Abstract: Based on the total pollutant emission accounting model of industrial parks and taking the total VOCs emission accounting of a key petrochemical industry park on the southeast coast of China as an example, a comparative study of inversion performances was compared on three optimization algorithms, including Nelder-Mead simplex method (NM), dual annealing optimization algorithm (DA) and particle swarm optimization algorithm (PSO), under different random error intensity and number of failure sites. Besides, the inversion performance of three optimization objective functions constructed by deviation sum of squares (Objective Function 1), logarithm transformation (Objective Function 2) and hyperbolic cosine transformation (Objective Function 3) was compared. The results showed that the inversion performance of PSO was related to the number of particles, but the overall performance implied that it was not suitable for the inversion optimization under the current conditions. NM and DA had better inversion accuracy (MARE<30%), but the inversion computation efficiency of NM was about 11-20 times higher than that of DA, and NM had the best inversion performance. All the three optimization objective functions were suitable for inversion optimization under current conditions (MARE<30%). Objective Function 1 and Function 3 performed better when the random error intensity was small and the number of failure sites was small, while Objective Function 2 performed better when the random error intensity was relatively large and the number of failure sites was relatively large.