Plant-wide control for the economic operation of modified single mixed refrigerant process for an offshore natural gas liquefaction plant

The marine operation of floating liquefied natural gas (FLNG) demands process compactness, flexibility, simplicity of operation, safety, and higher efficiency. The modified single mixed refrigerant (MSMR) process satisfies the FLNG process requirements and is accepted as a suitable technology for FLNG operation. The aim of this study was to develop a plant-wide control structure or strategy that can sustain the economic efficiency of the MSMR process. The NGL recovery and liquefaction units were integrated in the MSMR process to provide a compact plant structure with an efficient operation. Steady-state optimality analysis was intensively conducted in a rigorous dynamic simulation environment to determine the correct variable to sustain the economic efficiency of MSMR process. The results showed that the flow rate ratio of heavy and light mixed refrigerant (HK/LK ratio) is a promising self-optimizing controlled variable. Controlling this variable can sustain the MSMR optimality, even when the process is operated under off-design operating conditions or in the presence of disturbances. Based on the control structure tests, the control configuration with the HK/LK ratio loop showed excellent performance, maintaining the process stability against a range of disturbances. The proposed approach can also be applied to any cryogenic liquefaction technology for determining a possible optimizing controlled variable.     

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     

A feedback control framework for safe and economically‐optimal operation of nonlinear processes

Maintaining safe operation of chemical processes and meeting environmental constraints are issues of paramount importance in the area of process systems and control engineering, and are ideally achieved while maximizing economic profit. It has long been argued that process safety is fundamentally a process control problem, yet few research efforts have been directed toward integrating the rather disparate domains of process safety and process control. Economic model predictive control (EMPC) has attracted significant attention recently due to its ability to optimize process operation accounting directly for process economics considerations. However, there is very limited work on the problem of integrating safety considerations in EMPC to ensure simultaneous safe operation and maximization of process profit. Motivated by the above considerations, this work develops three EMPC schemes that adjust in real‐time the size of the safety sets in which the process state should reside to ensure safe process operation and feedback control of the process state while optimizing economics via time‐varying process operation. Recursive feasibility and closed‐loop stability are established for a sufficiently small EMPC sampling period. The proposed schemes, which effectively integrate feedback control, process economics, and safety considerations, are demonstrated with a chemical process example. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2391–2409, 2016     

Economic model predictive control of nonlinear time‐delay systems: Closed‐loop stability and delay compensation

Closed‐loop stability of nonlinear time‐delay systems under Lyapunov‐based economic model predictive control (LEMPC) is considered. LEMPC is initially formulated with an ordinary differential equation model and is designed on the basis of an explicit stabilizing control law. To address closed‐loop stability under LEMPC, first, we consider the stability properties of the sampled‐data system resulting from the nonlinear continuous‐time delay system with state and input delay under a sample‐and‐hold implementation of the explicit controller. The steady‐state of this sampled‐data closed‐loop system is shown to be practically stable. Second, conditions such that closed‐loop stability, in the sense of boundedness of the closed‐loop state, under LEMPC are derived. A chemical process example is used to demonstrate that indeed closed‐loop stability is maintained under LEMPC for sufficiently small time‐delays. To cope with performance degradation owing to the effect of input delay, a predictor feedback LEMPC methodology is also proposed. The predictor feedback LEMPC design employs a predictor to compute a prediction of the state after the input delay period and an LEMPC scheme that is formulated with a differential difference equation (DDE) model, which describes the time‐delay system, initialized with the predicted state. The predictor feedback LEMPC is also applied to the chemical process example and yields improved closed‐loop stability and economic performance properties. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4152–4165, 2015     

Revisiting the steady states of NO/O2/C3H6 on monolithic Pt/BaO/Al2O3 using bifurcation analysis

While NOx storage and reduction is periodically operated, steady‐state studies have been widely carried out to investigate the involved reaction mechanisms and effects of operating parameters. Due to the complex reaction chemistry and its coupling with transport phenomena, multiplicity may exist. A steady‐state monolith reactor model accounting for microkinetics and reaction heat effects was proposed in this study to avoid the evaluation of enthalpies of microreaction steps. Three simplified versions of the monolith model were developed based on various assumptions of the axial gradients. Steady‐state behaviors of NO/O₂/C₃H₆ system were investigated. A predictor‐corrector (PC) continuation method that does not require explicit evaluation of Jacobian Matrix was developed to solve the nonlinear system with a variable parameter of feed temperature. Model predictions were compared with experimental results. © 2013 American Institute of Chemical Engineers AIChE J 60: 623–634, 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     

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     

Composite fast‐slow MPC design for nonlinear singularly perturbed systems

The design of a composite control system for nonlinear singularly perturbed systems using model predictive control (MPC) is described. Specifically, a composite control system comprised of a “fast” MPC acting to regulate the fast dynamics and a “slow” MPC acting to regulate the slow dynamics is designed. The composite MPC system uses multirate sampling of the plant state measurements, i.e., fast sampling of the fast state variables is used in the fast MPC and slow‐sampling of the slow state variables is used in the slow MPC. Using singular perturbation theory, the stability and optimality of the closed‐loop nonlinear singularly perturbed system are analyzed. A chemical process example which exhibits two‐time‐scale behavior is used to demonstrate the structure and implementation of the proposed fast–slow MPC architecture in a practical setting. © 2012 American Institute of Chemical Engineers AIChE J, 58: 1802–1811, 2012     

