Browsing by Subject "Process control"

Now showing 1 - 20 of 20

A generic real-time process control system
(Texas Tech University, 1989-12) Arrant, Edwin Keith
A major problem facing manufacturers is the design and implementation of flexible automation systems. This problem is complicated by the many differing requirements for automated systems. Every facility has its own specific requirements; therefore, a generic factory control system design could provide the flexibility and adaptability to solve a wide variety of automation needs. Since factory automation systems are implemented from the bottom up, it is necessary to ensure that the initial automated subsystems are going to be compatible with future system enhancements. This thesis describes the various levels of factory automation and establishes a functional specification for each subsystem in the automated facility. These specifications provide the general information required to define the design objectives for the facility subsystems. The first step toward obtaining a solution to the automation problem is to develop a generic software control system for process equipment. A first-generation control system has been developed and applied to a four-module HF vapor etcher system, used in the processing of semiconductor wafers.
A goodness of control monitor
(Texas Tech University, 1997-05) Venkataramanan, Ganesh
The objective of this research work is to develop a goodness of control monitor which can analyze noisy signals that can not be analyzed deterministically. If the process were noiseless, then it would be fairly easy to deterministically identify the several situations that indicate ineffective control. However, noise on the measurement is, in most cases, inevitable and prevents a deterministic evaluation of control effectiveness. Further, the amplitude of the noise often changes, with time and with operating region; so, any practical method must be insensitive to both the noise amplitude and to changes in noise amplitude. This work tries to meet the above requirements.
Advanced process control and optimal sampling in semiconductor manufacturing
(2008-08) Lee, Hyung Joo, 1979-; Edgar, Thomas F.
Semiconductor manufacturing is characterized by a dynamic, varying environment and the technology to produce integrated circuits is always shifting in response to the demand for faster and new products, and the time between the development of a new profitable method of manufacturing and its transfer to tangible production is very short. The semiconductor industry has adopted the use of advanced process control (APC), namely a set of automated methodologies to reach desired process goals in operating individual process steps. That is because the ultimate motivation for APC is improved device yield and a typical semiconductor manufacturing process can have several hundred unit processes, any of which could be a yield limiter if a given unit procedure is out of control. APC uses information about the materials to be processed, metrology data, and the desired output results to choose which model and control plan to employ. The current focus of APC for semiconductor manufacturers is run-to-run control. Many metrology applications have become key enablers for the conventionally labeled “value-added” processing steps in lithography and etch and are now integral parts of these processes. The economic advantage of effective metrology applications increases with the difficulty of the manufacturing process. Frequent measurement facilitates products reaching its target but it increases the cost and cycle time. If lots of measurements are skipped, the product quality does not be guaranteed due to process error from uncompensated drift and step disturbance. Thus, it is necessary to optimize the sampling plan in order to quickly identify the sources of prediction errors and decrease the metrology cost and cycle time. The goal of this research intend to understand the relationship between metrology and advanced process control (APC) in semiconductor manufacturing and develop an enhanced sampling strategy in order to maximize the value of metrology and control for critical wafer features.
Advanced tabulation techniques for faster dynamic simulation, state estimation and flowsheet optimization
(2009-08) Abrol, Sidharth; Edgar, Thomas F.
