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

Taifu Li,Zhiqiang Liao

doi:10.1155/2019/2474909

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.

Highlights

With increasing attention paid to controlling production quality and costs in industrial production, various studies on reducing the costs and increasing production benefits have been widely explored in recent years [1, 2]. e operation parameter optimization is an effective method to promote the profit of industrial production processes
A scheme based on data-driven model and fluctuation analysis is proposed to obtain the robust optimal operation parameters of industrial processes. e Gaussian process regression based on the Bayesian statistical learning theory can build the model between the operation parameter and objective performance with the flexibility in nonparameter inferring and the self-adaptiveness to determinate hyperparameters. is data-driven method does not need to include too much information about the operating mechanism
As the equipment is exposed to the air during the production process, the production will be influenced by temperature, humidity, aging equipment, raw materials, and many other uncertain factors. e objective performance of the production process is measured by the conversion rate of hydrogen cyanide (HCN)

Summary

Introduction

With increasing attention paid to controlling production quality and costs in industrial production, various studies on reducing the costs and increasing production benefits have been widely explored in recent years [1, 2]. e operation parameter optimization is an effective method to promote the profit of industrial production processes. E operation parameter optimization is an effective method to promote the profit of industrial production processes. E data-driven method based on machine learning is an effective analysis method and has been widely used in many papers [5,6,7] It can describe the system model without too much prior knowledge. Based on the data-driven model, the optimization method can be performed to optimize the operation parameter. A scheme based on data-driven model and fluctuation analysis is proposed to obtain the robust optimal operation parameters of industrial processes.

Robust Optimization of Operation Parameter Design

Experiments and Discussions

Findings

Fluctuation Analysis