Optimizing model predictive control of an industrial distillation column

The main scope of this work is the implementation of an MPC that integrates the control and the economic optimization of the system. The two problems are solved simultaneously through the modification of the control cost function that includes an additional term related to the economic objective. The optimizing MPC is based on a quadratic program (QP) as the conventional MPC and can be solved with the available QP solvers. The method was implemented in an industrial distillation system, and the results show that the approach is efficient and can be used, in several practical cases.     

基于(火用)价格的精馏过程综合的研究

过程系统综合通常以系统的经济效益最优为设计目标。根据精馏系统的用能特点，把分离过程综合问题表述为一个混合整数非线性规划问题。基于提出的热炯价格模型方案，以[火用]经济为目标，用改进的模拟退火算法研究了十组分烃类的精馏过程综合问题。在算例中，精馏系统的公用工程分别采用不用的类型。结果表明以[火用]经济为目标优化分离过程综合问题，充分满足了节能与节钱并重的要求，可以从众多的分离方案中得到更合理的精馏流程结构方案；采用多种公用工程的精馏系统比采用一种公用工程的精馏系统经济效益更好。从分离方案中得到最优的精馏流程结构方案及数据，可以为工厂的设计提供非常有价值的参考。     

A multivariate statistical controller for on-line quality improvement

Producing good quality products is an important process control objective. However, achieving this objective can be very difficult in a continuous process, especially when quality measurements are not available on-line or they have long time delays. In this paper, a control approach using multivariate statistical models is presented to achieve this objective. The goal of the control approach is to decrease variations in product quality without real time quality measurements. A PCA model which incorporates time lagged variables is used, and the control objective is expressed in the score space of this PCA model. A controller is designed in the model predictive control (MPC) framework, and it is used to control the equivalent score space representation of the process. The score predictive model for the MPC algorithm is built using partial least squares (PLS). The proposed controller can be developed from and implemented on top of existing PID control systems, and it is demonstrated in two case studies, which involve a binary distillation column and the Tennessee Eastman process.     

State estimation and inferential control for a reactive batch distillation column

An optimal reflux ratio profile is obtained for a reactive batch distillation system utilizing the capacity factor as the objective function in a nonlinear optimization problem. Then, an Artificial Neural Network (ANN) estimator system, which utilizes the use of several ANN estimators, is designed to predict the product composition values of the distillation column from temperature measurements inferentially. The network used is an Elman network with two hidden layers. The designed estimator system is used in the feedback inferential control algorithm, where the estimated compositions and the reflux ratio information are given as inputs to the controller to see the performance of the ANN. In the control law, a scheduling policy is used and the optimal reflux ratio profile is considered as pre-defined set-points. It is found that, it is possible to control the compositions in this dynamically complex system by using the designed ANN estimator system with error refinement whenever necessary.     

Analog neural nonderivative optimizers

Continuous-time neural networks for solving convex nonlinear unconstrained programming problems without using gradient information of the objective function are proposed and analyzed. Thus, the proposed networks are nonderivative optimizers. First, networks for optimizing objective functions of one variable are discussed. Then, an existing one-dimensional optimizer is analyzed, and a new line search optimizer is proposed. It is shown that the proposed optimizer network is robust in the sense that it has disturbance rejection property. The network can be implemented easily in hardware using standard circuit elements. The one-dimensional net is used as a building block in multidimensional networks for optimizing objective functions of several variables. The multidimensional nets implement a continuous version of the coordinate descent method.     

Model based supervisory control of a ball mill grinding circuit

Optimum operation of grinding mills are important for the economic recovery of the valuable minerals, for energy efficiency, as well as from the point of view of pollution control. In many mineral beneficiation operations, the economic objectives translate into the maximization of throughput with suitable constraints on the product particle size distribution. While normal PID type controllers at the regulatory level are capable of controlling the process at the desired values of the process variables, a supervisory control system based on a process model will be required for optimizing the operation. In the present paper the ball mill model, together with the hydrocyclone separation system model is used in a simulation study to generate a response surface relating the control variables with plant load variables and manipulated variables. This response surface together with a suitably formulated economic objective function is used for on-line optimization to determine the optimum setpoints for the controlled variables in the supervisory control system. The model based supervision together with the regulatory layer control has been tested using simulation. ©     

Process control interface system for a distillation plant

This paper presents the development of a process control interface system (PCIS) for a distillation pilot plant that is based on the communication protocol called MODBUS. MODBUS communicates the computer (used as a control unit) with the process because it allows an efficient, flexible, and reliable system. The interface system is presented in an operator-friendly application, which has the capability to monitor the variables, manipulate actuators, and tune the controllers in the distillation process.     

