Distillation optimization

The optimization of operating conditions for distillation processes is studied. The numerical performance of several equation-oriented, Newton-like methods are compared on a variety of example problems. Numerical results show that the thermodynamically constrained hybrid method provides a reliable and efficient way of solving distillation optimization problems.     

Optimal robust optimization approximation for chance constrained optimization problem

Chance constraints are useful for modeling solution reliability in optimization under uncertainty. In general, solving chance constrained optimization problems is challenging and the existing methods for solving a chance constrained optimization problem largely rely on solving an approximation problem. Among the various approximation methods, robust optimization can provide safe and tractable analytical approximation. In this paper, we address the question of what is the optimal (least conservative) robust optimization approximation for the chance constrained optimization problems. A novel algorithm is proposed to find the smallest possible uncertainty set size that leads to the optimal robust optimization approximation. The proposed method first identifies the maximum set size that leads to feasible robust optimization problems and then identifies the best set size that leads to the desired probability of constraint satisfaction. Effectiveness of the proposed algorithm is demonstrated through a portfolio optimization problem, a production planning and a process scheduling problem.     

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.     

两群微粒群算法及其在油品调和优化中的应用

针对复杂的非线性约束优化问题,提出了一种含变异算子的两群微粒群算法。算法构造了两个粒子群,分别设置了不同的搜索速度上限,并设计了粒子群间的协调机制和变异算子,使算法的寻优能力得到增强。针对油品调和配方优化进行了实例仿真,研究结果表明所提出的算法能获得较理想的调和配方,在满足调和利润最大的同时能保证对调和指标的卡边,使调和成品油的指标富余量大大降低。     

实时优化技术在乙烯装置在线优化中的应用

乙烯产业是重要的石化基础产业,对国民经济、石油化工产业及工业社会起着举足轻重的作用,近年来 在我国经济增长过程中保持着蓬勃发展的态势,但是,由于我国乙烯生产总体技术水平的相对滞后以及乙烯工 业所供原料复杂等因素,导致我国目前乙烯装置与世界乙烯生产先进水平存在一定差距,为了减小这一差距, 达到世界先进水平,离不开工业过程自动化和运用实时优化技术。为此,本文介绍了实时优化技术的国内外发 展现状和该技术在乙烯装置的应用情况,并通过实时优化系统的实施,建立了乙烯装置的全流程严格机理模型, 可以实时跟踪乙烯装置的生产情况,持续不断对装置进行在线优化,使装置的操作达到最佳的经济效益操作点, 此外,在优化中以装置的原料、产品和公用工程等价格为导向,对全装置生产过程自动操作执行,减少了优化 计算和结果执行中的人为干预。分析表明实时优化技术有效实现了乙烯装置增产、节能和降耗的目标,可为乙 烯生产企业创造新增经济效益。     

φ3.2m×10m中卸烘干磨的操作优化

０引言烘干磨具有同时对原料进行烘干和粉磨的功能,在南方多雨地区,入磨物料水分偏高,易导致糊磨等不良工况,影响磨机产量,增加粉磨电耗。而烘干磨在解决因这一问题上作用明显。我厂生料磨采用合肥院设计的３．２m×１０m中卸烘干磨。投产初期,系统粉磨效率偏低。后通过采用预破碎降低入磨粒度,降低入磨物料水分,增加选粉机循环风量等措施,生料粉磨系统的操作面貌得以改善。目前,在只用４３t研磨体的情况下（较投产初期少２０多个）,烘干磨的产量可达６２t／h,较投产初期增产１１８％;粉磨系统电耗降到１７．２２kWh／t（较投…     

改进生物地理学算法对正丁烷异构反应模型的优化

针对生物地理学优化（biogeography-based optimization,BBO）算法在寻优过程中容易陷入早熟的现象,提出了一种基于三维变异的生物地理学优化（three-dimensional variation biogeography-based optimization,Tdv-BBO）算法。该算法是在BBO算法的基础上,引入了三维变量的变异,解决了BBO算法后期搜索动力不足的问题,加快了BBO算法的寻优速度。同时,提出将改进的Tdv-BBO算法应用到正丁烷异构反应动力学模型的优化中,对反应动力学模型的参数进行了优化和整定。仿真实验表明：改进的Tdv-BBO算法提高了个体种群的多样性,增强了算法的搜索能力,加快了寻优速度。用该方法优化得到的反应动力学模型,模型精度较高,泛化能力强;可为正丁烷异构反应的建模提供一种有效的方法。     

Process synthesis and optimization of a sustainable integrated biorefinery via fuzzy optimization

