基于BP神经网络和遗传算法的硫化工艺参数优化

采用BP神经网络建立硫化工艺参数与性能指标的网络模型,以神经网络预测的性能输出作为目标函数的求解方法,以遗传算法作为硫化工艺参数的优化方法,建立硫化工艺参数的优化系统.对橡胶联轴器硫化工艺参数优化的实际应用效果表明,优化后可大大提高橡胶制品的质量.     

控制与工艺集成优化设计研究进展

控制与工艺集成优化设计是一种系统化的过程优化设计方法。以典型二元精馏塔设计为例阐述了其过程系统的数学描述，给出其最优工艺设计和控制设计的求解框架和求解策略，以及其中混合整数动态优化问题(MIDO)的求解方法。通过与传统的工艺与控制分步序贯设计比较，讨论了二者各自的优势和不足，指出控制与工艺集成优化设计具有良好的发展前景。     

过程系统能量集成同步最优综合法

将换热网络超结构混合整数非线性规划多目标同步最优综合方法进一步扩展到与过程系统的联合优化,提出了改进的过程热集成同步综合方法,并以反应分离过程与换热网络能量集成为例,建立了同步优化超结构模型,采用混合整数非线性规划的遗传算法求解,可同时得到热集成系统最优的流程结构和操作条件。     

基于改进状态转移算法的串级平推流反应器动力学参数估计

实际化工过程反应系统通常由若干个相互关联的平推流反应器串级构成，要建立其机理模型，并估计其动力学参数，就必须反复求解大规模非线性微分方程组，计算代价较高。针对智能优化算法在求解昂贵优化问题时计算成本过高，而工业过程模型往往只需要找到一组满意解即可的特点，提出了一种基于改进状态转移算法的串级平推流反应器动力学参数估计方法。该算法在保留标准状态转移算法全局寻优能力和快速收敛能力的同时，利用对立算子优化初始种群，并根据误差阈值判定满意解中止条件，可有效节约计算时间。以实际炼油企业的加氢裂化装置为研究对象，采用所提方法对其反应系统的动力学参数进行估计，仿真结果表明了所提方法的有效性。     

磷酸奥司他韦关键中间体的合成工艺优化研究

研究磷酸奥司他韦关键中间体的合成工艺，并对工艺参数进行了优化。以环氧化合物为起始物料，对甲苯的体积、氯化镁的用量、反应温度、淬灭温度进行了筛选，对其稳定性进行了研究，优化后的工艺路线反应条件温和，生产过程安全，适合工业化生产。     

复杂壳体类塑料件气体辅助注射成型工艺参数优化研究

以21英寸彩电前壳作为研究对象,将Moldflow 2010作为CAE模拟试验平台,以熔体温度、模具温度、熔体注射时间、气体延迟时间、气体压力为关键工艺因素,考察了复杂壳体类塑料件气体辅助注射成型(GAIM)时制件的翘曲变形量和气体穿透情况。以正交试验设计方法为基础,利用遗传算法并结合径向基神经网络建立GAIM工艺参数优化系统,可用于工艺参数组合的快速确定,为GAIM过程中工艺参数优化提供了一种新的求解思路。     

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。     

磺化工艺过程控制方案

以多管膜式磺化反应工艺为例,描述了磺化反应的制取方法、生产过程及控制流程。详细介绍了反应过程中对温度、压力、流量等反应条件的控制方案。     

霍夫曼重排反应过程的研究进展

霍夫曼重排反应作为制备伯胺及其衍生物的一种重要手段,在药物合成和功能材料制备等领域有广泛的应用。传统霍夫曼重排反应工艺存在反应效率低、流程烦琐和安全性差等问题。针对以上问题,研究者们主要从反应条件优化和过程强化两方面入手。通过反应条件优化,发展了许多温和、高选择性的反应工艺条件,为不同酰胺底物的反应方案设计提供了更多选择;微波辅助、电化学合成以及微化工技术等新型反应过程强化技术的出现,为实现高效绿色的霍夫曼重排反应创造了有利的条件。本文重点阐述了霍夫曼重排反应在反应条件优化和过程强化方面的最新研究进展。在此基础上,对该反应未来的研究方向进行了展望。     

基于多目标教学优化算法在二甲苯吸附分离过程优化中的应用

以C8芳烃混合物的吸附分离过程作为研究对象, 应用多目标教学优化算法(multi-objective teaching-learning-based optimization algorithm, MOTLBO)对模拟移动床多目标优化问题进行求解。采用TMB方法, 建立了模拟移动床模型, 并对两个典型的模拟移动床多目标操作优化问题进行了优化设计。通过与NSGA-Ⅱ算法的比较, 证明了多目标教学优化算法在求解模拟移动床多目标优化问题上的有效性和优势。此外, 还分析了抽出液流量、抽余液流量以及步进时间等对多目标优化非劣解的影响, 优化结果为模拟移动床分离过程的工艺设计和操作提供了依据。     

Process optimization at the design stage with an objective function linear in profit and investment

In the optimization of chemical processes at the design stage, it is suggested that the optimization of a linear function of profit and investment allows the economics of the process to be presented in a particularly simple way. All technical considerations have been taken into account and the final design choice can be made from a purely economic point of view. The results can be represented most conveniently by curves in the profit-investment plane. The approach is illustrated with simplified examples of cross-current extraction and a first-order reversible reaction carried out in a sequence of continuous stirred tank reactors.     

