Similarities and differences between the concepts of operability and flexibility: The steady‐state case

This article presents a comparative review on the operability and flexibility concepts and their application to process design and control. First, the operability and flexibility methodologies are summarized. Then the application of the operability framework to steady‐state and dynamic systems is illustrated through the examination of several example categories such as linear and nonlinear, square and non‐square systems. The flexibility approach based on the active set strategy is used to study the same examples from the flexibility point of view. The discussed results show that the operability and flexibility approaches examine a process from different perspectives and provide valuable complementary information. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Bilevel and parallel programing‐based operability approaches for process intensification and modularity

Process operability emerged in the last decades as a powerful tool for the design and control of chemical processes. Recent efforts in operability have been focused on the calculation of the desired input set for process design and intensification of natural gas utilization applications described by nonlinear models. However, there is still a gap in terms of problem dimensionality that nonlinear operability methods can handle. To fill this gap, in this article, the incorporation of bilevel and parallel programing approaches into classical process operability concepts is discussed. Results on the implementation of the proposed method show a reduction in computational time up to two orders of magnitude, when compared to the original results without parallelization. These results could be extrapolated for use in a supercomputer as presented in the computational time analysis performed. In terms of intensification, the proposed approach can produce a natural gas combined cycle plant modular design with a dramatic reduction in size, from the original 400 to 0.11 MW, while still keeping the high net plant efficiency. This approach thus provides a computationally efficient framework for process intensification of high‐dimensional nonlinear energy systems toward modularity. The proposed approach also enables the verification of a modular design and conditions that can be obtained according to economic and physical constraints associated with a specific natural gas well production. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3042–3054, 2018     

Integrated process design,scheduling, and model predictive control of batch processes with closed-loop implementation

Simultaneous evaluation of multiple time scale decisions has been regarded as a promising avenue to increase the process efficiency and profitability through leveraging their synergistic interactions. Feasibility of such an integral approach is essential to establish a guarantee for operability of the derived decisions. In this study, we present a modeling methodology to integrate process design, scheduling, and advanced control decisions with a single mixed-integer dynamic optimization (MIDO) formulation while providing certificates of operability for the closed-loop implementation. We use multi-parametric programming to derive explicit expressions for the model predictive control strategy, which is embedded into the MIDO using the base-2 numeral system that enhances the computational tractability of the integrated problem by exponentially reducing the required number of binary variables. Moreover, we apply the State Equipment Network representation within the MIDO to systematically evaluate the scheduling decisions. The proposed framework is illustrated with two batch processes with different complexities.     

Multi-agent reinforcement learning for process control: Exploring the intersection between fields of reinforcement learning,control theory,and game theory

The application of reinforcement learning (RL) in process control has garnered increasing research attention. However, much of the current literature is focused on training and deploying a single RL agent. The application of multi-agent reinforcement learning (MARL) has not been fully explored in process control. This work aims to: (i) develop a unique RL agent configuration that is suitable in a MARL control system for multiloop control, (ii) demonstrate the efficacy of MARL systems in controlling multiloop process that even exhibit strong interactions, and (iii) conduct a comparative study of the performance of MARL systems trained with different game-theoretic strategies. First, we propose a design of an RL agent configuration that combines the functionalities of a feedback controller and a decoupler in a control loop. Thereafter, we deploy two such agents to form a MARL system that learns how to control a two-input, two-output system that exhibits strong interactions. After training, the MARL system shows effective control performance on the process. With further simulations, we examine how the MARL control system performs with increasing levels of process interaction and when trained with reward function configurations based on different game-theoretic strategies (i.e., pure cooperation and mixed strategies). The results show that the performance of the MARL system is weakly dependent on the reward function configuration for systems with weak to moderate loop interactions. The MARL system with mixed strategies appears to perform marginally better than MARL under pure cooperation in systems with very strong loop interactions.     