Large-scale processes that are modeled using differential algebraic equations based on mass and energy balance calculations at times require excessive computation time to simulate. Depending on the complexity of the model, these simulations may require many iterations to converge and in some cases they may not converge at all. Application of a storage and retrieval technique, named in situ adaptive tabulation or ISAT is proposed for faster convergence of process simulation models. Comparison with neural networks is performed, and better performance using ISAT for extrapolation is shown. In particular, the requirement of real-time dynamic simulation is discussed for operating training simulators (OTS). Integration of ISAT to a process simulator (CHEMCAD®) using the input-output data only is shown. A regression technique based on partial least squares (PLS) is suggested to approximate the sensitivity without accessing the first-principles model. Different record distribution strategies to build an ISAT database are proposed and better performance using the suggested techniques is shown for different case studies. A modified ISAT algorithm (mISAT) is described to improve the retrieval rate, and its performance is compared with the original approach in a case study. State estimation is a key requirement of many process control and monitoring strategies. Different nonlinear state estimation techniques studied in the past are discussed with their relative advantages/disadvantages. A robust state estimation technique like moving horizon estimation (MHE) has a trade-off between accuracy of state estimates and the computational cost. Implementation of MHE based ISAT is shown for faster state estimation, with an accuracy same as that of MHE. Flowsheet optimization aims to optimize an objective or cost function by changing various independent process variables, subject to design and model constraints. Depending on the nonlinearity of the process units, an optimization routine can make a number of calls for flowsheet (simulation) convergence, thereby making the computation time prohibitive. Storage and retrieval of the simulation trajectories can speed-up process optimization, which is shown using a CHEMCAD® flowsheet. Online integration of an ISAT database to solve the simulation problem along with an outer-loop consisting of the optimization routine is shown using the sequential-modular approach.
An investigation of the job-type performance of priority scheduling in manufacturing cells
(Texas Tech University, 1991-12) Gunal, Ali Kamil
In traditional job-shop studies, it is usually assumed that jobs arrive randomly, they have random processing requirements, and measures of performance are taken over all jobs completed within a time period. There are many instances in practice, however, where a group of machines is formed to process a certain set of similar job types. A number of flexible manufacturing systems and group technology cells are examples of such manufacturing environments. In these types of manufacturing environments, the performance by job type is as important as the performance over all jobs. Problems may arise when jobs of a certain type are delayed more than the other job types. Such a situation is more likely to be seen when priorities are assigned based on information that is dependent on the type of a job. This study is an investigation of such variation in the job-type performance of the scheduling rules. It may be looked upon as an exploratory study since there is a lack of published results in this field of scheduling literature. Some fundamental questions regarding the job-type performance of a typical manufacturing cell are answered through a large-scale simulation experiment.
Application of neural network control to distillation
(Texas Tech University, 1997-05) Dutta, Priyabrata
Distillation control is challenging due to its coupled, nonlinear, nonstationary, and slow dynamic behavior. Like distillation columns, most chemical processes are usually nonlinear and nonstationary. This nonlinearity greatly limits the effectiveness of linear controllers, especially when the process is operated away from the nominal operating region. Nonlinear controllers, based on phenomenological models, can be developed. However, it is still a very difficult task in real practice, in terms of computational power, to implement these controllers on-line, because the entire model needs to be solved within each control interval. Neural networks give us an alternative approach to model a nonlinear process, and a controller based on this model can overcome the issues of on-line computational problems. Besides nonlinearity, many practical control problems possess constraints on the input, state, and output variables. The ability to handle constraints is essential for any algorithm to be implemented on real processes. Thus strategies for constraint handling within model-based controllers have become one of the more popular research topics. In this dissertation, a constrained optimization technique for control which uses a neural network gain prediction approach has been developed and implemented on a laboratory distillation column as well as on a dynamic simulator. Here, the neural networks are trained based on a phenomenological model. Also, experimental results have been obtained to confirm the applicability of a neural network model-based controller using an inverse of a state-prediction approach that was developed and simulated earlier by Ramchandran and Rhinehart (1994). In addition, two separate single-input-singleoutput (SISO) controllers using the inverse of the state-prediction approach are implemented on the feed and reflux preheaters of the column.
Control-friendly scheduling algorithms for multi-tool, multi-product manufacturing systems
(2011-12) Bregenzer, Brent Constant; Qin, Joe; Hasenbein, John J.; Edgar, Thomas F.; Hwang, Gyeong S.; Kutanoglu, Erhan; Bonnecaze, Roger T.
