基于被控变量在线建模的化工过程实时优化方法

选择合适的被控变量可对过程进行实时优化(RTO),但现有方法在设计阶段确定被控变量后,不允许对其进行在线调整,导致了RTO效果的局限性。针对这一问题,提出了一种基于被控变量在线建模的方法,使用局部建模技术在线寻找相似样本并建立一阶最优性必要条件(NCO)的估计模型,将其作为被控变量更新控制回路,在反馈控制作用下达到更好的RTO效果。对一个蒸发过程的研究表明,此方法能够通过对NCO的在线准确建模,增加生产过程的经济效益。     

压差——一个有待认识的精馏塔被控变量

以过程控制及化工原理为基础，论证了采用压差作为精馏塔基本被控变量的可靠性，指出了在哪几种工况下的精馏可采用压差来控制热媒流量并有其独特优点，拓展了压差这一基本被控变量在精馏中的及地位。     

化工过程的自优化控制:原理、发展与应用展望

自优化控制(SOC)是一种通过选择被控变量实现化工过程实时优化的控制系统设计方法,具有控制结构简单、优化效果好等优点,近年来得到了快速发展。详细阐述了SOC的基本工作原理及其在过程控制系统中的定位与作用,对其发展历程和研究现状进行了系统性的总结,主要包括:被控变量求解方法、变量筛选快速算法、可变约束集问题、间歇过程SOC方法和混合实时优化策略等内容。最后,全面回顾了现阶段SOC在化工过程中的应用案例,并从理论和实践角度对其未来发展方向进行展望。     

应用分支定界法选择化工过程的操作变量

一种通用的子集合选择方法－分支定界法被应用于控制结构选择问题。这种方法的优点是不必评价所有可能的操作变量组合而找到最优解。用最小奇异值作为控制结构选择的评价函数并对其单调性条件作了说明。所讨论了ＨＡＤ系统实例显示了这一方法可达到较高的计算效率。     

一种面向化工生产系统的监控变量分步选取方法

提出一种基于信息熵的监控变量分步选取方法。以信息熵与监控变量所含系统信息量关系为基础,采用互信息广义相关系数度量变量间的广义相关性,完成监控变量分步选取。通过Friedman数学模型验证了所提方法的可行性,用Lorenz、Rossler等方程验证了基于信息熵选取单变量的可执行性。最后进行某煤化工企业实际DCS监控变量选取,结果显示:在化工实际生产中监控变量选取是可行的。     

一种分步约简的炼油生产敏感变量选择方法

变量筛选是现代工业过程产品质量预测研究中的热点问题之一。过滤式变量选择方法因其计算速度快且不易造成过拟合得到了广泛应用,但其存在容易忽略变量相关性且不能准确反映工况信息的问题,在高维数据维度灾难问题日渐突出的当今不再适用。针对这一问题,提出一种分步约简的敏感变量选择方法。该方法在明确敏感变量和关键敏感变量的基础上,根据变量对工况的描述能力和辅助变量与主导变量的净相关性定义了敏感性指标,实现敏感变量的初选;接着,构建加权余弦马田系统以解决变量冗余性问题,实现敏感变量的精选。所提方法应用于加氢裂化产品质量预测,实际工业应用结果表明,该方法不仅可以提高模型的预测精度,而且可以有效地降低模型复杂性。     

一种新颖的面向化工过程的实时优化方法(英文)

In this paper, a novel approach termed process goose queue (PGQ) is suggested to deal with real-time optimization (RTO) of chemical plants. Taking advantage of the ad-hoc structure of PGQ which imitates biologic nature of flying wild geese, a chemical plant optimization problem can be re-formulated as a combination of a multi-layer PGQ and a PGQ-Objective according to the relationship among process variables involved in the objective and constraints. Subsequently, chemical plant RTO solutions are converted into coordination issues among PGQs which could be dealt with in a novel way. Accordingly, theoretical definitions, adjustment rule and implementing procedures associated with the approach are explicitly introduced together with corresponding enabling algorithms. Finally, an exemplary chemical plant is employed to demonstrate the feasibility and validity of the contribution.     