Dynamic operability analysis of nonlinear process networks based on dissipativity

Most modern chemical plants are complex networks of multiple interconnected nonlinear process units, often with multiple recycle and by‐pass streams and energy integration. Interactions between process units often lead to plant‐wide operability problems (i.e., difficulties in process control). Plant‐wide operability analysis is often difficult due to the complexity and nonlinearity of the processes. This article provides a new framework of dynamic operability analysis for plant‐wide processes, based on the dissipativity of each process unit and the topology of the process network. Based on the concept of dissipative systems, this approach can deal with nonlinear processes directly. Developed from a network perspective, the proposed framework is also inherently scalable and thus can be applied to large process networks. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

A trust-region framework for integration of design and control

A trust-region approach is presented to address the simultaneous design and control of large-scale systems under uncertainty. The key idea is to represent the system using power series expansions (PSEs) as piecewise models in an iterative manner while the validity of those expansions is certified in a trusted region. The mean of squared errors is used as a metric to quantify the accuracy of the PSE approximations. Identified search regions specify the boundaries of the decision variables for the PSE-based optimization problems. The proposed algorithm shows a significant accomplishment in locating dynamically feasible and near-optimal design and operating conditions. The proposed approach was tested in a wastewater treatment plant and the Tennessee Eastman process. The results indicate that the proposed methodology leads to more economically attractive and reliable designs while maintaining the dynamic operability of the system in the presence of disturbances and uncertainty.     

Modelling and explicit model predictive control for PEM fuel cell systems

We present an analytical dynamic model and a general framework for the optima control design of a PEM fuel cell system. The mathematical model consists of a detailed model for the PEM fuel cell stack and simplified models for the compressor, humidifier and cooling system. The framework features (i) a detailed dynamic process model, (ii) a reduced order approximating model obtained by performing dynamic simulations of the system and (iii) the design of an explicit/multi-parametric model predictive controller. The derived explicit/multi-parametric controller is tested and validated off-line on several operating conditions.     

催化裂化装置反再系统动态模拟精细化与控制系统“工艺优先”配对设计

催化裂化是目前炼油厂中的核心加工工艺,其反应-再生系统是一个多变量紧密耦合的复杂系统,动态模拟和控制系统设计难度较大。目前,催化裂化装置在进行动态建模时设置了大量假设条件,与实际状况存在诸多不符,另外当前的控制回路配对方法未考虑工艺要求,也不适用于催化裂化这样的开环不稳定系统。基于以上原因,以已建立的反应-再生系统数学模型为基础,建立精细化动态模型,对反应器和再生器模型进行真实逼近,不再忽略气相动态变化,将原模型中气相对时间的导数项恢复,通过离散化的分布参数系统模型,对离散化模型中每段提升管和烧焦罐的时变变量加入时滞。仿真结果表明,精细化动态模型更加接近实际化工生产过程。根据上述模型搭建仿真平台,通过对不稳定的反再系统进行工艺优先的控制系统设计,首先根据化工工艺设计控制回路保证系统的稳定性,然后基于相对增益阵方法设计剩余变量配对,在降低了高维系统设计复杂度的同时保证了生产过程安全。设计结果表明,对于催化裂化装置反再系统,基于工艺特性完成控制回路配对后,剩余变量无须再添加多余的控制回路就能保证控制系统的稳定性和适当的控制性能。     

Dynamic risk analysis using alarm databases to improve process safety and product quality: Part I—Data compaction

In most industrial processes, vast amounts of data are recorded through their distributed control systems (DCSs) and emergency shutdown (ESD) systems. This two‐part article presents a dynamic risk analysis methodology that uses alarm databases to improve process safety and product quality. The methodology consists of three steps: (i) tracking of abnormal events over an extended period of time, (ii) event‐tree and set‐theoretic formulations to compact the abnormal‐event data, and (iii) Bayesian analysis to calculate the likelihood of the occurrence of incidents. Steps (i) and (ii) are presented in Part I and step (iii) in Part II. The event‐trees and set‐theoretic formulations allow compaction of massive numbers (millions) of abnormal events. For each abnormal event, associated with a process or quality variable, its path through the safety or quality systems designed to return its variable to the normal operation range is recorded. Event trees are prepared to record the successes and failures of each safety or quality system as it acts on each abnormal event. Over several months of operation, on the order of 10⁶ paths through event trees are stored. The new set‐theoretic structure condenses the paths to a single compact data record, leading to significant improvement in the efficiency of the probabilistic calculations and permitting Bayesian analysis of large alarm databases in real time. As a case study, steps (i) and (ii) are applied to an industrial, fluidized‐catalytic‐cracker. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Statistical evaluation and design of an evaporator control system

The utility of a statistical procedure suggested by Box and Jenkins is assessed for the evaluation and modification of an industrial evaporator control system. Results, obtained from the analysis of routine unperturbed operating data, indicate that this approach is (i) readily adaptable to the analysis of the dynamic behaviour of a full scale chemical unit process, (ii) characterized by effective model form identification, and (iii) extremely useful in deriving conventional and more novel control functions to supplement existing control schemes.     

