Synthesis of PID controller for unstable and integrating processes

Properly designed controllers provide stable closed-loop response for open-loop unstable processes. Internal model controller equivalent PID tuning rules for low order unstable plus dead time systems are synthesized in this work. The controller is approximated near the vicinity of zero (origin). Controller parameters are derived by equating the closed-loop response to a control-signature (desired closed-loop response) involving a user defined tuning parameter, λ. Simulations are carried out to show the performance of the proposed tuning scheme for both set point and disturbance rejection cases.     

Identification of integrating and unstable processes from relay feedback

In this paper, the exact relay response expressions are derived for integrating and unstable 1st or 2nd order processes. Identification algorithms are subsequently developed in terms of the measurable parameters under relay feedback and fitting conditions of the limit cycle. It is demonstrated that the limit cycle can be undoubtedly formed for an integrating process under a biased/unbiased relay feedback test. A tighter limiting condition is given for identification of unstable processes with the standard relay feedback structure. Denoising methods are presented to cope with measurement noise. Illustrative examples are given to demonstrate the effectiveness and merits of the proposed identification algorithms.     

Analytical design of enhanced PID filter controller for integrating and first order unstable processes with time delay

An analytical design for a proportional-integral derivative (PID) controller cascaded with a first order lead/lag filter is proposed for integrating and first order unstable processes with time delay. The design algorithm is based on the internal model control (IMC) criterion, which has a single tuning parameter to adjust the performance and robustness of the controller. A setpoint filter is used to diminish the overshoot in the servo response. In the simulation study, the controllers were tuned to have the same degree of robustness by measuring the maximum sensitivity, Ms, in order to obtain a reasonable comparison. Furthermore, the robustness of the controller was investigated by inserting a perturbation uncertainty in all parameters simultaneously to obtain the worst case model mismatch, and the proposed method showed more robustness against process parameter uncertainty than the other methods. For the selection of the closed-loop time constant, λ, a guideline is also provided over a broad range of time-delay/time-constant ratios. The simulation results obtained for the suggested method were compared with those obtained for other recently published design methods to illustrate the superiority of the proposed method.     

PID controller design for integrating processes with time delay

A simple IMC-PID controller design technique is proposed on the basis of the IMC principle for two representative integrating processes with time delay. Further, it is extended to integrating processes with negative and positive zero as well. The proposed PID controller design method is mainly focused on the disturbance rejection, which causes the overshoot in the setpoint response, and a two-degree-of-freedom (2DOF) control structure is used to eliminate this overshoot. The simulation results show the superiority of the proposed tuning rule over other existing methods, when the controller is tuned to have the same robustness level by evaluating the peak of the maximum sensitivity (Ms). The closed loop time constant (λ) has only one user-defined tuning parameter in the proposed method. A guideline is suggested for the selection of λ for different robustness levels by evaluating the value of Ms over a wide range of θ/τ ratios.     

Decentralized fault-tolerant control system design for unstable processes

Fault-tolerant control is an important issue in control of mission critical processes. In this paper, a new approach to fault-tolerant control of unstable processes is proposed based on the Passivity Theorem. The control system is designed in two sequential steps: A multi-loop proportional controller is used to stabilize the unstable process; a passivity-based decentralized unconditionally stabilizing (DUS) controller is then applied to the stabilized process. While the multi-loop stabilizing controllers need to be built with redundancy, the DUS controller is inherently fault tolerant and can maintain closed-loop stability when any of its loops fail. By using a stabilizing proportional controller with the fewest loops, control redundancy can be reduced to the minimum level.     

Moving horizon approach of integrating scheduling and control for sequential batch processes

Online integration of scheduling and control is crucial to cope with process uncertainties. We propose a new online integrated method for sequential batch processes, where the integrated problem is solved to determine controller references rather than process inputs. Under a two‐level feedback loop structure, the integrated problem is solved in a frequency lower than that of the control loops. To achieve the goal of computational efficiency and rescheduling stability, a moving horizon approach is developed. A reduced integrated problem in a resolving horizon is formulated, which can be solved efficiently online. Solving the reduced problem only changes a small part of the initial solution, guaranteeing rescheduling stability. The integrated method is demonstrated in a simulated case study. Under uncertainties of the control system disruption and the processing unit breakdown, the integrated method prevents a large loss in the production profit compared with the simple shifted rescheduling solution. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1654–1671, 2014     

Closed-loop identification of wafer temperature dynamics in a rapid thermal process

Single wafer rapid thermal processing (RTP) can be used for various wafer fabrication steps such as annealing, oxidation and chemical vapor deposition. A key issue in RTP is accurate temperature control, i.e., the wafer temperatures should be rapidly increased while maintaining uniformity of the temperature profile. A closed-loop identification method that suppresses RTP drift effects and maintains a linear operating region during identification tests is proposed. A simple graphical identification method that can be implemented on a field controller for autotuning and a nonlinear least squares method have been investigated. Both methods are tested with RTP equipment based on a design developed by Texas Instruments.     