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     

Hybrid FSC membrane for CO2 removal from natural gas: Experimental,process simulation,and economic feasibility analysis

The novel fixed‐site‐carrier (FSC) membranes were prepared by coating carbon nanotubes reinforced polyvinylamine/polyvinyl alcohol selective layer on top of ultrafiltration polysulfone support. Small pilot‐scale modules with membrane area of 110–330 cm² were tested with high pressure permeation rig. The prepared hybrid FSC membranes show high CO₂ permeance of 0.084–0.218 m³ (STP)/(m² h bar) with CO₂/CH₄ selectivity of 17.9–34.7 at different feed pressures up to 40 bar for a 10% CO₂ feed gas. Operating parameters of feed pressure, flow rate, and CO₂ concentration were found to significantly influence membrane performance. HYSYS simulation integrated with ChemBrane and cost estimation was conducted to evaluate techno‐economic feasibility of a membrane process for natural gas (NG) sweetening. Simulation results indicated that the developed FSC membranes could be a promising candidate for CO₂ removal from low CO₂ concentration (10%) NGs with a low NG sweetening cost of 5.73E?3 $/Nm³ sweet NG produced. © 2014 American Institute of Chemical Engineers AIChE J 60: 4174–4184, 2014     

Analysis of Steady‐State Temperature Profiles from a Laboratory Reactor by Means of a Process Simulator

A process simulator was used for the analysis of steady‐state results from a laboratory‐scale tubular reactor for the oxidation of carbon monoxide over a platinum catalyst. From a set of 14 steady‐state experiments, temperature profiles were simulated with two adjustable parameters recovered by optimizing the fit: k°, the pre‐exponential portion of the rate constant, and h_out, the outer wall heat transfer coefficient for the reactor tube. Simulation showed that despite elaborate insulation the reactor did not behave adiabatically. Simulation also predicted fairly well the magnitude of phenomena such as ignition, extinction, and rate hysteresis (caused by changes in feed temperatures or concentrations) but at temperatures below the experimental values.     

Smart manufacturing: Handling preventive actuator maintenance and economics using model predictive control

Integrating components and systems of the manufacturing process is an important area of research to enable the future development and deployment of the Smart Manufacturing paradigm. An economic model predictive control (EMPC) scheme is proposed that effectively integrates scheduled preventive control actuator maintenance, process economics, and process control into a unified methodology. To accomplish this goal, a Lyapunov‐based EMPC (LEMPC) scheme is formulated for handling changing number of online actuators (i.e., changing number of manipulated inputs). Closed‐loop stability under the proposed LEMPC is proven. Subsequently, the LEMPC is applied to a chemical process network used for benzene alkylation to demonstrate that the LEMPC can maintain stability and improve dynamic economic performance of the process network in the presence of changing number of available control actuators resulting from scheduled preventive maintenance tasks. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2179–2196, 2014     

Economic model predictive control of nonlinear process systems using Lyapunov techniques

In this work, we develop model predictive control (MPC) designs, which are capable of optimizing closed‐loop performance with respect to general economic considerations for a broad class of nonlinear process systems. Specifically, in the proposed designs, the economic MPC optimizes a cost function, which is related directly to desired economic considerations and is not necessarily dependent on a steady‐state—unlike conventional MPC designs. First, we consider nonlinear systems with synchronous measurement sampling and uncertain variables. The proposed economic MPC is designed via Lyapunov‐based techniques and has two different operation modes. The first operation mode corresponds to the period in which the cost function should be optimized (e.g., normal production period); and in this operation mode, the MPC maintains the closed‐loop system state within a predefined stability region and optimizes the cost function to its maximum extent. The second operation mode corresponds to operation in which the system is driven by the economic MPC to an appropriate steady‐state. In this operation mode, suitable Lyapunov‐based constraints are incorporated in the economic MPC design to guarantee that the closed‐loop system state is always bounded in the predefined stability region and is ultimately bounded in a small region containing the origin. Subsequently, we extend the results to nonlinear systems subject to asynchronous and delayed measurements and uncertain variables. Under the assumptions that there exist an upper bound on the interval between two consecutive asynchronous measurements and an upper bound on the maximum measurement delay, an economic MPC design which takes explicitly into account asynchronous and delayed measurements and enforces closed‐loop stability is proposed. All the proposed economic MPC designs are illustrated through a chemical process example and their performance and robustness are evaluated through simulations. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Highly scalable ZIF‐based mixed‐matrix hollow fiber membranes for advanced hydrocarbon separations

