离散粒子群算法的波纹管优化设计研究

为了解决波纹管所涉及的非线性约束离散变量的优化设计问题,文中尝试将粒子群算法与惩罚函数法相结合,建立新型离散粒子群算法,实现离散变量与连续变量之间的转化。提出了一种新的离散惩罚因子更新策略,以保证离散解的精度及算法的收敛性。通过著名的容器设计算例验证,文中方法优于文献方法。用该方法对波纹管工程实例进行优化设计,优化目标值比在用产品提高了79.96%,且与理论值接近,离散解的精度满足要求,进一步证明了该方法在求解波纹管工程非线性约束离散变量优化设计问题时的有效性。     

Fault detection and diagnosis with parametric uncertainty using generalized polynomial chaos

This paper presents a new methodology to identify and diagnose intermittent stochastic faults occurring in a process. A generalized polynomial chaos (gPC) expansion representing the stochastic inputs is employed in combination with the nonlinear mechanistic model of the process to calculate the resulting statistical distribution of measured variables that are used for fault detection and classification. A Galerkin projection based stochastic finite difference analysis is utilized to transform the stochastic mechanistic equation into a coupled deterministic system of equations which is solved numerically to obtain the gPC expansion coefficients. To detect and recognize faults, the probability density functions (PDFs) and joint confidence regions (JCRs) of the measured variables to be used for fault detection are obtained by substituting samples from a random space into the gPC expansions. The method is applied to a two dimensional heat transfer problem with faults consisting of stochastic changes combined with step change variations in the thermal diffusivity and in a boundary condition. The proposed methodology is compared with a Monte Carlo (MC) simulations based approach to illustrate its advantages in terms of computational efficiency as well as accuracy.     

基于微粒群优化算法的不确定性调和调度

Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimization problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization （PSO） algorithm is developed for nonlinear optimization problems with both continuous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.     

Optimization-based design of reactive distillation columns using a memetic algorithm

The design optimization of reactive distillation columns (RDC) is characterized by complex nonlinear constraints, nonlinear cost functions, and the presence of many local optima. The standard approach is to use MINLP solvers that work on a superstructure formulation where structural decisions are represented by discrete variables and lead to an exponential increase in the computational effort. The mathematical programming (MP) methods which solve the continuous sub-problems provide only one local optimum which depends strongly on the initialization. In this contribution a memetic algorithm (MA) is introduced and applied to the global optimization of four different formulations of a computational demanding real-world design problem. An evolution strategy addresses the global optimization of the design decisions, while continuous sub-problems are efficiently solved by a robust MP solver. The MA is compared to MINLP techniques. It is the only algorithm that finds the global solution in reasonable times for all model formulations.     

Simultaneous optimization approach for integrated water-allocation and heat-exchange networks

A systematic design methodology is developed in this work for simultaneously synthesizing the multi-contaminant water-allocation and heat exchange network (WAHEN) in any chemical process. Specifically, a modified state-space representation is adopted to capture the structural characteristics of the integrated WAHEN, and a mixed-integer nonlinear program (MINLP) is formulated accordingly to minimize the total annualized cost (TAC) of the network design. In the proposed mathematical programming model, not only all possible water reuse and treatment options are incorporated, but also the direct and indirect heat-exchange opportunities are considered as well. To enhance the solution quality and efficiency, a stochastic perturbation procedure is introduced to generate reliable initial guesses for the deterministic optimization procedures and also, an interactive iteration method is developed to guide the search toward a potential global optimum. Three examples are presented in this paper to demonstrate the validity and advantages of the proposed approach.     

A formulation methodology for multicomponent distillation sequences based on stochastic optimization

Based on stochastic optimization strategy, a formulation methodology is proposed for synthesizing distillation column sequences, allowing more than one middle component as the distributing components between a pair of key components in the non-sharp split. In order to represent and manipulate the distillation configuration structures, a new coding procedure is proposed in the form of one-dimensional array. Theoretically, an array can represent any kind of split (non-sharp and sharp).With the application of a binary sort tree approach, a robust flow sheet encoding and decoding procedure is developed so that the problem formulation and solution becomes tractable. In this paper, the synthesis problem is formulated as a mixed-integer nonlinear programming (MINLP) problem and an improved simulated annealing approach is adopted to solve the optimization problem. Besides, a shortcut method is applied to the evaluation of all required design parameters as well as the total function.     

Inferential Control of Reactive Destillation Columns – An Algorithmic Approach

Decentralized control system design comprises the selection of a suitable control structure and controller parameters. Here, mixed integer optimization is used to determine the optimal control structure and the optimal controller parameters simultaneously. The process dynamics is included explicitly into the constraints using a rigorous nonlinear dynamic process model. Depending on the objective function, which is used for the evaluation of competing control systems, two different formulations are proposed which lead to mixed‐integer dynamic optimization (MIDO) problems. A MIDO solution strategy based on the sequential approach is adopted in the present paper. Here, the MIDO problem is decomposed into a series of nonlinear programming (NLP) subproblems (dynamic optimization) where the binary variables are fixed, and mixed‐integer linear programming (MILP) master problems which determine a new binary configuration for the next NLP subproblem. The proposed methodology is applied to inferential control of reactive distillation columns as a challenging benchmark problem for chemical process control.     