The fabrication of semiconductor devices is a highly competitive and capital intensive industry. Due to the high costs of building wafer fabrication facilities (fabs), it is expected that products should be made efficiently with respect to both time and material, and that expensive unit operations (tools) should be utilized as much as possible. The process flow is characterized by frequent machine failures, drifting tool states, parallel processing, and reentrant flows. In addition, the competitive nature of the industry requires products to be made quickly and within tight tolerances. All of these factors conspire to make both the scheduling of product flow through the system and the control of product quality metrics extremely difficult. Up to now, much research has been done on the two problems separately, but until recently, interactions between the two systems, which can sometimes be detrimental to one another, have mostly been ignored. The research contained here seeks to tackle the scheduling problem by utilizing objectives based on control system parameters in order that the two systems might behave in a more beneficial manner. A non-threaded control system is used that models the multi-tool, multi-product process in a state space form, and estimates the states using a Kalman filter. Additionally, the process flow is modeled by a discrete event simulation. The two systems are then merged to give a representation of the overall system. Two control system matrices, the estimate error covariance matrix from the Kalman filter and a square form of the system observability matrix called the information matrix, are used to generate several control-based scheduling algorithms. These methods are then tested against more tradition approaches from the scheduling literature to determine their effectiveness on both the basis of how well they maintain the outputs near their targets and how well they minimize the cycle time of the products in the system. The two metrics are viewed simultaneously through use of Pareto plots and merits of the various scheduling methods are judged on the basis of Pareto optimality for several test cases.
Design of an embedded expert system for process model-based real-time control
(Texas Tech University, 1989-05) Cox, Mirick Trammell
An expert system has been embedded within an environment representative of a process model-based controller (PMBC) which performs as an on-line real-time diagnostic consultant utilizing forward chaining and backward chaining. The system runs in a multitasking environment on a PC, and has the capability to perform corrective actions in real-time, or provide corrective action recommendations to the operator. An information translation module was developed to translate the sensor readings from the numerically intensive computation environment of the PMBC to the symbolically intensive computation environment of the expert system. The embedded expert system, written in CLIPS, is then spawned as a separate process. The PMBC is put to sleep awaiting an appropriate response from the expert system. When the first appropriate response is delivered, corrective action is taken (if possible), and the PMBC is awakened in order to resume control. The expert system then continues searching for alternative responses as the PMBC is running concurrently.
Dynamic modeling of post-combustion amine scrubbing for process control strategy development
(2016-05) Walters, Matthew Scott; Rochelle, Gary T.; Edgar, Thomas F.; Baldea, Michael; Akella, Maruthi R; Chen, Eric
Intensified process designs with advanced solvents have been proposed to decrease both capital and operating costs of post-combustion carbon capture with amine scrubbing. These advanced flowsheets create process control challenges because process variables are designed to operate near constraints and the degrees of freedom are increased due to heat recovery. Additionally, amine scrubbing is tightly integrated with the upstream power plant and downstream enhanced oil recovery (EOR) facility. This work simulated an amine scrubbing plant that uses an intercooled absorber and advanced flash stripper configuration with aqueous piperazine to capture CO2 from a 550 MWe coal-fired power plant. The objective of this research was to develop a process control strategy that resulted in favorable closed-loop dynamics and near-optimal conditions in response to disturbances and off-design operation. Two models were created for dynamic simulation of the amine scrubbing system: a medium-order model of an intercooled absorber column and a low-order model of the entire plant. The purpose of the medium-order model was to accurately predict the absorber temperature profile in order to identify a column temperature that can be controlled by manipulating the solvent circulation rate to maintain a constant liquid to gas ratio. The low-order model, which was shown to sufficiently represent dynamic process behavior through validation with pilot plant data, was used to develop a plantwide control strategy. A regulatory control layer was implemented and tested with bounding cases that represent either electricity generation requirements, CO2 emission regulations, or EOR constraints dominating the control strategy. Satisfying the operational and economic objectives of one system component was found to result in unfavorable dynamic performance for the remainder of the system. Self-optimizing control variables were identified for the energy recovery flowrates of the advanced flash stripper that maintained good energy performance in off-design conditions. Regulatory control alone could not satisfactorily achieve the set point for CO2 removal rate from the flue gas. A supervisory model predictive controller was developed that manipulates the set point for the stripper pressure controller in order to control removal. The straightforward single-input, single-output constrained linear model predictive controller exhibited a significant improvement compared to PI control alone.