化工过程预测控制的在线优化实现机制

多层结构的预测控制已逐渐成为工业过程控制领域的主流控制方案。在此控制架构基础上,根据操作工或工艺优化所给定期望值的不同,将稳态优化问题具体化为两种基本情况,并对此提出基于复合目标函数的优化问题,可针对不同过程要求退化为线性、二次或二者兼有的优化问题形式。为保证最优目标的可行性并在一定程度上避免关键变量饱和,对不可行的期望值适当调整。将所得最优目标增量化处理后送入模型预测控制动态控制层,确保了上下层之间变量传递的一致性。包含约束的全混槽反应器系统仿真实例表明,流程的优化实现层可针对不同的过程要求有效给出最优目标以便动态控制,说明了该优化流程的可行性。     

流量同时作为工艺优化变量和串级控制副变量时的工艺优化实现与分析

针对如何通过现存的主被控变量-流量这种典型的化工过程串级控制实现流量优化的在线优化问题,对在线优化的实现结构进行分析,给出一种流量优化变量通过一阶惯性环节改变主回路被控变量设定值的在线优化的理想方案,通过CSTR的仿真实例证明了优化实现方案的可行性。     

Structure analysis of periodically controlled chemical processes

Calculation of characteristic multipliers corresponding to every periodic states is combined with the previously proposed computer-aided stroboscopic approach for analyses of existence and stability of periodic and almost periodic states appearing in periodically controlled chernical processes. If all characteristic multipliers corresponding to a periodic state have values other than e^j2πk′/k (k and k′ are relatively prime positive integers and k ≥ k′), the periodic state is structurally stable for small change in the period of control. Otherwise, some structural change such as bifurcation of periodic states, disappearance of periodic states and so on due to change in the period may occur. This technique is applied to a periodically controlled CSTR with success.     

New approach of constructing biosensing matrices by physical and chemical crosslinking of biotin-alginate with alginate-pyrrole

A new matrix was designed and constructed; enabling the physical and chemical co-crosslinking of alginate-biotin (Alg-B) with alginate-pyrrole (Alg-py), namely biotinylated-glucose oxidase (B-GOx) was conjugated to Alg-B through avidin (Av) bridges, thereafter mixed with Alg-Py. The alginate was physically crosslinked via calcium ions followed by pyrrole covalent crosslinking through electro-generation of polypyrrole networks. The pyrrole electropolymerization on the electrode surface created spatial wires which tightened the polymeric coating to the electrode surface, packed the 3D structure and reinforced its structure by the covalent bonds. These matrices exhibited better stability than those without Alg-Py even under vigorous stirring applied during the amperometric measurements. When the concentration of the glucose analyte was determined by hydrogen peroxide oxidation, the current was conducted faster to the electrode surface, therefore providing a low response time and higher sensitivity value; using a relatively low enzyme load.     

Two-layer model predictive control for chemical process model with integrating controlled variables

This paper proposes an overall solution to the two-layer model predictive control (MPC) for the integrating controlled variables in the process model. The scheme includes three modules, that is, the open-loop prediction module, the steady-state target calculation (SSTC) module, and the dynamic control module. Based on the real-time output measurements and past inputs, the open-loop prediction module predicts the future outputs in the presence of disturbances. The economic optimization of SSTC is comprised of the feasibility stage and the economics stage, considering constraints of multi-priority ranks. The dynamic control module receives the steady-state targets from SSTC and calculates the control signals. The optimization problems of SSTC and dynamic control operate with the same frequency. This overall method guarantees the consistency of three modules with respect to the model, the constraints, and the targets. The simulation example illustrates that steady-state targets are adjusted dynamically after the occurrence of disturbances, and offset-free control is achieved.     

The chemical engineering approach to some electrochemical processes

In recent years much research has been carried out in combining electrochemical processes with chemical engineering techniques, and in general, to apply principles of analysis and design of this discipline in search for new electrochemical technologies. This paper reviews seven major areas of activity in modern applied electrochemistry where the chemical engineering approach has been successful: Fluidized bed electrochemical reactors; Bipolar particulate reactors; Pulsed electrochemical reactors; Gas phase electrochemical reactors; Electrocrystallization and electrodissolution; Enhancement of heat and mass transfer in electric fields; dynamics, modelling and optimization of electrochemical reactors.     