Robust identification and control of batch processes

An approach is proposed for the robust identification and control of batch and semibatch processes. The batch experiments used for model identification are designed by minimizing the magnitude of the parameter uncertainties, and the effect of these uncertainties on the product quality achievable by optimal control is used as a stopping criterion for the identification procedure. The optimal control approach incorporates a quantification of the impact of both parameter and control implementation uncertainties on the performance of the optimal control policy. The approach is applied to the nucleation and growth of crystals with multiple characteristic dimensions, where the nominal parameters used in the simulation study are quantified from experimental data.     

Closed-loop identification of systems using hybrid Box-Jenkins structure and its application to PID tuning

The paper describes a closed-loop system identification procedure for hybrid continuous-time Box-Jenkins models and demonstrates howit can be used for IMC based PID controller tuning. An instrumental variable algorithmis used to identify hybrid continuous-time transfer function models of the Box-Jenkins formfromdiscretetime prefiltered data, where the process model is a continuous-time transfer function, while the noise is represented as a discrete-time ARMA process. A novel penalizedmaximum-likelihood approach is used for estimating the discrete-time ARMA process and a circulatory noise elimination identification method is employed to estimate process model. The input-output data of a process are affected by additive circulatory noise in a closedloop. The noise-free input-output data of the process are obtained using the proposed method by removing these circulatory noise components. The process model can be achieved by using instrumental variable estimation method with prefiltered noise-free input-output data. The performance of the proposed hybrid parameter estimation scheme is evaluated by the Monte Carlo simulation analysis. Simulation results illustrate the efficacy of the proposed procedure. The methodology has been successfully applied in tuning of IMCbased flowcontroller and a practical application demonstrates the applicability of the algorithm.     

A survey on applications of optimization-based integrating process design and control for chemical processes

Process design and control are closely related to each other in chemical engineering activities. Traditionally, process design and control system design are carried out in sequence. However, the integration of process design and control (IPDC) can bring greater economic benefits and process dynamic performance than traditional sequential design methods. This is true, particularly for modern chemical processes, in which various process units become more interacting and compact owing to the widespread use of heat integration and recycled streams, and the resulted impacts between process design and control begin to significantly influence both the capital and operational costs. Recently, considerable studies about the IPDC for chemical processes have been reported in published literature. The purpose of the paper is to survey the applications of optimization-based integrating process design and control for chemical processes. Firstly, attention has been focused on the applications of IPDC to different process units, for example, chemical reactors and separation columns. Then, the survey is extended to the applications of IPDC to plant-wide chemical processes. Finally, the future research challenges in the application of IPDC to chemical processes have been briefly discussed.     

Diffusion as a rate controlling step in heavy-oil biodesulfurization processes

Microbial degradation of hydrocarbons depends on various chemical and physical factors (viz, temperature, electron acceptors, nutrients, pH, substrate characteristics) and on the presence of degrading microorganisms. Even if all these factors are optimized, oil biodegradation can still be retarded by diminished availability of the compound. Setti et al. (1992) have shown that n-alkanes affect the biodegradation of aromatic sulfur compounds by an aerobic Pseudomonas sp.

This communication illustrates further investigations into the diffusion effect that controls the biodegradation of aromatic sulfur compounds.

Polycyclic aromatic hydrocarbons (PAHs) can be converted in the dissolved state only. This implies that mass-transfer from the solid phase to the aqueous phase might be rate-limiting. Dibenzothiophene (DBT) biodegradation is a good example of this model.

The presence of a fatty acid (hexadecanoic acid, aC16) during the fermentation affects DBT degradation. We propose a model in which aC16 surrounds the DBT molecule forming a micelle which favors a co-metabolic process between the fatty acid and the aromatic sulfur compound.

Similar behavior is seen when DBT is dissolved in n-dodecane (C12) and n-hexadecane (C16). There are two important parameters that affect DBT degradation in this system: the surface area of the substrate and the DBT concentration in C12. DBT degradation is limited by the diffusion of the sulfur compound from the organic phase to the adsorbed microorganism on the hydrocarbon. Diffusion increases with the DBT concentration, and the DBT degradation rate is significantly higher than that reported for DBT alone. In this case, a carrier effect of the n-alkane in the DBT degradation is shown. A barrier effect is suggested when the DBT concentration is below the limit for which the degradation rate is lowest.

Our investigations show that the presence of a co-substrate, such as fatty acids or n-alkanes, affects the bioavailability of the aromatic sulfur compound in aerobic conditions.     