Global optimization based on subspaces elimination: Applications to generalized pooling and water management problems

A global optimization strategy based on the partition of the feasible region in boxed subspaces defined by the partition of specific variables into intervals is described. Using a valid lower bound model, we create a master problem that determines several subspaces where the global optimum may exist, disregarding the others. Each subspace is then explored using a global optimization methodology of choice. The purpose of the method is to speed up the search for a global solution by taking advantage of the fact that tighter lower bounds can be generated within each subspace. We illustrate the method using the generalized pooling problem and a water management problem, which is a bilinear problem that has proven to be difficult to solve using other methods. © 2011 American Institute of Chemical Engineers AIChE J, 58: 2336–2345, 2012     

塑料注塑成型浇口位置优化

塑料注塑成型可以制作大量具有高精度和复杂型腔形状的制件,其中注塑模浇口位置的设定决定了聚合物流动方向和流动平衡性,产品质量可以通过浇口位置的优化得到显著提高.通过对导致制品翘曲和影响材料性能的主要因素进行定量分析构造了浇口位置优化问题,结合遗传算法和数值模拟技术对优化问题求解,得到最优浇口位置设计.算例证明该方法不仅适用于单浇口位置优化,也适用于多浇口位置优化.     

Deterministic global optimization for error-in-variables parameter estimation

Standard techniques for solving the optimization problem arising in parameter estimation by the error-in-variables (EIV) approach offer no guarantee that the global optimum has been found. It is demonstrated here that the interval-Newton approach can provide a powerful, deterministic global optimization methodology for the reliable solution of EIV parameter estimation problems in chemical process modeling, offering mathematical and computational guarantees that the global optimum has been found. Although this methodology is typically regarded as being applicable only to very small problems, it is successfully applied here to problems with over 200 variables. It is a general-purpose technique and is applied here to a diverse group of problems, including examples in reactor modeling, in modeling vapor-liquid equilibrium, and in modeling a heat exchanger network.     

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.     

Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes

In this article, we address a partition-based distributed state estimation problem for large-scale general nonlinear processes by proposing a Kalman-based approach. First, we formulate a linear full-information estimation design within a distributed framework as the basis for developing our approach. Second, the analytical solution to the local optimization problems associated with the formulated distributed full-information design is established, in the form of a recursive distributed Kalman filter algorithm. Then, the linear distributed Kalman filter is extended to the nonlinear context by incorporating successive linearization of nonlinear subsystem models, and the proposed distributed extended Kalman filter approach is formulated. We conduct rigorous analysis and prove the stability of the estimation error dynamics provided by the proposed method for general nonlinear processes consisting of interconnected subsystems. A chemical process example is used to illustrate the effectiveness of the proposed method and to justify the validity of the theoretical findings. In addition, the proposed method is applied to a wastewater treatment process for estimating the full-state of the process with 145 state variables.     

Generalized polynomial programming

Conventional design procedures use physical reasoning to construct equations describing the system. To find the optimal design for some specified performance criterion, it is desirable to change these equations into inequalities so that generalized polynominal programming, a highly nonlinear optimization technique, can be used. For illustration, a chemical reactor system optimal design problem with 11 variables and 8 constraints is formulated as an equality constrained optimization problem, then transformed into an inequality constrained generalized polynominal program and finally solved on the computer.     

Heterogeneous Catalytic Reactor Design with Non‐Uniform Catalyst Considering Shell‐Progressive Poisoning Behavior

A novel methodology has been developed to design an optimum heterogeneous catalytic reactor, by considering non‐uniform catalyst pellet under shell‐progressive catalyst deactivation. Various types of non‐uniform catalyst pellets are modelled in combination with reactor design. For example, typical non‐uniform catalyst pellets such as egg‐yolk, egg‐shell and middle‐peak distribution are developed as well as step‐type distribution. A progressive poisoning behavior is included to the model to produce correct effectiveness factor from non‐uniform catalyst pellet. As opposed to numerical experiment with limited type of kinetic application to the model in the past, this paper shows a new methodology to include any types of kinetic reactions for the modeling of the reactor with non‐uniform catalyst pellet and shell‐progressive poisoning. For an optimum reactor design, reactor and catalyst variables are considered at the same time. For example, active layer thickness and location inside pellet are optimised together with reactor temperature for the maximisation of the reactor performance. Furthermore, the temperature control strategy over the reactor operation period is added to the optimization, which extends the model to three dimensions. A computational burden has been a major concern for the optimization, and innovative methodology is adopted. Application of profile based synthesis with the combination of SA (Simulated Annealing) and SQP (Successive Quadratic Programming) allows more efficient computation not only at steady state but also in dynamic status over the catalyst lifetime. A Benzene hydrogenation reaction in an industry scale fixed‐bed reactor is used as a case study for illustration.     

