低浓度DMF废水的热集成回收新工艺

针对普通精馏回收低浓度废水中的DMF能耗高问题,本文提出了6种热集成节能方案,尽可能充分利用各物流的潜热和显热,以期达到降低能耗的目的:1)初始三效精馏工艺;2)增加多级进料预热器的三效精馏;3)四效精馏工艺;4)带热泵的三效精馏;5)单塔热泵精馏;6)改进的单塔热泵精馏。并应用Aspen Plus流程模拟软件对6种节能方案进行了模拟计算。结果表明:对于低浓度DMF废水体系,多效精馏和热泵精馏与普通单塔精馏相比,均有显著的节能效果;增加多级进料预热系统,充分利用物流的潜热和显热,能耗明显降低;四效精馏与增加多级进料预热器的三效精馏节能效果相当;热泵精馏比多效精馏更加节能;改进的单塔热泵精馏最节能,节能率达到79%,每小时仅需3.6t的低压蒸汽,就可处理10t的废水,回收0.5t的DMF。六种节能方案比普通精馏节省约37.1%、47.6%、47.2%、67.9%、73.3%、79.0%的能耗。本文的研究结果对老装置的改造和新装置的设计有一定的指导意义。     

多效精馏流程的优化设计计算

本文针对多效平流，顺流，逆流三种精馏过程分别建立了设计计算的通用数学模型，并以年总消耗费用最小为目标函数，对三种精馏过程通过实例进行了优化设计计算，结果表明，文中给出的优化设计计算方法，可方便地求出多效精馏过程设计所需的基础数据及系统所需的最小加热蒸汽量。     

多效精馏烷烃混合物的Aspen Plus模拟分离

精馏是石油化工能耗最大的单元操作之一,因此对其过程的节能研究非常重要。本文利用Aspen Plus化工模拟软件中的RADFRAC模块和PENG-ROB热力学计算模型,系统模拟了多效精馏顺流(DQF、IQF)、逆流(DQB、IQB)及常规精馏(DQ、IQ)的烃类混合物异丁烷/正丁烷/正己烷的分离。根据各组分的分离要求,通过灵敏度分析优化各工艺参数;同时比较了各精馏构造的有效能损失和热力学效率,并计算了它们的年度总费用(TAC)。结果表明:与间接精馏方案IQ相比,精馏方案IQF、DQB、DQ分别节能20.6%、16.0%、8.4%;热力学效率方面,多效精馏IQF为19.6%,DQB22.2%,普遍比常规精馏的DQ10.0%、IQ10.3%要高;至于年度总费用(TAC),相比于IQ,DQB多耗资12.1%,而IQF、DQ少耗资9.0%和8.7%。综合节能效果、热力学效率、年度总费用得知,IQF这种多效精馏方案最优。     

化工精馏高效节能技术开发及应用

高效节能精馏流程通过优化构造流程结构,合理利用精馏塔顶蒸汽的冷凝潜热,可以大幅度降低精馏过程的能耗.本文系统研究各种节能型精馏流程,重点分析3种热泵精馏、4种多效精馏和4种热偶精馏的流程特点,建立节能型精馏流程优化模拟及分析系统,将模拟优化计算的结果与普通精馏流程对比.结果表明,热泵精馏节能率可达80%以上,多效精馏40%以上,热偶精馏20%以上,为工业化应用提供设计依据和友好的平台.     

用序贯模块法模拟多组分完全复杂的精馏流程 （Ⅰ）模型与算法策略

本文基于序贯模块法开发了对由多个侧线汽提和／或侧线精馏塔构成的复杂精馏流程的模拟算法．针对所涉流程及序贯模块法的特点，采取了多种加速收敛的策略，这些策略能够显著地改善模拟算法的收敛性能，从而提高其收敛的速度和稳定性．     

复杂平流多效精馏系统能量集成模型及过程模拟

为了更合理利用能量,将平流多效精馏系统中各效塔底产品、第1效至第n-1效塔顶产品及首效再沸器中加热生蒸汽的冷凝液用于预热原料液,建立由多效精馏及多级预热2个子系统构成的复杂平流多效精馏系统的能量集成模型。用于模拟具有切点系统的高度非理想溶液的平流多效精馏过程,用迭代法结合矩阵法求解。结果表明:用系统余热预热原料液可节省加热生蒸汽消耗量25%～35%,是1种非常有效的节能措施;各效精馏塔回流比及再沸器的有效传热温差、首效塔底再沸器中加热生蒸汽压力(或温度)、末效塔顶压力(或温度)、原料液的预热温度等参数,对平流多效精馏系统的操作费用及设备费用影响很大。     

多效精馏回收DMF工艺的研究

针对传统的DMF回收工艺的高能耗,本文提出多效精馏回收DMF的双塔、三塔和四塔工艺流程.利用ASPEN PLUS 化工模拟软件中的RADFRAC模块和NRTL热力学计算模型,系统模拟了多效顺流、多效逆流回收工艺,以处理不同浓度的DMF废水工况.并以能耗最低为目标函数、各塔顶废水蒸出量为决策变量,确定各种回收工艺的最佳条件(包括塔压分布).利用ASPEN PLUS软件中的流体力学计算模块,算出各塔的设备参数,包括各附属冷换设备的面积.并以多效精馏与单塔精馏总费用的相对值最低为目标,确定的最佳DMF回收分离方案为三塔逆流精馏工艺,模拟计算出该工艺各效最佳工艺操作条件.研究结果表明,处理浓度分别为10%、20%和25%的DMF废水时,与单塔精馏工艺相比,三塔逆流精馏工艺节能分别为78%、79%和87%.     

