Robust Optimization of Industrial Process Operation Parameters Based on Data‐Driven Model and Parameter Fluctuation Analysis

Abstract

The fluctuation of industrial process operation parameters will severely influence the production process. How to find the robust optimal process operation parameters is an effective method to address this problem. In this paper, a scheme based on data-driven model and variable fluctuation analysis is proposed to obtain the robust optimal operation parameters of industrial process. The data-driven modelling method: multivariate Gaussian process regression (MGPR) based on Bayesian statistical learning theory can map the process operation parameters to objective performance with the flexibility in nonparameter inferring and the self-adaptiveness to determinate hyperparameters. According to the minimum variance criterion, the parameter fluctuation analysis can be performed through multiobjective evolutionary algorithm based on the MGPR model. To analyze the robustness influence of a single parameter, cross validation is applied to evaluate the model output with 2% fluctuation. After that, the robust optimal process operation parameters can be obtained and applied to guide the production. The effectiveness and reliability of the proposed method have been verified with the hydrogen cyanide production process and compared with other model methods and single objective optimization method.

Quality parameter adaptive optimization for spinning process using dynamic non-dominated sorting algorithm

“…Zhang et al [13] generated the selective disassembly sequence solution space by using hybrid graph model, and then combined with the ant colony algorithm to search for the optimal or near-optimal single target part selective disassembly sequence from all feasible sequences. Li et al [14] used the expected target variance as the measure target robustness criterion to conduct the process parameter uctuation analysis, and performed the parameter optimization with the robustness criterion and the prediction model as the tness function of parameter optimization. Lin et al [15] for the limit axial cutting depth of different process parameter optimization problem, put forward to establish a generalized regression neural network prediction model to predict the limit axial cutting depth, with the limit axial cutting depth as the milling stability evaluation index, milling stability, tool life, power, surface roughness as the optimization model constraints for process parameters optimization.…”