Over the last decade, utilization of biomasses is highly encouraged to conserve scarce resources, reduce dependency on energy imports as well as protect the environment. Integrated biorefinery emerged as noteworthy concept to integrate several conversion technologies to have more flexibility in product generation with energy self‐sustained and reduce the overall cost of the process. Integrated biorefinery is a processing facility that converts biomass feedstocks into a wide range of value added products via multiple technologies. In this work, a systematic approach for the synthesis and optimization of a sustainable integrated biorefinery which considers economic, environmental, inherent safety, and inherent occupational health performances is presented. Fuzzy optimization approach is adapted to solve four parameters simultaneously as they are often conflicting in process synthesis and optimization of an integrated biorefinery. An integrated palm oil‐based biorefinery case study is solved to demonstrate the proposed approach. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4212–4227, 2013     

Barrier NLP methods with structured regularization for optimization of degenerate optimization problems

Barrier nonlinear programming (NLP) solvers exploit sparse Newton-based algorithms and are characterized by fast performance and global convergence properties. This makes them especially suitable for very large process optimization problems. On the other hand, they are frequently challenged by degenerate and indefinite problems, which lead to ill-conditioned Karush–Kuhn–Tucker (KKT) systems. Such problems arise when process optimization models contain linearly dependent constraints, or the reduced Hessian is not positive definite at the solution. This can lead to poor solver performance and may preclude finding successful NLP solutions. Moreover, such optimization models occur in blending problems and NLP subproblems generated by MINLP or global optimization strategies. To deal with these difficulties we present a structured regularization strategy for barrier methods that identifies and excludes dependent constraints in the KKT system while leaving independent constraints unchanged. As a result, more accurate Newton directions can be obtained and much faster convergence can be expected for the KKT system over the conventional regularization approach. Numerical experiments with examples derived from the CUTE and COPS test sets as well as two nonlinear blending problems demonstrate the effectiveness of the proposed method and significantly better performance of the NLP solver.     

沙库巴曲的工艺优化

以N-[(1R)-2-[1,1′-联苯]-4-基-1-(羟基甲基)乙基]氨基甲酸叔丁酯(化合物Ⅰ)为起始原料,经TEMPO(2,2,6,6-四甲基哌啶氧化物)氧化、Wittig反应和脱乙酯,重结晶后得到中间体Ⅴ;接着经10％Pd/C氢化还原后用m(正庚烷):m(乙酸乙酯)=1:1重结晶得到关键中间体Ⅵ;最后经乙酯保护,酰胺化缩合得到目标产物沙库巴曲(化合物Ⅷ).对关键氢化步骤的反应溶剂、添加剂、催化剂用量、反应温度、H2压力、反应时间以及重结晶溶剂等反应参数进行了筛选,关键中间体Ⅵ的手性结构经手性HPLC方法分析,手性纯度99.98％;目标产物沙库巴曲的总收率约50％,产物经HPLC检测,纯度达100％.     

Large scale unconstrained optimization

This paper concerns recent developments in methods for minimizing unconstrained nonlinear functions in many variables. Recent theoretical results and computational experience with truncated Newton methods and Buckley's variable storage preconditioned conjugate gradient method are stressed.     

双电层电容器活性炭电极的优化

通过物理方法对双电层电容器用活性炭电极进行改性实验,探讨了活性炭电极的结构（比表面积、孔径分布、孔容）和性能（比电容、充放电特性）的优化问题.改性后活性炭电极BET比表面积从1739.77 m²•g^-1增至2215.40 m²•g^-1,其中微孔比表面积增幅22％,中孔比表面积增幅35％,孔容积也有20%～30％的增幅量,孔径分布更为合理.优化的活性炭电极结构改善了电极材料的电化学特性,比电容量可达424 F•g^-1,增幅10％.     

Economic process optimization strategies

The following deliberations are concerned with the application of economy criteria to the optimization of process-engineering plants. We are therefore not concerned here with criteria for investment decisions, i.e., decisions on the implementation orabandonment of an investment project, nor with criteria for the selection of the most profitable investment options from a series of alternatives. Instead, we are interested here in the question of how a plant, while at the planning stage, can best be designed, i.e., the most rational method of selecting process parameters, such as throughputs, pressures, temperatures, concentrations, etc. The problem of plant optimization only occurs, however, when there are design parameters which can be selected at will or can, at least, be varied within certain limits. This is however, virtually always the case. There is then a need for an optimization strategy in the form of an objective function which is either reduced to a minimum, where lowest possible production costs are the target, for instance, or advanced to a maximum, where high profitability is the aim. The inclusion of engineering economy functions, in the form, namely, of the net present value function and the internal rate of return function for definition of such objective functions, provides better defined information on the best possible choice of process parameters than was possible with methods previosly used, such as the annuity method, for instance. One obtains different values for the process parameters to be optimized, depending on the objective functionused, and therefore different investment costs and utility consumptions. These differences are delineated and quantified using a process-engineering example in the course of the following discussion. The example selected is a gas scrubber, the function of which is to remove and recover useful components from a flow of gas. The specific problem examined is one of heat recovery.     

