Kompleksnoe Ispolzovanie Mineralnogo Syra = Complex use of mineral resources

Predicting Copper Production Cycles in Hydrometallurgy with Interpretable Machine Learning

2025-10-06T07:51:33+00:00

Accurate production forecasting in industrial hydrometallurgy is essential for process optimization yet is often hindered by the scarcity of extensive historical data. This study demonstrates the effectiveness of classical machine learning models as a data-efficient and interpretable alternative to complex deep learning methods for predicting total copper mass. We evaluated four models—Random Forest, Gradient Boosting, Decision Tree, and Linear Regression—using a methodology centered on two key strategies: synthetically expanding a limited 150-day dataset into 10,000 simulated cycles (approximately 1.5 million data points) via data augmentation, and engineering 10-day lag features to provide the models with a temporal perspective for a 10-step-ahead forecasting task. The results revealed exceptional predictive accuracy, with ensemble techniques proving superior. The Random Forest model emerged as the top performer, achieving an R² of 0.974, an MAE of 0.088, and an RMSE of 0.111, closely followed by Gradient Boosting (R² of 0.971). All models successfully captured the distinct 150-day cyclical dynamics of the production process, showing a near-zero phase lag (0.00 ± ≤0.05 days). While performance on new, independent data requires further validation, this work establishes a robust and transparent framework for developing reliable forecasting tools in data-limited industrial environments.

Use of Industrial By-products from Metallurgical Production for the Development of Heat-Resistant Building Mixes and their Molding in an Improved Device

2025-08-25T05:44:51+00:00

In the context of the increasing volume of industrial waste and stricter environmental requirements, the urgent task is to efficiently process them to produce products with high added value. In this work, the composition of industrial products of vanadium production formed during the hydrometallurgical processing of rare metals is investigated, and the possibility of their use for the production of heat-resistant building mixes is substantiated. A comprehensive analysis, including X-ray, X-ray fluorescence, and scanning electron microscopic methods, revealed a high content of silica, aluminum oxides, and refractory minerals that determine the heat resistance of the material. Optimal compositions of building mixes based on Portland cement, liquid glass, and chamotte have been developed, providing compressive strength up to 45 MPa and resistance to thermal cycling at temperatures up to 1800 ° C. The design of a device for forming building blocks based on industrial waste from metallurgical production by vibration pressing is proposed, designed to ensure high density and geometric stability of products. The results obtained confirm the possibility of complex industrial waste disposal with the simultaneous creation of environmentally safe, durable, and heat-resistant building materials used in energy, metallurgy, the chemical industry, and civil engineering.