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     

化工过程危险与可操作性及爆炸事故分析技术

介绍了化工生产开、停车过程人工误操作危险与可操作性分析系统（MO-HAZOP）、基于层次分析法的计算机辅助HAZOP分析方法和化工过程爆炸事故分析软件,系统地从物料、设备、工艺、人为因素和外部环境等方面对化工过程中潜在危险进行全面的分析。提出以发生概率值大小为依据对所有的危险路径进行排序,找出最容易发生的潜在危险路径。在分析的完备性的同时,增强了防范措施的针对性和可操作性。将导致爆炸事故的原因分为五类46种,对防范类似事故发生具有指导性意义。以某双苯厂硝基苯初馏塔分离工艺为例,阐述了防范控制人工误操作及爆炸事故分析技术的应用。分析结果表明,应用此技术,采用预防控制措施是可以防爆炸事故于未然的。     

慢时变化工过程裕量释放机制分析

化工过程普遍存在慢时变特性,在一个运行周期内慢时变参数的变化造成化工装置性能逐渐下降。为此,过程设计时需要按照慢时变参数可能的“最坏”影响对设计变量留出足够的设计裕量,在一个运行周期内通过操作逐渐释放,补偿慢时变参数的不利影响,且理想操作是保证到运行周期结束时化工装置性能恰好达到过程约束边界。本文对慢时变过程设计裕量的释放机制进行了分析,考虑含慢时变参数的全周期操作优化通用动态模型,通过最优控制的极小值原理求解该优化问题,建立了最优裕量释放轨迹和慢时变参数变化曲线之间的联系,从而证明最优裕量释放只与慢时变化工过程的运行周期有关。以乙炔加氢反应器为例验证了该裕量释放机制,对于慢时变化工过程,设定的运行周期越短,设计裕量释放越快,仅能获得较高的短期经济效益;反之,设定较长的运行周期,设计裕量缓慢释放,能获得更高的长期经济效益。     

Release mechanism analysis of design margin for slowly-time-varying chemical processes

Slowly-time-varying characteristics are common in chemical processes, and the changes of slowly-time-varying parameters in an operating cycle gradually decrease the performance of chemical process. So, enough margins must be added for design variables during the phase of process design according to the possible worst-case influence of slowly-time-varying parameters. The design margins will be released gradually compensating the worse influence of slowly-time-varying parameters in an operating cycle. It can be called as a perfect operation that the operating point is on the boundary of process constraints when an operating cycle is ending. In this paper, the margin release mechanism of slowly-time-varying chemical processes is analyzed. Based on the universal dynamic model containing slowly-time-varying parameters, the full cycle operation optimization is solved by minimum principle of optimal control. It is found that the optimal margin release trajectory is related to the curve of slowly-time-varying parameter, ensuring that the optimal margin release is only dependent on the operating cycle. This mechanism is verified by the example of acetylene hydrogenation reactor. For slowly-time-varying chemical processes, the shorter the operating cycle is set, the faster the design margin is released, the higher temporary economic benefit is obtained; otherwise, the longer the operating cycle is set, the more integrated economic benefit is accomplished.     

Analysis of the stability and controllability of chemical processes

Chemical processes constitute strongly nonlinear systems and, for such systems, multiple steady-state solutions typically exist. In addition, the various steady state solutions are likely to differ in terms of stability and phase behaviors, which is an important consideration for practical applications. A chemical process is used in this paper to demonstrate how to analyze process stability and controllability. Finally, the conclusion is drawn that overall system stability and phase behaviors should be considered because the individual unit operations or subsystems differ from the total system in terms of these features. Therefore, the analysis of stability and controllability of process systems is important in terms of the design of inherently safer processes.     

Integration of scheduling,design, and control of multiproduct chemical processes under uncertainty

The development of a methodology that addresses the simultaneous design, scheduling, and control of multiproduct processes is focused. The proposed methodology takes into account the influence of disturbances by the identification of their critical frequency, which is used to quantify the worst‐case variability in the controlled variables via frequency response analysis. The uncertainty in the demands of products has also been addressed by creating critical demand scenarios with different probabilities of occurrence, while the nominal stability of the system has been ensured. Two case studies have been developed as applications of the methodology. The first case study focuses on the comparison of classical semisequential approach against the simultaneous methodology developed, while the second case study demonstrates the capability of the methodology in application to a large‐scale nonlinear system. © 2015 American Institute of Chemical Engineers AIChE J, 61: 2456–2470, 2015     

An algorithm for optimal waste heat recovery from chemical processes

We describe a computer algorithm designed to calculate the optimal energy extraction in the form of heat used for steam raising from chemical processes. The concepts are illustrated using chemical plant stream data in a process with multiple distillation columns. Pinch analysis is first applied to find the grand composite curve (GCC) of the problem, which is then used by the algorithm to determine the maximum mass flow rate of steam that can be produced from process waste heat. An analysis of the effects of the minimum temperature of approach ΔT_min on the optimal steam raising result is also conducted, and it is found that, in general, a higher ΔT_min will reduce the percentage heat recovery from the process.     