A comparative study of fiber breakage in compounding glass fiber-reinforced thermoplastics in a buss kneader,modular Co-rotating and counter-rotating twin screw extruders

The breakage of glass fibers was measured for several different types of continuous mixers including (i) Buss Kneader, (ii) modular intermeshing co-rotating twin screw extruder, and (iii) modular intermeshing counter-rotating twin screw extruder. Comparisons are made using different screw configurations, loadings, feeding ports, and mixing elements. Downstream feeding of glass fibers and milder screw configuration favor less breakage of glass fibers.     

Opportunities and challenges for process control in process intensification

This is a review and position article discussing the role and prospective for process control in process intensification. Firstly, the article outlines the classical role of control in process systems, presenting an overview of control systems’ development, from basic PID control to the advanced model based hierarchical structures. Further on, the paper reviews the research articles discussing control issues of intensified process equipment, specifically of reactive distillation, divided wall distillation, simulated moving bed reactors and micro-scale systems. In the next section, the focus is on more fundamental, dynamic characteristics of selected intensified process categories, which are elucidated in several examples. The goal of this analysis is to stress to the potential challenges for control of intensified processes. More importantly, the aim of this part is to emphasize to the opportunities for control, which are associated with new actuation possibilities arising from process intensification. Finally, a new concept of process synthesis is elaborated, which is based on process intensification and actuation improvement. The concept enables integration of process operation, design and control through dynamic optimization. This simultaneous synthesis approach should provide optimal operation and more efficient control of complex intensified systems. It may also suggest innovative process solutions which are more economically and environmentally efficient and agile.     

Towards the integration of process design,control and scheduling: Are we getting closer?

The integration of design and control, control and scheduling and design, control and scheduling, all have been core PSE challenges. While significant progress has been achieved over the years, it is fair to say that at the moment there is not a generally accepted methodology and/or “protocol” for such an integration – it is also interesting to note that currently, there is not a commercially available software [or even in a prototype form] system to fully support such an activity.Here, we present the foundations for such an integrated framework and especially a software platform that enables such integration based on research developments over the last 25 years. In particular, we describe PAROC, a prototype software system which allows for the representation, modeling and solution of integrated design, scheduling and control problems. Its main features include: (i) a high-fidelity dynamic model representation, also involving global sensitivity analysis, parameter estimation and mixed integer dynamic optimization capabilities; (ii) a suite/toolbox of model approximation methods; (iii) a host of multi-parametric programming solvers for mixed continuous/integer problems; (iv) a state-space modeling representation capability for scheduling and control problems; and (v) an advanced toolkit for multi-parametric/explicit Model Predictive Control and moving horizon reactive scheduling problems. Algorithms that enable the integration capabilities of the systems for design, scheduling and control are presented on a case of a series of cogeneration units.     

Multiscale molecular modeling in nanostructured material design and process system engineering

Atomistic-based simulations such as molecular mechanics, molecular dynamics, and Monte Carlo-based methods have come into wide use for material design. Using these atomistic simulation tools, we can analyze molecular structure on the scale of 0.1–10 nm. However, difficulty arises concerning limitations of the time and length scale involved in the simulation. Although a possible molecular structure can be simulated by the atom-based simulations, it is less realistic to predict the mesoscopic structure defined on the scale of 100–1000 nm, for example the morphology of polymer blends and composites, which often dominates actual material properties. For the morphology on these scales, mesoscopic simulations such as the dynamic mean field density functional theory and dissipative particle dynamics are available as alternatives to atomistic simulations. It is therefore inevitable to adopt a mesoscopic simulation technique and bridge the gap between atomistic and mesoscopic simulations for an effective material design. Furthermore, it is possible to transfer the simulated mesoscopic structure to finite elements modeling tools for calculating macroscopic properties for the systems of interest.In this contribution, a hierarchical procedure for bridging the gap between atomistic and macroscopic modeling passing through mesoscopic simulations will be presented and discussed. The concept of multiscale (or many scale) modeling will be outlined, and examples of applications of single scale and multiscale procedures for nanostructured systems of industrial interest will be presented. In particular the following industrial applications will be considered: (i) polymer-organoclay nanocomposites of a montmorillonite–polymer–surface modifier system; (ii) mesoscale simulation for diblock copolymers with dispersion of nanoparticels; (iii) polymer–carbon nanotubes system and (iv) applications of multiscale modeling for process systems engineering.     

