On the attainable region for process networks

In this work the attainable region (AR) concept for process networks with outlet flow rate specifications is introduced for the first time. For process unit models to which the infinite dimensional State‐space conceptual framework is applicable, it is shown that identification of AR boundary membership is equivalent to feasibility assessment of an infinite linear program (ILP). A number of important AR properties are then theoretically established, including AR convexity, and representation of the AR in a concentration state space of reduced dimension. Finite dimensional approximations of the aforementioned ILP are then employed in creating increasingly accurate approximations of the AR. A case study for the vapor‐liquid equilibrium‐based separation of a ternary azeotropic mixture is used to illustrate the proposed method. The quantified two‐ and four‐Dimensional ARs indicate that acetone mole fractions above 0.79 (acetone/methanol azeotrope) are attainable for the considered outlet flow rate ratios. © 2013 American Institute of Chemical Engineers AIChE J, 60: 193–212, 2014     

Equation‐oriented flowsheet simulation and optimization using pseudo‐transient models

Tight integration through material and energy recycling is essential to the energy efficiency and economic viability of process and energy systems. Equation‐oriented (EO) steady‐state process simulation and optimization are key enablers in the optimal design of integrated processes. A new process modeling and simulation concept based on pseudo‐transient continuation is introduced. An algorithm for reformulating the steady‐state models of process unit operations as differential‐algebraic equation systems that are statically equivalent with the original model is presented. These pseudo‐transient models improve the convergence of EO process flowsheet simulations by expanding the convergence basin. This concept is used to build a library of pseudo‐transient models for common process unit operations, and this modeling concept seamlessly integrates with a previously developed time‐relaxation optimization algorithm. Two design case studies are presented to validate the proposed framework. © 2014 American Institute of Chemical Engineers AIChE J 60: 4104–4123, 2014     

Distributed dissipative model predictive control for process networks with imperfect communication

Results are developed to ensure stability of a dissipative distributed model predictive controller in the case of structured or arbitrary failure of the controller communication network; bounded errors in the communication may similarly be handled. Stability and minimum performance of the process network is ensured by placing a dissipative trajectory constraint on each controller. This allows for the interaction effects between units to be captured in the dissipativity properties of each process, and thus, accounted for by choosing suitable dissipativity constraints for each controller. This approach is enabled by the use of quadratic difference forms as supply rates, which capture detailed dynamic system information. A case study is presented to illustrate the results. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1682–1699, 2014     

On the tuning of predictive controllers: Impact of disturbances,constraints, and feedback structure

The impact of problem formulation modifications on predictive controller tuning is investigated. First, the proposed tuning method is shown to adapt to disturbance characteristic changes and thus, takes full economic advantage of the scenario. The second topic concerns point‐wise‐in‐time constraints and the impact of constraint infeasibility. Specifically, we shift the tuning question from selection of nonintuitive weighting matrix parameters to that of a few key parameters and results in a rather intuitive trade‐off between expected profit and expected constraint violations. Finally, we show that simple modifications will allow for the consideration of various feedback structures, including computational delay and partial state information. The overall conclusions of the work are that the results of the automated algorithm will help build an intuitive understating of the dynamics of the process and ultimately result in a higher level trade‐off between profit and constraint observance. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3473–3489, 2014     

A superstructure‐based mixed‐integer programming approach to optimal design of pipeline network for large‐scale CO2 transport

Pipeline transport is the major means for large‐scale and long‐distance CO₂ transport in a CO₂ capture and sequestration (CCS) project. But optimal design of the pipeline network remains a challenging problem, especially when considering allocation of intermediate sites, like pump stations, and selection of pipeline routes. A superstructure‐based mixed‐integer programming approach for optimal design of the pipeline network, targeting on minimizing the overall cost in a CCS project is presented. A decomposition algorithm to solve the computational difficulty caused by the large size and nonlinear nature of a real‐life design problem is also presented. To illustrate the capability of our models. A real‐life case study in North China, with 45 emissions sources and four storage sinks, is provided. The result shows that our model and decomposition algorithm is a practical and cost‐effective method for pipeline networks design. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2442–2461, 2014     

