Identification and control of a riser‐type FCC unit

This paper addresses the use of feedforward neural networks for the steady‐state and dynamic identification and control of a riser type fluid catalytic cracking unit (FCCU). The results are compared with a conventional PI controller and a model predictive control (MPC) using a state space subspace identification algorithm. A back propagation algorithm with momentum term and adaptive learning rate is used for training the identification networks. The back propagation algorithm is also used for the neuro‐control of the process. It is shown that for a noise‐free system the adaptive neuro‐controller and the MPC are capable of maintaining the riser temperature, the pressure difference between the reactor vessel and the regenerator, and the catalyst bed level in the reactor vessel, in the presence of set‐point and disturbance changes. The MPC performs better than the neuro controller that in turn is superior to the conventional multi‐loop diagonal PI controller.     

Temperature Inferential Control of Heat‐Integrated Distillation Column Based on Variable Sensitive Stage Temperature Set‐point

Heat‐integrated distillation is an improved distillation technique with remarkable energy‐saving potential. A control scheme with a variable sensitive stage temperature set‐point is proposed to solve the control problem of a heat‐integrated distillation column (HIDiC). An online estimator is designed to support the variation of the set‐point. The locations of the stage temperature measurements are carefully selected based on a combination strategy with three steps. First, the sensitive stages are selected. Then, the following stages are determined by a PCA‐based method. Finally, a maximum differentiation method provides the remaining measurement selections. According to the profile parameters estimated by the proposed estimator, the set‐point of the sensitive stage temperature is adjusted adaptively to reduce the influence of the disturbances. Two commonly‐used PID controllers, the sensitive temperature control and the temperature differential control, are developed as the comparative study. The simulation results show that the proposed control scheme has a distinct advantage in restraining different disturbances.     

Delay‐range‐dependent guaranteed cost control for batch processes with state delay

A guaranteed cost control scheme is proposed for batch processes described by a two‐dimensional (2‐D) system with uncertainties and interval time‐varying delay. First, a 2‐D controller, which includes a robust feedback control to ensure performances over time and an iterative learning control to improve the tracking performance from cycle to cycle, is formulated. The guaranteed cost law concept of the proposed 2‐D controller is then introduced. Subsequently, by introducing the Lyapunov–Krasovskii function and adding a differential inequality to the Lyapunov function for the 2‐D system, sufficient conditions for the existence of the robust guaranteed cost controller are derived in terms of matrix inequalities. A design procedure for the controller is also presented. Furthermore, a convex optimization problem with linear matrix inequality (LMI) constraints is formulated to design the optimal guaranteed cost controller that minimizes the upper bound of the closed‐loop system cost. The proposed control law can stabilize the closed‐loop system as well as guarantee H_∞ performance level and a cost function with upper bounds for all admissible uncertainties. The results can be easily extended to the constant delay case. Finally, an illustrative example is given to demonstrate the effectiveness and advantages of the proposed 2‐D design approach. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2033–2045, 2013     

改进Smith预估控制器的解析设计

针对化工过程常见的积分和不稳定时滞对象,基于改进的史密斯预测控制提出了两自由度控制方案。首先根据鲁棒控制理论H2最优性能指标设计设定值跟踪控制器,然后在分析稳定性和抗扰性的基础上设计了扰动抑制控制器,设定值跟踪控制器和扰动抑制控制器可通过性能参数独立调节和优化。同时针对存在的乘性不确定性过程分析了系统的鲁棒稳定性。最后通过仿真实例验证了该控制方案的有效性。     

积分和不稳定时滞对象的改进内模控制

针对化工过程中一阶积分和不稳定时滞对象,基于内模控制提出了两自由度控制方案。首先根据鲁棒控制理论H2最优性能指标设计设定值跟踪控制器,然后采用期望闭环补灵敏度函数确定扰动抑制控制器。设定值跟踪控制器和扰动抑制控制器可通过性能参数独立调节而无需再取折衷,同时保证系统具有较好的鲁棒稳定性。最后通过仿真实例验证了该控制方案的有效性。     