Dynamic modeling, optimization, and control of monoethanolamine scrubbing for CO2 capture
(2012-08) Ziaii Fashami, Sepideh; Rochelle, Gary T.; Edgar, Thomas F.; Seibert, A F.; Masada, Glenn Y.; Freeman, Benny D.
This work seeks to develop optimal dynamic and control strategies to operate post combustion CO2 capture in response to various dynamic operational scenarios. For this purpose, a rigorous dynamic model of absorption/stripping process using monothanolamine was created and then combined with a simplified steady state model of power cycle steam turbines and a multi-stage variable speed compressor in Aspen Custom Modeler. The dynamic characteristics and interactions were investigated for the plant using 30% wt monoethanolamine (MEA) to remove 90% of CO2 in the flue gas coming from a 100 MW coal-fired power plant. Two load reduction scenarios were simulated: power plant load reduction and reboiler load reduction. An ACM® optimization tool was implemented to minimize total lost work at the final steady state condition by adjusting compressor speed and solvent circulation rate. Stripper pressure was allowed to vary. Compressor surge limit, run off condition in rich and lean pumps, and maximum allowable compressor speed were found as constraints influencing the operation at reduced loads. A variable speed compressor is advantageous during partial load operations because of its flexibility for handling compressor surge and allowing the stripper and reboiler to run at optimal conditions. Optimization at low load levels demonstrated that the most energy efficient strategy to control compressor surge is gas recycling which is commonly applied by an anti-surge control system installed on compressors. Trade offs were found between initial capital cost and optimal operation with minimal energy use for large load reduction. The examples are, designing the stripper in a way that can tolerate the pressure two times larger than normal operating pressure, over sizing the pumps and over designing the compressor speed. A plant-wide control procedure was used to design an effective multi-loop control system. Five control configurations were simulated and compared in response to large load variations and foaming in the stripper and the absorber. The most successful control structure was controlling solvent rate, reboiler temperature, and stripper pressure by liquid valve, steam valve, and compressor speed respectively. With the investigated disturbances and employing this control scheme, development of an advanced multivariable control system is not required. This scheme is able to bring the plant to the targeted set points in about 6 minutes for such a system designed initially with 11 min total liquid holdup time.Frequency analysis used for evaluation of lean and rich tanks on the dynamic performances has shown that increasing the holdup time is not always helpful to damp the oscillations and rejecting the disturbances. It means there exists an optimum initial residence time in the tanks. Based on the results, a 5-minute holdup can be a reasonable number to fulfill the targets.
Electrical parameter control for semiconductor manufacturing
(2007-12) Schoene, Clare Butler, 1979-; Qin, S. Joe; Edgar, Thomas F.
The semiconductor industry is highly competitive environment where modest improvements in the manufacturing process can translate to significant cost savings. An area where improvements can be realized is reducing the number of wafers that fail to meet their electrical specifications. Wafers that fail to meet electrical specifications are scrapped, which negatively impacts yield and increases manufacturing costs. Most of the existing semiconductor process control research has focused on controlling individual steps during the manufacturing process via run-to-run control, but almost no work has looked at directly controlling device electrical characteristics. Since meeting electrical specifications is so critical to reducing scrap a fab-wide electrical parameter control scheme is proposed to directly control electrical parameter values. The goal of the controller is reducing the variation in the electrical parameters. The control algorithm uses a model to predict electrical parameter values after each processing step. Based on this prediction the decision to make a control move is made. If a control move is necessary, optimal adjustments for the subsequent processing steps are determined. The process model is continually updated so that it reflects the current process. A simple implementation using a least squares model is first proposed. Simulations and an industrial case study demonstrate the potential improvements that can be achieved with the algorithm and the limitations of the simple implementation are discussed. A partial least squares modeling and control algorithm combined with missing data algorithms are proposed as enhancements to the electrical parameter control algorithm to address many of the issues faced when implementing such a control strategy in real manufacturing environments. The enhancements take the input variable correlations into account when making control moves and utilize the correlation structure to make better model predictions. Simulations are performed to determine the effectiveness of the enhancements. A cost function formulation and a Bayesian based alternative are also presented and evaluated. The cost function implementation uses a different method to determine the optimal set points for the subsequent processing steps than the other implementations use. Simulations are used to compare the cost function formulation with the other methods presented. The Bayesian implementation addresses the stochastic nature of the manufacturing process by dealing with the probabilities of events occurring. A simulation of the Bayesian algorithm is preformed and the algorithms limitations are discussed.