A novel hybrid feedstock to liquids and electricity process: Process modeling and exergoeconomic life cycle optimization

This article proposes a novel hybrid low‐rank coal (LRC)/biomass/natural gas process for producing liquid fuels and electricity. The hybrid process highlights coexistence of indirect and direct liquefaction technologies, cogasification of char and biomass, and corefinery of LRC syncrude and Fischer–Tropsch syncrude. A process simulation based on detailed chemical kinetics is present to illustrate its feasibility. In addition, we propose an exergoeconomic life cycle optimization framework that seeks to maximize the primary exergy saving ratio, primary total overnight cost saving ratio, life cycle waste emissions avoidance ratio, and primary levelized cost saving ratio by comparing the proposed hybrid process to its reference stand‐alone subsystems. From the results, we can determine four optimal designs which yield competitive breakeven oil prices ranging from $1.87/GGE to $2.13/GGE. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3739–3753, 2014     

Ensembles‐based and GA‐based optimization for landfill gas production

The efficient and economic production of landfill gases (LFG) by optimally adjusting LFG production settings is of high interest as a promising source of biomass energy. A key obstacle in LFG production optimization is the large‐scale and complex system with overwhelming uncertainty and heterogeneity. We propose a simplified ensemble‐based optimization (EnOpt) method to solve the LFG production optimization problem when constraints are not a concern, where the gradient information is obtained from an ensemble of realizations of the system. For constrained optimization, a novel parameterless genetic algorithm is proposed and successfully applied to the simulated LFG process. The effectiveness of the proposed (EnOpt) method and the parameterless genetic algorithm is demonstrated with the simulation of a landfill and gas generation and transport therein, using a parallel computation strategy. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2063–2071, 2014     

Maximum‐likelihood maximum‐entropy constrained probability density function estimation for prediction of rare events

This work addresses the problem of estimating complete probability density functions (PDFs) from historical process data that are incomplete (lack information on rare events), in the framework of Bayesian networks. In particular, this article presents a method of estimating the probabilities of events for which historical process data have no record. The rare‐event prediction problem becomes more difficult and interesting, when an accurate first‐principles model of the process is not available. To address this problem, a novel method of estimating complete multivariate PDFs is proposed. This method uses the maximum entropy and maximum likelihood principles. It is tested on mathematical and process examples, and the application and satisfactory performance of the method in risk assessment and fault detection are shown. Also, the proposed method is compared with a few copula methods and a nonparametric kernel method, in terms of performance, flexibility, interpretability, and rate of convergence. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1013–1026, 2014     

Control structure synthesis for operational optimization of mixed refrigerant processes for liquefied natural gas plant

The best control structures for the energy optimizing control of propane precooled mixed refrigerant (C₃MR) processes were examined. A first principles‐based rigorous dynamic model was developed to analyze the steady‐state and dynamic behaviors of the C₃MR process. The steady‐state optimality of the C₃MR process was then examined in a whole operation space for exploring the feasibility of the energy optimizing control for possible control structures. As a result, the temperature difference (TD) between the warm‐end inlet and outlet MR streams was exploited as a promising controlled variable to automatically keep the liquefaction process close to its optimum. The closed‐loop responses were finally evaluated for every possible control structure candidate. Based on the steady‐state optimality and the dynamic performance evaluation, several control structures with a TD loop were proposed to be most favorable for the energy optimizing control of the C₃MR process. The proposed optimality approach can be applied to any natural gas liquefaction process for determining a proper controlled variable for optimizing operation. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2428–2441, 2014     

Distributed model predictive control based on dissipativity

A noncooperative approach to plant‐wide distributed model predictive control based on dissipativity conditions is developed. The plant‐wide process and distributed control system are represented as two interacting process and controller networks, with interaction effects captured by the dissipativity properties of subsystems and network topologies. The plant‐wide stability and performance conditions are developed based on global dissipativity conditions, which in turn are translated into the dissipative trajectory conditions that each local model predictive control MPC must satisfy. This approach is enabled by the use of dynamic supply rates in quadratic difference forms, which capture detailed dynamic system information. A case study is presented to illustrate the results. © 2012 American Institute of Chemical Engineers AIChE J, 59: 787–804, 2013     

