Keywords: Model-based Control, Modeling and Identification
Abstract: This paper gives an overview of recent developments in set-theoretic methods for nonlinear systems, with a particular focus on the activities in our own research group. Central to these approaches is the ability to compute tight enclosures of the range of multivariate systems, e.g. using ellipsoidal calculus or higher-order inclusion techniques based on multivariate polynomials, as well as the ability to propagate these enclosures to enclose the trajectories of parametric or uncertain differential equations. We illustrate these developments with a range of applications, including the reachability analysis of nonlinear dynamic systems; the determination of all equilibrium points and bifurcations in a given state-space domain; and the solution of set-membership parameter estimation problems. We close the paper with a discussion about on-going research in tube-based methods for robust model predictive control.

Keywords: Process Applications, Process and Control Monitoring
Abstract: Automation is increasingly being employed in the life sciences. New control problems are arising as a result, few with simple off-the-shelf solutions. This paper discusses some of the scheduling and control problems associated with automation in synthetic biology. It specifically focuses on the challenges associated with robotics, drawing heavily from our own experiences at the University of Illinois at Urbana-Champaign. No solution are presented and only the problems discussed. The goal is to motivate research in the process systems engineering community to solve problems in this new field.

Keywords: Model-based Control
Abstract: Critically ill patients commonly suffer from stress hyperglycemia, or elevated glucose levels, following injury or disease. Hypoglycemia, or low glucose level, is a frequent and serious complication of treating hyperglycemia. In order to reduce the incidence of hyper- and hypoglycemia, a linear zone model-predictive controller with moving horizon state estimation and output regulation is developed. Critical care patient data from an observational study was used to construct virtual patients. Closed-loop control in these virtual patients, versus clinical standard of practice, results in a substantial increase in time spent in the target glucose zone and significant reductions in both hyperglycemia and hypoglycemia. Overall, the proposed controller significantly enhances targeted glucose control in critically ill patients in silico, which may translate to improved clinical decision making and patient outcomes in the clinic.

Keywords: Model-based Control, Optimization and Scheduling
Abstract: We consider the robust output feedback control of uncertain systems utilizing a robust predictive control scheme based on reduced order models. In detail, we assume that for the plant an uncertain, linear full order model is available. For this model a reduced order model is derived. The proposed control scheme utilizes an estimator based on the reduced order model, which is combined with a robust model predictive controller to robustly stabilize the system. The proposed framework allows to guarantee robust satisfaction of input and output constraints as well as robust stability. An efficient implementation is possible, because online only the solution of a standard model predictive control problem and a simple state estimation problem are needed, which both involve the reduced model. The applicability of the control scheme is outlined using simulation examples.

Keywords: Model-based Control, Process Applications, Process and Control Monitoring
Abstract: Multi-stage Nonlinear Model Predictive Control (NMPC) is a promising strategy for the design of robust NMPC controllers which is based on describing the evolution of the uncertainty as a scenario tree. The scenario tree makes it possible to consider explicitly that the future control inputs can be adapted to the future information (measurements), thus reducing the conservativeness of the robust approach. This paper reviews the multi-stage approach and illustrates its main advantages using a nonlinear CSTR example. We also provide guidelines for possible multi-stage NMPC users that could help to identify the problems where the use of multi-stage NMPC can result in a significant improvement with respect to standard NMPC or other robust NMPC approaches. Finally, we summarize the different modifications that can be done to the multi-stage approach to enhance its performance. The possible enhancements include: improved performance using parameter estimation, rigorous guarantee of constraint satisfaction, and stability guarantees for the case of discrete-valued uncertainties.

Keywords: Model-based Control, Process Applications, Optimization and Scheduling
Abstract: A robust tuning problem for a two-degree-of-freedom model predictive controller is explored for single-input, single-output uncertain paper-making processes. The objective is to achieve satisfactory closed-loop responses, as measured by overshoots, settling times and output oscillations with user-specified parametric uncertainties. As the output variation cannot be easily specified by the end users, two methods are proposed to connect a total variation specification to user-friendly indices, based on which two algorithms are designed to solve the tuning problem. An application to a process extracted from the pulp and paper industry is employed to verify the effectiveness of the proposed algorithms.

