Keywords: Modelling and Identification, Inferential sensing, State Estimation and Sensor development, Control Applications
Abstract: Mathematical models of biochemical reaction networks are important tools in systems biology and systems medicine to understand the reasons for diseases like cancer, and to make predictions for the development of effective treatments. In synthetic biology, for instance, models are used for the design of circuits to reliably perform specialized tasks. For analysis and predictions, plausible and reliable models are required, i.e., models must reflect the properties of interest of the considered biochemical networks. One remarkable property of biochemical networks is robust functioning over a wide range of perturbations and environmental conditions. Plausible mathematical models of such robust networks should also be robust. However, capturing, describing, and analyzing robustness in biochemical reaction networks is challenging. First, including uncertainty in the structures, parameters, and perturbations into the model is not straightforward due to different types of uncertainties encountered. Second, robustness as well as system and thus model properties are often itself inherently uncertain, such as qualitative (i.e., nonquantitative) descriptions. Finally, analyzing nonlinear models subject to different uncertainties and with respect to quantitative and qualitative properties is still in its infancy. In the first part of this perspective article, network functions and behaviors of interest are formally defined. Furthermore, different classes of uncertainties and perturbations in the data and model are consistently described. In the second part, we review frequently used approaches and present our own recent developments for robustness analysis, estimation, and model-based prediction. We illustrate their capabilities to deal with the different types of uncertainties and robustness requirements.

Keywords: Process Optimization, Control, Interaction Between Design and Control, Batch Process Modelling, Optimization and Control
Abstract: The present article looks at the problem of iterative controller tuning, where the parameters of a given controller are adapted in an iterative manner to bring a user-defined performance metric to a local minimum for some repetitive process. Specifically, we cast the controller tuning problem as a real-time optimization (RTO) problem, which allows us to exploit the available RTO theory to enforce both convergence and performance guarantees. We verify the effectiveness of the proposed methodology on an experimental torsional system and note that the results are particularly promising considering the simplicity of the method.

Keywords: Process Optimization, Control
Abstract: The paper deals with nonlinear control under input constraints of a non isothermal Continuous Stirred Tank Reactor (CSTR) using thermodynamic concepts. More precisely, the paper presents an extension of the previous work (Hoang et al. (2012)) where the jacket temperature is used as the only control input. Constrained input control strategy is based on the observation that the chemical reactor operates at an unique stationary temperature when the lower or upper bound of the input variable is imposed. This control design results in a global asymptotic stabilizing feedback law that provides a locally exponentially stable behaviour of the overall system.

Keywords: Process Optimization, Control, Control Applications
Abstract: In the process industry, advanced control is usually implemented to ensure stability and constraints satisfaction. Moreover, a competitive global market and environmental regulations results in the necessity for the economic optimization of the process operation. Real Time Optimization (RTO), which is based on an economic criterion, is usually performed in an upper level of the control structure and sends optimizing targets to the lower dynamic control layer where the advanced control drives the system to optimum targets. In this structure, the RTO employs a complex stationary non-linear model of the process for the optimization and the advanced control is usually implemented through a MPC based on a linear model. In this paper, the application of such optimization structure to an industrial Propylene/Propane (PP) splitter is tested in a simulation platform based on the integration of the commercial dynamic simulator Dynsim® and the rigorous steady-state optimizer ROMeo® with real-time facilities of Matlab. The advanced control is represented by an Infinite Horizon Model Predictive Controller (IHMPC), based on a space-state model in the incremental form that reproduces the step response model and considers the existence of zone control, optimizing targets for the inputs and can accommodate time delays. In this simulation platform the optimization and advanced control of a Propylene/Propane splitter of an oil refinery is studied. The simulation results show that proposed RTO/advanced control structure is stable and can be implemented in the real system.