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

State estimation from plant measurements plays an important role in advanced monitoring and control technologies, especially for chemical processes with nonlinear dynamics and significant levels of process and sensor noise. Several types of state estimators have been shown to provide high‐quality estimates that are robust to significant process disturbances and model errors. These estimators require a dynamic model of the process, including the statistics of the stochastic disturbances affecting the states and measurements. The goal of this article is to introduce a design method for nonlinear state estimation including the following steps: (i) nonlinear process model selection, (ii) stochastic disturbance model selection, (iii) covariance identification from operating data, and (iv) estimator selection and implementation. Results on the implementation of this design method in nonlinear examples (CSTR and large dimensional polymerization process) show that the linear time‐varying autocovariance least‐squares technique accurately estimates the noise covariances for the examples analyzed, providing a good set of such covariances for the state estimators implemented. On the estimation implementation, a case study of a chemical reactor demonstrates the better capabilities of MHE when compared with the extended Kalman filter. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

The interactions of design control and operability in reactive distillation systems

In this work the design and control of a reactive distillation column, described by a rigorous dynamic model, is tackled via two different optimization approaches. In the first, the steady-state process design and the control system are optimized sequentially. It is shown that operability is a strong function of the process design and potential operability bottlenecks are identified. In the second approach, the process design and the control system are optimized simultaneously leading to a more economically beneficial and better controlled system than that obtained using the sequential approach.     

通过化工产品工程开发可持续的技术来控制斑纹蚌种群

The zebra mussel is an important aquatic pest that causes great damage to freshwater-dependent industries, due to biofouling. The main goal of the project discussed here is to develop improved solutions to control this species. Three approaches have been explored in an attempt to design innovative application strategies for existing biocides： （i） encapsulation of toxins; （ii） combination of toxins; （iii） investigation of the seasonal variation of the species＇ tolerance to toxins. In this paper, the principles behind these approaches and the major results on each topic are presented. The benefits of adopting a chemical product engineering approach in conducting this project are also discussed.     

The Different Types of Noise and How They Effect Data Analysis

Random (noisy) processes can be characterized by the way consecutive data are correlated. The data can be uncorrelated (white noise), short-range correlated (often called red noise), or long-range correlated (sometimes called pink noise). Here we describe the properties and applications of these different kinds of noise. We discuss, how they influence (i) the diffusion process, (ii) the occurrence of rare extreme events and (iii) the detection of an external trend that is superimposed on the noise; (ii) and (iii) are particularly relevant in the context of detecting anthropogenic global warming by data analysis.     

Nichtlineare stochastische Optimierung unter Unsicherheiten

Nonlinear Stochastic Optimization under Uncertainty Robust decision making under uncertainty is considered to be of fundamental importance in numerous disciplines and application areas. In dynamic chemical processes in particular there are parameters which are usually uncertain, but may have a large impact on equipment decisions, plant operability, and economic analysis. Thus the consideration of the stochastic property of the uncertainties in the optimization approach is necessary for robust process design and operation. As a part of it, efficient chance constrained programming has become an important field of research in process systems engineering. A new approach is presented and applied for stochastic optimization problems of batch distillation with a detailed dynamic process model.     

Quantitative comparison of dynamic controllability between a reactive distillation column and a conventional multi-unit process

A comparison of the steady-state economic optimum designs of two alternative chemical processes was presented in a previous paper [Kaymak, D. B., & Luyben, W. L. (2004). A quantitative comparison of reactive distillation with conventional multi-unit reactor/column/recycle systems for different chemical equilibrium constants. Industrial & Engineering Chemistry Research, 43, 2493–2507]. A generic exothermic reversible reaction A + B ↔ C + D occurs in both flowsheets, which consist of a conventional multi-unit reactor/separator/recycle structure and a reactive distillation column. Results showed that the reactive distillation process is significantly less expensive than the conventional process for a wide range of the chemical equilibrium constant when there is no mismatch between the temperature favorable for reaction and the temperature favorable for vapor–liquid separation.

A reactive distillation column has fewer control degrees of freedom than a conventional multi-unit system. Therefore a reactive distillation column may have worse dynamic response than a conventional process. The purpose of this paper is to compare the dynamic controllability of these two alternative processes.

Three different chemical equilibrium constants are considered. Several control structures are developed for each flowsheet, and their effectiveness is evaluated. Disturbances in production rate and fresh feed compositions are considered.

The conventional multi-unit process provides significantly better control. The operability region is much larger, there is less variability in product quality and the dynamic responses are faster than those of the reactive column. Thus, these results demonstrate that there is a significant trade-off in this system between optimum economic steady-state design and dynamic controllability.