The invention relates to the technical field of
sintering production, in particular to a self-
adaptive control method for an ignition process of a metallurgical
sintering machine, which comprises the following steps of: acquiring key parameters such as material
layer thickness,
hearth pressure,
sintering machine speed and air box temperature in a sintering
machine system in real time, and transmitting acquired data to an
ignition control model unit; and parameters such as fuel and air flow and
air volume are dynamically adjusted by adopting a self-
adaptive optimization algorithm, so that rapid improvement and stable control of the ignition efficiency are realized. According to the method, temperature and fuel in the ignition process are evaluated through a
big data analysis technology in combination with actual requirements of the sintering process, and an
ignition control strategy is optimized in combination with an intelligent regulation and
control algorithm. Aiming at the complexity of an ignition process of the metallurgical sintering machine, an
adaptive optimization technology is utilized to realize accurate control of the ignition process, and optimization suggestions are comprehensively given according to an ignition
temperature curve and a fuel consumption condition to timely guide an operator to adjust process parameters, so that the ignition efficiency is improved, the
energy consumption is reduced, and the sintering quality is improved.