Adaptive Internal Model Control of a High‐Purity Heat‐Integrated Air Separation Column

An internal model control scheme based on a second‐order internal model (SI‐IMC) is proposed for the heat‐integrated air separation column (HIASC). An adaptive internal model control (ASI‐IMC) scheme is further presented to make the model more accurate. The IMC scheme based on the first‐order model (F‐IMC) and the multi‐loop PID (M‐PID) scheme are also explored as the comparison basis of ASI‐IMC and SI‐IMC schemes. Comparative researches among these four control schemes are carried out in detail. The results indicate that ASI‐IMC presents the best performances among the four control schemes in both servo control and regulatory control, which proves the improvement of ASI‐IMC over the SI‐IMC and the superiority of ASI‐IMC for the high‐purity HIASC.     

Integrating data‐based modeling and nonlinear control tools for batch process control

A data‐based multimodel approach is developed in this work for modeling batch systems in which multiple local linear models are identified using latent variable regression and combined using an appropriate weighting function that arises from fuzzy c‐means clustering. The resulting model is used to generate empirical reverse‐time reachability regions (RTRRs) (defined as the set of states from where the data‐based model can be driven inside a desired end‐point neighborhood of the system), which are subsequently incorporated in a predictive control design. Simulation results of a fed‐batch reactor system under proportional‐integral (PI) control and the proposed RTRR‐based design demonstrate the superior performance of the RTRR‐based design in both a fault‐free and faulty environment. The data‐based modeling methodology is then applied on a nylon‐6,6 batch polymerization process to design a trajectory tracking predictive controller. Closed‐loop simulation results illustrate the superior tracking performance of the proposed predictive controller over PI control. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Simulation‐Based Design of Dual‐Mode Controller for Non‐Linear Processes

This paper presents a simulation‐based approach for designing a non‐linear override control scheme to improve the performance of a local linear controller. The higher‐level non‐linear controller monitors the dynamic state of the system and calculates an override control action whenever the system is predicted to move outside an acceptable operating regime under the local controller. The design of the non‐linear override controller is based on a cost‐to‐go function, which is constructed by using simulation or operation data. The cost‐to‐go function delineates the admissible region of state space within which the local controller is effective, thereby yielding a switching rule.     

On‐Line PEMFC Control Using Parameterized Nonlinear Model‐Based Predictive Control

In this work, a fast nonlinear model‐based predictive control (NMPC) strategy is designed and experimentally validated on‐line on a real fuel cell. Regarding NMPC strategies, the most challenging part remains to achieve on‐line implementation, especially when dealing with fast dynamic systems. As previously demonstrated in a recent work, the proposed control strategy is ideally suited to address this problem. Indeed, it is 30 times faster than classical NMPC controllers. This strategy relies on a specific parameterization of the control actions to reduce the computational time and achieve on‐line implementation. Due to its short computational time compared to mechanistic models, an artificial neural network model is designed and experimentally validated. This model is employed as internal model in the NMPC controller to predict the system behavior. To confirm the applicability and the relevance of the proposed NMPC controller varying control scenarios are investigated on a test bench. The built‐in controller is overridden and the NMPC controller is implemented externally and executed on‐line. Experimental results exhibit the outstanding tracking capability and robustness against model‐process mismatch of the proposed strategy. The parameterized NMPC controller turns out to be an excellent candidate for on‐line applications.     

A delay‐dependent robust fuzzy MPC approach for nonlinear CSTR

Based on Takagi–Sugeno (T–S) fuzzy models, a robust fuzzy model predictive control (MPC) algorithm is presented for a class of nonlinear time‐delay systems with input constraints. Delay‐dependent sufficient conditions for the robust stability of the closed‐loop system are derived, and the condition for the existence of the fuzzy model predictive controller is formulated in terms of nonlinear matrix inequality via the parallel distributed compensation (PDC) approach. By using a novel matrix transform technique, a receding optimization problem with linear matrix inequality (LMIs) constraints is constructed to design the desired controllers with an on‐line optimal receding horizon guaranteed cost. Finally, an example of continuous stirred tank reactors (CSTR) is given to demonstrate the effectiveness of the proposed results.     

