基于被控变量在线建模的化工过程实时优化方法

选择合适的被控变量可对过程进行实时优化(RTO),但现有方法在设计阶段确定被控变量后,不允许对其进行在线调整,导致了RTO效果的局限性。针对这一问题,提出了一种基于被控变量在线建模的方法,使用局部建模技术在线寻找相似样本并建立一阶最优性必要条件(NCO)的估计模型,将其作为被控变量更新控制回路,在反馈控制作用下达到更好的RTO效果。对一个蒸发过程的研究表明,此方法能够通过对NCO的在线准确建模,增加生产过程的经济效益。     

设计余量对运行优化和化学过程控制性能的影响简

Operation optimization is an effective method to explore potential economic benefits for existing plants. The m.aximum potential benefit from operationoptimization is determined by the distances between current operating point and process constraints, which is related to the margins of design variables. Because of various ciisturbances in chemical processes, some distances must be reserved for fluctuations of process variables and the optimum operating point is not on some process constraints. Thus the benefit of steady-state optimization can not be fully achied（ed while that of dynamic optimization can be really achieved. In this study, the steady-state optimizationand dynamic optimization are used, and the potential benefit-is divided into achievable benefit for profit and unachievable benefit for control. The fluid catalytic cracking unit （FCCU） is used for case study. With the analysis on how the margins of design variables influence the economic benefit and control performance, the bottlenecks of process design are found and appropriate control structure can be selected.     

INTEGRATING THE EFFECTS OF PROCESS CONTROLLERS WITH STEADY-STATE, EQUATION-BASED SIMULATION

Process controllers have a significant influence on the steady-state, as well as the dynamic, behavior of chemical processes. Thus, the steady-state simulation of processes should include the effects of control. A new method for including controllers in steady-state simulation is presented in this paper. The method provides equations that represent the steady-state control algorithm and can be solved simultaneously with the process model to yield the steady-slate behaviour of the closed-loop system. Most importantly, the controller models include saturation effects and can be formulated and solved within an open-form model. The method is general and can be applied to single-loop controllers, to complex control designs including split range and signal select, and to several single-loop controllers in a multiloop controller design.     

Model-free real-time optimization of process systems using safe Bayesian optimization

Conventional real-time optimization (RTO) requires detailed process models, which may be challenging or expensive to obtain. Model-free RTO methods are an attractive alternative to circumvent the challenge of developing accurate models. Most model-free RTO methods are based on estimating the steady-state cost gradient with respect to the decision variables and driving the estimated gradient to zero using integral action. However, accurate gradient estimation requires clear time scale separation from the plant dynamics, such that the dynamic plant can be assumed to be a static map. For processes with long settling times, this can lead to prohibitively slow convergence to the optimum. To avoid the need to estimate the cost gradients from the measurement, this article uses Bayesian optimization, which is a zeroth order black-box optimization framework. In particular, this article uses a safe Bayesian optimization based on interior point methods to ensure that the setpoints computed by the model-free steady-state RTO layer are guaranteed to be feasible with high probability (i.e., the safety-critical constraints will not be violated at steady-state). The proposed method can thus be seen as a model-free variant of the conventional two-step steady-state RTO framework (with steady-state detection), which is demonstrated on a benchmark Williams-Otto reactor example.     

On the use of extensive variables in process dynamics and control

A novel approach is presented for the physical interpretation of the slow and fast modes of process dynamics and the synthesis of multivariable and (or) nonlinear control structures by the use of extensive thermodynamic variables. For the several example processes examined, the dynamic modes are related to the total energy or mass contents of a set of units, or to the energy or mass balance of a unit, or, for chemical reactors, to the deviation of the reaction rate from its steady-state value. The nonlinear controllers synthesized contain nonlinearities naturally implied by the nonlinearities of the process, and succeed in providing uniform closed-loop behaviour when the open-loop characteristics change with the desired steady state. The multivariable control structures synthesized with the proposed method are characterized by minimal dynamic and zero steady-state interaction for all process designs examined. The method is applied to an isothermal and an adiabatic CSTR, a nonlinear level control problem, a network of two stirred tank heaters in series, and a network of two heat exchangers in parallel, resembling the catalytic cracking process. The method is also successfully used for the deisgn of noninteracting control structures for a prototype distillation column.     