模糊序贯决策算法优化设计及Matlab实现

对模糊序贯决策算法进行了优化设计,构建了一种基于模糊关系矩阵的模糊序贯决策算法,并利用Matlab程序实现了算法,给出了源程序,通过实例分析说明了算法的简洁性。     

平流双效精馏过程的模拟和优化

对平流双效精馏过程进行了详细研究，建立了最优化设计的数学模型并编制了通用计算机最优化设计程序，用实例对所建立的数学模型及优化设计方法进行了考核计算，发现平流双效精馏节能效果显著，具有很高的应用价值。     

进料组成对双效精馏节能效果的影响

以乙醇-水系统为研究物系,通过计算机稳态模拟计算,考察进料组成对三组不同结构的节能型双效精馏流程节能率的影响。研究结果表明:双效精馏流程A随进料中轻组分含量的增加,节能率均呈快速递增趋势,最高节能率均可达50%左右,2种流程均适合于进料中轻组分含量较高的体系分离。双效精馏流程B在进料组成0.2~0.4范围内,随进料组成的变化节能率变化很小,但在进料组成为0.5~0.6范围内,2种流程的节能率均有较大幅度增长,最高节能率分别达到34.29%和31.22%。双效精馏流程C在进料组成0.2~0.4范围内,2种流程的节能率受组成变化影响甚微,但在相同的进料组成下,双效精馏C-2的节能率高于双效精馏C-1,而在进料组成为0.5~0.6范围内,2种流程的节能率有很大的差别,双效精馏C-1节能率最低下降到6.2%,而双效精馏C-2节能率上升到24.89%。研究结果可为效精馏流程的选择提供理论依据。     

Sequential versus simultaneous approach in the location and design of two new facilities using planar Huff-like models

Companies frequently decide on the location and design for new facilities in a sequential way. However, for a fixed number of new facilities, the company might be able to improve its profit by taking its decisions for all the facilities simultaneously. In this paper we compare three different strategies: simultaneous location and independent design of two facilities in the plane, the same with equal designs, and the sequential approach of determining each facility in turn. The basic model is profit maximization for the chain, taking market share, location costs and design costs into account. The market share captured by each facility depends on the distance to the customers (location) and its quality (design), through a probabilistic Huff-like model. Recent research on this type of models was aimed at finding global optima for a single new facility, holding quality fixed or variable, but no exact algorithm has been proposed to find optimal solutions for more than one facility. We develop such an exact interval branch-and-bound algorithm to solve both simultaneous location and design two-facility problems. Then, we present computational results and exhibit the differences in locations and qualities of the optimal solutions one may obtain by the sequential and simultaneous approaches.     

Constrained MIMO dynamic discrete-time modeling exploiting optimal experimental design

This article presents a new multiple input, multiple output (MIMO) constrained discrete-time modeling (DTM) approach for dynamic block-oriented processes that does not require the nonlinear steady state characteristics to be known prior to model development. This approach uses an efficient statistical experimental design to provide design points for sequential step tests. The DTM is developed from this data in two stages. In the first stage, the ultimate response (steady state) model is determined from just the ultimate response data of the sequential step tests. In the second stage, the dynamic parameters are estimated under the constraint of the fitted ultimate response model obtained in the first stage. The constrained formulation is given for MIMO Hammerstein and Wiener block-oriented systems. Comparison of the proposed constrained DTM method is made with unconstrained DTM and constrained continuous-time modeling (CTM). Prediction accuracy of the proposed method is significantly better than unconstrained DTM and comparable to constrained CTM for the process studied.     

一类参数线性系统辨识的最优输入设计

对未知参数进行估计时,得到的结果与激励系统所选用的输入信号有较大的关系.针对一类参数可线性化系统,本文提出了一种利用多维同步正交信号和直接配点法设计最优输入信号的方法.首先根据最小二乘原理,利用法矩阵构造Mayer型性能指标函数.然后利用不同频率的正弦基函数构造相互正交的多维输入,通过添加幅值与相位的等式约束,使得输入信号在初/末时刻取值均为零.之后采用直接配点法离散状态变量,将动态的最优输入问题转化为静态的非线性规划问题.最后采用从可行解到优化解的串行优化策略进行求解,不仅提高了寻优效率,还确保了优化结果为原问题的可行解.仿真结果表明,与工程上常用的输入信号相比,本文方法获取的最优输入信号可以提高参数估计精度并加快收敛速率.     