Process design and control optimization: A simultaneous approach by multi‐parametric programming

We present a framework for the application of design and control optimization via multi‐parametric programming through four case studies. We develop design dependent multi‐parametric model predictive controllers that are able to provide the optimal control actions as functions of the system state and the design of the process at hand, via our recently introduced PAROC framework (Pistikopoulos et al, Chem Eng Sci. 2015;136:115–138). The process and the design dependent explicit controllers undergo a mixed integer dynamic optimization (MIDO) step for the determination of the optimal design. The result of the MIDO is the optimal design of the process under optimal operation. We demonstrate the framework through case studies of a tank, a continuously stirred tank reactor, a binary distillation column and a residential cogeneration unit. © 2017 American Institute of Chemical Engineers AIChE J, 2017     

AN INDIRECT FEEDFORWARD COMPOSITION CONTROLLER FOR DISTILLATION COLUMNS

This paper addresses the problem of designing an indirect feedforward controller, also called cascade control, to regulate the product composition of distillation columns to overcome load disturbances. The overall controller is composed of two cascaded controllers. The primary controller is basically a low-gain controller, which uses slow (delayed) composition measurements to provide servo responses (composition control). The secondary controller has the structure of a high-gain proportional-integral (PI) controller with an antireset windup scheme, which uses fast temperature measurements at a given distillation tray to track the output of the primary controller. In this way, the secondary controller also provides protection against load disturbances. A simulated example is used to illustrate the functioning of the proposed controller.     

An indirect feedforward composition controller for distillation columns

This paper addresses the problem of designing an indirect feedforward controller, also called cascade control, to regulate the product composition of distillation columns to overcome load disturbances. The overall controller is composed of two cascaded controllers. The primary controller is basically a low-gain controller, which uses slow (delayed) composition measurements to provide servo responses (composition control). The secondary controller has the structure of a high-gain proportional-integral (PI) controller with an antireset windup scheme, which uses fast temperature measurements at a given distillation tray to track the output of the primary controller. In this way, the secondary controller also provides protection against load disturbances. A simulated example is used to illustrate the functioning of the proposed controller.     

Sequential and iterative architectures for distributed model predictive control of nonlinear process systems

In this work, we focus on distributed model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the distributed controllers at each sampling time and a model of the plant is available, we propose two different distributed model predictive control architectures. In the first architecture, the distributed controllers use a one‐directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bi‐directional communication strategy, are evaluated in parallel and iterate to improve closed‐loop performance. In the design of the distributed model predictive controllers, Lyapunov‐based model predictive control techniques are used. To ensure the stability of the closed‐loop system, each model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov‐based controller. We prove that the proposed distributed model predictive control architectures enforce practical stability in the closed‐loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. © 2010 American Institute of Chemical Engineers AIChE J, 2010     

Modeling, simulation and control of a methanol synthesis fixed-bed reactor

In this paper, the dynamic behavior and control of the low pressure methanol synthesis fixed bed reactor have been investigated. For simulation purpose, a heterogeneous one-dimensional model has been developed. First, the reactor simulation is carried out under steady-state condition and the effects of several parameters such as shell temperature, feed composition (especially CO₂ concentration) and recycle ratio on the methanol productivity and reactor temperature profile are investigated. Using the steady state model and a trained feedforward neural network that calculates the effectiveness factor, an optimizer which maximizes the reactor yield has been developed. Through the dynamic simulation, the system open loop response has been obtained and the process dynamic is approximated by a simple model. This model is used for the PID controller tuning and the performances of fixed and adaptive PID controllers are compared for load rejection and set point tracking. Finally the proposed optimizer is coupled with a controller for online optimization and hot spot temperature protection.     

