Robust Parameter Design for Cyclone System Based on Dual-Response Surface Method and Multiobjective Genetic...

Area of Science:

• Industrial Engineering
• Process Optimization
• Quality Management

Background:

• Coal preparation quality control is crucial for maximizing production and meeting blending requirements.
• Current methods may not sufficiently ensure product quality aligns with minimum standards.
• Predictive control is needed to shift from reactive to proactive quality management.

Purpose of the Study:

• To investigate the feasibility of using the double response surface method and multiobjective genetic algorithm for robust coal preparation quality control.
• To establish a predictive model for optimizing coal washing product quality.
• To identify optimal parameter settings for enhanced production and quality.

Main Methods:

• Robust parameter design tests using the product table method.
• Development of second-order polynomial models for response characteristics (mean and standard deviation).
• Application of multiobjective genetic algorithm for optimization.

Main Results:

• Established robust parameter settings at 150.68 kpa, 0.18143.73 kpa, and 30%.
• Achieved optimal output of 8.499% ash content with a 69.54% yield.
• Demonstrated superiority over traditional optimization methods.

Conclusions:

• The proposed method enables effective predictive control of coal preparation quality.
• This approach supports the transition to preventive quality management in coal preparation plants.
• Provides a framework for accurate control and systematic management of production processes.