Toward Mechanism-Driven Control: A Soft-Sensor for Zeta Potential and Settling-Decisive Parameters in Coal Slime Water Treatment

Intelligent dosing in coal slime water treatment remains a challenge due to the lack of real-time and solid hardware-based measurement of key microscopic parameters governing the settling process, particularly zeta potential. This study proposes a soft-sensor method using Sparrow Search Algorithm-optimized Extreme Learning Machine (SSA-ELM) to simultaneously predict four critical settling process parameters: settling velocity, supernatant turbidity, sediment layer height, and zeta potential. Key variables influencing the coal slime water settling process, including coal slime water concentration, fines content, water hardness, pH, and chemical dosage, were investigated, and the experimental data were used as inputs for the development of the prediction model. The prediction performance of the proposed SSA-ELM model was evaluated against standard ELM and SSA-optimized Back Propagation (BP) models. The results demonstrate that the SSA-ELM model achieved superior prediction accuracy for all parameters, with R2 values ranging from 0.95 to 0.98, while maintaining favorable computational efficiency. This study establishes a method for virtual measurement of zeta potential, providing a crucial data foundation for developing mechanism-driven, intelligent dosing systems aimed at precise intelligent control and reduced chemical consumption for coal preparation plants.