Three-way data analysis with time lagged window for on-line batch process monitoring

In this paper, on-line batch process monitoring is developed on the basis of the three-way data structure and the time-lagged window of process dynamic behavior. Two methods, DPARAFAC (dynamic parallel factor analysis) and DTri-PLS (dynamic trilinear partial least squares), are used here depending on the process variables only or on the process variables and quality indices, respectively. Although multivariate analysis using such PARAFAC (parallel factor analysis) and Tri-PLS (trilinear partial least squares) models has been reported elsewhere, they are not suited for practicing on-line batch monitoring owing to the constraints of their data structures. A simple modification of the data structure provides a framework wherein the moving window based model can be incorporated in the existing three-way data structure to enhance the detectability of the on-line batch monitoring. By a sequence of time window of each batch, the proposed methodology is geared toward giving meaningful results that can be easily connected to the current measurements without the extra computation for the estimation of unmeasured process variables. The proposed method is supported by using two sets of benchmark fault detection problems. Comparisons with the existing two-way and three-way multiway statistical process control methods are also included.     

过程预测控制中约束可行性研究与在线调整

化工过程控制中,普遍存在着各种对输入和输出变量的约束条件。系统与约束之间的矛盾有可能造成约束预测控制的优化问题不可行,为生产带来负面影响。基于线性系统离散状态空间的动态模型,从凸多面体距离角度,对有约束预测控制的可行性分析和不可行时的约束处理问题进行讨论,提出在每步求解约束预测控制律之前进行必要的可行性分析和合理的约束调整的在线滚动算法,从而使约束条件在整个时域得到满足,并且保证系统的控制性能。通过CSTR模型的控制仿真实验证明了该算法的有效性。     

Optimal operation of integrated processing systems: Part II: Closed-loop on-line optimizing control

A method for tracking the economically optimal operating conditions of a chemical process in the presence of constraints is developed. The technique is based on an on-line search rather than a fundamental model. The most profitable operating point is found by fitting a dynamic model of the process based on data obtained from experimental moves on the plant. This model is used to compute gradients of the economic objective and of the constraints so that a direction of economic improvement inside the allowed operating region of the plant is always obtained. Constraint violations during the transients are prevented by a multivariable regulator. A new regulation method (Internal Model Control) is used which permits explicit handling of constraints and which can be made robust against modelling errors. This combined optimization/regulation approach is tested in a demonstrative simulation example and shown to be reliable for following a moving optimum and safely handling complex constraint moves.     

微分进化算法应用于换热网络全局最优化

前言换热网络是过程系统中实现能量回收和高效利用的重要环节,其性能直接关系到整个系统的能量利用水平。目前,换热网络优化设计的方法主要分为三类,分别是夹点法[1-2]、数学规划法[3-4]与启发式方法[5-7]。     

混合粒子群算法用于共沸精馏塔的最优设计

提出了一种适于求解混合整数非线性规划问题的混合粒子群优化算法,并将其与化工过程模拟软件相结合,用于共沸精馏塔的最优设计。优化模型以年度总费用最小为目标,以精馏段板数、提馏段板数和回流比为优化变量,并引入流体力学约束使得优化结果更具实际价值,并以效率更高的"轮盘赌"式策略处理整数变量,约束处理采用Deb方法。最终以C++实现优化算法,C#编制界面,通过商业模拟软件Aspen Plus计算粒子适应度,将本方法用于一个醋酸甲酯/甲醇/水三元共沸组成的分离案例,所获最优年度总费用优于文献结果。     

Development of real-time state estimators for reaction–separation processes: A continuous flash fermentation as a study case

The development of reliable on-line state estimators applicable to reaction–separation processes is addressed in this work. Artificial Neural Network-based software sensors (ANN-SS) are proposed to allow on-line measurement of key variables, with an estimation algorithm that uses secondary variables as inputs. A continuous laboratory-scale flash fermentation for bioethanol production is considered as a case study. The process consists of three interconnected units: fermentor, filter (tangential microfiltration for cell recycling) and vacuum flash vessel (for the continuous separation of ethanol from the broth). The concentrations of ethanol in the fermentor and of ethanol condensed from the flash are successfully monitored on-line using ANN-SS. The proposed model contributes to improve the understanding of the complex relationships between process variables in the reaction and separation units, which is of major importance to allow the operation of the ethanol production process near its optimum performance.     

Nonlinear model-based dissolved oxygen control in a biological wastewater treatment process

The dissolved oxygen (DO) concentration has been an important process parameter in the biological wastewater treatment process (WWTP). In this paper, we propose a nonlinear control scheme to maintain the dissolved oxygen level of an activated sludge system. Without any linearization or model reduction, it can directly incorporate the nonlinear DO process model with on-line estimation of the respiration rate (R) and the oxygen transfer rate (K_La). Simulation results show that it outperforms a control performance of the PID controller. Since it incorporates the process disturbance and nonlinearity in the controller design, the suggested method can efficiently deal with the operating condition changes that occur frequently in the wastewater treatment process.     