Optimization of pulse frequency modulated control systems via modified maximum principle

The problem of finding the optimum control function for the pulse frequency modulated (PFM) system is considered in this paper. In the PFM systems discussed here, the control function consists of a series of standard pulses. The optimization procedure consists of determining the polarity and positions of the pulses which make up the control function. The performance index is assumed to be a linear combination of the final values of the state variables. This does not exclude the problem of optimizing a system with respect to an integral, provided that the integrand is linear with respect to the state variables, but not necessarily with respect to the control function. In the PFM systems considered, the control function is fixed for a period of time following the initiation of each pulse. This fact precludes the direct application of the existing standard optimizing techniques. The Modified Maximum Principle is presented. It is based on Pontryagin's Maximum Principle and is applicable to open-loop systems with linear plants with fixed operating time. The Modified Maximum Principle is valid for systems with and without final value constraints on the state variables.     

数据挖掘技术在煤焦油萘回收中的应用研究

用模式识别、数理统计、动态序列分析等数据挖掘技术分析双炉双塔精馏工艺下的煤焦油萘回收数据，找到了萘回收工艺中影响精馏塔塔底洗油含萘的主要因素，建立了可以有效控制洗油含萘量的数学模型，据此提出了提高萘回收率的方法与建议，在实践生产中得到了很好的结果。说明数据挖掘技术在类似的复杂动态工业系统中有着重要作用。     

MPC for tracking zone regions

This paper deals with the problem of tracking target sets using a model predictive control (MPC) law. Some MPC applications require a control strategy in which some system outputs are controlled within specified ranges or zones (zone control), while some other variables – possibly including input variables - are steered to fixed target or set-point. In real applications, this problem is often overcome by including and excluding an appropriate penalization for the output errors in the control cost function. In this way, throughout the continuous operation of the process, the control system keeps switching from one controller to another, and even if a stabilizing control law is developed for each of the control configurations, switching among stable controllers not necessarily produces a stable closed loop system. From a theoretical point of view, the control objective of this kind of problem can be seen as a target set (in the output space) instead of a target point, since inside the zones there are no preferences between one point or another. In this work, a stable MPC formulation for constrained linear systems, with several practical properties is developed for this scenario. The concept of distance from a point to a set is exploited to propose an additional cost term, which ensures both, recursive feasibility and local optimality. The performance of the proposed strategy is illustrated by simulation of an ill-conditioned distillation column.     

Novel developments in process optimisation using predictive control

In industrial practice, constrained steady state optimisation and predictive control are separate, albeit closely related functions within the control hierarchy. This paper presents a method which integrates predictive control with on-line optimisation with economic objectives. A receding horizon optimal control problem is formulated using linear state space models. This optimal control problem is very similar to the one presented in many predictive control formulations, but the main difference is that it includes in its formulation a general steady state objective depending on the magnitudes of manipulated and measured output variables. This steady state objective may include the standard quadratic regulatory objective, together with economic objectives which are often linear. Assuming that the system settles to a steady state operating point under receding horizon control, conditions are given for the satisfaction of the necessary optimality conditions of the steady-state optimisation problem. The method is based on adaptive linear state space models, which are obtained by using on-line identification techniques. The use of model adaptation is justified from a theoretical standpoint and its beneficial effects are shown in simulations. The method is tested with simulations of an industrial distillation column and a system of chemical reactors.     

Integrated design optimization of structural size and control system of piezoelectric curved shells with respect to sound radiation

Simultaneously optimizing the thickness of the base structure and the location of piezoelectric sensors/actuators as well as control gains is investigated for minimizing the sound radiation from the vibrating curved shell integrated with sensors/actuators under harmonic excitation. The finite element formulation of the piezoelectric curved shell structure is described. The piezoelectric element is coupled into the base shell element using nodal displacement constraint equations. The active control of structural vibration-acoustic radiation is formulated using the velocity feedback algorithm. Based on both passive and active control measures, an integrated optimization model of the vibro-acoustic problem is proposed, in which the sound power is taken as the objective function. The thickness of the base shell elements and the parameters of control system, including the location of sensors/actuators and control gains, are chosen as the design variables. In order to restrict the complexity of the control system, the number of sensors/actuators is considered as a constraint. A simulated annealing algorithm is extended to handle the vibro-acoustic optimization problem with the continuous and discrete variables co-existing. Numerical examples demonstrate the effectiveness of the optimization scheme and the correctness of the computation program.     