Keywords: Model-based Control, Optimization and Scheduling
Abstract: Model predictive control (MPC) is an optimization-based tool that is widely used in the chemical industry, and nonlinear MPC (NMPC) expands the technology to handle more detailed models that are accurate across a wider range of state values. Many works in the literature have studied NMPC using Input-to-State Stability (ISS). The purpose of this work is to provide a method for calculating state trajectory bounds for NMPC using ISS theory. These predictive bounds are derived in terms of parameters that may be found from a series of open loop calculations in the general nonlinear case. Results are shown for a scalar linear system and a nonlinear CSTR, and the challenges involved with higher dimensional problems are discussed.

Keywords: Model-based Control, Optimization and Scheduling, Modeling and Identification
Abstract: Dual model predictive control (DMPC) optimally combines plant excitation and control based on current and predicted parameter estimation errors. Exact solution of dual control problems with constraints is in general computationally prohibitive. Our deterministic equivalent of the stochastic optimal control problem enables convergence toward optimality for a specific class of finite-horizon problems. The cost function shows that the optimal controls are functions of the current and future parameter-estimate error covariances. Our proposed objective-function reformulation provides the optimal combination of caution, probing, and nominal control. We show that the nonconvex optimization problem can be solved as a quadratic program with bilinear constraints. This type of problem can be efficiently solved with existing algorithms based on branch and bound with McCormick-type estimators. We demonstrate the application of DMPC to a singe-input single-output (SISO) finite impulse response (FIR) system. In the simulation example the parameter estimates converge quickly, and accurate and precise estimates are obtained even though the excitation vanishes.

Keywords: Model-based Control, Modeling and Identification
Abstract: This paper deals with a design problem of an adaptive predictive control for uncertain multi-rate sampled systems. In the proposed method, an adaptive predictive control using a simple adaptive output estimator, which has been previously proposed for single-rate sampled system, will be expanded to a multi-rate sampled system. A robust and model-free design method of feedforward compensators for designing the adaptive output estimator for predictive control and for setting an input constraint, almost strictly positive real (ASPR) constraint, for stable control system will also be provided for the considered multi-rate systems. The effectiveness of the proposed method will be confirmed through an experiments of two-tank process control.

Keywords: Energy Processes and Control
Abstract: This paper is concerned with the stabilization of tubular reactors in which convection, dispersion, conduction phenomena as well as chemical reaction take place. The stabilization is performed by using a Lyapunov function derived from the second law of thermodynamics called availability function. This function is used to design a stabilizing distributed control law around a stationary profile of a tubular reactor driven far from the thermodynamic equilibrium. A numerical example illustrates the proposed control strategy.

Keywords: Energy Processes and Control, Model-based Control
Abstract: This paper further explores the link between irreversible thermodynamics and system theory, and its use for port-based modeling for reaction systems. More specifically we show here that a pseudo Hamiltonian representation with R(x) > 0 can be obtained by considering the Brayton-Moser formulation via a unified potential function that verifies a thermodynamic evolution criterion. As a consequence, it gives additional degrees of freedom (i.e. to construct alternate pseudo Hamiltonian models with new passive outputs) usable for further studies on the control design. A representative example of irreversible processes via the non isothermal continuous stirred tank reactor model is used to illustrate the theoretical developments.

Keywords: Energy Processes and Control, Modeling and Identification
Abstract: On this work is shown how to derive a structural representation of a class of thermo-mechanical systems in the Port Hamiltonian framework in order to express explicitly the dissipation along the trajectories of the dynamics. To achieve this goal the entropy is used as the storage function. The dissipation structures are correlated with irreversible processes, while the conservative processes are correlated with reversible or isentropic processes. Finally, three study cases are presented: the first one is an adiabatic gas-piston system, the second is an adiabatic two chambers gas-piston-gas system and the last one is an adiabatic liquid-pendulum system.