AN ADAPTIVE STRATEGY FOR CONSTRAINED GENERIC MODEL CONTROL

The performance of control systems on industrial processes is often constrained—constraints on the process inputs and outputs. Effective control algorithms must be cognizant of the presence of these constraints. Generic Model Control (GMC) is a model-based control framework for both linear and nonlinear systems without explicit constraint handling. In this paper, it is shown that an adaptive approach can be incorporated within GMC to accommodate the constraints by adapting one of the two GMC parameters during the control procedure. Adaptation is determined to be necessary when the predicted process state and output variables as calculated by the process model violate their constrained values. The adaption is achieved through assessing the sensitivities of the constraints to the GMC parameters. Two non-linear examples are presented which demonstrate the efficiency of the approach.     

一种基于混合建模技术的MIMO软测量建模方法

针对复合肥产品中几种养分含量需要同时预报的一类多输入/多输出(MIMO)软测量建模问题,提出一种基于混合建模方法的复合肥养分含量MIMO软测量模型。该混合模型首先对几个不能实时测量的关键辅助变量采用基于限定记忆部分最小二乘算法的数据驱动建模方法建立自适应软测量模型,然后采用简化机理模型实时计算三种养分含量。基于实际工业过程数据的仿真结果表明,所建模型运算速度快、预测精度高,可以满足复合肥养分含量在线预报的要求。     

On improving accuracy of computationally efficient nonlinear predictive control based on neural models

For nonlinear processes the classical model predictive control (MPC) algorithm, in which a linear model is used, usually does not give satisfactory closed-loop performance. In such nonlinear cases a suboptimal MPC strategy is typically used in which the nonlinear model is successively linearised on-line for the current operating point and, thanks to linearisation, the control policy is calculated from a quadratic programming problem. Although the suboptimal MPC algorithm frequently gives good results, for some nonlinear processes it would be beneficial to further improve control accuracy. This paper details a computationally efficient nonlinear MPC algorithm in which a neural model is linearised on-line along the predicted trajectory in an iterative way. The algorithm needs solving on-line only a series of quadratic programming problems. Advantages of the discussed algorithm are demonstrated in the control system of a high-purity ethylene–ethane distillation column for which the classical linear MPC algorithm does not work and the classical suboptimal MPC algorithm is slow. It is shown that the discussed algorithm can give practically the same control accuracy as the algorithm with on-line nonlinear optimisation and, at the same time, the algorithm is significantly less computationally demanding.     

Simulation of mass exchange networks using modified genetic algorithms

The optimum water usage network leads to both a minimum of freshwater consumption and a minimum of generated wastewater. This work is to develop a mass-exchange networks (MENs) module for a minimum freshwater usage target. This module works as an interface to retrieve supplemental data of chemical processes from a process simulator and to communicate this to the genetic algorithm optimizer. A reuse system and a regeneration/recyclingsystem with a single contaminant are considered as approaches for freshwater minimization. In the formulated model, as mixed integer nonlinear programming (MINLP), all of the variables are divided into independent and dependent variables. The values of independent variables come from randomization, whereas the values of dependent variables come from simultaneous solutions of a set of equality constraints after assigning the values of independent variables. This method is applied to the steps of initialization, crossover and mutation. The MENs module is validated with a tricresylphosphate process consisting of five unit operations. Water is used to remove a fixed content of cresol. From the result, the module gives a reliable solution for freshwater minimization, which can satisfy mass balance and constraints. The results show that reuse and regeneration/recycling strategies can reduce freshwater consumption, including wastewater generated. Reuse cannot decrease the mass load of the contaminant, while regeneration/recycling can. In addition, regeneration requires less freshwater than the reuse process.     

Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements☆

State estimation of biological process variables directly influences the performance of on-line monitoring and op-timal control for fermentation process. A novel nonlinear state estimation method for fermentation process is proposed using cubature Kalman filter (CKF) to incorporate delayed measurements. The square-root version of CKF (SCKF) algorithm is given and the system with delayed measurements is described. On this basis, the sample-state augmentation method for the SCKF algorithm is provided and the implementation of the proposed algorithm is constructed. Then a nonlinear state space model for fermentation process is established and the SCKF algorithm incorporating delayed measurements based on fermentation process model is presented to implement the nonlinear state estimation. Finally, the proposed nonlinear state estimation methodology is applied to the state estimation for penicillin and industrial yeast fermentation processes. The simulation results show that the on-line state estimation for fermentation process can be achieved by the proposed method with higher esti-mation accuracy and better stability.     

一种化工过程优化的稀疏SQP算法

根据开放式方程模型结构统一、所得优化命题普遍稀疏的特点 ,提出了一种稀疏SQP算法 .利用一阶 /二阶导数构造Hessian矩阵 ,保持了系统的稀疏结构 .通过一个预处理过程获得命题的稀疏结构信息 ,显著减少构造高维矩阵所需工作量 .计算示例表明 ,该算法优于传统SQP法 ,也表明该算法的有效性     

Gradient technique and lagrange multiplier – A technique for optimizing complex processes

The gradient technique combined with Lagrange multiplier is used to find the optimum of a complex heterogeneous process with two recycle streams. Both the reaction stages and the separation stages for the separation of a six component mixture are considered. In spite of the complexity of the process, only two minutes computation (IBM 7094) is needed to obtain the solution. Techniques for handling inequality constraints on the control and state variables are also discussed.     