逆向工程中测量数据曲面重构技术研究

文中首先介绍了自由曲面的描述方式,并提出了一种对点云数据进行NURBS曲面重建的改进算法。该算法通过对目标函数的修正及迭代处理,将测点数据的有理B样条曲线拟合问题转化为一单变量的优化问题。从而容易求得控制点三维坐标、权值、各测点所对应的参数值及最优控制点的数目,进而有效地压缩测量点云数据量。该方法克服了现有方法存在的需要求解多变量的非线性优化问题,不能对最优控制点数目进行求取等缺陷。实践表明,文中提出的方法是行之有效的。     

Existence of an optimal strategy for stochastic train energy-efficient operation problem

When we formulate the optimal train control model for optimizing the operation strategy, it is necessary to consider the uncertain disturbances arising from the whether, route and locomotive rolling conditions. Stochastic energy-efficient operation has proved to be an efficient approach to reduce the effect of uncertainty by estimating the resistance coefficients as random variables. The purpose of this paper is to prove the existence of an optimal operation strategy. First, we prove the existence of a feasible operation strategy satisfying the nonnegativity, boundary conditions, trip distance and the motion equation. Furthermore, we prove the Lipschitz continuity for the stochastic objective function. Finally, we prove the existence theorem for an optimal operation strategy.     

Simultaneous optimization of design and operation of multicomponent batch distillation column—single and multiple separation duties

A method is presented for the simultaneous optimization of a batch distillation column design and its operation, for single and multiple separation duties, each involving different multicomponent mixtures and complex operations with intermediate cuts. For operation structures selected a priori, the formulation presented permits the use of general distillation design and cost models. The objective function and constraints include capital and operating cost. In particular, the number of internal plates is optimized along with the most significant operating variables (recoveries in various cuts and reflux ratio profiles and times). The multiple duty formulation presented accounts for the different importance of each duty and setup time between batches. Application of the method to single duty multicomponent separation from the literature shows that significant profit improvements can be achieved within acceptable computing times. For multiple separation duties (two binary mixtures), the method clearly shows the importance of including allocation time to each duty and setup time for each batch in the objective function.     

Robust production and maintenance planning in stochasticmanufacturing systems

In this paper the authors consider a preventive maintenance and production model of a flexible manufacturing system with machines that are subject to breakdown and repair. The preventive maintenance can be used to reduce the machine failure rates and improve the productivity of the system. The control variables are the rate of maintenance and the rate of production; the objective is to choose a control process that optimizes a robust cost of inventory/shortage, production, and maintenance. The value function is shown to be locally Lipschitz and to satisfy a Hamilton-Jacobi-Isaacs equation. A sufficient condition for optimal control is obtained. Finally, an algorithm is given for solving the optimal control problem numerically     

基于神经网络模型的非线性多步预测学习控制器

构造出一种建模网络,通过对它的学习来辨识过程动态,通过对广义预测控制目标函数的在线优化求得控制律.仿真结果验证了该算法的有效性.     

一种工业过程稳态优化控制算法

针对带有输出关联约束的工业过程,提出一种确定其稳态优化控制的算法.首先通过对数变换将原问题转化为一个等价而且可在对数空间求解的优化控制问题;然后为避免事先选择一个合适罚系数的困难,在算法中引入了目标函数的线性化形式.该优化算法不仅能收敛到正确的系统最优解,而且可用现有的二次规划算法计算.应用简单的滤波技术,改善了算法在有量测噪声情况下的性能.仿真结果表明,所提出的优化算法是有效的.     

Optimal design of composite laminated plates with the discreteness in ply angles and uncertainty in material properties considered

An algorithm is developed for optimizing laminated plate stacking sequences and determining thicknesses, which incorporates discrete ply angles and considers the uncertainties of material properties in a two-step optimization process. The branch and bound method was modified to handle discrete variables; and convex modeling was used to allow the consideration of variable material properties. The numerical results obtained show that the optimal stacking sequences are determined with fewer evaluations of the objective function than might be expected from considerations of the size of the design space. Our results also show that the optimal thickness increases when elastic moduli uncertainties are considered, which indicates that such uncertainties should not be ignored at the design stage.