自底向上优化神经网络的方法

引荐了一种自动优化神经网络的新方法。这种启迪方法综合采用了相关有效算法,通过快速自底向上构造神经网络算法,可以获得优化结构的神经网络,即时选定参数算法动态优化神经网络的学习参数,并且快速交叉校验算法为解决过度适应问题提供了捷径。实验证明,这种启迪方法能自动有效地优化神经网络,与其它算法相较而言,具有更好的归纳性能、优化的网络结构和更快的学习速度。     

Product variety optimization under modular architecture

In this paper, product variety design is discussed under an optimization viewpoint. Product variety design means the challenge to simultaneously design multiple products for achieving higher optimality beyond ordinary design methods for a single product. When the possibilities of computational optimization for product variety design under fixed product architecture are explored, optimization is demanded to determine the contents of modules and their combinations under fixed modular architecture. This indicates that product variety optimization includes three classes of optimization problems: attribute assignment, module combination, and simultaneous design of both. Following problem classification, the domains and situations of such optimization problems are investigated. Then, two typical optimization examples are demonstrated through aircraft design for simultaneous optimal attribute assignment and through design of television receiver circuits for optimal module combination, respectively. The discussion concludes with the roles of problem classification and the direction of future works.     

Adaptive value function approximation for continuous-state stochastic dynamic programming

Approximate dynamic programming (ADP) commonly employs value function approximation to numerically solve complex dynamic programming problems. A statistical perspective of value function approximation employs a design and analysis of computer experiments (DACE) approach, where the “computer experiment” yields points on the value function curve. The DACE approach has been used to numerically solve high-dimensional, continuous-state stochastic dynamic programming, and performs two tasks primarily: (1) design of experiments and (2) statistical modeling. The use of design of experiments enables more efficient discretization. However, identifying the appropriate sample size is not straightforward. Furthermore, identifying the appropriate model structure is a well-known problem in the field of statistics. In this paper, we present a sequential method that can adaptively determine both sample size and model structure. Number-theoretic methods (NTM) are used to sequentially grow the experimental design because of their ability to fill the design space. Feed-forward neural networks (NNs) are used for statistical modeling because of their adjustability in structure-complexity . This adaptive value function approximation (AVFA) method must be automated to enable efficient implementation within ADP. An AVFA algorithm is introduced, that increments the size of the state space training data in each sequential step, and for each sample size a successive model search process is performed to find an optimal NN model. The new algorithm is tested on a nine-dimensional inventory forecasting problem.     

Integrated topology and controller optimization of motion systems in the frequency domain

In the precision mechatronics industry the limits of current technology are expanded by a very tight integration of the design disciplines involved, such as structural mechanics and control systems technology. In this article a framework is presented which allows the simultaneous and integrated design of a structure and controller. The structure is designed using topology optimization and the objectives and constraints are related to the closed-loop control performance and defined in the frequency domain. For simple examples it is shown that this approach leads to at least as good performance as a sequential approach consisting of eigenfrequency optimization followed by controller tuning, and leads to lighter designs. The framework allows rapid development of prototype designs, which may save a number of the costly design iterations which are currently made in industrial practice. Examples are found in the semiconductor industry, microscopy, micro–electromechanical devices, medical devices and robotics.     

MATLAB并流多效蒸发过程的模拟

并流多效蒸发作为化学工业中常用的操作过程,蒸汽的消耗量、各效溶剂蒸发量、各效蒸发器的传热面积等需要计算的参数,是并流多效蒸发操作和设计的主要依据,因此对多效并流蒸发过程的模拟具有重要的意义。而已知的多效并流蒸发模拟计算的数学模型,常因模型中存在过多的近似简化或模型求解算法太复杂,造成模型的精确性低、可操作性差。联立多效蒸发系统的物料恒算方程、热量恒算方程、传热速率方程、汽液相温度平衡方程及热力学数据方程,建立了多效蒸发设计计算的数学模型,模型思路简单,推导严密,系统性强。提出了非线性方程组的MATLAB工具箱函数解法,并分析了1个三效蒸发实例,计算结果表明该方法方便快捷,收敛性好,该模型及其求解方法值得推广使用。     

A dynamic programming approach to sequential pattern recognition

This paper presents the dynamic programming approach to the design of optimal pattern recognition systems when the costs of feature measurements describing the pattern samples are of considerable importance. A multistage or sequential pattern classifier which requires, on the average, a substantially smaller number of feature measurements than that required by an equally reliable nonsequential classifier is defined and constructed through the method of recursive optimization. Two methods of reducing the dimensionality in computation are presented for the cases where the observed feature measurements are 1) statistically independent, and 2) Markov dependent. Both models, in general, provide a ready solution to the optimal sequential classification problem. A generalization in the design of optimal classifiers capable of selecting a best sequence of feature measurements is also discussed. Computer simulated experiments in character recognition are shown to illustrate the feasibility of this approach.