一类连续/间歇过程的迭代学习模型预测控制

An iterative learning model predictive control (ILMPC) technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control (ILC) handling repetitive signal and the flexibility of model predictive control (MPC). By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial appli-cation show that the proposed ILMPC method is effective for a class of continuous/batch processes.     

Optimization of coal-fired boiler SCRs based on modified support vector machine models and genetic algorithms

An integrated combustion optimization approach is presented for the combined considering the trade offs in optimization of coal-fired boiler and selective catalyst reaction (SCR) system, to balance the unit thermal efficiency, SCR reagent consumption and NO_x emissions. Field tests were performed at a 160 MW coal-fired unit to investigate the relationships between process controllable variables, and optimization targets and constraints. Based on the test data, a modified on-line support vector regression model was proposed for characteristic function approximation, in which the model parameters can be continuously adapted for changes in coal quality and other conditions of plant equipment. The optimization scheme was implemented by a genetic algorithm in two stages. Firstly, the multi-objective combustion optimization problem was solved to achieve an optimal Pareto front, which contains optimal solutions for lowest unit heat rate and lowest NO_x emissions. Secondly, best operating settings for the boiler, and SCR system and air preheater were obtained for lowest operating cost under the constraints of NO_x emissions limit and air preheater ammonium bisulfate deposition depth.     

Economic model predictive control of chemical processes with parameter uncertainty

This work proposes an EMPC (Economic Model Predictive Control) algorithm that integrates RTO (Real Time Optimization) and EMPC objectives within a single optimization calculation. Robust stability conditions are enforced on line through a set of constraints within the optimization problem.A particular feature of this algorithm is that it constantly calculates a set point with respect to which stability is ensured by the aforementioned constraints while searching for economic optimality over the horizon. In contrast to other algorithms reported in the literature, the proposed algorithm does not require terminal constraints or penalty terms on deviations from fixed set points that may lead to conservatism.Changes in model parameters over time are also compensated for through parameter updating. The latter is accomplished by including the parameters’ values as additional decision variables within the optimization problem.Several case studies are presented to demonstrate the algorithm’s performance.     

A symbolic reformulation/spatial branch-and-bound algorithm for the global optimisation of nonconvex MINLPs

This paper presents an algorithm for the solution of nonconvex mixed integer nonlinear programming (MINLP) problems involving general constraints and objective functions. The algorithm employs a symbolic reformulation step that brings the original MINLP problem to an equivalent standard form for which a convex relaxation can be constructed. The reformulated problem is then solved using a spatial branch-and-bound algorithm which branches on both integer and continuous variables. Issues relating to the efficient implementation of this algorithm and its parallelisation are also discussed. The algorithm has been incorporated within the gPROMS process modelling environment and tested on several MINLP problems arising from process engineering applications.     

Optimization‐Based Nonlinear Centralized Controller Tuning of Liquid‐Liquid Extraction Processes

Abstract

The control problem of an agitated contactor is considered in this work. A Scheibel extraction column is modeled using the non‐equilibrium backflow mixing cell model. Model dynamic analysis shows that this process is highly nonlinear, thus the control problem solution of such a system needs to tackle the process nonlinearity efficiently. The control problem of this process is solved by developing a multivariable nonlinear control system implemented in MATLAB?. In this control methodology, a new controller tuning method is adopted, in which the time‐domain control parameter‐tuning problem is solved as a constrained optimization problem. A MIMO (multi‐input multi‐output) PI controller structure is used in this strategy. The centralized controller uses a 2×2 transfer function and accounts for loops interaction. The controller parameters are tuned using an optimization‐based algorithm with constraints imposed on the process variables reference trajectories. Incremental tuning procedure is performed until the extractor output variables transient response satisfies a preset uncertainty which bounds around the reference trajectory. A decentralized model‐based IMC (internal model control) control strategy is compared with the newly developed centralized MIMO PI control one. Stability and robustness tests are applied to the two algorithms. The performance of the MIMO PI controller is found to be superior to that of the conventional IMC controller in terms of stability, robustness, loops interaction handling, and step‐change tracking characteristics.     

基于动态模型的在线反馈优化

针对化工过程动态波动明显、优化模型存在较多的不确定性等特点,提出了一种考虑过程不确定性、基于过程动态模型的在线反馈优化策略。将过程动态模型按一定周期离散化为差分方程,基于差分方程进行动态优化,优化目标函数为优化时域的终端时刻的经济指标,优化变量为过程的操作变量,采用非线性规划作为优化算法;优化结果在实施后根据可测输出进行在线反馈,在优化模型的差分方程中引入误差修正项,将对应时刻的状态变量和相关变量的实际值代入可求出误差修正项,从而实现在线反馈优化。仿真结果表明,与传统的稳态操作优化相比,基于动态模型的反馈优化同样可将过程运行于最优操作点,同时具有很强的实时性,在外界干扰出现时可以立即作出反应,将过程推向最优操作点。