Equation-oriented modeling, simulation, and optimization of integrated and intensified process and energy systems
(2016-12) Pattison, Richard C.; Baldea, Michael; Edgar, Thomas F.; Rochelle, Gary T; Bonnecaze, Roger T; Biros, George
Process intensification, defined as unconventional design and/or operation of processes that results in substantial performance improvements, represents a promising route toward reducing capital and operating expenses in the chemical/petrochemical process industry, while simultaneously achieving improved safety and environmental performance. In this dissertation, intensification is approached from three different angles: reactor design and control, process flowsheet design and optimization, and production scheduling and control. In the first part of the dissertation, three novel concepts for improving the controllability of intensified microchannel reactors are introduced. The first concept is a latent energy storage-based temperature controller, where a phase change material is confined within the walls of an autothermal reactor to improve local temperature control. The second concept is a segmented catalyst layer which modulates the rate of heat generation and consumption along the length of an autothermal reactor. Finally, the third concept is a thermally actuated valve, which uses small-scale bimetallic strips to modulate flow in a microchannel reactor in response to temperature changes. The second part of the dissertation introduces a novel framework for equation-oriented flowsheet modeling, simulation and optimization. The framework consists of a pseudo-transient reformulation of the steady-state material and energy balance equations of process unit operations as differential-algebraic equation (DAE) systems that are statically equivalent to the original model. I show that these pseudo-transient models improve the convergence properties of equation-oriented process flowsheet simulations by expanding the convergence basin in comparison to conventional steady state equation-oriented simulators. A library of pseudo-transient unit operation models is developed, and several case studies are presented. Models for more complex unit operations such as a pseudo-transient multistream heat exchanger and a dividing-wall distillation column are later introduced, and can easily be included in the flowsheet optimization framework. In the final part of the dissertation, a paradigm for calculating the optimal production schedule in a fast changing market situation is introduced. This is accomplished by including a model of the dynamics of a process and its control system into production scheduling calculations. The scheduling-relevant dynamic models are constructed to be of lower order than a detailed dynamic process model, while capturing the closed-loop behavior of a set of scheduling-relevant variables. Additionally, a method is given for carrying out these production scheduling calculations online and in "closed scheduling loop,"' i.e., recalculating scheduling decisions upon the advent of scheduling-relevant process or market events. An air separation unit operating in a demand response scenario is used as a representative case study.
Evaluation and extension of threaded control for high-mix semiconductor manufacturing
(2010-12) Patwardhan, Ninad Narendra; Flake, Robert H.; Edgar, Thomas F.
In the recent years threaded run-to-run (RtR) control algorithms have experienced drawbacks under certain circumstances, one such trait is when applied to high-mix of products such as in Application Specific Integrated Circuits (ASIC) foundries. The variations in the process are a function of the product being manufactured as well as the tool being used. The presence of semiconductor layers increases the number of times the lithography process must be repeated. Successive layers having different patterns must be exposed using different reticles/masks in order to maximize tool utilizations. The objectives of this research are to develop a set of methodologies for evaluation and extension of threaded control applied to overlay. This project defines methods to quantify the efficacy of threaded controls, finds the drawbacks of threaded control under production of high mix of semiconductors and suggests extensions and alternatives to improve threaded control. To evaluate the performance of threaded control, extensive simulations were performed in MATLAB. The effects of noise, disturbances, sampling and delays on the control and estimation performance of threaded controller were studied through these simulations. Based on the results obtained, several ideas to extend threaded control by reducing overall number of threads, by improving thread definitions and combination have been introduced. A unique idea of sampling the measurements dynamically based on the estimation accuracy is also presented. Future work includes implementing the extensions to threaded control suggested in this work in real production data and comparing the results without the use of those methods. Future work also includes building new alternatives to threaded control.