Global optimization methods for chemical process design: Deterministic and stochastic approaches

Process optimization often leads to nonconvex nonlinear programming problems, which may have multiple local optima. There are two major approaches to the identification of the global optimum: deterministic approach and stochastic approach. Algorithms based on the deterministic approach guarantee the global optimality of the obtained solution, but are usually applicable to small problems only. Algorithms based on the stochastic approach, which do not guarantee the global optimality, are applicable to large problems, but inefficient when nonlinear equality constraints are involved. This paper reviews representative deterministic and stochastic global optimization algorithms in order to evaluate their applicability to process design problems, which are generally large, and have many nonlinear equality constraints. Finally, modified stochastic methods are investigated, which use a deterministic local algorithm and a stochastic global algorithm together to be suitable for such problems. Partly presented at PSE Asia 2000 (December 6–8, Kyoto, Japan)     

Optimization of injection molding design. Part II: Molding conditions optimization

The quality of an injection molded part is affected by many factors. These include geometric parameters associated with the mold design and the cooling system design as well as process parameters such as the molding conditions during the filling phase. In the companion paper, the problem of automatic optimization of gate location was addressed. In this paper, a methodology for molding condition optimization is presented. The optimization problem can be broken into three parts. An approximate feasible molding space (AFMS) is first determined to constrain the search space for the optimization algorithm. Quality is quantified as a function of flow simulation outputs and constitutes the objective function that must be minimized. The resulting optimization is solved by iterative search in the constrained space based on numerical optimization algorithms. The proposed methodology is not dependent on any particular simulation package and may be applied for any thermoplastic material and any complex mold geometry.     

Optimum design of heat exchanger networks

The optimum design of heat exchanger networks is considered in two stages. In the first, the optimum configuration for fixed values of the continuous variables is determined using an implicit enumeration algorithm. In the second stage the optimization of continuous variables of the network is performed solving a large scale nonlinear programme. The proposed method for discrete variables lessens considerably the computational effort for solving the combinatorial problem in the first stage. The results show the importance of optimizing the continuous variables of these networks.     

Optimal control of linear and nonlinear systems by two-level optimization

The decomposition of a system into simpler subsystems, followed by the optimization of the individual subsystems, and then co-ordinating the subsystem optimal solutions to yield the optimal control policy for the original system, is considered. Lasdon's co-ordination algorithm, in which the subsystems are regarded as being completely independent, is improved by including the interdependence of the adjacent subsystems. For linear systems with a quadratic performance index, this procedure yields the optimal control policy in a single iteration from an arbitrary initial guess of the decomposition parameters. For nonlinear systems, the method includes linearizing the state equations, the calculation of the optimal control for the linearized system, and the adjustment of the corresponding decomposition parameters of the original problem until convergence is achieved. For the optimization of a nonlinear continuous stirred tank reactor such a procedure requires considerably more computation time than the standard techniques, and the optimum policies are less accurate.     

基于相对增益和优先级的化工过程协调优化裕量设计

化工过程设计裕量一般是通过设计经验或经济优化给出的,设计经验无法保证经济性能的优化,而经济优化需要求解大规模非线性优化问题,计算复杂,容易陷入局部极值点,设计结果有时与设计经验违背。本文用非方相对增益阵和非方相对能量增益阵描述化工过程设计的自变量和因变量的灵敏度关系,将自变量划分为操作变量和设计变量,将因变量划分为经济指标和约束变量。相对增益具有无量纲化和归一化的优点,因此可根据经济指标和约束变量的相对增益对操作变量和设计变量划分优先级,针对过程不确定性的大小按照优先级依次调整各个操作变量和设计变量,找到对过程经济性能影响最小并有效移动操作点、远离约束边界的裕量设计方案。以串联反应釜为例对该设计方法进行了验证,结果表明,与求解经济最优化问题的裕量设计方法相比,本设计方法得到了经济性能与之接近的设计结果,计算简单,无须求解最优化问题。     

基于带补偿随机规划的蒸汽动力系统优化设计

在蒸汽动力系统优化设计中,考虑不确定因素的优化策略能避免基于确定性设计策略的保守设计,并能针对不确定因素的实现提出相应的调度调节策略.本研究分析了蒸汽动力系统设计包含的不确定因素的特性及其对蒸汽动力系统优化目标和约束条件的影响.不确定因素的表达分成两类:基于时间变化表达和基于发生概率表达.对基于时间变化表达的因素,转化为多周期问题进行处理;对外部工艺过程变化引起的汽电需求不确定波动等基于发生概率表达的因素,应用随机规划策略,补偿不确定参数的实现可能引起的约束背离.基于本研究建立的多周期带补偿的二阶段随机规划MILP模型,求解蒸汽动力系统结构,同时优化调度调节策略,用调节决策和惩罚不足应对汽电需求等不确定因素的实现,实现系统安全稳定运行和经济效益最优.