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Dive into the research topics where Yale Zhang is active.

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Featured researches published by Yale Zhang.


Journal of Process Control | 2002

Real-time optimization under parametric uncertainty: a probability constrained approach

Yale Zhang; Dayadeep S. Monder; J. Fraser Forbes

Abstract Uncertainty is an inherent characteristic in most industrial processes, and a variety of approaches including sensitivity analysis, robust optimization and stochastic programming have been proposed to deal with such uncertainty. Uncertainty in a steady state nonlinear real-time optimization (RTO) system and particularly making robust decisions under uncertainty in real-time has received little attention. This paper discusses various sources of uncertainty within such closed loop RTO systems and a method, based on stochastic programming, that explicitly incorporates uncertainty into the RTO problem is presented. The proposed method is limited to situations where uncertain parameters enter the constraints nonlinearly and uncertain economics enter the objective function linearly. Our approach is shown to significantly improve the probability of a feasible solution in comparison to more conventional RTO techniques. A gasoline blending example is used to demonstrate the proposed robust RTO approach.


Computers & Chemical Engineering | 2000

Extended design cost: a performance criterion for real-time optimization systems

Yale Zhang; J. Fraser Forbes

Abstract This paper presents a performance metric and design criterion for model-based real-time optimization (RTO) systems, named extended design cost. Extended design cost is a systematic, comprehensive approach for evaluating the performance of RTO design alternatives based on fundamental principles from optimization, control and statistics theory. This metric is defined as the total loss in performance of the RTO system during some pre-specified evaluation period due to design imperfections, and is shown to consist of contributions from both the transient and steady-state behavior of the closed-loop RTO system. The proposed method is demonstrated using several examples. This paper concludes by presenting a systematic approach for selecting which model parameters to adjust on-line using extended design cost and the method is illustrated in the William–Otto reactor case study.


Annual Reviews in Control | 2003

Integrated Monitoring Solution to Start-Up and Run-Time Operations for Continuous Casting

Yale Zhang; Michael S. Dudzic; Vit Vaculik

Multivariate statistical (MVS) technologies can be applied to both continuous and batch operations for process monitoring and fault diagnosis. Dofasco has developed an on-line MVS monitoring application for its #2 Continuous Caster that combines both continuous and batch MVS technologies into an integrated monitoring solution. Continuous MVS-based monitoring is used for continuous, run-time casting operation. Batch MVS-based monitoring is applied during the start-up operation while the process is in the transition to the run-time operation. This integrated application provides a real-time indication of the stability of the casting operation, which has resulted in improved process safety and economic performance.


Journal of Process Control | 2001

Results analysis for trust constrained real-time optimization

Yale Zhang; Daniel Nadler; J. Fraser Forbes

Abstract In real-time optimization (RTO), results analysis is used to ensure that RTO predictions can be implemented and are not the result of the unnecessary variance transmission around the RTO loop. Miletic and Marlin [I. Miletic, T.E. Marlin, On-line statistical results analysis in real-time operations optimization, Ind. Eng. Chem. Res. 37 (1998) 3670–3684.] proposed a statistical framework for analyzing RTO results; however, their method cannot effectively deal with inequality constraints. Many industrial RTO implementations include bounds on the changes that the RTO system can make to the process operation (i.e. trust-region constraints). Such trust-region constraints can seriously degrade the performance of existing results analysis methods. In this paper, a results analysis procedure is proposed that incorporates statistical testing on both the primal and dual variables of the optimization problem to effectively analyze steady-state RTO results in the presence of trust-region constraints. The proposed method is illustrated using two small case studies, one of which is the same Williams and Otto [T.J. Williams, R.E. Otto, A generalized chemical processing model for the investigation of computer control, AIEE Trans 79 (1960) 458–473.] reactor example used in Miletic and Marlin (1998) .


