Young-Hak Lee

Intelligent integrated plant operation system for Six Sigma

Abstract Six Sigma has been widely adopted in a variety of industries as a proven management innovation methodology to produce high-quality products and reduce the cost at all the levels of an enterprise. This paper proposes a plant operation system which can guide plant engineers and operators to pursue Six Sigma activities by providing supports for key elements of Six Sigma: measurement, analysis, improvement and control. Multivariate statistical process control (MSPC) techniques have been employed as key technologies for the system, along with the plant information systems. This paper also discusses the future research issues that should be addressed to implement the described system.

Real-time classification of petroleum products using near-infrared spectra

Abstract This study describes a real-time classification methodology for petroleum products based upon the near-infrared (NIR) spectra. The proposed real-time classifier (RTC) is designed based on the combination of principal component analysis (PCA) and a Bayesian classifier. Principal component analysis is employed to extract essential features that are selected considering both classification power and easiness of implementation for classification of the spectra. Bayesian classifier minimizes classification error. The RTC based on NIR spectra offers the faster and more accurate identification capacity of products on-line than the conventional analyzers. The proposed RTC has been applied to classify six petroleum products: diesel, gasoline, kerosene, light gas oil, light straight-run, and naphtha. It has shown good classification power for industrial petroleum products.

RMBatch: Intelligent real-time monitoring and diagnosis system for batch processes

Abstract Batch Process-generally exhibits batch-to-batch variation that makes it difficult to produce a uniform high quality product. The variation arises from sources such as: composition disturbances, deviations from specified trajectories. equipment malfunction and heat transfer limitation. Extensive research has been done to develop a model for the efficient and reliable monitoring and diagnosis for these batch processes. Various algorithms such as multivariate statistical process control based on multhvay principal component analysis and partial least square have been proposed for the monitoring of batch processes. A prototype of intelligent Real-time Monitoring and diagnosis system for Batch process (“RMBatch”) has been developed as a tool that offers real-time monitoring and diagnosis for batch processes. Our developed RMBatch is composed of plant information module, data preprocessing module, data analysis and modeling module, monitoring and diagnosis module. The design and the various functions of RMBatch will be illustrated using its application to an industrial batch process. The application has shown that RMBatch can help operators monitor, diagnose and improve the operation of batch processes.

Quality improvement in the chemical process industry using Six Sigma technique

Abstract Although Six Sigma has been so successful in many organizations, the successful applications of Six Sigma are rare for the chemical processes due to highly nonlinear causality. This paper provides a successful case for the chemical process industry in which degraded quality has been improved by performing the proposed Six Sigma strategy. The strategy consists of required techniques in each phase and the techniques include not only existing tools such as 5M1E approach, partial least squares (PLS), and design of experiment (DOE) but also a new methodology to discover exact key sources of quality degradation. This case study has been a great success; from 3.5 initial sigma to 5.5 sigma has been achieved by applying the proposed methodology to the quality degradation problem.

Toward Intelligent Integrated Plant Operation System for Six Sigma

Abstract Six sigma has been widely adopted in a variety of industries as a proven management innovation methodology to produce high-quality products and reduce the cost at all the levels of an enterprise. However, in case of process industries, the application of six sigma activities has had only limited successes. This paper proposes a plant operation system which can guide plant engineers and operators to pursue six sigma activities by providing supports for key elements of six sigma: measurement, analysis, improvement and control. Multivariate statistical process control (MSPC) techniques have been employed as key technologies for the system, along with the plant infonnation systems. This paper also discusses the future research issues that should be addressed to implement the described system.

Application of multivariate statistical process control to supervising NOx emissions from large-scale combustion systems

Abstract Computer modeling of NOx formation in combustion systems provides a tool that can be used to investigate and improve understanding of the systems. In this work the systematic method based on the data-driven model was proposed to supervise nitrogen oxides emitted from a large number of fired heaters which go through one common stack. The relationships between process variables in the fired heaters and the NOx were investigated by employing projection to least squares (PLS) regression in a hierarchical manner. Multivariate statistical contributions of fired heaters and process variables in a fired heater to NOx emissions were introduced as a key means to cope with the NOx overemissions. The proposed approach was evaluated by using data collected from an industrial combustion system.

Controller Fault Detection Based on Multivariate Statistical Process Control

Abstract The ability to detect controller malfunctions is crucial in the monitoring of chemical process because malfunctioning controllers cause abnormal process operation, excess energy consuming and critical process upset. Especially, this paper is concerned to monitor the controller status in MIMO case. The proposed method is aimed to provide a practical basis on the earlier detection of degraded or malfunctioning controller. In this article, a new approach to controller performance monitoring in an online manner is presented that is based on multivariate statistical process control combined with time series analysis. Simulation case to distillation column is applied to demonstrate the suggested approach.