Design and Control of a Fully Thermally Coupled Distillation Column Modified from a Conventional System

A modified fully thermally coupled distillation column is proposed, with utilization of the existing distillation columns of the conventional system, and its control scheme is suggested here. The proposed distillation system is applied to a benzene‐toluene‐xylene (BTX) separation process, of which the system design and control performance evaluation are conducted using the HYSYS software. The performance of the suggested 3 × 3 control is examined in the set‐point tracking of product specification and the regulation for the changes of feed flow and composition. The pairings of three proportional‐integral control loops are the reflux flow and the specification of overhead product, the prefractionator vapor flow and that of the side product, and the vapor boil‐up rate and that of the bottom product. The multi‐variable controllability using various indices is investigated for the proposed control scheme, and the controllability is compared with that of the cross‐pairing between the control loops of the side and bottom products.     

Dynamics and Control of a Biodiesel Transesterification Reactor

Biodiesel transesterification reactors resemble the heart of any biodiesel manufacturing plant. These reactors involve a highly complex set of chemical reactions and heat transfer characteristics. The high nonlinearity inherent in the dynamics of these reactors requires an efficient process control algorithm to handle the variation of operational process parameters and the effect of process disturbances efficiently. In this work, a multi‐model adaptive control strategy is considered for achieving the goal mentioned above. In order to implement the adaptive controller, a rigorous mechanistic model of the biodiesel transesterification reactor was developed and validated with published experimental results. The validated model was analyzed for stability and nonlinearity. The analysis revealed that the system is stable. However, its high nonlinearity necessitates an advanced control strategy to be considered. The input‐output relationship between the effective process variables was studied and the control system synthesis revealed a two‐by‐two control system. Two adaptive control loops were then designed and tuned to optimize the performance of the controller. Finally, a comparison with conventional controllers revealed the superiority of the new control system in terms of set‐point tracking and disturbance rejection. The results of this work prove that an adequately designed adaptive control system can be used to improve the performance of the transesterification reactor.     

State‐Space Digital PI Controller Design for Linear Stochastic Multivariable Systems with Input Delay

In this paper, a centralized digital PI control scheme is proposed for linear stochastic multivariable systems with input delay. The discrete linear quadratic regulator (LQR) approach with pole placement is used to achieve satisfactory set‐point tracking with guaranteed closed‐loop stability. In addition, the innovation form of Kalman gain is employed for state estimation with no prior knowledge of noise properties. Compared with existing designs, the proposed scheme provides an optimal closed‐loop design via the digitally implementable PI controller for linear stochastic multivariable systems with input delay. Its effectiveness will be demonstrated by the simulation study on examples from both industrial process control and aircraft control.     

Design and Control of Heterogeneous Azeotropic Distillation for Separating 2‐Methylpyridine/Water

In industrial processes, 2‐methylpyridine/water mixtures can be separated via the conventional heterogeneous azeotropic distillation (HAD) using benzene as entrainer. 2‐Methylpyridine and water can form a heterogeneous azeotrope by themselves, based on which an improved HAD process is proposed. This allows for reducing the total operating cost and total annual cost (TAC) by more than one‐tenth compared with the conventional HAD process. Two different control structures were established for the enhanced HAD process. The results indicate that applying the feedforward ratio controllers to the control structure can handle the feed disturbances and maintain product purities with smaller transient deviations and shorter settling times.     

Control of milk pasteurization process using model predictive approach

A milk pasteurization process, a nonlinear process and multivariable interacting system, is difficult to control by the conventional on–off controllers since the on–off controller can handled the temperature profiles for milk and water oscillating over the plant requirements. The multi-variable control approach with model predictive control (MPC) is proposed in this study. The proposed algorithm was tested for control of a milk pasteurization process in four cases of simulation such as set point tracking, model mismatch, difference control and prediction horizons, and time sample. The results for the proposed algorithm show the well performance in keeping both the milk and water temperatures at the desired set points without any oscillation and overshoot and giving less drastic control action compared to the cascade generic model control (GMC) strategy.