I-Lung Chien

Nonlinear identification and control of a high-purity distillation column: a case study

Abstract Identification and control of ill-conditioned, interactive and highly nonlinear processes pose a challenging problem to the process industry. In the absence of a reasonably accurate model, these processes are fairly difficult to control. Using a high-purity distillation column as an example, model identification and control issues are addressed in this paper. The structure of the identified models is that of the polynomial type nonlinear autoregressive models with exogenous inputs (NARX). While most of the work in this area has concentrated on linear models (one-time scale and two-time scale models), this work is aimed at identifying the inherent nonlinearities. Comparisons are drawn between the identified models based on statistical criteria (AIC etc.) and other validation tests. Simulation results are provided to demonstrate the closed-loop performance of the nonlinear ARX models in the control of the distillation column. The controller employed is based on a nonlinear model predictive scheme with state and parameter estimation.

Simple control method for integrating processes with long deadtime

Abstract Control of integrating processes with long deadtime is a challenging problem. Using original Smith predictor control structure will result in an offset problem during load disturbance. Using the internal model control (IMC) principle to design the control structure will result in large overshoot during servo response. In this work, a simple modified Smith predictor controller design is proposed for this important type of system. The overall control structure has only two physically meaningful tuning parameters. One is used to set the speed of the closed-loop servo response and the other tuning parameter is used to set the speed of the closed-loop load response. Two examples are used to demonstrate the improved closed-loop performance of the proposed controller design.

Simplified IMC-PID tuning rules

Abstract We have significantly simplified the IMC-PID tuning rules. These new rules cover the vast majority of control loops encountered in the chemical industry. This work is the result of a great deal of experience in successfully applying IMC-PID tuning rules and an effort to prepare a training course on controller tuning. The simplified rules are very similar in form to the classic open loop Ziegler-Nichols rules and use the process reaction curve method for process testing. The two differences are that these rules are based on a less aggressive performance criteria and that we adapt the rules for some commonly encountered special cases. This paper presents the relationship between the simplified IMC-PID rules, the general IMC-PID rules, the Ziegler-Nichols rules and the Cohen-Coon rules. We show that the simplified rules are less sensitive to parameter mis-estimation than other more aggressive tuning rules. We also proposed rules for a fourth action; filtering. Filtering is available in digital controllers and smart field transmitters. We report that filtering and derivative action cancel each other and therefore should not be used together. We briefly outline the contents of the tuning course and finish the paper with an industrial example where the simplified rules have been successfully applied.

IMC-PID Controller Design - An Extension

Abstract PID controller design based on Internal Model Control (IMC) design procedure by Rivera and co-workers has been extended to cover a wider range of process models. PID tuning parameters for processes up to second-order with first-order numerator dynamics and dead time will be given with only one tuning parameter (closed-loop time constant). In order to obtain the PID formulation, dead time is approximated by either a first-order Pade or a first-order Taylor series expansion depending on whether a PI or PID controller is desired. Various industrial PID implementations which require different PID settings will also be examined in this paper. Several examples including a first-order plus dead time, second-order plus dead time and inverse response, an industrial level control problem, and a nonlinear distillation column model will be used to compare the performance of IMC-PID controller design with more traditional tuning rules, eg, Ziegler-Nichols, Cohen-Coon, and frequency response maximum peak criterion. Model mismatches in process gain, dead time, and time constant will also be introduced to compare the robustness of various controller design methods.

Coordinated control of blending systems

Blending is an important unit operation in chemical process industries. Based on Luybens work (1990), a coordinated control is proposed to overcome the system interaction and process nonlinearity in blending processes. Blending systems with ideal and nonideal mixing are explored. Furthermore, unusual dynamic behavior is observed for blending systems coupled with complex dynamics arisen from the process side. Controlling the blending of streams from different sidestream locations of a distillation column is studied and, based on physical insight, a simple procedure is proposed for the selection of sidedraw locations. The disturbance rejection aspects of blending control are discussed and practically important issues such as model uncertainties and measurement noise are also treated. Simulation results show that the control of blending systems is not as trivial as it was thought and, more importantly, improvement in the performance can be achieved using the proposed coordinated control.

