Planning coal drawing control system based on process engine

Current research on intelligent fully mechanized coal caving mining primarily focuses on perception, with limited studies on the intelligence of the coal drawing process. Existing automatic coal drawing control technologies face issues such as insufficient adaptability, low efficiency, and difficulty in quality control. To enhance the intelligence and operational efficiency of the coal drawing process, a planning coal drawing control system based on a process engine was designed. This system consisted of a coal drawing management unit and a window decision-making unit. The planning coal drawing management unit employed an asynchronous progressive scheduling strategy, flexible switching technology, and a process editing engine to achieve automated sequential coal drawing with weak correlation to the mining machine's position and online process editing. By associating with the load of the rear scraper conveyor, the system dynamically adjusted process starts and stops, ensuring safe operation of the scraper conveyor. The window decision-making unit utilized a PID control algorithm to dynamically adjust the tail beam angle, implementing feedback control of the coal drawing window. A genetic algorithm optimized a BP neural network to make intelligent decision about the size of the coal drawing window to adapt to varying operating conditions and improve coal drawing quality. Field application results indicated that the asynchronous progressive scheduling strategy and flexible switching technology enhanced the efficiency of automatic operation, eliminating the need for manual intervention. The number of automated operations per shift increased by 33.3%. The system's associated rear scraper conveyor load, pump station, and other equipment could dynamically adjust process starts and stops, resulting in a 61.1% decrease in the average stopping frequency of the rear scraper conveyor per shift, ensuring operational safety. The process editing engine accommodated various applications, substantially reducing adjustment time. The overlap of rear and front actions shortened the average operation time by 9.3%, increasing extraction efficiency. The correlation control of the tilt angle and intelligent decision-making for the planning coal release window improved daily calorific value by 10.3%, enhancing coal drawing quality.