Experimental comparison of advanced control strategies
(Texas Tech University, 1995-08) Joshi, Ninad V.
The objective of this research endeavor is to compare experimentally several advanced control strategies on a heat exchanger and fluid flow system. The experimental set-up was established a few years back and consists of a shell-and-tube heat exchanger with several control valves. This heat exchanger uses steam or hot water on its shell side to heat either cold or hot or a mixture of hot and cold water passing through its tube side to a desired temperature. The apparatus also contains many pneumatic control valves for controlling the flow rates of hot or cold water or steam. An experimental comparison of three control strategies (classical PID, internal model control [IMC], and process modelbased control [PMBC]) was done a couple of years earlier. The objective of this study, along with the previous one, was to implement some advanced control strategies, and present a broad-based overall perspective on the advantages and disadvantages of different control strategies. This study picks up where the last study left off, and implemented some more control strategies under similar experimental conditions. The different advanced control strategies ultimately to be implemented were neural network-based control (both inverse and normal model), model predictive control, a combination of model predictive control and neural network-based control, and heuristic-based fuzzy logic control. Thus, as a part of this study, eight different strategies were implemented. Studies on the fuzzy logic strategy were carried out separately by another graduate student.
Neural networks and evolutionary computation for real-time quality control
(Texas Tech University, 1997-05) Patro, Sanjukta
Quality control in general and automated quality control in particular are assuming major importance in modem society as technological SNStems are becoming increasingly complex and highly interconnected. Traditional statistical process control techniques are inadequate to address control problems in automated processes because of the high degree of data correlation characterized by such processes. Classical process control methods depend on simplifying assumptions of plant linearity and time-invariance to make the problem analytically tractable. They are therefore limited in effectiveness of the control of complex, nonlinear, multivariable processes. This dissertation attempts to overcome some of the limitations and shortcomings of traditional quality control methods through the integration of two technologies, neural networks and evolutionary computation. An autonomous control system prototype has been developed to control (maintain quality variables within desired limits) a process by providing high level adaptation to changes in the plant, environment, and control objectives. This technology utilizes memory and learning techniques to overcome limitations of traditional control methods, namely data autocorrelation, requirements of simplifying assumptions, and requirements of a priori information about the process. The robustness and applicability of this integrated technology is demonstrated though results obtained from tests involving simulated processes of varying degrees of complexity.
Performance monitoring of run-to-run control systems used in semiconductor manufacturing
(2008-08) Prabhu, Amogh V., 1983-; Edgar, Thomas F.
Monitoring and diagnosis of the control system, though widely used in the chemical processing industry, is currently lacking in the semiconductor manufacturing industry. This work provides methods for performance assessment of the most commonly used control system in this industry, namely, run-to-run process control. First, an iterative solution method for the calculation of best achievable performance of the widely used run-to-run Exponentially Weighted Moving Average (EWMA) controller is derived. A normalized performance index is then defined based on the best achievable performance. The effect of model mismatch in the process gain and disturbance model parameter, delays, bias changes and nonlinearity in the process is then studied. The utility of the method under manufacturing conditions is tested by analyzing three processes from the semiconductor industry. Missing measurements due to delay are estimated using the disturbance model for the process. A minimum norm estimation method coupled with Tikhonov regularization is developed. Simulations are then carried out to investigate disturbance model mismatch, gain mismatch and different sampling rates. Next, the forward and backward Kalman filter are applied to obtain the missing values and compared with previous examples. Manufacturing data from three processes is then analyzed for different sampling rates. Existing methods are compared with a new method for state estimation in high-mix manufacturing. The new method is based on a random walk model for the context states. This approach is also combined with the recursive equations of the Kalman filter. The method is applied to an industrial exposure process by extending the random walk model into an integrated moving average model and weights used to give preference to the context that is more frequent. Finally, a performance metric is derived for PID controllers, when they are used to control nonlinear processes. Techniques to identify nonlinearity in a process are introduced and polynomial NARX models are proposed to represent a nonlinear process. A performance monitoring technique used for MIMO processes is then applied. Finally, the method is applied to an EWMA control case used before, a P/PI control case from literature and two cases from the semiconductor industry.