IFAC Proceedings Volumes | 2003

Start Cast Breakouts Preventative Prediction Using Multi-Way PCA Technology

Yale Zhang; Vit Vaculik; Michael S. Dudzic; Ivan Miletic; A. Smyth; T. Holek

Abstract Breakouts during continuous caster start-up operations are of major concern in the steel-making industry, because they can lead to severe damage to equipment, significant process downtime, and potential safety consequences. As a multivariate statistical (MVS) analysis tool, Multi-way PCA (MPCA) is applied for monitoring the start-up operation of a continuous caster in order to predict potential start cast breakouts so the caster can be automatically stopped to avoid the catastrophic event. It is shown to provide good prediction of start cast breakouts resulting in significant savings in operating and maintenance costs. An on-line start cast monitoring system has been successfully implemented at Dofascos #2 continuous caster.


IFAC Proceedings Volumes | 2004

On-Line Industrial Implementation of Process Monitoring / Control Applications Using Multivariate Statistical Technologies: Challenges and Opportunities

Michael S. Dudzic; Yale Zhang

Abstract The global steel industry is stnvmg to improve product quality through excellence in operation. To support this, significant investments have been made in upgrading instrumentation, data acquisition and computing infrastructures. The expectation is that with more process and product data readily available, useful information and better process knowledge can be gained in a timely fashion. The problem that has developed is that with the large volumes of data available, the associated data analysis and modeling have become increasingly complex. As a result, much of the data is either not used or summarized / heavily compressed. This means that a significant amount of the information and knowledge resident in the data is lost, diminishing the returns from the investment made in the information technology infrastructure. A class of technologies that Dofasco has used to meet this data challenge is multivariate statistics (MVS), with a primary focus on Principal Components Analysis (PCA) and Projection to Latent Structures (PLS). These methods have been successfully applied to analyze data for a variety of purposes, which includes the development of online predictive models and process monitoring systems. Since 1993, Dofasco has been involved with over 70 off-line / on-line applications of this technology at our steel facility in Hamilton, Ontario, Canada. Through these applications, significant fmancial returns to the company have been generated.


IFAC Proceedings Volumes | 1999

A New Design Metric for Real-Time Optimization

Yale Zhang; J. Fraser Forbes

Abstract This paper presents a new design metric for model-based Real-Time Optimization (RTO) system, named Extended Design Cost. Extended Design Cost is a systematic, comprehensive approach to evaluate different RTO design alternatives and is based on fundamental principles from optimization and statistics theory. It is defined as the loss in performance of the RTO system during some pre-specified evaluation period, due to design imperfections, and decomposed into three separate terms: Bias Cost. Transition Cost and Variance Cost. The proposed method is demonstrated using a chemical reactor optimization case study in which the adjustable parameters are selected.


IFAC Proceedings Volumes | 2004

Industrial Experience on Process Transition Monitoring for Continuous Steel Casting Operation

Yale Zhang; Michael S. Dudzic

Abstract Process transitions are common in the iron and steel industry. Our investigation shows that more than 50% of catastrophic process failures in continuous steel casting operation are related to abnormal operations during the process transition period. As a multivariate statistical (MVS) analysis tool, Multi-way PCA (MPCA) is applied in this paper to monitor one important process transition in the continuous casting process: submerged entry nozzle (SEN) change. A novel scheme is proposed for synchronizing process trajectories over the SEN change and the missing data existing in the synchronized trajectories are handled subsequently in both mode ling and monitoring parts. The monitoring results are demonstrated by an industrial example. It is shown to provide good detectability of various process abnormalities. The proposed scheme can be further extended to monitor other process transitions in continuous casting process such as flying tundish change and product grade change.


IFAC Proceedings Volumes | 2000

Robust Real-Time Optimisation Under Parametric Uncertainty

Yale Zhang; Dayadeep S. Monder; J. Fraser Forbes

Abstract Uncertainty is an inherent characteristic in most industrial processes, and a variety of approaches including sensitivity analysis, stochastic programming and robust optimisation have been proposed to deal with such uncertainty. Uncertainty in Real-Time Optimisation (RTO), particularly making robust decisions under uncertainty in real-time has received little attention. This paper discusses various sources of uncertainty within the closed RTO loop. A method, based on stochastic programming, that explicitly incorporates uncertainty into the RTO problem is presented and allows solution using conventional optimisation algorithms. A gasoline blending example is used to demonstrate the proposed robust RTO approach.


Archive | 2004

Real-time system and method of monitoring transient operations in continuous casting process for breakout prevention

Yale Zhang; Michael S. Dudzic

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