Optimal design of large‐scale chemical processes under uncertainty: A ranking‐based approach

An approach for the optimal design of chemical processes in the presence of uncertainty was presented. The key idea in this work is to approximate the process constraint functions and model outputs using Power Series Expansions (PSE)‐based functions. The PSE functions are used to efficiently identify the variability in the process constraint functions and model outputs due to multiple realizations in the uncertain parameters using Monte Carlo (MC) sampling methods. A ranking‐based approach is adopted here where the user can assign priorities or probabilities of satisfaction for the different process constraints and model outputs considered in the analysis. The methodology was tested on a reactor–heat exchanger system and the Tennessee Eastman process. The results show that the present method is computationally attractive since the optimal process design is accomplished in shorter computational times when compared to the use of the MC method applied to the full plant model. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3243–3257, 2014     

A study on the improvement of controllability of chemical processes based on structural analysis

Controllability should be considered at the design stage before control system is designed, as it is an inherent property of process. Structural information makes controllability assessment possible by giving insights on the pathways of disturbances in the process. In this study, a simple procedure to evaluate controllability is suggested to select design alternatives. This procedure is useful in screening out the design alternatives before using detailed controllability evaluation methods such as dynamic simulation.     

A survey on applications of optimization-based integrating process design and control for chemical processes

Process design and control are closely related to each other in chemical engineering activities. Traditionally, process design and control system design are carried out in sequence. However, the integration of process design and control (IPDC) can bring greater economic benefits and process dynamic performance than traditional sequential design methods. This is true, particularly for modern chemical processes, in which various process units become more interacting and compact owing to the widespread use of heat integration and recycled streams, and the resulted impacts between process design and control begin to significantly influence both the capital and operational costs. Recently, considerable studies about the IPDC for chemical processes have been reported in published literature. The purpose of the paper is to survey the applications of optimization-based integrating process design and control for chemical processes. Firstly, attention has been focused on the applications of IPDC to different process units, for example, chemical reactors and separation columns. Then, the survey is extended to the applications of IPDC to plant-wide chemical processes. Finally, the future research challenges in the application of IPDC to chemical processes have been briefly discussed.     

The use of particle swarm optimization for dynamical analysis in chemical processes

Particle swarm optimization is employed here to evaluate the parametric regions where different dynamic phenomena (periodic oscillations, double-period oscillations, chaos) can be expected in dynamic models. The proposed algorithm comprises two fundamental steps: the rough evaluation of regions where the desired solutions can be found and solution refining. The refining step allows the search for unstable solutions that may coexist with the other stable attractors. No preliminary bifurcation analysis is required. Simulations performed for distinct dynamic models show that the proposed algorithm is indeed able to locate different dynamic phenomena in the parameter space and that the algorithm may be of help for those interested in increasing the speed of more traditional dynamic bifurcation analysis.     

Steady-state optimization of chemical processes with guaranteed robust stability and controllability under parametric uncertainty and disturbances

A new methodology is proposed for the steady-state optimal design of chemical processes under parametric uncertainty and disturbances. The methodology allows the integration of uniform constraints for robust controllability and stability in an optimization problem by using the Routh–Hurwitz test and zero dynamics based method. The underlying mathematical problem is difficult to solve because it involves infinite stability and controllability constraints. We developed an algorithm where an infinite number of constraints can be implemented as several relaxation problems that are solved iteratively. Additionally, the dynamic simulation results under parametric uncertainty and disturbances are also used to estimate the bound of the state perturbations rather than assumptions based on experience, which may lead to overly conservative or non-implementable design. To illustrate the methodology, three different examples are presented and robustly stable and controllable designs are obtained.     

State‐of‐the‐art and progress in the optimization‐based simultaneous design and control for chemical processes

Significant progress in the area of simultaneous design and control for chemical processes has been achieved and various methodologies have been put forward to address this issue over the last several decades. These methods can be classified in two categories (1) controllability indicator‐based frameworks that are capable of screening alternative designs, and (2) optimization‐based frameworks that integrate the process design and control system design. The major objective is to give an up‐to‐date review of the state‐of‐the‐art and progress in the challenging area of optimization‐based simultaneous design and control. First, motivations and significances of simultaneous design and control are illustrated. Second, a general classification of existing methodologies of optimization‐based simultaneous design and control is outlined. Subsequently, the mathematical formulations and relevant theoretical solution algorithms, their merits, strengths and shortcomings are highlighted. Last, based on the recent advances in this field, challenges and future research directions are discussed briefly. An attempt is made with the help of this review article to stimulate further research and disseminate the simultaneous design methods to challenging problem areas. In particular, the application of optimization‐based simultaneous design and control methods to large‐scale systems with highly inherent nonlinear dynamics often the case in industrial chemical processes remains a challenging task and yet to be solved. © 2012 American Institute of Chemical Engineers AIChE J, 58: 1640–1659, 2012     

Design standardization of unit operations for reducing the capital intensity and cost of small-scale chemical processes

A methodology is proposed to reduce the cost and capital intensity of small-scale chemical processes by creating new opportunities for economies of numbers through standardizing the equipment designs across multiple processes. We depart from asynchronous design of single-processes and adopt a common-functionality based simultaneous design of multiple processes that use similar unit operations. A generalized cost function is used to appropriately balance the trade-offs between economies of scale and economies of numbers. An optimization-based framework for design standardization is developed and illustrated using two case studies. The first involves the simultaneous synthesis of methanol and ammonia processes, and the second addresses the optimal synthesis of multi-column natural gas liquid (NGL) fractionation processes for different natural gas sources. We observe that considerable reduction in capital intensity of small-scale processes is possible through equipment standardization.     