Verfahrenstechnik für den Umweltschutz

Industrial process engineering and environmental protection . Industrial process engineering involves not only the realization of chemical-physical processes but also the optimization of the economics of a plant and the minimization of environment pollution. Since the incorporation of additional facilities in a process for the reduction of emission has been brought to a high technical standard in recent years, more and more attention will now be directed to achieving a high selectivity in favor of the product in the chemical reaction by suitable process control. It is shown with the aid of schematically represented mass balances and cost diagrams that emission can be reduced by process engineering optimization of a process, leading to a decrease in both the costs incurred from environmental pollution and the costs for the preventative measures taken.     

化工过程分离循环系统的多目标模糊优化研究

Separation-recycle system is an important part in chemical process, and its optimization is a multiobjective problem. In this paper the process optimization procedure is proposed. The fuzzy optimization algorithm with the concept of relative importance degree (RID) is utilized to transfer multi-objective optimization (MO-O) model into a single-objective optimization (SO-O) framework. The treatment of process condensate in synthesisa mmonia plant is taken as example to illustrate the optimization procedure, and the satisfactory result demonstrates feasibility and effectiveness of the suggested method.     

Analysis of the particle swarm algorithm in the optimization of a three-phase slurry catalytic reactor

The Particle Swarm Optimization (PSO) method was employed to optimize an industrial chemical process characterized by being difficult to be optimized by conventional deterministic methods. The chemical process is a three phase catalytic slurry reactor (tubular geometry) in which the reaction of the hydrogenation of o-cresol producing 2-methyl-cyclohexanol is carried out. The optimization problem was formulated considering as input variables the operating conditions of the reactor and as objective function the maximization of productivity, subject to the environmental constraint of conversion. The process was represented by a multivariable non-linear rigorous mathematical model and in order to solve the optimization problem, the performance of the PSO algorithm was evaluated considering four sets of parameters values suggested by the literature. PSO demonstrated to be efficient and robust to solve the constrained optimization problem, independently of the values of the PSO parameters. The solution of the rigorous mathematical model of the reactor was associated with a high computational burden, and although the PSO algorithm presented high rate of convergence, the attempt to make possible the optimization in a timeframe suitable to real time applications failed because the algorithm lost robustness (fraction of the number of runs the algorithm reached the optimization goal) when run with a reduced number of function evaluations. Therefore, if this type of application is desired, simplified mathematical models with fast and simple numerical methods must be preferred.     

OPTIMIZATION OF HETEROGENEOUS CATALYTIC PROCESSES IN FLUIDIZED BED

A number of large-scale chemical processes is carried out in fluidized-bed reactors (FBR). Optimization of processes in FBR is thus of great importance both for the increase of efficiency of the existing plants and for the design of new ones. Selection of optimal conditions for exothermic processes should recognize conditions for heat removal and multiplicity and stability of stationary states rather than just the reaction kinetics and fluidized bed hydrodynamics. Mathematically this problem is rather cumbersome. A process in FUR is described by the boundary problem for differential equations while statement of an optimization problem requires some additional limitations to be imposed on concentrations at the input and output of the reactor, on temperature, design parameters etc. One should take special notice of the limitation on the process stability which (owing to great reverse thermal agitation) is much more important in FBRs than in packed-bed reactors.     

OPTIMIZATION OF HETEROGENEOUS CATALYTIC PROCESSES IN FLUIDIZED BED

A number of large-scale chemical processes is carried out in fluidized-bed reactors (FBR). Optimization of processes in FBR is thus of great importance both for the increase of efficiency of the existing plants and for the design of new ones. Selection of optimal conditions for exothermic processes should recognize conditions for heat removal and multiplicity and stability of stationary states rather than just the reaction kinetics and fluidized bed hydrodynamics. Mathematically this problem is rather cumbersome. A process in FUR is described by the boundary problem for differential equations while statement of an optimization problem requires some additional limitations to be imposed on concentrations at the input and output of the reactor, on temperature, design parameters etc. One should take special notice of the limitation on the process stability which (owing to great reverse thermal agitation) is much more important in FBRs than in packed-bed reactors.     

计算机辅助现有化工过程操作最佳化

应用计算机技术来改进现有化工过程操作,对提高化工企业的经济效益有重要的意义.模拟调优通过计算机辅助过程优化技术寻求过程系统的最佳条件,可以使过程达到最佳运行.本文对基于序贯二次规划的两层法作了改进,在二次规划中引入额外变量,构造了新的二次规划问题,并提出了此规划问题的分解策略,从而保证分解矩阵为奇异时仍能对其进行分解.本文提出的两层次-序贯二次规划法,数值稳定性好,收敛较快.用此算法对合成甲醇过程进行优化计算得到了满意的结果,巳在工业装置上实现.     

Discrete-time contraction constrained nonlinear model predictive control using graph-based geodesic computation

Modern chemical processes need to operate around time-varying operating conditions to optimize plant economy, in response to dynamic supply chains (e.g., time-varying specifications of product and energy costs). As such, the process control system needs to handle a wide range of operating conditions whilst optimizing system performance and ensuring stability during transitions. This article presents a reference-flexible nonlinear model predictive control approach using contraction based constraints. Firstly, a contraction condition that ensures convergence to any feasible state trajectories or setpoints is constructed. This condition is then imposed as a constraint on the optimization problem for model predictive control with a general (typically economic) cost function, utilizing Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade-off between the rate of target trajectory convergence and economic benefit (away from the desired process objective). The proposed approach is illustrated by a simulation study on a CSTR control problem.     

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.