Uncertainties in the calorific value and composition of municipal solid waste (MSW), the variability in operations, equipment performance decline, maintenance needs, and other dynamic factors make it challenging for operational experts to accurately gauge the overall combustion impact under fluctuating conditions. There is a need for a visual numerical simulation of the full MSW incineration (MSWI) process, with a focus on the effectiveness of the grate speed and air volume ratio. Unfortunately, software like Aspen Plus struggles to visually depict the effect of typically manipulated variables such as grate speed, and it is not well-suited for simulating solid-phase combustion and flue gas treatment. In response to these challenges, our study introduces a numerical simulation method for the MSWI process using a multi-software coupling approach. This simulation method incorporates (a) the use of custom software for simulating solid-phase combustion on the grate, (b) the application of Computational Fluid Dynamics (CFD) software for gas-phase combustion in the furnace, and (c) the utilization of chemical process simulation software for non-grate solid-phase combustion. The benchmark condition was determined using real-time running data from the Beijing MSWI Power Plant. The results show that for the incinerator’s third flue, flue gas G1, and flue gas G3, the relative errors between the simulated and actual values for flue gas temperature and oxygen concentration were 0.3% and 1.1%, 3.5% and 20%, and 0.2% and 20% respectively. In addition, we used eight non-benchmark conditions to study the effects of grate speed and air volume ratio. When the grate speed was gradually increased from 6 m/h to 8 m/h, O2, CO2, sulfur dioxide (SO2), sulfur trioxide(SO3), nitrogen monoxide (NO), and nitrogen oxide (NO2) at G1 had error fluctuation ranges of 5.9%− 48.5%, 2.5%− 11.5%, 10.42–28.57%, 7–23%, 29.10–45.52%, and 24.39–48.78%, respectively. When the primary air ratio was gradually increased from 0.84 to 0.96, the O2 concentration at G1 exhibited a steady upward trend. The simulation results provide insights into the adjustment range of the manipulated variables and offer additional support for the visualized and optimized control operation of the MSWI plant.