Keywords: Model-based Control, Batch Process Modeling and Control, Process Applications
Abstract: This paper studies the properties of closed reacting systems described by mass-action kinetics. Following recent developments in potential-driven kinetic representations and stability analysis of (open) reacting systems, this paper seeks to develop a framework where both thermodynamically-consistent reaction fluxes and stoichiometry play a role in the identification of invariants and on stability analysis. Following some observations from the literature, the proposed approach seek to re-express mass-action kinetics in terms a potential-driven dynamical system. The proposed approach consists in the homotopy decomposition of the mass-action kinetic to compute such a potential-driven representation. An example is considered to illustrate the proposed approach.

Keywords: Model-based Control, Batch Process Modeling and Control, Process Applications
Abstract: This paper studies geometric properties of a class of irreversible dynamical systems, referred to in the literature as metriplectic systems. This class of systems, related to generalized (or dissipative) Hamiltonian systems, are generated by a conserved component and a dissipative component and appear, for example, in non-equilibrium thermodynamics. In non-equilibrium thermodynamics, the two potentials generating the dynamics are interpreted as generalized energy and generalized entropy, respectively. Stability and stabilization results for metriplectic systems have been presented in the literature, however, some aspects are still poorly understood, in particular the existence of dynamical invariants such as periodic orbits. In this note, we study the properties of metriplectic systems by considering a lift from the n-dimensional state space to a (2n+1)-dimensional contact space, following an approach introduced in recent years to study irreversible control systems. This lift leads to a deeper geometric characterization of metriplectic systems in the extended space. An example is provided to illustrate the approach proposed in this paper.

Keywords: Model-based Control, Process and Control Monitoring
Abstract: The problem of regulating the effluent concentration in an open-loop unstable exothermic jacketed reactor is addressed. The coolant temperature must be adjusted according to temperature as well as flow measurements. First, the robust nonlinear feedforward-output feedback stabilizing control problem is addressed with advanced control theory, yielding: (i) solvability conditions with sensor location criterion, and (ii) closed-loop robust stability coupled with simple tuning guidelines. Then, the behavior of the advanced controller is recovered with a PI temperature controller equiped with: (i) antiwindup protection, and (ii) feedforward dynamic setpoint compensation driven by measured (feed temperature and volumetric flow rate) disturbances. The approach is applied to a representative case example through numerical simulations.

Keywords: Batch Process Modeling and Control, Process and Control Monitoring
Abstract: An observer-based robust control strategy is proposed for controlling overflow metabolism cultures operated in fed-batch mode. In order to maximize the biomass productivity, the controller is designed to regulate the inhibitory by-product concentration at small levels keeping the substrate concentration close to its critical level. To this end, a reduced order nonlinear model of the bioprocess dynamics is obtained and a partial feedback linearizing strategy is applied. The resulting free linear dynamics is designed by means of a convex optimization problem aiming at mitigating the effects of non canceled nonlinearities and model uncertainties. An adaptive extended Luenberger observer is also designed for estimating the by-product concentration from the measurements of biomass and substrate concentrations. Realistic numerical simulations demonstrate that the proposed observer based robust control strategy is able to maximize the biomass concentration despite large disturbances on the measurements (30% for substrate and 15% for biomass concentrations).

Keywords: Model-based Control, Process and Control Monitoring, Process Applications
Abstract: The key feature of this paper is the development of a control strategy for the lactic acid production process from wheat flour in a continuous bioreactor. As lactic acid has inhibition effects on bacterial growth and its own production, the regulation of its concentration is required. In this paper, a control strategy is proposed in order to maximize the process productivity. First, the optimal setpoint is determined. Then, a controller based on a state-feedback linearizing control strategy is proposed to regulate the product concentration at its optimal value. The developed control law requires measurement or estimation of online state variables and a good knowledge of model parameters. In this paper the estimation of lactic acid production rate is proposed as an alternative to reduce the complexity of the control law. Different production rate estimators are studied and tested. Finally, the proposed control strategy results in a controller that involves the estimation of the production rate by a Kalman filter. The effectiveness of the developed strategy is illustrated by simulation results

Keywords: Process Applications, Optimization and Scheduling, Process and Control Monitoring
Abstract: This paper presents a possible approach for the dynamic optimization of biomass productivity in continuous cultures of microalgae, using light intensity as the manipulated variable. Extremum seeking control is applied as the real-time optimization algorithm to evaluate feasibility. Two different models of microalgae growth (of different complexities) were used in this study, with parameters representative of the Isochyris galbana specie. Results showed relatively good performances of the optimization procedure despite parameter variations and the presence of measurement noise.

