反应精馏合成乙酸乙酯的实验研究与模拟

本文以Amberlyst-36Wet离子交换树脂为催化剂,采用间歇搅拌釜式反应器,在消除内外扩散影响的条件下,测得不同温度下反应速率常数.研究自制反应精馏塔中(直径25 mm,高2.2 m)乙酸乙酯的合成工艺,得到反应精馏的工艺参数.在实验基础上,建立改进工艺的Aspen Plus模拟流程图.实验结果与模拟计算值吻合良好,表明所建立的Aspen Plus模型能够很好地描述反应精馏合成乙酸乙酯过程.以乙醇转化率、产品乙酸乙酯的收率和塔顶油相乙酸乙酯的质量分率为考察目标,通过流程模拟和灵敏度分析,确定该工艺的最佳工艺参数:精馏段、反应段和提馏段的理论板数分别为9、7和7;醋酸和乙醇的最佳进料位置在第9块和第16块塔板上;回流比R为1.6.在此工艺条件下,产品乙酸乙酯的含量是95.2%(wt),乙醇转化率为96.1%.     

乙酸丁酯反应精馏过程模拟设计

反应精馏偶合了反应和精馏两种单元操作,通过精馏促进反应,可以提高反应转化率和收率,为可逆反应的化工过程生产提供了新的设计途径。基于严格热力学分析计算,利用计算机模拟和优化手段。提出了乙酸丁酯反应精馏、分离纯化的生产流程。采用UNIQUAC方程表征乙酸-正丁醇-乙酸丁酯-水四元非理想体系的汽液平衡,首先,根据实验数据回归了热力学模型中的交互作用参数,并预测了体系中5个共沸物组成,模型的计算结果与实际数据吻合。基于平衡级模型,提出了由平衡反应器、反应精馏塔、倾析器和纯化塔构成的可行流程,对提出的设计流程进行了模拟、优化,得到了操作工艺参数。模拟结果对工业过程的设计和改造具有一定的指导意义。     

State estimation and inferential control for a reactive batch distillation column

An optimal reflux ratio profile is obtained for a reactive batch distillation system utilizing the capacity factor as the objective function in a nonlinear optimization problem. Then, an Artificial Neural Network (ANN) estimator system, which utilizes the use of several ANN estimators, is designed to predict the product composition values of the distillation column from temperature measurements inferentially. The network used is an Elman network with two hidden layers. The designed estimator system is used in the feedback inferential control algorithm, where the estimated compositions and the reflux ratio information are given as inputs to the controller to see the performance of the ANN. In the control law, a scheduling policy is used and the optimal reflux ratio profile is considered as pre-defined set-points. It is found that, it is possible to control the compositions in this dynamically complex system by using the designed ANN estimator system with error refinement whenever necessary.     

复合萃取精馏分离乙酸乙酯-乙醇-水的研究

介绍了乙酸乙酯在工业生产中的应用；在前人的研究基础上，采用合适的萃取剂，在萃取塔上考察了不同溶剂比、回流比等因素对产品纯度的影响，并摸索复合萃取分离乙酸乙酯－乙醇－水三元体系的适宜操作条件，在溶剂比为1：1：1，R=4时，能一次得到高浓度（99．5％）的乙酸乙酯，同时得到95％的乙醇溶液，得率高、能耗低，为工业试验提供了基础数据。     

Nonlinear model-based control of a batch reactive distillation column

The inherent trade off between model accuracy and computational tractability for model-based control applications is addressed in this article by the development of reduced order nonlinear models. Traveling wave phenomena is used to develop low order models for multicomponent reactive distillation columns. A motivational example of batch esterification column is used to demonstrate the synthesis procedure. Tight control of the column is obtained with the use of reduced model in a model predictive control algorithm.     