Impact of the coating process on the molecular structure of starch‐based barrier coatings

Molecular analysis of starch structure can be used to explain and predict changes in physical properties, such as water vapor and oxygen barrier properties in packaging materials. Solution casting is a widely used technique to create films from starch formulations. This study compared the molecular properties of these standard films with those of experimental coatings applied to paper in laboratory‐scale and pilot‐scale trials, with all three techniques using the same starch formulation. The results revealed large differences in molecular structure, i.e., cross‐linking and hydrolysis, between films and coatings. The main differences were due to the shorter drying time allowed to laboratory‐scale coatings and the accelerated drying process in pilot trials owing to the high energy output of infrared dryers. Furthermore, surface morphology was highly affected by the coating technique used, with a rougher surface and many pinholes occurring in pilot‐scale coatings, giving lower water vapor permeability than laboratory‐scale coatings. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 41190.     

Tear force of physically crosslinked poly(vinyl alcohol) gels with different submicrometer‐scale network structures

Poly(vinyl alcohol) (PVA) gels can be easily prepared by either the freeze‐thawing (FT gel) method or by the cast‐drying (CD gel) method. Although the resulting nanostructured networks of the FT and CD gels are similar, their physical properties are quite different; while CD gels are transparent and elastic, FT gels are opaque and less elastic. Moreover, the tear energy of the FT gels is much greater than that of the CD gels, which is a direct result of micrometer‐scale differences in their network structures. In order to control the distribution of microcrystallites on nano‐ and micrometer scales, FT gels were prepared from PVA solutions with different water contents. As a result, the gel gradually became more transparent as the initial water content was decreased; and accordingly, the tear energy decreased. Tear resistance was improved in the case of FT gels by repeating the number of FT cycles, whereas with CD gels, this was achieved by increasing the gelation temperature. These results indicate that the microscopic network structures are characterized by a micrometer‐scale bundled‐polymer (fibril), which determines the tear energy of FT gels. Simple methods to control the fibril network structure of FT gels using a unidirectional freezing method are presented herein, with the swelling and mechanical properties of modified FT gels discussed in terms of their multiple‐scale network structures. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41356.     

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     

Real‐time economic model predictive control of nonlinear process systems

Closed‐loop stability of nonlinear systems under real‐time Lyapunov‐based economic model predictive control (LEMPC) with potentially unknown and time‐varying computational delay is considered. To address guaranteed closed‐loop stability (in the sense of boundedness of the closed‐loop state in a compact state‐space set), an implementation strategy is proposed which features a triggered evaluation of the LEMPC optimization problem to compute an input trajectory over a finite‐time prediction horizon in advance. At each sampling period, stability conditions must be satisfied for the precomputed LEMPC control action to be applied to the closed‐loop system. If the stability conditions are not satisfied, a backup explicit stabilizing controller is applied over the sampling period. Closed‐loop stability under the real‐time LEMPC strategy is analyzed and specific stability conditions are derived. The real‐time LEMPC scheme is applied to a chemical process network example to demonstrate closed‐loop stability and closed‐loop economic performance improvement over that achieved for operation at the economically optimal steady state. © 2014 American Institute of Chemical Engineers AIChE J, 61: 555–571, 2015     

Distributed lyapunov‐based model predictive control with neighbor‐to‐neighbor communication