Keywords: Batch Process Modeling and Control, Model-based Control, Modeling and Identification
Abstract: This paper presents a realisation of a closed-loop controlled autotrophic fed-batch cultivation of Ralstonia eutropha H16 with the aim to maximise the amount of biomass and internal storage compound PHB. The specific control unit is a model-based on-line trajectory planning with a sigma-point Kalman Flter for state estimation and an additional gas phase controller. For the control, a medium-sized structured model describing the process is proposed and compared to cultivation data. The model considers the internal storage of PHB by R. eutropha and pays special attention to the production of the oxygen-tolerant membrane-bound hydrogenase. This enzyme is in the center of biological research as a component of a biological fuel cell. It is shown that on-line optimisation works for maximising the amounts of biomass and PHB.

Keywords: Process and Control Monitoring
Abstract: In order to mantain hybridoma cell cultures in optimal operating conditions, on-line measurements of glutamine and glucose concentrations are required, implying the availability of probes, which are expensive and with poor durability. A way to overcome this problem is to design software sensors. In this work, both Extended and Unscented Kalman Filters are developed in order to estimate glucose and glutamine concentrations, based on biomass, lactate and ammonia on-line measurements. System observability conditions are first examined. The performances of both software sensors are analyzed with simulations of hybridoma cell cultures in fed-batch and continuous bioreactor operating modes. Three different tests are conducted in order to compare the performance of both observers: continuous culture with constant feeding profile, fed-batch culture with both constant and exponential feeding profiles. Also, two different sets of parameters are investigated: the ones obtained by using the least-squares method in order to minimize the error between model predictions and experimental measurements, and the ones which are modified by minimizing a cost function combining the usual least-squares criterion with a state estimation sensitivity criterion.

Keywords: Process Applications, Batch Process Modeling and Control
Abstract: The continuous fermentation processes involving yeast Saccharomyces cerevisiae or bacterium Zymomonas mobilis often exhibit oscillatory behavior. Because, the oscillatory mode of operation may lead to higher or lower average ethanol concentrations, hence, there is a natural need to control this behavior. The idea presented in this paper is based on the use of a mixture of two substrates that are continuously fed into the reactor chamber and the continuous fermentation process is described by an unstructured mathematical model with a product inhibition on cell growth. The relative contribution of both substrates will be treated as a new control variable. Moreover, it is assumed that the microorganisms exhibit diauxic growth and that the occurrence of the oscillatory behavior is related to a time delay in the response of cells to changes in the environment. From the bifurcation analysis of the system, it is shown that an appropriate ratio of both substrates to the mixture allows for induction and elimination of the oscillatory behavior.

Keywords: Process Applications, Process and Control Monitoring, Modeling and Identification
Abstract: In this work, integrated process design and control of reactive distillation processes is presented. Simple graphical design methods that are similar in concept to non-reactive distillation processes are used, such as reactive McCabe-Thiele method and driving force approach. The methods are based on the element concept, which is used to translate a system of compounds into elements. The operation of the reactive distillation column at the highest driving force and other candidate points is analyzed through analytical solution as well as rigorous open-loop and closed-loop simulations. By application of this approach, it is shown that designing the reactive distillation process at the maximum driving force results in an optimal design in terms of controllability and operability. It is verified that the reactive distillation design option is less sensitive to the disturbances in the feed at the highest driving force and has the inherent ability to reject disturbances.