一种分步约简的炼油生产敏感变量选择方法

变量筛选是现代工业过程产品质量预测研究中的热点问题之一。过滤式变量选择方法因其计算速度快且不易造成过拟合得到了广泛应用,但其存在容易忽略变量相关性且不能准确反映工况信息的问题,在高维数据维度灾难问题日渐突出的当今不再适用。针对这一问题,提出一种分步约简的敏感变量选择方法。该方法在明确敏感变量和关键敏感变量的基础上,根据变量对工况的描述能力和辅助变量与主导变量的净相关性定义了敏感性指标,实现敏感变量的初选;接着,构建加权余弦马田系统以解决变量冗余性问题,实现敏感变量的精选。所提方法应用于加氢裂化产品质量预测,实际工业应用结果表明,该方法不仅可以提高模型的预测精度,而且可以有效地降低模型复杂性。     

Enhanced control of integrating cascade processes with time delays using modified Smith predictor

This paper presents a simple cascade controller in the enhanced modified Smith predictor structure for control of integrating processes. The proposed structure consists of two control loops, a secondary inner loop and a primary outer loop. The method has totally three controllers of which the secondary loop has one controller and the primary loop has two controllers. The secondary loop controller is designed using IMC technique. The primary loop set-point tracking and disturbance rejection controllers are designed using direct synthesis method. The primary set-point tracking controller is designed as a PID with lag filter and the primary disturbance rejection controller is designed as a PD with lead-lag filter. Simulation studies have been carried out on various cascade integrating processes with/without zero. The present method gives significant disturbance rejection both in the inner and outer loops and also shows significant improvement when compared to the recently reported methods.     

Improved frequency response model identification method for processes with initial cyclic‐steady‐state

A new nonparametric process identification method is proposed to obtain the frequency response model from given process input and output data. The proposed algorithm can estimate exact models for all desired frequencies. It is applicable to various process conditions (initial/final steady‐state, initial steady‐state/final cyclic‐steady‐state, and initial/final cyclic‐steady‐state) and requires a smaller amount of memory than previous methods. Also, it provides the exact models even in the presence of a static disturbance and shows an acceptable robustness to measurement noises. © 2011 American Institute of Chemical Engineers AIChE J, 2011     

Improved system identification method for Hammerstein-Wiener processes

We propose a new system identification method for Hammerstein-Wiener processes, in which an input static nonlinear block, a linear dynamic block, and an output static nonlinear block are connected in a series. The proposed method can estimate the model parameters in a very simple way without solving the full-dimensional nonlinear optimization problem by activating the process with a specially designed test signal, composed of a relay feedback signal, a binary signal and a multi-step signal. The proposed method analytically identifies the output nonlinear static function and the input nonlinear static function from the relay signal and the multi-step signal, respectively. The linear dynamic subsystem is identified from the relay feedback signal and the binary signal with existing well-established linear system identification methods. We demonstrate with a simple example that the proposed method can be successfully applied to identify the Hammerstein-Wiener-type nonlinear process.     

Two‐step principal component analysis for dynamic processes monitoring

In this study, a two‐step principal component analysis (TS‐PCA) is proposed to handle the dynamic characteristics of chemical industrial processes in both steady state and unsteady state. Differently from the traditional dynamic PCA (DPCA) dealing with the static cross‐correlation structure and dynamic auto‐correlation structure in process data simultaneously, TS‐PCA handles them in two steps: it first identifies the dynamic structure by using the least squares algorithm, and then monitors the innovation component by using PCA. The innovation component is time uncorrelated and independent of the initial state of the process. As a result, TS‐PCA can monitor the process in both steady state and unsteady state, whereas all other reported dynamic approaches are limited to only processes in steady state. Even tested in steady state, TS‐PCA still can achieve better performance than the existing dynamic approaches.     

Effect of blade-tip shape on the doctoring step in gravure printing processes

A fundamental limit to the gravure printing process is in the doctoring step, in which a residual film defines the lower bound on allowable feature size. The resolution of finer features requires thin residual films, but these thin films increase the likelihood of wearing the doctor blades. A computational model was used to study the effect of blade-tip shape on residual film thickness while also minimizing the likelihood of wear. The blade-tip shape is altered by varying the bevel angles and the predicted film thickness is computed under various wiping speeds, configurations, and applied forces. In all cases studied, a slower wiping speed resulted in a thinner residual film, which is due to the doctoring step being governed by elastohydrodynamic lubrication. In some cases, a reversal of the wiping configuration created a thinner film, but it had no impact on the likelihood of wearing. Higher applied force leads to thinner residual film but the blade shape can have a more significant influence, indicating that lubrication forces dominate at this scale. Lastly, the likelihood of blade wear was predicted to vary within a small range for a fixed blade-tip shape over all conditions studied, which suggests that tip shape is the primary factor to consider when minimizing blade wear.     

Model predictive control for multivariable unstable processes with constraints on manipulated variables

The original MPC(Model Predictive Control) algorithm cannot be applied to open loop unstable systems, because the step responses of the open loop unstable system never reach steadystates. So when we apply MPC to the open loop unstable systems, first we have to stabilize them by state feedback or output feedback. Then the stabilized systems can be controlled by MPC. But problems such as valve saturation may occur because the manipulated input is the summation of the state feedback output and the MPC output. Therefore, we propose Quadratic Dynamic Matrix Control(QDMC) combined with state feedback as a new method to handle the constraints on manipulated variables for multivariable unstable processes. We applied this control method to a single-input-single-output unstable nonlinear system and a multi-input-multi-output unstable system. The results show that this method is robust and can handle the input constraints explicitly and also its control performance is better than that of others such as well tuned PI control. Linear Quadratic Regulator (LQR) with integral action.