Keywords: Process Optimization, Control, Batch Process Modelling, Optimization and Control, Control Applications
Abstract: Model-based optimization is an increasingly popular way of determining the values of the degrees of freedom for a process. The drawback is that the available model is often inaccurate. An iterative set-point optimization method called “modifier adaptation” overcomes this obstacle by incorporating process measurements into the optimization framework. We extend this technique to optimization problems where the model inputs do not correspond to the plant inputs. Using the example of an incineration plant, we argue that this occurs in practice when a complex process cannot be fully modeled and the missing part encompasses additional degrees of freedom. This paper shows that the modifier-adaptation scheme can be modified accordingly. This extension makes modifier adaptation much more flexible and applicable, as a wider class of models can be used. The proposed method is illustrated through a simulated CSTR example.

Keywords: Process Optimization, Control, Control Applications
Abstract: In this paper we apply model order reductions techniques to efficiently implement simultaneous model predictive control and moving horizon estimation for high dimensional chemical processes. Two model approximation schemes that both combine order reduction and linearization are employed and compared. The approach is demonstrated on a benchmark distillation column example model.

Keywords: Process Optimization, Control
Abstract: An approach to distributed multi-rate control for large-scale systems, and in particular process networks, is presented. Where the local measurements, local control and controller communication are allowed to operate at different sampling rates. Dissipative systems theory is used to facilitate stability and performance analysis of the process network, based upon dynamic supply rates which have been lifted into a global sampling rate. Quadratic difference forms are used as supply rates and storage functions, which facilitate less conservative stability and performance conditions as compared to classical types of supply rates.

Keywords: Process Optimization, Control, Interaction Between Design and Control, Control Applications
Abstract: We consider the output tracking of a set-point using cooperative distributed model predictive control. We propose a framework to avoid the loss of feasibility, guarantee stability, constraint satisfaction as well as convergence to admissible set-points. To enable a distributed implementation of the model predictive control law we utilize a cyclic varying horizon length. A simulation example illustrates the approach and its applicability for interconnected systems.

Keywords: Inferential sensing, State Estimation and Sensor development, Process and Performance Monitoring, Fault Detection, Supervision and Safety
Abstract: In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection and, we investigate its applicability to monitoring material’s properties from spectroscopic observations. Motivated by continuity preservation, the SDPP is a linear projection method where the local geometry of the points in the low-dimensional subspace mimics the geometry of the points in the response space. Such a mapping facilitates an efficient regressor design and it may also uncover useful information for visualisation. An experimental evaluation is conducted to show the performance of the SDPP and compare it with a number of state-of-the-art approaches for unsupervised and supervised dimensionality reduction. For the task, the results obtained on a benchmark problem consisting of a set of NIR spectra of diesel fuels and six different chemico- physical properties of those fuels is discussed. Based on the experimental results, the SDPP leads to accurate and parsimonious projections that can be used in the design of efficient regression models.

Keywords: Process Optimization, Control, Modelling and Identification, Interaction Between Design and Control
Abstract: We present a model predictive control (MPC) approach to solve the dual adaptive control problem. The cost function minimized by the controller rewards probing the system for information when the parameter estimates are poor. The control algorithm is designed to handle poorly identified models and excites the system so that information can be gathered to achieve the optimal trade-off between process control and identification. This excitation is achieved without requiring the input to be persistently exciting; rather, the probing objective is based on an exact formulation of the expected value of the output error at the first time stage. The resulting expression is also used for the second time stage; this ensures that a proper trade-off between excitation and output regulation is maintained. The algorithm can be viewed as the merging of adaptive control with MPC and its design can easily be implemented with modifications to an existing MPC. As an example we consider a first-order linear process system with two unknown parameters. Our proposed algorithm probes the system even when the output error is small and quickly gathers enough information to correctly identify the unknown plant parameters.

