Abstract
AbstractAn optimal design method of operating parameters in hydrogen cyanide (HCN) production process is presented. Firstly the soft sensor model of HCN conversion rate is established by BP neural network. Then considering the practical constraints of operating parameters and taking the maximum HCN conversion rate as objective function, an improved particle swarm optimization (IPSO) algorithm is introduced to optimize the operating parameters, which takes into account the dynamic inertia weight and the particle average position to avoid falling into local optimum and accelerate the convergence. Simulation results with actual production data show that, compared with genetic algorithm (GA) optimization, this method can obtain the optimal operating parameters of the HCN production process.KeywordsParticle swarm optimizationBP neural networkHCN conversion rateOperating parameters optimizationSoft sensing model
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