粗糙集与模糊推理相集成的过程建模方法及其应用

针对复杂化工过程机理建模困难的问题,采用适应性较广的模糊方法经验建模,鉴于模糊法对于高维、强相关的样本数据很难导出规则,本文提出先用粗糙集方法消除冗余性,约简系统,获取最小规则集,在此基础上构建结构合理、参数可适当初始化的模糊-神经网络,并采用LM算法训练,收敛速率快,模型预测性能良好.将此法用于PTA装置溶剂脱水塔精馏过程的经验建模,效果令人满意,性能优于现代统计方法和前馈神经网络.     

基于分子矩阵的炼油过程全厂模拟与优化

利用物流分子矩阵对多个生产单元过程进行建模,将单元过程的分子矩阵模型和全厂模拟优化相结合,采用分解优化策略对原料、产品和操作参数等进行优化.分子矩阵为模拟优化提供了更详尽的信息,尤其加强了对环境影响和产品质量的考虑.结果表明分子信息有助于掌握物质在炼油过程中的变化情况,基于分子矩阵模型的优化可以在满足产品质量和环境要求的条件下最大程度地提高企业的效益.     

催化裂化装置主分馏塔的过程模拟研究

综合现有相关文献的方法和模拟经验,采用Aspen Hysys 9.0对某石化220万吨/年催化裂化装置主分馏塔进行建模研究,逐步分析建模过程中的关键点。采用PR物性方法,采用Aspen Hysys的Petroleum Assay Manager功能建立组分列表和蒸馏数据,采用"反推合成法"进行进料表征,采用单个带冷凝器的精馏塔模型,脱过热段的人字板用两块塔板模拟。通过模型调整,综合对比全塔温度分布、产品蒸馏数据以及中段取热数据,使模拟结果与实际标定数据吻合。     

基于ANSYS的大型风机叶片建模研究

有限元校核分析是叶片设计过程中的重要一环,而叶片外形与结构的复杂性决定了其有限元模型的建立将十分繁琐.本文针对750KW风机叶片,利用有限元软件ANSYS,详细介绍了大型叶片的建模过程,并对建模方法提出了一些建议.在建模过程中,采取自下而上的方法建立叶片的实体模型,利用壳单元SHELL91和SHELL99与实常数相结合,在划分网格时采用面赋值法和单元赋值法来模拟复合材料的铺层结构.通过设置SHELL91的单元参数,利用其"三明治"的特性模拟叶片的夹芯结构.叶片有限元模型的建立为大型叶片的设计与分析校核做了铺垫.     

玻璃配合料-成品质量定量关系模型设计与分析

结合某瓶罐玻璃生产企业的实际情况,对玻璃配合料与成品质量的关系进行研究,提出了基于模糊粗糙集和ANFIS相结合的定量模型建模方法。首先,针对所研究问题的特点,构建了基于TL模糊相似关系的模糊信息系统,该相似关系在实际生产过程中更容易满足,进而,采用自适应神经模糊系统ANFIS对配合料配比与玻璃成品质量的关系进行了定量建模,最后,采用某企业实际生产数据对上述模型进行了应用验证。     

基于分子矩阵的炼油过程的全厂模拟

针对复杂的炼油生产过程及其物流成分,采用分子矩阵表征相关的物流分子组成,对催化重整、加氢脱硫、汽油稳定塔和油品调和四个过程建立了基于分子矩阵的过程模型. 通过物性-分子矩阵转换关系可以方便地从已知物流物性计算分子矩阵以及由分子矩阵得到物流物性. 基于分子矩阵的模型计算不仅可以提供比传统虚拟组分模型更为详尽的分子信息,而且可以统一传统建模中不同过程虚拟组分和集总参数之间的差异,将多个过程模型集成实现全厂的模拟.     