Process model based control of a fluidized bed gasifier: a comparison of two strategies
(Texas Tech University, 1988-08) Pandit, Hemant Gopal
NOT AVAILABLE
Simulation and Analysis of the Process Control for a Solar Gridiron Power System
(Texas Tech University, 1981-12) Jiwani, Enayet Aly
Not Available.
The stability and performance of the EWMA and double-EWMA run-to-run controllers with metrology delay
(2004) Good, Richard Paul; Qin, S. Joe
Because of the ever-increasing demands on product quality, feedback con- trol has become a necessary enabling component in the manufacture of modern semiconductor devices. The nature of semiconductor manufacturing is such that measurements of device quality characteristics are not available during the processing of the product. Measurements are not made until after the product is processed and necessary changes to tool setting can only be made to subsequent production runs. This control scheme, termed run-to-run control, has become the cornerstone of process control in the semiconductor manufacturing industry. In addition to the ever-increasing demands on product quality, the semi- conductor manufacturing industry continues to see stringent growth in throughput requirements. Because of the demands on production throughput, it is rarely possi- ble to perform quality measurements on a batch of wafers before processing begins on the following batch of wafers. The delay between product manufacturing and product metrology coupled with inaccurate process models can lead to process in- stabilities and deterioration in controller performance. This dissertation investigates the robust stability requirements of processes controlled with EWMA and double- EWMA run-to-run controllers with delays between processing and metrology. In addition, the effects of model mismatch and metrology delay on the closed-loop performance of the EWMA and double-EWMA run-to-run controllers are derived by extending the robust stability methodology. Finally, these robust performance requirements are used to find the optimal tuning parameters for the double-EWMA controller. These tuning parameters allow for the largest model uncertainty while guaranteeing a predetermined minimum closed-loop transient performance.
Superfractionator process control
(Texas Tech University, 1998-08) Hurowitz, Scott Edward
An in-depth study is conducted regarding product composition control of superfractionators with an emphasis on control configuration selection. A propylenepropane (C3) splitter is chosen as a representative column by which to investigate superfractionator process control issues. An ethylene-ethane (C2) splitter is also investigated for comparative purposes. Detailed steady state and dynamic simulations of a C3 and C2 splitter are developed and benchmarked against industrial C3 and C2 splitter process data. These simulations are used to investigate single-ended and dual Proportional-Integral (PI) composition control. For C3 splitter single-ended PI composition control, the (L, V) configuration provides the best control performance. For C3 splitter dual PI composition control, the (L, B) and (L, V/B)configurations provide the best control performance. The (L, V) and (L, V/B) configurations are determined as optimal for dual PI composition control of the C2 splitter. The control benefits provided by the use of decoupling techniques and feedforward compensation for dual PI composition control are also investigated. An evaluation of the control benefits realized by feedforward compensation indicate that, when a material balance (product) stream is used to control composition, feedforward compensation will provide a significant improvement in composition control performance. Dynamic Matrix Control (DMC), a model-based control algorithm, is applied to the C3 and C2 splitters, and its performance is compared to that obtained by PI control. Dynamic Matrix Control generally provides control performance that is equal to or better than that obtained by PI control for unconstrained, 2x2 distillation composition control, provided that the process is adequately modeled by the DMC controller. A technique is developed for predicting closed-loop product variabilities based on a signal processing analysis of feed composition data, from which usefiil information can be extracted and used to predict closed-loop product variabilities. This technique is applied to a C3 splitter for demonstrative purposes and is shown to accurately predict the product variabilities that result from feed composition disturbances.