Method and algorithm for Bayesian estimation of kinetic parameters of chemical processes with fluctuating independent variables

A method for the calculation of the effect of fluctuations of independent variables on the estimates of kinetic parameters is developed. Fluctuations of variables are random deviations from the accepted constant values or predetermined time dependences. The method is based on the application of the generalized probability function and the Bayesian approach. An algorithm for the realization of the suggested method is also developed. The proposed method and algorithm were used to estimate the kinetic parameters of chemical reactions under temperature fluctuations. The given example is described in detail.     

A hybrid science-guided machine learning approach for modeling chemical processes: A review

This study presents a broad perspective of hybrid process modeling combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach. We divide the approach into two major categories: ML complements science, and science complements ML. We review the literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models. For applying ML to improve science-based models, we present expositions of direct serial and parallel hybrid modeling and their combinations, inverse modeling, reduced-order modeling, quantifying uncertainty in the process and even discovering governing equations of the process model. For applying scientific principles to improve ML models, we discuss the science-guided design, learning and refinement. For each subcategory, we identify its requirements, strengths, and limitations, together with their published and potential applications. We also present several examples to illustrate different hybrid SGML methodologies for modeling chemical processes.     

Parameter estimation in nonlinear chemical and biological processes with unmeasured variables from small data sets

We propose an algorithm for parameter estimation in nonlinear chemical and biological stochastic processes with unmeasured variables and small data sets. The algorithm relies on an iterative approach wherein random samples of parameters and unmeasured variables are generated, from their respective posterior density functions, through Markov chain Monte Carlo simulations. The random samples are then used in approximating the posterior density functions of the parameters. The effectiveness of the algorithm is demonstrated through two biological examples—a feed-forward loop genetic regulatory network and a JAK–STAT signal transduction pathway.     

State estimation for high-dimensional chemical processes

The application of conventional observer designs for high-dimensional systems may not always be practical due to high computational requirements or the resulting observers being too sensitive to measurement noise. In order to address these issues, this paper presents two observer design techniques for state estimation of high-dimensional chemical processes. One technique is used for systems with inputs, whereas the other one is specifically geared towards systems that are not excited from the outside. Both of these observers are applicable to linear and with a modification to non-linear systems.The main idea behind the presented observer designs is that a reduced-order observer is implemented instead of a conventional state estimator. The motivation is that subspaces, which are close to being unobservable, cannot be correctly reconstructed in a realistic setting due to measurement noise and inaccuracies in the model. The presented approaches make use of this observation and only reconstruct the parts of the system where accurate state estimation is possible. The observer designs are illustrated on a 30-tray distillation column model. Additionally, it has been shown that the location of process measurements has a major effect on the performance of the presented reduced-order observers.     

Systematic controllability analysis for chemical processes

Modern chemical industrial processes are becoming more and more integrated and consist of multiple interconnected nonlinear process units. These strong interactions profoundly complicate a system's inherent properties and further alter the plant‐wide process dynamics. This may lead to a poor control performance and cause plant‐wide operability problems. To ensure entire processes run robustly and safely, with considerable profitability, it is crucial to recognize the inherent characteristics that can jeopardize controllability and process behavior at the early design stage. With a focus on inherently safer designs, from a plant‐wide perspective, a systematic method for chemical processes controllability analysis is addressed in this study. In the proposed framework, based on open‐loop stability/instability and minimum/nonminimum‐phase behavior, the entire operating zone of the process can be categorized into distinct subregions with different inherent properties. Variations in the inherent characteristics of a plant‐wide process with the operation and design conditions, over the feasible operation region, can be probed and analyzed. An attempt of this framework is made to illustrate how to clarify the roots of the poor controllability that arise in the design and operation of a large scale chemical process, and the results can provide guidance for both deciding the optimal operation conditions and selecting the most suitable control structure. Singularity theory is also applied in the framework to improve the computational efficiency. The framework is illustrated with two case studies. One involves a reactor‐external heat exchanger network and the other a more complex plant‐wide process, comprising a reactor, an extractor, and a distillation column. © 2012 American Institute of Chemical Engineers AIChE J, 58: 3096–3109, 2012