Keywords: Process Optimization, Control, Inferential sensing, State Estimation and Sensor development, Control Applications
Abstract: Control of product purity is of paramount importance in effective control of tightly integrated process such as reactive distillation. In practice, however, measurements of product concentrations may be unavailable or may be available at slower sampling rates when compared with other measurements. A cost effective approach to improve control of such systems is to develop a state estimator that can accommodate measurements at multiple rates and use it in controller development. In this work, DAE EKF formulation developed by Mandela et al. (2010) is modified to accommodate measurements available at multiple sampling rates. A successive linearization based nonlinear MPC scheme is then developed for controlling a system modeled as DAEs. Observer error feedback approach developed by Hunag et al. (2012) has been extended to achieve offset reduction and to improve regulatory control of a multi-rate sampled data system. The efficacy of the proposed approach is demonstrated by conducting simulation studies on an ideal reactive distillation system. Analysis of the simulation results reveals that the feedback introduced using the multi-rate concentration measurements reduces the offset significantly in the face of unmeasured disturbances of moderate magnitude.

Keywords: Process Optimization, Control, Process Scheduling and Decision support, Integration between Scheduling and Control, Interaction Between Design and Control
Abstract: Interpolation-based off-line MPC for LPV systems is presented in this work. The on-line computational time is reduced by pre-computing off-line the sequences of state feedback gains corresponding to the sequences of ellipsoidal invariant sets. At each sampling time, the real-time state feedback gain is calculated by linear interpolation between the pre-computed state feedback gains. Four interpolation techniques are presented. In the first technique, the smallest ellipsoid containing the current state measured is approximated and the corresponding real-time state feedback gain is calculated. In the second technique, the pre-computed state feedback gains are interpolated in order to get the largest possible real-time state feedback gain while robust stability is still guaranteed. In the third technique, the real-time state feedback gain is calculated by minimizing the violation of the constraints of the adjacent inner ellipsoids so the real-time state feedback gain calculated has to regulate the state from the current ellipsoids to the adjacent inner ellipsoids as fast as possible. In the last technique, the real-time state feedback gain is calculated by minimizing the one-step cost function so the real-time state feedback gain calculated has to regulate the next predicted state to the origin as fast as possible. A case study of nonlinear CSTR is presented to illustrate the implementation of the proposed techniques. The results show that the proposed interpolation techniques 2, 3 and 4 tend to produce less sluggish responses than the technique 1.

Keywords: Process Scheduling and Decision support, Integration between Scheduling and Control
Abstract: A systematic plantwide control design procedure was proposed in [Skogestad, 2000].The main goal of this procedure is, to design an optimal control structure for a complete chemical plant based on steady state plant economics, also known as economic plantwide control. In this work, we automated a key step of this procedure, which is the selection of controlled variables, based on quantitative local methods. We applied the economic plantwide control design procedure to a typical chemical plant process, which consists of a reactor, a separator and a recycle stream with purge. We evaluated the economic performance of the designed control structures for various disturbances and found that, although the automatic selection of the controlled variables was based on local methods, the control structures performed quite well, even for large disturbances.

Keywords: Process Optimization, Control
Abstract: This article considers the robust regulation problem for a class of constrained linear switched systems with bounded additive disturbances. The proposed solution extends the existing robust tube based model predictive control (RTBMPC) strategy for non-switched linear systems to switched systems. RTBMPC utilizes nominal model predictions, together with tightened sets of state and input constraints, to obtain a control policy that guarantees robust stabilization of the dynamic systems in presence of bounded uncertainties. Similar to RTBMPC for non-switched systems, a disturbance rejection proportional controller is used to ensure that the closed loop trajectories of the switched linear system are bounded in a tube centered on the nominal system. To account for the switching dynamics, the gain of this controller is chosen to simultaneously stabilize all switching dynamics. The RTBMPC for the switched system requires an on-line solution of a Mixed Integer Quadratic Program (MIQP). To reduce the complexity of the MIQP, a sub-optimal design is proposed, which considers the notion of a pre-terminal set in addition to the usual terminal set used to ensure stability. The RTBMPC design with the pre-terminal set aids in tuning the trade-off between the complexity of the control algorithm with the optimal performance of the closed-loop system while ensuring robust stability. Examples are presented to illustrate features of the proposed MPC.

