Design and control of pressure-swing distillation for azeotropes with different types of boiling behavior at different pressures

The mixture of n-heptane and isobutanol presents minimum- and maximum-boiling azeotropes as the pressure changes. Two different pressure-swing distillation (PSD) processes are available for this mixture if the pressure of the low-pressure column is set as atmospheric. The steady states of the two available PSDs are optimized by minimizing the total annual cost (TAC) following a sequential iterative optimization procedure. On the basis of the steady-state results, the dynamic control of the two available PSDs and a comparison between them are presented. The conventional PSD (CPSD) is more economical but less controllable than the unusual PSD (UPSD), which has seldom been studied in the published literature. These two different PSD processes should be considered for the separation of this type of mixture according to their advantages.     

Adaptive dynamic matrix control of a distillation column with closed-loop online identification

This paper presents an online identification technique where a process is identified in terms of pseudo impulse response coefficients and subsequently used to update convolution type models to accommodate process-model mismatch. As an example, dynamic matrix control has been applied adaptively to control the top product composition of a distillation column for both servo and regulatory problems. The algorithm automatically detects a large step-like disturbance requiring fresh identification of the process and subsequently adapts the controller to the new model. Simulation studies using an analytical dynamic full order model of a distillation column demonstrated the usefulness of the adaptation scheme. Experimentation on a pilot scale distillation unit vindicated the simulation results.     

Application of predictive control techniques to a distillation column

Model-based predictive control techniques are widely recognized as having useful application to control problems characterized by complex dynamics and severe time delay. The establishment of a representative process model is the key step in the procedure and for anything other than trivially simple systems is a major hurdle. This paper describes the application of predictive control techniques to a distillation problem which embodies a pure time delay of 2–3 h and time constants of 3–4 h. A sampled-data process model is identified from monitored input/output data and from this a predictive control algorithm is designed. The application of the controller has resulted in very effective closed-loop control of the base composition of the distillation column, where previously only manual supervision was possible.     

Experimental investigation of conventional control strategies for a heterogeneous azeotropic distillation column

In this work, a laboratory scale sieve plate distillation column was constructed to investigate the conventional control strategies of an isopropyl alcohol (IPA), cyclohexane (CyH) and water (H₂O) heterogeneous azeotropic distillation column. Steady state process analysis showed that the optimal operation point should be located at a critical reflux, a transition point at which the distillation path switches from a route that passes through IPA+H₂O azeotrope to one that passes through IPA+CyH azeotrope. At this critical reflux, a high purity IPA product can be obtained with minimum energy consumption and maximum product recovery. However, the steady state is extremely sensitive to feed disturbances. A good control strategy must be able to maintain a steady column temperature profile that shows a plateau near 70°C to ensure passage around IPA+CyH azeotrope. In this study, an inverse double loop control strategy is recommended. Through experimental testing, the proposed control strategy was demonstrated to keep the product IPA purity at the desired high-purity level under all feed disturbance changes while other conventional control strategies fail.     

Dynamics and control of benzene hydrogenation via reactive distillation

This work develops a dynamic, first principles-based model of a reactive distillation column used for benzene hydrogenation of a reformate stream and investigates different control structures for this process. The model is used initially to develop and evaluate a feedback control strategy which provides good regulatory performance for small disturbances, however, it tends to be sluggish for significant disturbances in the feed composition. In order to address this point, adding a feedforward controller to the feedback structure has also been investigated. However, the feedforward controller can only be implemented if composition measurements of the feed are taken. As online composition measurements are expensive in practice, several different scenarios have been investigated where samples of the feed are taken and subsequently analyzed in a lab, as represented by measurement time delays. Simulation results show that adding feedforward control to the feedback scheme can be very beneficial for this process, however, this is only the case if the composition disturbance measurements do not involve a significant time delay.     

Model predictive control of reactive distillation for benzene hydrogenation

Benzene hydrogenation via reactive distillation is a process that has been widely adopted in the process industry. However, studies in the open literature on control of this process are rare and seem to indicate that conventional decentralized PI control results in sluggish responses when the reactive distillation column is subjected to disturbances in the feed concentration. In order to overcome this performance limitation, this work investigates model predictive control (MPC) strategies of a reactive distillation column model, which has been implemented in gPROMS. Several MPCs based upon different sets of manipulated and controlled variables are investigated where the remaining variables remain under regular feedback control. Further, MPC controllers with output disturbance correction and, separately, with input disturbance correction have been investigated. The results show that the settling time of the column can be reduced and the closed loop dynamics significantly improved for the system under MPC control compared to a decentralized PI control structure.     

Object-oriented approach applied to ANFIS modeling and control of a distillation column

Neurofuzzy networks are hybrid systems that combine neural networks with fuzzy systems, and the Adaptive Neuro-Fuzzy inference system (ANFIS) is a particular case in which a fuzzy system is implemented in the framework of an adaptive neural network. This neurofuzzy approach represents an effective structure to the modeling of plant dynamics, and the oriented-object programming environments offer an intuitive way to address this task. In this paper the MODELICA object-oriented environment has been applied to the ANFIS modeling and indirect control of the heavy and light product composition in a binary methanol-water distillation column by using the adaptive Levenberg–Marquardt approach. The results obtained demonstrate the potential of the adaptive ANFIS scheme under MODELICA for the dual control of composition both for changes in set points with null stationary error even when disturbances are present.     

