펄프 산업에서의 보일러 공정을 위한 확률론적 최적화에 관한 연구

Abstract

This paper aims to find the stochastic optimal solution for a boiler process. The target is to maximize steam generation while complying with pollutant emissions regulations.

For a simulation base-model, Support Vector Regression (SVR) is employed to present a general discrete-time dynamical system of a boiler and a stochastic optimization was performed to take inherent process uncertainties into account. To generate a stochastic optimization problem, Sample Average Approximation (SAA) based on Monte-Carlo sampling is introduced due to the properties of a SVR. Moreover, a gradient free-based Particle Swarm Optimization (PSO) technique is applied to find the optimal parameters of SVR and investigate the stochastic optimal solution for the boiler process.

The results show that the stochastic optimal solution provides at least 1.914% more steam generation under uncertainties: this indicates that the stochastic optimal solution provides more realistic results compared to the deterministic approach.

The proposed methodology can be applied straightforwardly to black box models, or when the use of gradient-based optimization solvers is restricted.