A multi-priority hierarchical optimization method for double-layer model predictive control

Considering the demand for the sequential regulation of manipulated variables in actual industrial process control, the conventional solution of double-layer model predictive control faces the problem that the weight coefficients are difficult to tune. This paper proposes an improved hierarchical optimization method for manipulated variables in the steady-state optimization layer of double-layer model predictive control. The proposed method can adjust the manipulated variables sequentially without an accurate weight coefficient to avoid difficulty in tuning the weight coefficients. The relation between the optimal solution and the feasible region of the steady-state optimization layer is analysed to describe the reoptimization of the key manipulated variables. The impact of the economic cost coefficient on the optimal solution with the sensitivity analysis method is studied, and the complexity of using the weight coefficient to solve the priority optimization problem of the manipulated variables is assessed. The steady-state optimization solution procedure is improved based on the theory of the multiobjective complete hierarchical method. The hierarchical and sequential optimization of the manipulated variables results in expanding the space and freedom of the key manipulated variables, increasing efficiency, reducing consumption, and improving economic performance. The improved hierarchical optimization method is direct and simple in achieving optimization sequentially and satisfies the need for adjusting the manipulated variables according to human intentions.     

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。     

大型系统控制结构设计的交互分析和分解原理简

Industrial processes are mostly large-scale systems with high order. They use fully centralized control strategy, the parameters of which are difficult to tune. In the design of large-scale systems, the decomposition according to the interaction between input and output variables is the first step and the basis for the selection of control structure. In this paper, the decomposition principle of processes in large-scale systems is proposed for the design of control structure. A new variable pairing method is presented, co_nsidering the steady-state information anddynamic response of large-scale system. By selecting threshold values, the related matrix can be transformed into the adjoining matrixes, which directly measure the couple among different loops. The optimal number of controllers can be obtained after decomposing the large-scale system. A practical example is used to demonstrate the validity and feasibility of the proposed interaction decomposition principle in process large-scale systems.     

动态实时优化策略下的分层式预测控制及其在化工过程中的应用

针对化工过程中那些因存在批处理、含有物料回流环节而很难达到稳态的过程以及一些因扰动的存在而很难精确地操作在一个设定点处的非线性过程,采用常规的稳态优化会产生低效或失效优化解的问题,提出一种动态实时优化策略。即在多层控制结构中的RTO层采用动态优化而非常规的稳态优化,依照过程的优化操作信息在满足过程动态规律和物料、产品市场价格变化的条件下实现生产的经济利润最优,事例仿真结果表明该方法的可行性和有效性。     

Control structure selection of increased-pressure extractive distillation process for DMC-MeOH azeotropic mixture

Producing dimethyl carbonate (DMC) as a green chemical with the desired purity is important in the industry. Although studies on the steady-state design of energy-efficient extractive distillation processes are important for the purification of DMC-methanol (DMC-MeOH) azeotropic mixtures, the dynamic controllability of these processes is also critical in the case of feed condition changes, and it should be investigated carefully. Results of the limited studies in the literature show that changing the operating pressures in extractive distillation processes might have different effects on the dynamic controllability of different systems. Thus, in this study, alternative control strategies are developed for a recently proposed increased-pressure extractive distillation process to separate DMC-MeOH mixture. All control structures are designed using inferential temperature controllers, which have a general acceptance in industrial applications. Effects of different ratio controllers are investigated by evaluating the dynamic responses of control structures for disturbances in feed flowrate and composition. Two metrics including integral absolute error and steady-state deviation of purities are used in the evaluation of alternatives. Results of dynamic simulations show that a control structure including reflux ratio controller is not a suitable strategy for this process. It is demonstrated that a control structure including reflux to feed ratio controller for both distillation columns is necessary for the robust and efficient control of a pressure-increased extractive distillation process. These efficient dynamic results support the economic advantage of increased-pressure extractive distillation process separating DMC-MeOH azeotropic mixtures.     