Multivariable adaptive predictive control of a binary distillation column

This paper presents a tutorial review of an adaptive predictive control system (APCS). Special emphasis is given to the key issues involved in the practical application of APCS to real processes. These practical issues are illustrated by actual application of SISO and MIMO control of a pilot scale binary distillation column. The experimental evaluation of this method reveals the simplicity of the adaptive algorithm and its excellent performance in an industrial type environment. The experimental results easily outperformed well-tuned classical PID controllers. A brief review of other applications of adaptive control to chemical processes is also included in this paper.     

Two-point control of a reactive distillation column for composition and conversion

Reactive distillation is a hybrid process with dual process objectives: reactant conversion and product composition. Control schemes for reactive distillation frequently neglect the effect of the principal operating parameters on the reactant conversion, and this has a detrimental effect on the overall process profitability. An ETBE reactive distillation column has been used as a case study to show how a two-point control configuration, which recognises the importance of both composition and conversion, can be developed and implemented for a reactive distillation process. The combined composition and conversion control configuration was tested using SpeedUp dynamic simulations and proved to be effective in maintaining a high isobutylene conversion despite process disturbances. The two-point control scheme also had superior disturbance rejection capability, especially for feed rate changes, and composition set-point sensitivity compared with a one-point control scheme.     

Combined internal model and inferential control of a distillation column via closed-loop identification

A procedure for designing distillation control systems with spe cified nominal properties is presented. The desired behaviour of the control system for both setpoint changes and disturbances in the feed flow rate and the feed composition can be specified. Both types of specifications can be handled because the disturbances can be inferred from the behaviour of the inventory control system. The control system is realized as a combined internal model and inferential control (CIMIC) system. A disturbance rejecting and decoupling (DRD) control structure is obtained as a special case. The performance of the control system is demonstrated experimentally on a pilot-scale distillation column. For comparison, experiments with pure internal model control (IMC) are also illustrated. A preliminary model of the distillation column was determined from step tests carried out in open-loop operation, but the final model used in the control system designs was obtained via a control-relevant closed-loop identification.     

A geometric approach to multivariable control system design of a distillation column

A multivariable control problem of a distillation column is considered, where the object is to maintain two output variables, the compositions of the distillate and the bottom product at some desired values by manipulating the reflux flow rate and the boil-up rate.Based on a linearized model, a geometric approach is applied to the design problem of disturbance rejection control. In other words, a feedback control strategy is desired which enables the complete rejection of the effect of disturbances on both output variables.In obtaining the feedback control, the problem of how many and what state variables are to be measured and fed back has been made clear. In this control strategy, only five state variables are fed back. Thus, only five columns of the feedback gain matrix have non-zero values. Furthermore, two out of these five columns are uniquely determined, and the other three columns can be assigned arbitrary values and used for pole assignment of the controlled system.For the disturbances in composition and flow rate of the feed stream, Δx_F and ΔL_F, the effect of the disturbance Δx_F is completely rejected by the feedback controller, but the effect of the disturbance ΔL_F can only be eliminated from the output Δx_D.A digital simulation of a distillation column composed of nine plates, a condenser and a reboiler was carried out to confirm these results and to show that the linearized model used in this paper is valid for fairly large step changes.     

Multimodel identification for control of an ill-conditioned distillation column

Identification for control of an ill-conditioned system requires special techniques. The directionality of such a system should be taken into account in the design of identification experiments. In distillation, information about the directionality properties can be obtained from certain flow gains, which are easy to determine in practice. Based on such information, the high- and low-gain directions of the plant can be explicitly excited. In this paper, a pilot-scale distillation column is identified by this approach at two different operating points. At each operating point, a nominal second-order plus time-delay model with logarithmic outputs is determined. This model structure makes it possible to capture the dynamic directionality of the plant. In addition, models describing variations and uncertainties in the high- and low-gain directions are determined by a special technique. The models obtained are superior to models determined via traditional step tests. The former satisfy integral controllability requirements, while the latter tend to violate them.     

多组分精馏过程推断控制及鲁棒性仿真

在化工生产实践中,基于被控变量检测的反馈控制系统占有绝对比重。不过,实际生产过程中却存在着这样一类情况,过程的被控制变量,甚至过程的扰动均无法测量或难以测量,如精馏塔塔顶、塔底产品的组成,因而难于实现反馈控制或前馈控制。精馏塔是化工等行业中广泛使用的分离设备,其控制方案在化工过程中具有十分关键的作用。为满足工艺要求和节能,需将塔顶和塔底产品流控制在设计值。理论上,将产品组成直接作为被控变量是最佳的,但仍有一些问题限制了其在实践中的推广使用。基于可测辅助变量推断难以直接测量或测量滞后太大的关键过程变量的推断控制能弥补这一缺陷。本文以多组分精馏过程为例,在MATLAB平台进行了多变量推断控制控制设计及仿真,并对其鲁棒性进行了分析,达到较好的控制仿真效果。     

Nonlinear parametric predictive temperature control of a distillation column

The integration of a nonlinear reduced process model with Parametric Predictive Control (PPC) is discussed for the bottom temperature control of a stabilizer distillation column. One of the main objectives is ensure the quality of the bottom product despite disturbances and complex dynamics. The purpose is to balance nonlinear control with simplicity, facilitating implementation in a DCS. The controllers developed were first tested in a simulated environment and then in the field, showing good performance under a wide range of operating conditions. The use of an estimator to compensate for modeling errors and unmeasured disturbances is also discussed.     