Keywords: Process Optimization, Control, Modelling and Identification, Control Applications
Abstract: Industrial applications of model predictive control rely mostly on linear empirical models obtained by employing time series analysis approaches. These models can quickly become obsolete and require maintenance when the operating conditions become significantly different from the design conditions. The need to generate good predictions in the face of changing operating conditions and / or plant characteristics can be fulfilled through updating the linear model parameters online. This work is aimed at the development of adaptive MPC (AMPC) scheme based on ARX models, which are parameterized using generalized orthonormal basis filters (GOBF). The proposed model structure, in addition to capturing the dynamics with respect to the manipulated inputs, facilitates modeling of stationary as well as non-stationary components of the unmeasured disturbances. The feasibility of using the proposed AMPC scheme is established by conducting experimental studies on a benchmark Heater-Mixer setup.

Keywords: Process Optimization, Control
Abstract: This paper is concerned with integrated design and operation of energy systems that are subject to significant uncertainties. The problem is cast as a two-stage stochastic nonconvex mixed-integer nonlinear program, in which the first and second stages include design decisions and operational decisions, respectively. By exploiting the separable and decomposable structure of the problem, an efficient global optimization method, called nonconvex generalized Benders decomposition (NGBD), is developed based on convex relaxation and generalized Benders decomposition. The efficiency of NGBD can be further improved via the notion of piecewise convex relaxations. The advantages of the proposed formulation and solution method are demonstrated through case studies of two industrial energy systems, a natural gas production network and a polygeneration plant. The first example shows that the stochastic programming formulation can result in better expected economic performance than the deterministic formulation, and that the NGBD solution method is dramatically more efficient than a state-of-the-art global optimization solver, especially for large numbers of scenarios. The second example further shows that the integration of piecewise convex relaxations can improve the efficiency of NGBD by at least an order of magnitude.

Keywords: Process Optimization, Control, Process Scheduling and Decision support, Integration between Scheduling and Control
Abstract: In this study, we propose a mixed integer nonlinear programming (MINLP) model for superstructure based optimization of biodiesel production from microalgal biomass. The proposed superstructure includes a number of major processing steps for the production of biodiesel from microalgal biomass, such as harvesting of microalgal biomass, pretreatments including drying and cell disruption of harvested biomass, lipid extraction, transesterification, and post-transesterfication purification. The proposed model is used to find the optimal processing pathway among the large number of potential pathways that exist for the production of biodiesel from microalgae. The proposed methodology is tested by implementing on a specific case study. The MINLP model is implemented and solved in GAMS using a database built in Excel. The results from the optimization are analyzed and their significances are discussed.

Keywords: Control Applications, Process Optimization, Control
Abstract: Complex process networks featuring multiple energy integration agents (process-to-process heat exchangers) offer significant cost benefits while adding additional operational constraints. These networks show potential for multi-time scale dynamics owing to the presence of energy flows spanning multiple orders of magnitude. In previous work, we have developed a graph-theoretic framework to systematically uncover this time scale multiplicity. In this paper, we present an application of this framework to a reactor-heat exchanger system used for naphtha reforming. This system involves energy flows spanning three orders of magnitude and the underlying energy balance variables evolve over two time scales. The framework allows for the derivation of control-relevant models in each time scale and classifies the control objectives leading to a hierarchical control strategy. We demonstrate that the analysis uses minimum process information, is efficient, and scalable to large networks.

Keywords: Process Optimization, Control, Batch Process Modelling, Optimization and Control, Modelling and Identification
Abstract: This paper deals with the problem of uncertainty management in real time optimization (RTO). It proposes a new architecture in the modifier-adaptation methodology, reformulating the algorithm as a nested optimization problem with two layers. Using this approach, it is possible to find a point that satisfies the KKT conditions of a process using an inaccurate model, but unlike the original modifier method, with no need to estimate the experimental gradients of the process. The proposed method has been tested in the Otto Williams Reactor considering structural mismatches and perfect and noisy measurements. The results are compared with the previous modifier adaptation methodology using dual control optimization showing that the method finds a KKT point of the process with the advantage that no experimental gradient information is required and with less sensitivity to process noise.