This work considers distributed predictive control of large‐scale nonlinear systems with neighbor‐to‐neighbor communication. It fulfills the gap between the existing centralized Lyapunov‐based model predictive control (LMPC) and the cooperative distributed LMPC and provides a balanced solution in terms of implementation complexity and achievable performance. This work focuses on a class of nonlinear systems with subsystems interacting with each other via their states. For each subsystem, an LMPC is designed based on the subsystem model and the LMPC only communicates with its neighbors. At a sampling time, a subsystem LMPC optimizes its future control input trajectory assuming that the states of its upstream neighbors remain the same as (or close to) their predicted state trajectories obtained at the previous sampling time. Both noniterative and iterative implementation algorithms are considered. The performance of the proposed designs is illustrated via a chemical process example. © 2014 American Institute of Chemical Engineers AIChE J 60: 4124–4133, 2014     

Selection of control configurations for economic model predictive control systems

Economic model predictive control (EMPC) is a feedback control method that dictates a potentially dynamic (time‐varying) operating policy to optimize the process economics. The objective function used in the EMPC system may be a general nonlinear function that describes the process/system economics. As this function is not derived on the sole basis of classical control considerations (stabilization, tracking, and optimal control action calculation) but rather on the basis of economics, selecting the appropriate control configuration, and quantifying the influence of a given input on an economic cost is an important task for the proper design and computational efficiency of an EMPC scheme. Owing to these considerations, an input selection methodology for EMPC is proposed which utilizes the relative degree and the sensitivity of the economic cost with respect to an input to identify and select stabilizing manipulated inputs with the most dynamic and steady‐state influence on the economic cost function to be assigned to EMPC. Other considerations for input selection for EMPC are also discussed and integrated into a proposed input selection methodology for EMPC. The control configuration selection method for EMPC is demonstrated using a chemical process example. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3230–3242, 2014     

Process design methodology for energy‐efficient processes operating below and across ambient temperature

This article presents a targeting and design methodology that can be implemented for any process where pressure‐based exergy, also known as mechanical exergy, has an important contribution to the total exergy conversion and transfer. However, in this article it is applied to processes that operate at sub‐ambient conditions, or processes where the ambient conditions are crossed. Exergy efficiencies, new Exergetic Composite Curves, Cascades, and Extended Grid Diagrams are tools that had to be implemented, improved, or invented, to develop a methodology with considerable potential for energy‐efficient process design. The appropriate placement (correct integration) of compressors and expanders in heat exchanger networks is also analyzed to minimize the number of units. An example is used to demonstrate the methodology, where several simplifying assumptions are made to facilitate understanding and to explain the design method. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2324–2340, 2016     

Dynamic real‐time optimization and control of a hybrid energy system

A proactive energy management strategy for a stand‐alone hybrid renewable energy system is presented. The study was motivated by the system built in Lambton College (Sarnia, Ontario, Canada) which includes photovoltaic arrays, wind turbine, battery, electrolyzers, hydrogen storage tanks, and fuel cells. The control architecture consists of two levels of hierarchy: (1) optimal predictive scheduling at the supervisory level and (2) local controllers for each of the system units. A “day‐ahead” approach is followed at the supervisory level and a bidirectional communication between the supervisory, proactive control, and the low‐level control layer is established. The proposed energy management strategy accounts for external (i.e., weather and demand) and internal disturbances. The efficacy of the proposed strategy is demonstrated through case studies. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2546–2556, 2014     

Economic model predictive control with time‐varying objective function for nonlinear process systems

Economic model predictive control (EMPC) is a control scheme that combines real‐time dynamic economic process optimization with the feedback properties of model predictive control (MPC) by replacing the quadratic cost function with a general economic cost function. Almost all the recent work on EMPC involves cost functions that are time invariant (do not explicitly account for time‐varying process economics). In the present work, we focus on the development of a Lyapunov‐based EMPC (LEMPC) scheme that is formulated with an explicitly time‐varying economic cost function. First, the formulation of the proposed two‐mode LEMPC is given. Second, closed‐loop stability is proven through a theoretical treatment. Last, we demonstrate through extensive closed‐loop simulations of a chemical process that the proposed LEMPC can achieve stability with time‐varying economic cost as well as improve economic performance of the process over a conventional MPC scheme. © 2013 American Institute of Chemical Engineers AIChE J 60: 507–519, 2014