Constrained multivariable control of a distillation column using a simplified model predictive control algorithm

Distillation columns are important process units in petroleum refining and need to be maintained close to optimum operating conditions because of economic incentives. Model predictive control has been used for control of these units. However, the constrained optimization problem involved in the control has generally been solved in practice in a piece-meal fashion. To solve the problem without decomposition, the use of a linear programming (LP) formulation using a simplified model predictive control algorithm has been suggested in the literature. In this paper, the LP approach is applied for control of an industrial distillation column. The approach involved a very small size optimization problem and required very modest computational resources. The control algorithm eliminated the large cycling in the product composition that was present using SISO controllers. This resulted in a 2.5% increase in production rate, a 0.5% increase in product recovery, and a significant increase in profit.     

A wave propagation approach for reduced dynamic modeling of distillation columns: Optimization and control

Reduced models enable real-time optimization of large-scale processes. We propose a reduced model of distillation columns based on multicomponent nonlinear wave propagation (Kienle 2000). We use a nonlinear wave equation in dynamic mass and energy balances. We thus combine the ideas of compartment modeling and wave propagation. In contrast to existing reduced column models based on nonlinear wave propagation, our model deploys a hydraulic correlation. This enables the column holdup to change as load varies. The model parameters can be estimated solely based on steady-state data. The new transient wave propagation model can be used as a controller model for flexible process operation including load changes. To demonstrate this, we implement full-order and reduced dynamic models of an air separation process and multi-component distillation column in Modelica. We use the open-source framework DyOS for the dynamic optimizations and an Extended Kalman Filter for state estimation. We apply the reduced model in-silico in open-loop forward simulations as well as in several open- and closed-loop optimization and control case studies, and analyze the resulting computational speed-up compared to using full-order stage-by-stage column models. The first case study deals with tracking control of a single air separation distillation column, whereas the second one addresses economic model predictive control of an entire air separation process. The reduced model is able to adequately capture the transient column behavior. Compared to the full-order model, the reduced model achieves highly accurate profiles for the manipulated variables, while the optimizations with the reduced model are significantly faster, achieving more than 95% CPU time reduction in the closed-loop simulation and more than 96% in the open-loop optimizations. This enables the real-time capability of the reduced model in process optimization and control.     

醋酸脱水萃取精馏过程的控制研究

通过Aspen Plus软件首先模拟了醋酸脱水萃取精馏系统。然后在稳态模拟的基础上,为该过程建立了2种控制结构,并采用动态模拟软件Aspen Dynamics考察了在进料流量发生±20%和进料组成中醋酸发生±5wt%干扰时其控制性能。传统萃取精馏控制结构CS₁不能较好地处理-5 wt%HAC进料组成干扰,醋酸产品质量的稳态余差较大,为0.43 wt%。为了减小该稳态余差,在CS₁基础上添加一个温度控制回路,提出一种改进的控制结构CS₂。模拟结果显示,CS₂对于进料流量和组成干扰均能处理得较好,将该进料组成干扰的稳态余差降到0.03 wt%。     

Online model predictive control of industrial processes using low level control hardware: A pilot-scale distillation column case study

Throughout the years, the computing power of industrial controllers has steadily increased. Together with the development of efficient quadratic program (QP) solvers, this raises the question whether these devices can host an online model predictive controller (MPC). The applicability of online MPC is investigated using a programmable automation controller (PAC) and a programmable logic controller (PLC) for the control of an industrially relevant process, i.e., a pilot scale distillation column. It is demonstrated that both devices are capable of hosting MPC, however the limitations of the PLC are reached for the investigated set-up. Finally, guidelines and pitfalls for use in practice are highlighted.     

分壁式萃取精馏塔制取高纯度甲缩醛的模拟与优化

建立一种利用分壁式萃取精馏塔制取高纯度甲缩醛的新工艺,并用AspenPlus软件对该工艺进行模拟和优化。最优工艺参数为:主塔塔板数39,隔板底端位置在第3l块板,原料进料在第17块板,萃取剂进料在第4块扳,回流比为0.9,溶剂比为1.0,气相分配比为0.12。最优参数下的严格模拟结果显示:本工艺可得到质量分数99.96%的甲缩醛和99.24%的甲醇;与常规萃取精馏工艺相比,再沸器和冷凝器热负荷分别降低14.8%和16.9%。说明分壁式萃取精馏塔制取高纯度甲缩醛不仅技术上可行,而且能减少一个塔的投资和节约分离过程的能耗,在经济上也具有显著的优势。