Chun-Cheng Chang

Simulation of dynamics and phase pattern changes for an azeotropic distillation column

Abstract In this work, a dynamic model for an azeotropic distillation column capable of predicting the appearance and disappearance of stages with multiple liquid phases was developed. The dynamic behavior of an azeotropic distillation column separating ethanol and water using benzene as the entrainer was extensively studied using this model. Responses of the column to feed upsets in flowrates and composition, as well as changes in operating parameters such as aqueous reflux rate, reboiler duty and entrainer makeup were simulated. Appearance of heterogeneous two-phase liquid in various equilibrium stages, and the movement of the temperature front were predicted. Moreover, response of the column with temperature of stage 23 being controlled by manipulating the aqueous reflux flowrate were simulated. It had been found that the formation and disappearance of a heterogeneous two-liquid phase in stages inside the column were critical to the dynamic behavior of the azeotropic column.

Threaded EWMA Controller Tuning and Performance Evaluation in a High-Mixed System

The exponentially weighted moving average (EWMA) controller is a very popular run-to-run (RtR) control scheme in the semiconductor industry. However, in any typical step of semiconductor process, many different products are produced on parallel tools. RtR control is usually implemented with a ldquothreadedrdquo control framework, i.e., different controllers are used for different combinations of tools and products. In this paper, the problem of EWMA controller tuning and performance evaluation in a mixed product system are investigated by simulation and time-series analysis. It was found that as the product frequency changed, the tuning guidelines of a threaded EWMA controller were different for different types of tool disturbances. For a stationary ARMA(1,1) noise, the tuning parameter lambda should be decreased as product frequency decreases. If the tool exhibits nonstationary tool dynamics, e.g., ARIMA(1,1,1) noise, the tuning parameter should increase as the product frequency decreases.

Control Strategies For Flexible Operation Of Power Plant Integrated With Co2 Capture Plant

Abstract Amine-based CO 2 capture schemes have been developed to reduce CO2 emission from coal-fired power plant. Large electricity penalties will be incurred due to heat required for CO 2 desorption. It was suggested this loss can be partially compensated by flexible operation. However, daily large variations of liquid and gas flows may cause operation problems to packed columns. In this work, control schemes were proposed to improve the flexibility of power output without causing substantial hydraulic disturbances in the capture plant. They were verified by steady state and dynamic simulations using ASPEN Plus. In varying lean solvent flow strategy, the flow-rate of recycling solvent was manipulated to control the CO2 capture rate. The gas flow in the absorber and the gas/liquid ratio in the stripper will remain constant relatively. The liquid flow of the absorber and gas flow of the stripper will vary. In an alternative strategy, the lean solvent loading will be varied. Also, variation of gas throughput in the stripper is avoided by recycling part of CO2 vapor to stripper. This strategy provided the stable hydraulics condition in both columns. Power output is similar compared to the previous scheme.

An Adaptive-Tuning Scheme for G&P EWMA Run-to-Run Control

Run-to-run (RtR) control is an important method for improving process capability. The most common form of RtR controllers are exponentially weighted moving average (EWMA) controllers. The performance of EWMA RtR controllers is affected by the values of the selected tuning parameter. In practice, the tuning parameter usually remains unchanged, resulting in suboptimal performance. In this paper, we propose an adaptive-tuning method for a group and product (G&P) EWMA controller to improve the control performance. The G&P EWMA controller is developed for mixed run processes. We show that the optimum-tuning parameters for the next run of this G&P EWMA controller are obtained online using a window of historical input-output data. The performance improvement due to the proposed method is demonstrated by a simulation example and an industrial application.

The Effect of Initial State Estimates on Just-in-Time Adaptive Disturbanc Estimation

Run-to-Run control algorithms for high-mix semiconductor processes typically require that the initial product state estimates have sufficient accuracy for satisfactory control. In this paper, we use historical process data and apply single observation just-in-time adaptive disturbance estimation (JADE) to find the initial product state estimates. Single observation JADE with random selection, high-frequency sampling, and exclusion of the earliest data from the average is shown to provide satisfactory initial product state estimates. The effect of initial state estimate accuracy is demonstrated by several simulation and industrial data examples. We also provide a method to estimate relative confidence between individual product state estimates, information that may be used to determine assignment of process error between the tool and product state.

Estimating states in an unobservable system

Run-to-Run control methods as applied in semiconductor manufacturing can greatly benefit from partitioning the observed disturbance state into separate components of tool and product disturbance states. Obtaining sufficiently accurate initial estimates of tool and product disturbance terms from historical data greatly improves the effectiveness of this method. Such an estimation method is developed in this work based on the just-in-time adaptive disturbance estimator (JADE) using random, high frequency sampling of historical data.

FAULT DECTION AND VARIATION SOURCE IDENTIFICATION BASED ON STATISTICAL MULTIVARIATE ANALYSIS

Abstract Abstract This paper aims to solve the problems of fault diagnosis and variation reduction by using multivariate statistical techniques when the quality measurements are scarce. Both single stage process and multi-stage process are considered. For the single stage process, the nonparametric statistical method, Wilcoxon rank-sum test is used to identify the key variable/step that causes the fault of the un-qualified wafers. For the multi-stage process, the most important variables are first picked out by systematic statistical analysis, and the specifications of these key variables are designated using nonparametric method to improve the product yield. Gene map which gives visual images is used to assist the analysis. Industrial examples are given to show the effectiveness of the proposed method.

EWMA Controller Tuning and Performance Evaluation in a High Mixed System

The exponentially weighted moving average (EWMA) controller is a very popular run-to-run (RtR) control scheme in the semiconductor industry. However, in any typical step of semiconductor process, many different products are produced on parallel tools. RtR control is usually implemented with a ldquothreadedrdquo control framework, i.e., different controllers are used for different combinations of tools and products. In this paper, the problem of EWMA controller tuning and performance evaluation in a mixed product system are investigated by simulation and time-series analysis. It was found that as the product frequency changed, the tuning guidelines of a threaded EWMA controller were different for different types of tool disturbances. For a stationary ARMA(1,1) noise, the tuning parameter lambda should be decreased as product frequency decreases. If the tool exhibits nonstationary tool dynamics, e.g., ARIMA(1,1,1) noise, the tuning parameter should increase as the product frequency decreases.