferation of the Internet of Things, a large amount of data is generated constantly by industrial systems, corresponding in many cases to critical tasks. It is particularly important to detect abnormal data to ensure the accuracy of data. Aiming at the problem that the training data are contaminated with anomalies in autoencoder-based anomaly detection, which makes it difficult to distinguish abnormal data from normal data, this paper proposes a data anomaly detection method that combines an isolated forest (iForest) and autoencoder algorithm. In this method (iForest-AE), the iForest algorithm was used to calculate the anomaly score of energy data, and the data with a lower anomaly score were selected for model training. After the test data passed through the autoencoder trained by normal data, the data whose reconstruction error was larger than the threshold were determined as an anomaly. Experiment results on the electricity consumption dataset showed that the iForest-AE method achieved an F1 score of 0.981, which outperformed other detection methods, and a significant advantage in anomaly detection.
The control of matte grade determines the production cost of the copper smelting process. In this paper, an optimal matte-grade control model is established to derive the optimal matte grade with the objective of minimizing the cost in the whole process of copper smelting. This paper also uses the prediction capability of the BP (Backpropagation) neural network to establish a BP neural network prediction model for the matte grade, considering various factors affecting matte grade (including the input copper concentrate amount and its composition content, air drumming amount, oxygen drumming amount, melting agent amount, and other process parameters). In addition, the paper also uses the optimal matte grade to optimize the dosing, air supply/oxygen supply, and oxygen supply for the ISA and other furnaces. When using BP networks only, it is a nonconvex problem with gradient descent, which tends to fall into local minima and has some bias in the prediction results. This problem can be solved by optimizing its weights and thresholds through GA (Genetic Algorithm) to find the optimal solution. The analysis results show that the average absolute error of the simulation of the BP neural network prediction model for ice copper grade after GA optimization is 0.51%, which is better than the average absolute error of 1.17% of the simulation of the single BP neural network model.
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