CLOSED-LOOP DATA RECONCILIATION FOR THE CONTROL OF A BINARY DISTILLATION COLUMN

Data reconciliation is a procedure that makes use of process models along with process measurements to give more precise and consistent estimates for process variables. Data reconciliation has been traditionally used to provide a more representative set of data to calculate steady-state inventories and process yields. For dynamic systems, the use of data reconciliation is relatively nascent. This article examines the potential use of data reconciliation in closed-loop control as a filter to attenuate the noise in measurements of the controlled variables so that the controllers can access more accurate sets of data. Data reconciliation filters were implemented in simulations of a PID control system for a binary distillation column. Results showed that data reconciliation could efficiently reduce the propagation of measurement noise in control loops, so that the overall performance of the controller is enhanced.     

CLOSED-LOOP DATA RECONCILIATION FOR THE CONTROL OF A BINARY DISTILLATION COLUMN

Data reconciliation is a procedure that makes use of process models along with process measurements to give more precise and consistent estimates for process variables. Data reconciliation has been traditionally used to provide a more representative set of data to calculate steady-state inventories and process yields. For dynamic systems, the use of data reconciliation is relatively nascent. This article examines the potential use of data reconciliation in closed-loop control as a filter to attenuate the noise in measurements of the controlled variables so that the controllers can access more accurate sets of data. Data reconciliation filters were implemented in simulations of a PID control system for a binary distillation column. Results showed that data reconciliation could efficiently reduce the propagation of measurement noise in control loops, so that the overall performance of the controller is enhanced.     

Context-kernel support vector machines (KSVM) based run-to-run control for nonlinear processes

Past studies on multi-tool and multi-product (MTMP) processes have focused on linear systems. In this paper, a novel run-to-run control (RtR) methodology designed for nonlinear semiconductor processes is presented. The proposed methodology utilizes kernel support vector machines (KSVM) to perform nonlinear modeling. In this method, the original variables are mapped using a kernel function into a feature space where linear regression is done. To eliminate the effects of unknown disturbances and drifts, the KSVM expression for the KSVM controller is modified to include constants that are updated in a manner similar to the weights used in double exponential weighting moving average method and the control law for KSVM controllers is derived. Illustrative examples are presented to demonstrate the effectiveness of KSVM and its method in process modeling and control of processes. Even if there is limited data in process modeling, KSVM still has the good capability of characterizing the nonlinear behavior. The performance of the proposed KSVM control algorithm is highly satisfactory and is superior to the other MTMP control algorithms in controlling MTMP processes.     

间歇过程的批间自优化控制

针对间歇过程的实时优化问题,提出了一种基于最优性条件近似法的批间自优化控制策略。首先获取标称工作点的最优输入轨迹形态,将其参数化为少量决策变量,简化问题复杂度。然后根据参数化后的决策变量得到批间优化的最优性条件,并建立批次终端可测变量和最优性条件之间的回归模型,将其作为被控变量进行批间跟踪控制。对一个间歇反应器进行了仿真研究,结果表明方法能有效实现间歇过程的批间自优化控制。     

一种构造化工过程被控变量的方法

针对化工过程的控制结构设计问题,提出了一种构造被控变量的新方法.选取最优性条件(NCO)为被控变量,可在反馈控制器作用下使过程运行在最优点.针对某些NCO条件在线不可测的问题,基于神经网络找到可测变量的非线性函数关系来近似NCO条件,并选取这些函数关系为被控变量.对一个放热反应过程的研究表明,提出的方法可对过程近似优化...     

