DEVELOPMENT OF AN INFORMATION ALGORITHM FOR IDENTIFYING ENERGY SAVING OPPORTUNITIES CONSUMED IN A TECHNOLOGICAL PROCESS

Abstract

The paper addresses the critical issue of optimizing power consumption and developing effective energy-saving policies at modern energy-intensive industrial enterprises. Due to the systemic complexity of production lines, the stochastic nature of technological processes, and the continuous variability of raw material properties, traditional approaches fail to provide a comprehensive analysis of energy losses. This study proposes a new integrated method based on real-time monitoring and control of the interconnected impact of technological and energy parameters.

The research is carried out using the case study of an ore-grinding technological process, which includes coarse, medium, and fine crushing, milling, and slurry pumping. For each operation, the key controllable input and output factors affecting power consumption are identified. Utilizing matrix algebra, multi-factor regression equations in a standardized scale are derived. These equations allow a quantitative assessment of the degree of influence that individual parameters (such as throughput, particle size, and ore hardness) have on the resulting power consumption.

Based on experimental data from a single-stage grinding circuit, a correlation analysis is performed. The results confirm a strong linear relationship between the total power consumption and the hourly mill throughput of the finished product (r = 0.92), as well as the power of the mill drive motors (r = 0.787). The developed algorithm serves as an effective tool for energy audit in ore-dressing plants, enabling the rapid identification of irrational energy expenses, forecasting power consumption under changing raw material properties, and optimizing the operation modes of the equipment.