一类间歇生产过程的迭代学习控制算法及其收敛性分析

针对基于迭代学习控制的间歇过程产品质量优化控制算法难以进行收敛性分析的难题,并且考虑到实际生产中存在外部干扰和不确定因素的影响,本文对间歇过程模型参数动态更新问题进行了分析,建立了间歇生产过程产品质量的神经模糊(NF)预测模型,提出了一种新颖的批次轴参数自适应调节算法。在此基础上,构造了一种基于数据驱动的间歇生产过程产品质量迭代学习控制算法,并对优化问题的收敛性给出了严格的数学证明。最后,将本文提出的算法用于一类典型的间歇过程终点质量控制研究,仿真结果验证了本文算法的有效性和实用价值,为间歇过程的优化控制提供了一条新途径。     

化工过程稳定性分析研究进展

化工过程在实际操作过程中时刻面临着扰动,稳定性作为化工过程可操作性的一个重要元素,决定着化工过程在扰动消失之后能否回到原来的操作点,直接关系到产品的质量以及过程的安全性。对于稳定性的研究有助于理解及调控化工过程的非线性行为,例如多稳态现象和持续振荡等,从而保证化工过程在扰动的作用下能够长周期、平稳、安全运行。本文从多稳态现象和多稳态点的稳定性分析、考虑稳定性的化工过程优化方法、化工过程稳定性的调控以及不确定性条件下的稳定性分析4个方面综述了化工过程稳定性分析的研究进展。     

化工过程大范围工况变化的工况迁移控制策略判定

化工过程出现大范围的工况变化时，复杂的迁移控制策略会带来一定的操作不确定性，因此需要对控制策略进行选择判定。考虑到直接估计工况变化过程生产指标变化量带来的判定误差，引入了中间变量，提出了基于中间变量的控制策略选择判定方法，并分析给出了构建中间变量的基本准则。进而通过对某实际乙烯精馏塔工况变化过程的仿真研究，说明了中间变量的引入能够很大程度地降低对生产指标的估计误差，验证了基于中间变量的控制策略选择判定方法的可用性。     

Recent progress on equation-oriented optimization of complex chemical processes

Process optimization in equation-oriented (EO) modeling environments favors the gradient-based opti-mization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation.However,computational inefficiencies including that in initial-point-finding for Newton type methods have significantly limited its application.Recently,progress has been made in using a pseudo-transient (PT) modeling method to address these difficulties,providing a fresh way for-ward in EO-based optimization.Nevertheless,research in this area remains open,and challenges need to be addressed.Therefore,understanding the state-of-the-art research on the PT method,its principle,and the strategies in composing effective methodologies using the PT modeling method is necessary for further developing EO-based methods for process optimization.For this purpose,the basic concepts for the PT modeling and the optimization framework based on the PT model are reviewed in this paper.Several typical applications,e.g.,complex distillation processes,cryogenic processes,and optimizations under uncertainty,are presented as well.Finally,we identify several main challenges and give prospects for the development of the PT based optimization methods.     

Plantwide operability assessment for nonlinear processes using a microscopic level network analysis

The increase of raw material and energy costs has caused a shift in process design philosophy leading to more complex chemical plants utilising heat integration and material recycles. This warrants plantwide dynamic operability analysis in the process design stage. In our previous work, a networked plantwide operability analysis approach was developed, where the plantwide process is viewed as a network of process units connected via mass and energy flow. Such an analysis is based on the dissipativity of each process unit and the topology of the process network. However, to determine the dissipativity of multivariable nonlinear process units is often extremely difficult. In this work, we take the network approach to a microscopic level and treat each nonlinear multivariable process unit as a network of individual (single state) mass and energy balances (sub-systems). The plantwide process is then viewed as a network of such sub-systems rather than physical process units. The dissipativity of these simple sub-systems can often be determined more easily in comparison to that of multivariable sub-systems. The dissipativity property (in terms of supply rate) of the entire nonlinear process can be parametrised by the dissipativity of individual sub-systems, leading to a cluster of supply rates. The operability of the plantwide nonlinear process can then be determined based on the above parametrised dissipativity which can be much less conservative than existing nonlinear analysis. The effects of interactions caused by the interconnections are considered explicitly based on the network topology. The stability and stabilisability analysis problem is then converted into a feasibility problem with linear matrix inequalities which can be solved numerically. The application of the proposed approach requires successful determination of the dissipativity of nonlinear sub-systems.