System and method for forecasting yield of concentrate in whole mineral dressing process

Abstract

The invention relates to a system and method for forecasting the yield of concentrate in the whole mineral dressing process. The system comprises a data obtaining unit, a data processing unit, a single-model training unit, a model integration unit and a data storage unit. The method for forecasting the yield of the concentrate in the whole mineral dressing process includes the steps that production index historical data of the whole mineral dressing process are obtained, the yield of the concentrate is selected to serve as forecast index data, a new data set is formed with multiple production indexes as input index data, the new data set is divided into a training data set and a verification data set, a single-implicit-strata feedforward network model is built, the input index data serve as input of the model, the forecast index data serve as output of the model, an implicit-strata output matrix of the single-implicit-strata feedforward network model is obtained, the single-implicit-strata feedforward network is integrated, a concentrate yield forecast model of the whole mineral dressing production process is determined, production index data are collected in real time in the whole actual mineral dressing production process, the forecast model is effectively made use of and the yield of the mineral dressing is forecasted.

Description

technical field [0001] The invention belongs to the technical field of beneficiation production process, and in particular relates to a system and method for forecasting concentrate output in the whole process of beneficiation production. Background technique [0002] Mineral resources are an important basic raw material for the needs of economic development. They play a pivotal role in the development of the national economy, the construction of national defense science and technology, and in many fields such as metallurgy, construction, transportation, and chemical industry. At the same time, mineral resources are difficult to regenerate. natural resources, so countries are actively advocating sustainable development strategies. In this situation, mineral processing enterprises can no longer simply pursue economic benefits as in the past, but should pay more attention to product quality, reduce production costs, save resource consumption, and reduce environmental pollution...

AI Technical Summary

Problems solved by technology

[0003] The more traditional modeling methods for mineral processing problems include gray, regression analysis and time series methods, but most of the traditional methods focus on the analysis of its causal regression model and time series model, and the established models cannot comprehensively and essentially reflect the The complex internal structure and characteristics of the predicted data also easily lead to the loss of information

In addition, some scholars use the traditional neural network method to establish a forecast model for mineral processing, such as the production index forecast model based on fuzzy clustering RBF neural network. This model is based on the principle of empirical risk minimization, but its generalization ability is poor and overfitting , local optimal solution and low learning efficiency make the application of this model in this field subject to certain restrictions

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