化工过程系统优化的分布式并行计算

针对大规模化工过程系统优化计算能力不够的情况 ,讨论用机群系统建构成并行优化计算环境 .在分析并行计算的原理和现状后 ,对优化算法的并行化进行探讨 ,并且深入讨论了大规模优化算法SQP的并行化和如何提高机群系统效率的问题 .通过精馏塔优化算例 ,证明利用机群系统开展并行优化是行之有效的     

模式识别法在化工调优中的应用

模式识别调优的基本出发点是,以工艺参数为特征变量构筑模式空间,按调优目标划分样本的类别.采用模式特征抽提方法压缩工艺参数,找出影响目标的主要因素.分别对两类调优问题,即最优指标问题和最优方向问题,提出了寻找最优工况的具体处理方法.对多目标调优也作了简单的讨论.     

A rigorous targeting algorithm for resource allocation networks

Techniques of process integration can be applied to conserve resources such as energy, freshwater, cooling water, hydrogen, solvent, etc. Process integration methodologies are broadly classified into two categories: methodologies based on the mathematical optimization techniques and methodologies based on the conceptual approaches of pinch analysis. In this paper, a mathematically rigorous methodology is proposed to minimize the requirement of a natural resource in a chemical process industry. The proposed methodology combines the simplicity of the pinch analysis with the mathematical rigor of mathematical optimization techniques. Conservation of resource in a chemical process industry is posed as a network flow optimization problem and a simple algebraic methodology is proposed to solve the optimization problem. The proposed algebraic methodology is mathematically proved in this paper. The proposed algorithm is numerically faster than the general mathematical optimization methods used for solving optimal resource allocation problems.     

Multiple-objective decision analysis applied to chemical engineering

Chemical engineers are now being faced with new decisions involving multiple (and often conflicting) objectives as a result of increases in the scale and complexity of chemical plants and a re-evaluation of their performance criteria. Reliability and environmental impacts are now considered to be as important as economic efficiency, and this must be taken into account in process design, production planning and control.This paper describes methods for solving these multiple-objective optimization problems and gives an overview of the existing software. Selected applications of multiple-objective analysis are discussed—these include the design of a twin-screw extruder, the control of a film-hardening process and a production planning problem.     

Multistage adaptive optimization using hybrid scenario and decision rule formulation

Scenario-based stochastic programming and linear decision rule (LDR)-based robust optimization are prevalent methods for solving multistage adaptive optimization (MSAP) problems. In practical applications such as capacity expansion planning of chemical processes, often multiple sources of uncertainty affect the problem which introduces challenges to traditional stochastic optimization methods. While a large number of uncertain parameters exist in the problem, using scenario-based method results in very large problem size and the solution becomes computationally expensive. In addition, when the constraints include multiplication of uncertain parameters and adaptive variables, the constraints are not linear with respect to uncertain parameters when the LDR method is used. In order to address these challenges, we propose two different hybrid methods where scenario and decision rule methods are combined to solve the MSAP problem. The article demonstrates the computational performance of the proposed hybrid methods using two chemical process planning examples.     

One-stage optimization problem with chance constraints

Generally, the design of chemical processes (CP) is performed with the use of inaccurate mathematical models. Therefore, it is essential to create chemical processes that can satisfy all the design specifications at the operation stage in spite of the changes in internal and external factors. Consequently, the problem of chemical process optimization under uncertainty is of prime importance in chemical engineering. The paper considers one-stage optimization problems with chance constraints. The main issue in solving one-stage optimization problems is calculation of multiple integrals (calculating the expected value of the objective function and probabilities of constraints satisfaction). Here we consider a new approach to solving a one-stage optimization problem which is based on transformation of chance constraints into deterministic ones. A computational experiment has shown the efficiency of this approach.     

全局优化搜索新算法──列队竞争算法(Ⅱ)解网络综合问题

本文给出了列队竞争算法解组合优化问题的框架和确定变异领域的两条原则 .对管路网络综合问题和换热网络综合问题确定了相应的变异领域 ,用列队竞争算法分别解这两个网络综合问题 ,所得到的最优解优于文献报道的结果 .     

A NEW ALGORITHM FOR GLOBAL OPTIMIZATION SEARCHLINE-UP COMPETITION ALGORITHM (Ⅱ) SOLVING NETWORK SYNTHESIS PROBLEMS

本文给出了列队竞争算法解组合优化问题的框架和确定变异领域的两条原则 .对管路网络综合问题和换热网络综合问题确定了相应的变异领域 ,用列队竞争算法分别解这两个网络综合问题 ,所得到的最优解优于文献报道的结果 .     

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.     

基于PSO-控制变量参数化混合策略的间歇化工过程优化控制

控制变量参数化方法作为一种化工过程动态优化的梯度搜索算法，其求解效率过于依赖初始给定轨迹。目前初始轨迹一般都是设定在边界值或中间值，缺乏科学依据，从而大大影响了算法的收敛速度。针对这一问题，提出了一种粒子群优化(PSO)与控制变量参数化方法混合的策略，首先利用粒子群优化对间歇化工过程最优控制量进行求解，结果作为控制变量参数化方法初始给定轨迹，进行二次优化。双层优化的混合策略提高了控制变量参数化方法的收敛速度和粒子群优化算法的求解精度。将混合策略应用于两个间歇化工过程优化控制实例，仿真结果表明了该算法对求解化工过程动态优化问题具有可行性和有效性。     

混沌搜索方法及其在化工过程优化中的应用

提出利用混沌搜索方法结合精确不可微罚函数求解约束优化问题的新方法 ,并将该方法用于闪蒸过程优化 .结果表明 ,该方法算法简单 ,实现容易 ,求解精度和可靠性较高 ,是解决化工优化问题的有效方法 .     