A novel approach for ANFIS modelling based on full factorial design

Adaptive neural network based fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modelling and control of ill-defined and uncertain systems. ANFIS is based on the input–output data pairs of the system under consideration. The size of the input–output data set is very crucial when the data available is very less and the generation of data is a costly affair. Under such circumstances, optimization in the number of data used for learning is of prime concern. In this paper, we have proposed an ANFIS based system modelling where the number of data pairs employed for training is minimized by application of an engineering statistical technique called full factorial design. Our proposed method is experimentally validated by applying it to the benchmark Box and Jenkins gas furnace data and a data set collected from a thermal power plant of the North Eastern Electric Power Corporation (NEEPCO) Limited. By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced and thereby computation time as well as computation complexity is remarkably reduced. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model.     

常减压装置初馏塔顶产品干点软测量的应用研究

在常减压装置中,影响初馏塔顶石脑油干点的因素很多,反应十分复杂,故难以建立准确的机理模型.针对传统的主元分析法(PCA)或自适应模糊推理系统(ANFIS)建立软测模型中的缺点,本文提出采用主元分析法预处理输入变量,再结合自适应模糊推理系统,进行常减压装置初馏塔顶石脑油干点的软测量模型的改进,能及时测定化工过程的变量,对稳定生产过程,有效控制产品质量具有重要意义.通过MATLAB仿真,表明该改进型方法的软测建模效果较好,建模的训练时间大大节省了,且泛化能力和拟合精度很好.     

A wave propagation approach for reduced dynamic modeling of distillation columns: Optimization and control

Reduced models enable real-time optimization of large-scale processes. We propose a reduced model of distillation columns based on multicomponent nonlinear wave propagation (Kienle 2000). We use a nonlinear wave equation in dynamic mass and energy balances. We thus combine the ideas of compartment modeling and wave propagation. In contrast to existing reduced column models based on nonlinear wave propagation, our model deploys a hydraulic correlation. This enables the column holdup to change as load varies. The model parameters can be estimated solely based on steady-state data. The new transient wave propagation model can be used as a controller model for flexible process operation including load changes. To demonstrate this, we implement full-order and reduced dynamic models of an air separation process and multi-component distillation column in Modelica. We use the open-source framework DyOS for the dynamic optimizations and an Extended Kalman Filter for state estimation. We apply the reduced model in-silico in open-loop forward simulations as well as in several open- and closed-loop optimization and control case studies, and analyze the resulting computational speed-up compared to using full-order stage-by-stage column models. The first case study deals with tracking control of a single air separation distillation column, whereas the second one addresses economic model predictive control of an entire air separation process. The reduced model is able to adequately capture the transient column behavior. Compared to the full-order model, the reduced model achieves highly accurate profiles for the manipulated variables, while the optimizations with the reduced model are significantly faster, achieving more than 95% CPU time reduction in the closed-loop simulation and more than 96% in the open-loop optimizations. This enables the real-time capability of the reduced model in process optimization and control.     

反应精馏生产丁酸酐的过程模拟

丁酸酐是一种重要的有机原料,其现有的生产工艺为间歇操作,收率较低。本研究将反应精馏技术应用到生产丁酸酐的工艺中,建立了连续生产工艺的数学模型,在实验数据的基础上,用数据分析软件MATLAB对实验数据分析处理,得到相应的模型参数。采用ASPEN PLUS进行工艺方案的模拟计算,结果表明,利用反应精馏连续生产丁酸酐的工艺是可行的,以分离促反应,产品收率到达了97%以上,同时选择工艺操作参数,最佳条件为:酸酐比2:1、反应压力0.2 atm、塔板数为12块,确定反应区主要为进料板附近。     

A new hybrid method for time series forecasting: AR–ANFIS

In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR–ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR–ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.

     

基于ANFIS的焦炉火道温度预报模型研究

针对焦炉生产过程中直接检测火道温度成本高、精度低等问题,提出运用自适应神经网络模糊推理系统理论(ANFIS)建立焦炉火道温度预报模型,模型采用模糊减法聚类方法选取模糊规则数目,大大减少规则冗余量;结合最小二乘和误差反向传播混合算法对神经网络参数进行优化,采用现场的热工数据作为输入,将获得的模型与传统的线性回归模型和BP神经网络模型进行了比较,数值仿真结果表明所建立的模型具有学习速度快、预报精度高、泛化能力强等优点.     