化工过程大范围工况变化的工况迁移控制策略判定

化工过程出现大范围的工况变化时，复杂的迁移控制策略会带来一定的操作不确定性，因此需要对控制策略进行选择判定。考虑到直接估计工况变化过程生产指标变化量带来的判定误差，引入了中间变量，提出了基于中间变量的控制策略选择判定方法，并分析给出了构建中间变量的基本准则。进而通过对某实际乙烯精馏塔工况变化过程的仿真研究，说明了中间变量的引入能够很大程度地降低对生产指标的估计误差，验证了基于中间变量的控制策略选择判定方法的可用性。     

Two-layer model predictive control for chemical process model with integrating controlled variables

This paper proposes an overall solution to the two-layer model predictive control (MPC) for the integrating controlled variables in the process model. The scheme includes three modules, that is, the open-loop prediction module, the steady-state target calculation (SSTC) module, and the dynamic control module. Based on the real-time output measurements and past inputs, the open-loop prediction module predicts the future outputs in the presence of disturbances. The economic optimization of SSTC is comprised of the feasibility stage and the economics stage, considering constraints of multi-priority ranks. The dynamic control module receives the steady-state targets from SSTC and calculates the control signals. The optimization problems of SSTC and dynamic control operate with the same frequency. This overall method guarantees the consistency of three modules with respect to the model, the constraints, and the targets. The simulation example illustrates that steady-state targets are adjusted dynamically after the occurrence of disturbances, and offset-free control is achieved.     

基于多目标分层的预测控制定态优化技术

多目标分层优化要求是从复杂工业过程的现场实际应用背景出发而提出的.详细阐述了复杂工业过程的多目标分层优化要求的含义,在预测控制两级分层结构的基础上提出了基于二次规划的定态优化有效求解方法.通过Shell控制问题等仿真结果,说明所提出的定态优化技术具有良好的控制性能,能够适应生产过程的需要.     

A novel adaptive surrogate modeling‐based algorithm for simultaneous optimization of sequential batch process scheduling and dynamic operations

A novel adaptive surrogate modeling‐based algorithm is proposed to solve the integrated scheduling and dynamic optimization problem for sequential batch processes. The integrated optimization problem is formulated as a large scale mixed‐integer nonlinear programming (MINLP) problem. To overcome the computational challenge of solving the integrated MINLP problem, an efficient solution algorithm based on the bilevel structure of the integrated problem is proposed. Because processing times and costs of each batch are the only linking variables between the scheduling and dynamic optimization problems, surrogate models based on piece‐wise linear functions are built for the dynamic optimization problems of each batch. These surrogate models are then updated adaptively, either by adding a new sampling point based on the solution of the previous iteration, or by doubling the upper bound of total processing time for the current surrogate model. The performance of the proposed method is demonstrated through the optimization of a multiproduct sequential batch process with seven units and up to five tasks. The results show that the proposed algorithm leads to a 31% higher profit than the sequential method. The proposed method also outperforms the full space simultaneous method by reducing the computational time by more than four orders of magnitude and returning a 9.59% higher profit. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4191–4209, 2015     

Adaptation strategies for real-time optimization

Challenges in real-time process optimization mainly arise from the inability to build and adapt accurate models for complex physico-chemical processes. This paper surveys different ways of using measurements to compensate for model uncertainty in the context of process optimization. Three approaches can be distinguished according to the quantities that are adapted: model-parameter adaptation updates the parameters of the process model and repeats the optimization, modifier adaptation modifies the constraints and gradients of the optimization problem and repeats the optimization, while direct input adaptation turns the optimization problem into a feedback control problem and implements optimality via tracking of appropriate controlled variables. This paper argues in favor of modifier adaptation, since it uses a model parameterization and an update criterion that are well tailored to meeting the KKT conditions of optimality. These considerations are illustrated with the real-time optimization of a semi-batch reactor system.