Keywords: Control Applications, Process Optimization, Control
Abstract: While the notion of a hybrid fuel cell vehicles is conceptually promising, due to an off-loading of peak power demands from the fuel cell to the storage devices, the questions of device coordination is unsettled. Clearly the prominent role of equipment limitations, with respect to energy storage capacity and maximum power, suggests the use of predictive control for constraint enforcement. In this work an MPC tuning method specifically tailored to the hybrid vehicle application is presented. The approach is based on the notion of backed-off operating point selection and has the objective of minimizing energy losses from the storage devices. In addition, a soft constraint formulation unique to hybrid vehicle application is proposed.

Keywords: Inferential sensing, State Estimation and Sensor development
Abstract: The dynamics of Li-ion batteries are often defined by a set of coupled nonlinear partial differential equations called the pseudo two-dimensional model. It is widely accepted that this model, while accurate, is too complex for estimation and control. As such, the literature is replete with numerous approximations of this model. For the first time, an algorithm for state-of-charge estimation using the original pseudo two-dimensional model is provided. A discrete version of the model is reformulated into a state-space model by separating linear, nonlinear, and algebraic states. This model is high dimensional (of the order of tens to hundreds of states) and consists of implicit nonlinear algebraic equations. The degeneracy problems with high-dimensional state estimation are circumvented by developing a particle filter algorithm that sweeps in time and spatial coordinates independently. The implicit algebraic equations are handled by ensuring the presence of a `tether' particle in the algorithm. The approach is illustrated through simulations.

Keywords: Process Optimization, Control
Abstract: IGCC (Integrated Gasification Combined Cycle) is an alternative power generation system that can utilize fossil fuels in a more eco-friendly way than the conventional pulverized coal-fired plant. An IGCC plant requires an (Elevated Pressure) Air Separation Unit (EP ASU) that separates the air into pure oxygen and nitrogen, to be sent to the gasifier and the gas turbine, respectively. The ASU consumes about 10% of the gross power output generated in IGCC, so an economical operation of the ASU is important for lowering the overall power generation cost. In this research, controlled variable selection for an EP ASU is studied from the viewpoint of economics, i.e., with the objective of maintaining an economically (near-)optimal operation in the presence of load changes. Instead of the full-scale real-time optimization (RTO), we adopt a simpler approach known as self-optimizing control (SOC), which attempts to achieve the objective through a systematic selection of controlled variables. For the purpose of designing and testing a self-optimizing control structure, equation-based modeling of EP ASU is carried out using the software platform of gPROMS. Then, the SOC approach is applied based on the model to select the best set of controlled variables, which will lead to the most economical operation in the presence of load changes. Finally, PI control loops are designed and their dynamic control performances are tested. In addition, the economic loss in the presence of load changes is analyzed and compared with that achievable from the use of RTO.

Keywords: Process Optimization, Control, Process Scheduling and Decision support, Integration between Scheduling and Control
Abstract: Dry and semi-dry tight formation gas wells normally share the characteristic production profile defined by an initial high production, with an early steep decline and subsequent low pseudo steady-state gas rates. Small volumes of co-produced liquids will, even for dry gas wells, eventually bring the wells into the state of liquid loading, causing erratic unpredictable production rates deteriorating the performance of the wells. This state of the wells can be prevented by performing short shut-ins when the gas rate falls below the minimum rate needed to avoid liquid loading. Multi-well shut-ins may however lead to very high and low peak rates, possibly causing problems for the capacity of shared surface systems or lower and upper bounds on the total rate in a production plan. This paper presents a Lagrangian relaxation based scheme for scheduling of shut-in times for late-life tight formation gas wells with a shared gathering system. The proposed scheme includes a QP formulation for solving the Lagrangian dual, together with an aggregated construction and improvement heuristic for generating primal feasible solutions from the solution of the Lagrangian. We include several test examples to demonstrate the efficiency of the proposed decomposable scheme.