基于神经网络模型的混沌优化及其应用

研究一种新型优化算法－混沌优化,提出加快解的疏敛速度和精度新方法,并与精确不可微罚函数结合来求解非线性约束优化问题。对不能用数学解析式精确表达的优化问题利用神经网络建模,在此基础上进行混沌搜索寻优。该方法应用于甲醛生产过程的稳态优化,获得较好的经济效益。     

含滞后及不确定参数动态过程系统的定量优化方法与应用

许多动态化工系统含有不确定参数。当含不确定参数的过程系统又带有滞后环节时,系统的优化问题就变得非常复杂。对含不确定参数的化工系统的动态过程进行优化,若未考虑滞后环节对动态优化结果的影响,不能保证系统实际操作的优化及安全运行。本文采用改进的有限元正交配置优化算法,较好地解决了含有滞后、不确定参数的化工过程的动态优化问题。最后将一个具有循环返料的反应-分离器系统以及一个绝热反应釜作为案例对该优化方法的应用进行分析,根据案例的研究验证了该优化方法的有效性,从而为含有滞后、不确定参数的动态系统的设计和操作性能优化提供一种有效的定量分析方法和理论依据。     

Process simulator-based optimization of biorefinery downstream processes under the Generalized Disjunctive Programming framework

Downstream processing of biofuels and bio-based chemicals represents a challenging problem for process synthesis and optimization, due to the intrinsic nonideal thermodynamics of the liquid mixtures derived from the (bio) chemical conversion of biomass. In this work, we propose a new interface between the process simulator PRO/II (SimSci, Schneider-Electric) and the optimization environment of GAMS for the structural and parameter optimization of this type of flowsheets with rigorous and detailed models. The optimization problem is formulated within the Generalized Disjunctive Programming (GDP) framework and the solution of the reformulated MINLP problem is approached with a decomposition strategy based on the Outer-Approximation algorithm, where NLP subproblems are solved with the derivative free optimizer belonging to the BzzMath library, and MILP master problems are solved with CPLEX/GAMS. Several validation examples are proposed spanning from the economic optimization of two different distillation columns, the dewatering task of diluted bio-mixtures, up to the distillation sequencing with simultaneous mixed-integer design of each distillation column for a quaternary mixture in the presence of azeotropes.     

减少化工过程对环境的影响--绿色化学工程的目标

综述了绿色化学工程的研究现状,并对发展趋势作了探讨,认为要实现绿色化学工程,除研究绿色化学技术以降低原材料消耗、改进或开发新型分离技术以减少过程化学污染和提高能源有效利用外,还需在一个统一的评价平台上进行过程综合和优化;随着研究的深入,过程综合的范围将扩大到人和社会等因素;讨论了建立统一的评价平台的关键和难点。     

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。     

序贯优化化工动态问题的蚁群算法

针对化工动态优化问题,分析现有数值解法的不足,提出序贯执行蚁群寻优操作,逐步寻找最佳解的策略,构建序贯蚁群算法.算法首先对时间区间和控制变量搜索域实施离散化,以一组整数编码的蚁群路径表示可行控制策略,进而应用蚁群寻优操作寻找离散问题的最优控制策略.逐步收缩控制搜索域并反复上述步骤,不断改善寻优结果.序贯蚁群算法简便快捷,用于化工动态优化问题效果良好,计算结果体现了算法的稳健性.     

Chemical Composition Determination at the Bottom Region of a Recovery Boiler Furnace by Direct Minimization of Gibbs Free Energy

An optimization strategy has been applied to describe the chemical composition at the furnace bottom in the Kraft recovery boiler of a pulp production process. The concentrations of each involved chemical species were calculated through an optimization approach, minimizing the Gibbs free energy of the system. Various systems were proposed and tested, assuming different chemical species and phases number. Because serious initialization problems were found at this stage for some of the proposed systems, an optimization heuristic method (PSO) was used for the first approach to the problem. Once the appropriate phases number and chemical species in the system were determined, the initialization problems disappeared and the use of a deterministic optimization method (SQP) became viable. The proposed approach has shown to be satisfactory to reproduce industrial data and also data reported in the open scientific literature.     

化工过程设计的多目标不确定优化研究

In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.     

Design of Chemical Engineering Systems with Chance Constraints

In the design of chemical process under uncertainty in initial information, an important problem is to determine a structure in which the control system will guarantee that all constraints are satisfied despite variations in internal and external factors at the operation stage. A method has been proposed for solving onestage optimization problems with chance constraints in the design of optimal flexible chemical processes. The developed approach makes it possible to avoid multidimensional integration in each of the iterations of problem solving, thus reducing the computational effort. The efficiency of the proposed approach is illustrated by model examples.