集成轻烃回收单元代理模型的氢气网络多目标优化

当前炼油企业氢气需求持续增长,导致炼厂成本及生产过程温室气体排放增加,炼油企业通过增设轻烃回收单元对氢气和轻烃组分进行回收利用,能有效缓解这一现状。因此,在氢气网络优化中有必要考虑轻烃回收单元。本研究提出了一种集成轻烃回收单元的氢气网络多目标数学规划模型,对轻烃回收单元采用代理模型建模方法,解决了直接嵌入严格机理模型可能导致的高计算成本问题,以总年度费用最小为优化目标,同时将系统的环境影响也纳入优化目标。实例计算表明,所提出的方法能够有效降低氢气网络的年度费用及温室气体排放,并揭示了集成轻烃回收单元的氢气网络经济性能与环境影响之间的权衡关系,为工业应用提供了一定的理论基础。     

化工过程系统的研究和开发

介绍采用面向对象应用框架法开发的化工过程仿真系统的建模思想和开发过程。抽象的基本模型满足任何化工工艺过程的设备单元 ,保证了模型的通用性。用稳定的抽象设备单元对象和工艺物流对象构成系统的主体框架 ,化工工艺过程中涉及的所有设备和物流实例对象都可以直接或间接从构成系统框架的抽象对象派生。系统具有良好的通用性、稳定性和可扩充性。     

基于分阶段的LSSVM发酵过程建模

发酵过程建模是研究微生物发酵的重要课题,基于模型可实现被测参量的软测量、系统的优化控制。鉴于引入混合核函数的最小二乘支持向量机在过程建模中具有优良表现,采用基于混合核函数的最小二乘支持向量机建模。但由于发酵过程周期较长,最小二乘支持向量机的全局模型预测精度难以保证,算法复杂度很高,因此提出一种分阶段建模方法。首先,选择表征阶段特性的辅助变量,利用模糊C均值聚类算法对样本数据聚类,将发酵过程分成不同的阶段,然后为各个阶段分别建立最优混合核最小二乘支持向量机局部模型,最后将局部模型合成构成过程的完整模型。将此方法应用于青霉素发酵过程和重组大肠杆菌发酵过程中,验证了该方法的有效性。     

基于水管埋置单元模型的大体积混凝土温控研究

水管冷却是控制大体积混凝土温度裂缝产生的重要措施,采用水管埋置单元模型可以有效模拟冷却水管对混凝土的冷却作用。为了克服该方法水管须穿过混凝土单元结点时模拟才准确的缺陷,提出了一种改良的水管埋置单元模型。在该模型中,水管若不在单元结点上,可以将水管作为结点将常规的水管埋置单元进行再划分。在计算过程中,水管可以从单元的任意位置穿过,程序可以自动根据水管在单元中的位置判断是否将单元进行再划分。算例分析表明,该模型可以有效反映水管对混凝土的冷却作用,适用于实际工程计算。     

On the fuzzy logic for rule based modeling and process simulation

Some chemical processes are not well understood, and not easy to analyze and simulate with conventional mathematical approaches. A rule based approach is proposed in this paper to model such process units. Conversion between fuzzy and crisp numbers at the input and output of these units is discussed in details and a slicing conversion approach is proposed. It is shown from a case study that the approach provides a way to incorporate fuzzy models into conventional process simulators.     

pH控制在管状流动反应器中的应用(英文)

Tubular flow reactors are mainly used in chemical industry and waste water discharged units. Control of output variables is very difficult because of the existence of high dead-time in these types of reactors. In the present work, sodium hydroxide and acetic acid solutions were sent to the tubular flow reactor. The aim was to control pH at 7 in the nonlinear region. The pH control of a tubular flow reactor with high time delay and a highly nonlinear behavior in pH neutralization reaction was investigated experimentally in the face of the various load and set point changes. Firstly, efficiency of conventional Proportional-Integral-Derivative (PID) algorithm in the experiments was tested. Then self-tuning PID (STPID) control system was applied by using the ARMAX model. The model parameters were calculated from input–output data by using PRBS signal as disturbance and Bierman algorithm. Lastly, the experimental fuzzy control of pH based on fuzzy model was achieved to compare the success of fuzzy approach with the performance of other control cases studied.     

Application of pH control to a tubular flow reactor

Tubular flow reactors are mainly used in chemical industry and waste water discharged units. Control of output variables is very difficult because of the existence of high dead-time in these types of r...     

Online Flooding Supervision in Packed Towers: An Integrated Data‐Driven Statistical Monitoring Method

The development of simple and efficient monitoring methods for flooding supervision is an important but difficult task for the safe operation of packed towers. A data‐driven online flooding monitoring method named Bayesian integrated dynamic principal component analysis (IDPCA) is assessed. In the first step of IDPCA, using the fuzzy c‐means clustering method, the multivariate samples collected during plant operation are first classified into several groups. Then, in each subset a dynamic principal component analysis (DPCA) model is constructed to extract the process characteristics. To improve the monitoring performance, Bayesian inference is utilized to combine these DPCA models in a suitable manner. Consequently, the control limits are formulated using the probabilistic analysis. The superiority of IDPCA is illustrated using a lab‐scale packed tower by comparison with the conventional principal component analysis (PCA) and DPCA methods.     