A simple method for tuning cascade control systems

Abstract A simple method for tuning controllers in a cascade system is presented, In this method, all the relations that facilitate the tuning procedures are well prepared in terms of figures or simple equations. Using these figures and equations, the controller tuning for different configurations of cascade systems becomes easy and straightforward when process models are available. On the other hand, when process models are not available, a simple method that uses one single run of step input experiment to develop such models is proposed. Based on these developments in the controller tuning and process models, an autotuning system that uses relay feedback is presented. Unlike the existing autotuning systems, this proposed system conducts identification and controller tuning in a decoupled manner. As a result, no excessive trial- and-error efforts for modeling and tuning are required. Simulation results show the potential usage of such a method, It is interested to see that the resulting systems have almo...

Dynamic modeling and analyses of simultaneous saccharification and fermentation process to produce bio-ethanol from rice straw

The rice straw, an agricultural waste from Asians’ main provision, was collected as feedstock to convert cellulose into ethanol through the enzymatic hydrolysis and followed by the fermentation process. When the two process steps are performed sequentially, it is referred to as separate hydrolysis and fermentation (SHF). The steps can also be performed simultaneously, i.e., simultaneous saccharification and fermentation (SSF). In this research, the kinetic model parameters of the cellulose saccharification process step using the rice straw as feedstock is obtained from real experimental data of cellulase hydrolysis. Furthermore, this model can be combined with a fermentation model at high glucose and ethanol concentrations to form a SSF model. The fermentation model is based on cybernetic approach from a paper in the literature with an extension of including both the glucose and ethanol inhibition terms to approach more to the actual plants. Dynamic effects of the operating variables in the enzymatic hydrolysis and the fermentation models will be analyzed. The operation of the SSF process will be compared to the SHF process. It is shown that the SSF process is better in reducing the processing time when the product (ethanol) concentration is high. The means to improve the productivity of the overall SSF process, by properly using aeration during the batch operation will also be discussed.

Operation and decoupling control of a heterogeneous azeotropic distillatin column

Abstract In this work, proper operation and improved control of a heterogeneous azeotropic distillation of isopropyl alcohol (IPA) + water (H2O) with cyclohexane (CyH) as an entrainer were investigated. A simple operating procedure under inverse double loop control strategy is proposed to automatically adjust the manipulated variables to be at optimum operating point. A simple one-way decoupling control scheme is also proposed to account for the high interactions between two temperature control loops. Both fed rate and composition load disturbances will be introduced to test the proposed decoupling control scheme.

Modeling and control of a temperature-based high-purity distillation column

Industrial distillation columns typically lack adequate on-line instrumentation required for implementing high performance, model-based control systems which depend on composition measurements. The typical practice is to base product quality control schemes instead on tray temperature measurements since these are usually available on-line. While this strategy has been applied with success on a wide variety of distillation columns, some special considerations may be necessary in dealing with high-purity columns. This paper presents results which address some of the key issues involved with the use of tray temperature measurements as surrogates for composition measurements, and with the development of appropriate input/output models for multi variable control of high-purity columns. For the purpose of illustration, we investigate the performance of several model predictive control schemes based on a linear model, a “high frequency” model, and a nonlinear temperature transformation, along with that of conven...

Grade transition using dynamic neural networks for an industrial high-pressure ethylene―vinyl acetate (EVA) copolymerization process

In this paper, estimating of melt index (MI) in an industrial ethylene and vinyl acetate (EVA) copolymerization process will be studied. Three products with their melt indexes ranging from 2.49 to 167.21 are dynamically estimated by an artificial neural networks (ANN) model based on available plant measurements. With this dynamic estimator, a simple MI controller with only proportional-integral mode can be established for the purpose of grade transition by suitably adjusting the chain modifier feed rate. Simulation results demonstrate that significant reduction in the grade transition time can be gained in comparison with the base strategy by step-changing of the operating recipe. A simple model updating algorithm is also proposed for the adjustment of the predicted MI using infrequent lab measurement to handle plant-model mismatches.