Online Optimization of Cracking Furnace Based on Advanced PSO Algorithm and Neural Network Ensembles

The traditional Particle Swarm Optimization (PSO) algorithm is easily trapped in the local optimum and converges slowly. Due to the shortcomings above, a novel PSO algorithm based on the carrierwave (CWPSO) is presented in the paper, which searches through the carrierwave and takes a precise search by means of carrierwave extending. As a result, it overcomes the above shortcomings better, and has a shorter searching time as well. In addition, towards the online optimal requirements of the industrial cracking furnace, a neural network ensembled with dynamic weights is applied in the predictive modeling of C2H4 and C3H6 yield rates, then the online rolling optimization is carried out. The simulating result shows that the optimal method has sound effects for the cracking furnace, and there is a palpable improvement of C2H4 and C3H6 yield rates.