Model predictive control of reactive distillation for benzene hydrogenation

Benzene hydrogenation via reactive distillation is a process that has been widely adopted in the process industry. However, studies in the open literature on control of this process are rare and seem to indicate that conventional decentralized PI control results in sluggish responses when the reactive distillation column is subjected to disturbances in the feed concentration. In order to overcome this performance limitation, this work investigates model predictive control (MPC) strategies of a reactive distillation column model, which has been implemented in gPROMS. Several MPCs based upon different sets of manipulated and controlled variables are investigated where the remaining variables remain under regular feedback control. Further, MPC controllers with output disturbance correction and, separately, with input disturbance correction have been investigated. The results show that the settling time of the column can be reduced and the closed loop dynamics significantly improved for the system under MPC control compared to a decentralized PI control structure.     

基于IQPSO-GA优化ANFIS模型的服务器故障预警方法

针对服务器底层部分业务类硬件故障对系统稳定运行的影响,提出一种改进的量子行为粒子群优化(IQPSO)与遗传算法(GA)相结合的混合元启发式优化算法对自适应神经模糊推理系统(ANFIS)参数进行训练,以获得更准确的ANFIS规则进行硬件故障预警的方法。首先,通过分析服务器业务与硬件相关参数之间的映射关系,通过采集的数据集对ANFIS模型进行训练构造预测模型;其次,考虑ANFIS在梯度计算过程中存在容易陷入局部最优值的问题,设计了一种IQPSO算法结合GA中的交叉和变异算子操作混合元启发算法全局搜索ANFIS规则参数;最后,通过一组后处理样本数据集对所提方法有效性和稳定性进行了检验。实验结果表明,该方法可有效预警服务器硬件故障,基于所提混合元启发优化算法获得的ANFIS模型具备更快的收敛速度和更高的全局搜索精度,与传统ANFIS模型相比泛化精度提高了47%以上。     

间歇精馏设计软件的开发

利用Visual Basic 6.0语言开发间歇精馏常规设计及优化设计软件。软件可用于不同物系(二元理想及非理想溶液)，采用不同操作方式(恒馏出液组成操作和恒回流比操作)间歇精馏的常规设计和优化设计(包括单变量优化和多变量优化)计算。软件采用面向对象的编程技术，对精馏组分的物性参数、汽液相平衡数据实行数据库操作，界面友好，使用方便。     

Prediction of chaotic time series using computational intelligence

In this paper, two CI techniques, namely, single multiplicative neuron (SMN) model and adaptive neuro-fuzzy inference system (ANFIS), have been proposed for time series prediction. A variation of particle swarm optimization (PSO) with co-operative sub-swarms, called COPSO, has been used for estimation of SMN model parameters leading to COPSO-SMN. The prediction effectiveness of COPSO-SMN and ANFIS has been illustrated using commonly used nonlinear, non-stationary and chaotic benchmark datasets of Mackey–Glass, Box–Jenkins and biomedical signals of electroencephalogram (EEG). The training and test performances of both hybrid CI techniques have been compared for these datasets.     

Simultaneous optimization of design and operation of multicomponent batch distillation column—single and multiple separation duties

A method is presented for the simultaneous optimization of a batch distillation column design and its operation, for single and multiple separation duties, each involving different multicomponent mixtures and complex operations with intermediate cuts. For operation structures selected a priori, the formulation presented permits the use of general distillation design and cost models. The objective function and constraints include capital and operating cost. In particular, the number of internal plates is optimized along with the most significant operating variables (recoveries in various cuts and reflux ratio profiles and times). The multiple duty formulation presented accounts for the different importance of each duty and setup time between batches. Application of the method to single duty multicomponent separation from the literature shows that significant profit improvements can be achieved within acceptable computing times. For multiple separation duties (two binary mixtures), the method clearly shows the importance of including allocation time to each duty and setup time for each batch in the objective function.     