混沌粒子群算法及其在生化过程动态优化中的应用

化工过程的动态优化,大多较为复杂,有相当的难度.新近发展的粒子群优化算法,基于群智能机理,适于求解连续问题,但它不具备遍历特性,影响了全局搜索能力.本文拟引入混沌机制,以混沌变量的遍历性改进粒子群算法,使其更全面地获取目标函数的有用信息,并反映到逐代更新的个体极值和群体极值中,可更有效地带领粒子群移向最优解,提高了全局搜优效率.由此构建为混沌粒子群算法,经多个性能测试,表明其搜索能力优于经典粒子群算法,引入混沌机制是有效的.将其用于Park-Ramirez生物反应器补料流率的动态优化,也取得了满意的效果.     

Pyrolysis schedule optimization of benzoxazine-derived carbon/carbon composites through reaction rate optimization

Carbon-carbon composites (CCCs) are a form of carbon-fiber reinforced materials that exhibit excellent thermomechanical properties under extreme environmental conditions. To expand the applicability of CCCs, the fabrication process must be modified to reduce the cost or processing time. An optimization of this fabrication process was proposed for a thermoset benzoxazine-derived carbon-carbon composite and resulted in a 7 – hour pyrolytic schedule. This abbreviated schedule was achieved using a multi-stage nth – order kinetics model to limit individual reaction rates. These imposed limits reduced the internal pressures generated during thermal processing preventing layer separations and fiber rupture. The results of this modification were evaluated post heat treatment, via X-Ray 3D Computed Tomography, to ensure that the porous microstructure was fully interconnected with minimal closed void volume. Considering the absence of sample failure and closed void volumes of <1%, the pyrolysis schedule optimization was deemed successful in terms of producing a shortened cycle for a thermoset-derived CCC. To define the limitations of the optimization's applicability, a 1D model was proposed to predict the internal pressure generated during the final ramp as a function of decomposition kinetics, the through-thickness length, and the air permeability. Analysis of these predicted pressures resulted in a design chart that provided the upper bounds of the optimization protocol as it relates to sample thicknesses ≤50 mm and applied ramp rates ≤40 °C min⁻¹.     

分级优化用于边值固定的化工动态优化问题

针对边值固定的化工动态优化问题, 提出了分级优化策略, 包括约束优先与目标优先两种方案, 它们的基本思想是将原问题转化为一系列的边值无约束问题, 采用目前成熟的优化算法加以集成即可实现. 对于控制变量受箱型约束的问题, 采用三角函数转换将其转化为控制无约束问题. 分级优化策略避免了罚函数策略的缺陷. 实例研究显示了分级优化策略能以足够的精度满足边值约束, 三角函数转换法是可行的.     

改进的精英教学优化算法及其在甲醇合成优化中的应用

为了提高精英教学算法（ETLBO）的寻优能力,特别是精度差、寻优速度慢的问题,提出改进的精英教学算法。首先,通过自主学习过程,加强对优质解所在区域的局部勘探,提高算法的寻优效率。其次,引入“差异化帮扶”思想及自适应机制,对不同水平的学生施予适宜的、灵活的学习方式,有针对性的帮助,平衡了算法的勘探速度、精度。通过增加学生间的交流次数,提高了算法的全局勘探能力。标准函数优化结果表明,改进后的算法在寻优能力和勘探效率两方面都有明显提高。最后,建立甲醇合成的机理模型,将改进后的算法应用于甲醇合成过程的优化,取得了良好的效果。     

基于差分进化粒子群混合优化算法的软测量建模

针对乙烯生产过程中,用传统方法难以直接完成对乙烯收率的在线测量的问题,提出了一种新型差分进化粒子群混合优化算法,建立了乙烯收率软测量建模。改进算法将优化过程分成两阶段,两分群分别采用粒子群算法和差分进化算法同时进行。迭代过程中引入进化速度因子进行算法局部收敛性判断,通过两个群体间的信息交流阻止算法陷入局部最优。对高维复杂函数寻优测试表明,算法的整体优化性能均强于基本粒子群算法和差分进化算法。应用结果表明,基于改进算法的软测量模型具有测量精度较高、泛化性能较好等优点。     

Operational optimization of industrial steam systems under uncertainty using data-Driven adaptive robust optimization

This article addresses the operational optimization of industrial steam systems under device efficiency uncertainty using a data-driven adaptive robust optimization approach. A semiempirical model of steam turbine is first developed based on process mechanism and operational data. Uncertain parameters of the proposed steam turbine model are further derived from the historical process data. A robust kernel density estimation method is then used to construct the uncertainty sets for modeling these uncertain parameters. The data-driven uncertainty sets are incorporated into a two-stage adaptive robust mixed-integer linear programming (MILP) framework for operational optimization of steam systems to minimize the total operating cost. Integer variables are introduced to model the on/off decisions of the steam turbines and electrical motors, which are the major energy consumers of the steam system. By applying the affine decision rule, the proposed multilevel optimization model is transformed into its robust counterpart, which is a single-level MILP problem. The proposed framework is applied to the steam system of a real-world ethylene plant to demonstrate its applicability. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16500 2019