ZIF‐8/6FDA‐DAM, a proven mixed‐matrix material that demonstrated remarkably enhanced C₃H₆/C₃H₈ selectivity in dense film geometry, was extended to scalable hollow fiber geometry in the current work. We successfully formed dual‐layer ZIF‐8/6FDA‐DAM mixed‐matrix hollow fiber membranes with ZIF‐8 nanoparticle loading up to 30 wt % using the conventional dry‐jet/wet‐quench fiber spinning technique. The mixed‐matrix hollow fibers showed significantly enhanced C₃H₆/C₃H₈ selectivity that was consistent with mixed‐matrix dense films. Critical variables controlling successful formation of mixed‐matrix hollow fiber membranes with desirable morphology and attractive transport properties were discussed. Furthermore, the effects of coating materials on selectivity recovery of partially defective fibers were investigated. To our best knowledge, this is the first article reporting successful formation of high‐loading mixed‐matrix hollow fiber membranes with significantly enhanced selectivity for separation of condensable olefin/paraffin mixtures. Therefore, it represents a major step in the research area of advanced mixed‐matrix membranes. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2625–2635, 2014     

Control structure comparison for three-product Petlyuk column

The focus of this paper is to investigate different control structures (single-loop PI control) for a dividing wall (Petlyuk) column for separating ethanol, n-propanol and n-butanol. Four control structures are studied. All the results are simulations based on Aspen Plus. Control structure 1 (CS1) is stabilizing control structure with only temperature controllers. CS2, CS3 and CS4, containing also composition controllers, are introduced to reduce the steady state composition deviations. CS2 adds a distillate composition controller (CCDB) on top of CS1. CS3 is much more complicated with three temperature-composition cascade controllers and in addition a selector to the reboiler duty to control the maximum controller output of light impurity composition control in side stream and bottom impurity control in the prefractionator. CS4 adds another high selector to control the light impurity in the sidestream. Surprisingly, when considering the dynamic and even steady state performance of the proposed control structures, CS1 proves to be the best control structure to handle feed disturbances inserted into the three-product Petlyuk column.     

Integrated combined cycle from natural gas with CO2 capture using a Ca–Cu chemical loop

The integration in a natural gas combined cycle (NGCC) of a novel process for H₂ production using a chemical Ca–Cu loop was proposed. This process is based on the sorption‐enhanced reforming process for H₂ production from natural gas with a CaO/CaCO₃ chemical loop, but including a second Cu/CuO loop to regenerate the Ca‐sorbent. An integration of this system into a NGCC was proposed and a full process simulation exercise of different cases was carried out. Optimizing the operating conditions in the Ca–Cu looping process, 8.1% points of efficiency penalty with respect to a state‐of‐the‐art NGCC are obtained with a CO₂ capture efficiency of 90%. It was demonstrated that the new process can yield power generation efficiencies as high as any other emerging and commercial concepts for power generation from NGCC with CO₂ capture, but maintaining competing advantages of process simplification and compact pressurized reactor design inherent to the Ca–Cu looping system. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2780–2794, 2013     

Graph reduction of complex energy‐integrated networks: Process systems applications

We illustrate the application of a graph reduction method developed recently to analyze complex energy‐integrated process networks. The method uses information on the energy flow structure of the network and the orders of magnitude of the different energy flows to generate, automatically, information on the time scales where the process units evolve, canonical forms of the reduced models in each time scale, and controlled variables and potential manipulated inputs available in each time scale. Representative examples of reactor‐heat exchanger and distillation column networks are considered to illustrate the method and develop insights on effective control strategies for these processes. © 2014 American Institute of Chemical Engineers AIChE J, 60: 995–1012, 2014     

Error‐triggered on‐line model identification for model‐based feedback control

In industry, it may be difficult in many applications to obtain a first‐principles model of the process, in which case a linear empirical model constructed using process data may be used in the design of a feedback controller. However, linear empirical models may not capture the nonlinear dynamics over a wide region of state‐space and may also perform poorly when significant plant variations and disturbances occur. In the present work, an error‐triggered on‐line model identification approach is introduced for closed‐loop systems under model‐based feedback control strategies. The linear models are re‐identified on‐line when significant prediction errors occur. A moving horizon error detector is used to quantify the model accuracy and to trigger the model re‐identification on‐line when necessary. The proposed approach is demonstrated through two chemical process examples using a model‐based feedback control strategy termed Lyapunov‐based economic model predictive control (LEMPC). The chemical process examples illustrate that the proposed error‐triggered on‐line model identification strategy can be used to obtain more accurate state predictions to improve process economics while maintaining closed‐loop stability of the process under LEMPC. © 2016 American Institute of Chemical Engineers AIChE J, 63: 949–966, 2017     

Economic model predictive control of nonlinear process systems using empirical models

Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first principles or through system identification techniques. In industrial practice, it may be difficult in general to obtain an accurate first‐principles model of the process. Motivated by this, in the present work, Lyapunov‐based EMPC (LEMPC) is designed with a linear empirical model that allows for closed‐loop stability guarantees in the context of nonlinear chemical processes. Specifically, when the linear model provides a sufficient degree of accuracy in the region where time varying economically optimal operation is considered, conditions for closed‐loop stability under the LEMPC scheme based on the empirical model are derived. The LEMPC scheme is applied to a chemical process example to demonstrate its closed‐loop stability and performance properties as well as significant computational advantages. © 2014 American Institute of Chemical Engineers AIChE J, 61: 816–830, 2015