Adaptive neuro-fuzzy inference system–based model for elevation–surface area–storage interrelationships

In the developing of an optimal operation schedule for dams and reservoirs, reservoir simulation is one of the critical steps that must be taken into consideration. For reservoirs to have more reliable and flexible optimization models, their simulations must be very accurate. However, a major problem with this simulation is the phenomenon of nonlinearity relationships that exist between some parameters of the reservoir. Some of the conventional methods use a linear approach in solving such problems thereby obtaining not very accurate simulation most especially at extreme values, and this greatly influences the efficiency of the model. One method that has been identified as a possible replacement for ANN and other common regression models currently in use for most analysis involving nonlinear cases in hydrology and water resources–related problems is the adaptive neuro-fuzzy inference system (ANFIS). The use of this method and two other different approaches of the ANN method, namely feedforward back-propagation neural network and radial basis function neural network, were adopted in the current study for the simulation of the relationships that exist between elevation, surface area and storage capacity at Langat reservoir system, Malaysia. Also, another model, auto regression (AR), was developed to compare the analysis of the proposed ANFIS and ANN models. The major revelation from this study is that the use of the proposed ANFIS model would ensure a more accurate simulation than the ANN and the classical AR models. The results obtained showed that the simulations obtained through ANFIS were actually more accurate than those of ANN and AR; it is thus concluded that the use of ANFIS method for simulation of reservoir behavior will give better predictions than the use of any new or existing regression models.     

Modeling of transfer length of prestressing strands using genetic programming and neuro-fuzzy

In this study, the efficiency of neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the transfer length of prestressing strands in prestressed concrete beams was investigated. Many models suggested for the transfer length of prestressing strands usually consider one or two parameters and do not provide consistent accurate prediction. The alternative approaches such as GEP and ANFIS have been recently used to model spatially complex systems. The transfer length data from various researches have been collected to use in training and testing ANFIS and GEP models. Six basic parameters affecting the transfer length of strands were selected as input parameters. These parameters are ratio of strand cross-sectional area to concrete area, surface condition of strands, diameter of strands, percentage of debonded strands, effective prestress and concrete strength at the time of measurement. Results showed that the ANFIS and GEP models are capable of accurately predicting the transfer lengths used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.     

An efficient dynamic model for batch distillation

A novel, rigorous and efficient solution technique for multicomponent batch distillation modelling equations is proposed. Model predictions using the technique are shown to be in close agreement with experimental batch distillation data for a ten sieve tray, 15 cm diameter column separating ethanol and water. The results also show improved accuracy over commercially available programs for batch distillation. The method incorporates rigorous dynamic energy blances as well as accurate representation of both tray hydraulics and non-ideal mass transfer. The technique is based on a functional approximation for liquid enthalpy and makes a difficult-to-calculate temperature derivative implicit in other terms in the equations, eliminating the need for iterative solution techniques. The numerical efficiency of the method permits its utilization in model-based optimization and control calculations. The modelling approach is applicable to both batch and continuous dynamic distillation models.     

基于神经模糊系统的热工过程建模及预测

热工对象内部过程的物理性能比较复杂,其往往表现出非线性、严重时变、大迟延和不确定等特点,这就使得难以对其建立比较精确的模型。该文以自适应神经模糊推理系统(ANFIS)作为辨识器建立热工过程模型,用ANFIS分别建立锅炉-汽轮机的非线性模型、不同负荷工况点的线性模型,并根据现场采集的锅炉-汽轮机系统数据建立了ANFIS模型。对以上三个系统的建模仿真结果表明基于ANFIS建立的模型具有较高的模型精度和较好的预测能力,ANFIS可用于非线性系统、复杂系统的建模和预测,并具有较少的训练次数和较小的预测误差。