Synthesis of robust water reuse networks for single-component retrofit problems using symmetric fuzzy linear programming

Water integration techniques can be used to minimize the utility water consumption and effluent generation of process plants through the implementation of reuse or recycle networks. There are a number of graphical and mathematical programming techniques available for the synthesis of such water reuse networks. However, effective use of these methods requires the availability of reliable process data, which in reality might be difficult to acquire. This paper describes a procedure for the synthesis of robust water reuse networks from imprecise data using symmetric fuzzy linear programming (SFLP). Two model variants, one based on mass exchange units and the other on source/sink allocation, are presented. Each variant is illustrated with a numerical example.     

DYNAMIC FUZZY ADAPTIVE CONTROLLER FOR pH

Control of pH processes is very difficult due to nonlinear dynamics, high sensitivity at the neutral point, and changes in the concentrations of known or unknown chemical species. In this study, a dynamic fuzzy adaptive controller (DFAC) with a new inference mechanism is proposed and applied for the control of pH processes. The DFAC consists of a low-level basic control phase with a minimum rule base and a high-level dynamic learining phase with an updating mechanism to interact and modify the control rule base. The DFAC can self-adjust its fuzzy control rules using information from the process during on-line control and create new fuzzy control rules or modify the present control rules using its learning capability from past control trends. The controller is evaluated by applying it to a weak acid-strong base pH process with input disturbances and to another pH process that involve that has changes in acidic/buffering streams. The results of the DFAC with the new inference mechanism are compared with the known inference mechanisms, the fuzzy controller, the conventional PI controller, and also with an adaptive PID controller. The proposed DFAC provides better performance for set point tracking of the pH and rejection of load disturbances and buffering affects.     

自校正分级模糊控制器的设计开发及应用

针对蒸发结晶生产过程中存在的液相温升速率和蒸发量耦合性较强、控制难度大的问题,设计开发了一个多变量自校正分级模糊控制器。生产实践表明,该自校正分级模糊控制器是解决多变量耦合控制问题的一种实用有效算法。     

ADAPTIVE FUZZY MODEL BASED PREDICTIVE CONTROL OF AN EXOTHERMIC BATCH CHEMICAL REACTOR*

An adaptive fuzzy model based predictive control (AFMBPC) approach is presented to track the desired temperature trajectories in an exothermic batch chemical reactor. The AFMBPC incorporates an adaptive fuzzy modeling framework into a model based predictive control scheme to derive analytical controller output. This approach has the flexibility to cope with different fuzzy model structures whose choice also lead to improve the controller performance. In this approach, adaptation of fuzzy models using dynamic process information is carried out to build a predictive controller, thus eliminating the determination of a predefined fixed fuzzy model based on various sets of known input-output relations. The performance of the AFMBPC is evaluated by comparing to a fixed fuzzy model based predictive controller (FFMBPC) and a conventional PID controller. The results show the better suitability of AFMBPC for the control of highly nonlinear and time varying batch chemical reactors.     

Modeling and predictive control using fuzzy logic: Application for a polymerization system

In this study, a predictive control system based on type Takagi‐Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require considerable effort in controller tuning. The copolymerization of methyl methacrylate with vinyl acetate was considered for analysis of the performance of the proposed control system. A nonlinear mathematical model which describes the reaction plant was used for data generation and implementation of the controller. The modeling using the fuzzy approach showed an excellent capacity for output prediction as a function of dynamic data input. The performance of the projected control system and dynamic matrix control for regulatory and servo problems were compared and the obtained results showed that the control system design is robust, of simple implementation and provides a better response than conventional predictive control. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Fuzzy Control Of A Fluidized Bed Dryer

Fluidized bed dryers are utilised in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzy control inherently compensates for-process nonlinearities and exhibits more robust behaviour. In this study, a fuzzy logic controller is proposed; its design is based on a heuristic approach and its performance is compared against a conventional PI controller for a variety of responses. It is shown that the fuzzy controller exhibits a remarkable dynamic behaviour, equivalent if not better than the PI controller, for a wide range of disturbances. In addition, the proposed fuzzy controller seems to be less sensitive to the nonlinearities of the process, achieves energy savings and enables MIMO control.