Keywords: Process Applications, Batch Process Modeling and Control, Optimization and Scheduling
Abstract: This paper presents a comparison between worst-case and distributional uncertainty analysis in a thin film deposition process. The key idea is to evaluate the effect of model parameter uncertainties on thin film properties employing power series expansion (PSE). The worst-case deviation in the film properties is obtained under bounded parameter uncertainties while the probabilistic bounds are estimated under distributional uncertainties. To describe the growth process on the surface of a substrate, a multiscale approach that integrates kinetic Monte Carlo (KMC) simulations with continuum modelling is employed. The uncertainty analysis in this work is applied to estimate the optimal substrate temperature profile for robust optimization of the thin film end-point properties.

Keywords: Process Applications, Process and Control Monitoring
Abstract: The new Sequential Multi-block PLS algorithm is applied for establishing multivariate specification regions jointly on multiple types of raw materials. SMB-PLS distinguishes between process variations associated with raw materials from other orthogonal sources of variations such as operating policies. Combinations of raw material properties not compensated by the control schemes are clearly identified. Multivariate specifications are required for these combinations to avoid negative impact on process performance and product quality. The method was applied to an industrial aluminum smelter. Bad combinations of raw material properties were identified and validated against process knowledge.

Keywords: Energy Processes and Control, Model-based Control, Process Applications
Abstract: The problem of reducing the energy consumption in an industrial multicomponent distillation column is addressed. The column is subjected to step feed and distillate flow rates, the distillate impurity is regulated by adjusting the heat duty with a PI temperature controller, and the objective is to save energy through control upgrade. First, the nonlinear feedforward output-feedback robust advanced control problem is addressed, drawing the control construction and the solvability conditions. Then, the behavior of the advanced controller is recovered using a PI temperature controller with a dynamic feedforward setpoint compensator driven by the three measured flow rate disturbances. The approach is illustrated and tested through numerical simulations with a model calibrated with industrial data, finding that: the distillate impurity mean is maintained, its variability is reduced by 80 %, and the energy consumption is reduced by 15 %.

Keywords: Process Applications, Process and Control Monitoring, Model-based Control
Abstract: The plate heat exchanger (PHE) is a strong nonlinear cascade process, where the input is the steam valve position and the outputs are the steam flow-rate of inner loop and the supply water temperature of outer loop. In general the PHE operation is subjected to the large random disturbances caused by the outdoor temperature and the water randomly discharged by users. These disturbances will make the temperature and the flow rate of return water fluctuate a lot. This causes the supply water temperature to fluctuate outside the targeted range and leads to the frequently changes of steam flow-rate. To solve this problem, an intelligent cascade control method for the temperature interval of PHE is proposed for such a nonlinear process. The proposed method combines a feed forward compensation, a range limitation unit, rule base reasoning (RBR) and cascade control together. The intelligent control for the intervals of supply temperature method is established that includes a PI-based feed forward control for the supply water and a PI-based upper bound constraint control for the steam flow. The successful application to a real PHE confirms the effectiveness of the proposed method. In particular, the real application has shown that the proposed method can ensure the supply temperature to be within its technically specified range and can realize a small fluctuation range of steam flow-rate when the system is subjected to the disturbances of outdoor temperature and the randomly discharged water by users.

Keywords: Model-based Control, Optimization and Scheduling, Energy Processes and Control
Abstract: This paper addresses the optimal energy management of a cooling system, which comprises a building composed of a number of thermally conditioned zones, a chiller plant that converts the electrical energy in cooling energy, and a thermal storage unit. The electrical energy price is time-varying, and the goal is to minimize the electrical energy cost along some look-ahead time horizon while guaranteeing an appropriate level of comfort in the building. A key feature of the approach is that the temperatures in the zones are treated as control inputs together with the cooling energy exchange with the storage. This simplifies the enforcement of comfort, which can be directly imposed through appropriate constraints on the control inputs. Furthermore, a model that is easily scalable in the number of zones and convex as a function of the control inputs is derived based on energy balance equations. A convex constrained optimization program is then formulated to address the optimal energy management with reference to the forecasted operating conditions of the building. Simulation results show the efficacy of the proposed approach.

Keywords: Optimization and Scheduling
Abstract: Robust optimization can provide safe and tractable analytical approximation for the chance constrained optimization problem. In this work, we studied the application of robust optimization approximation in solving chance constrained planning and scheduling problem under uncertainty. Four different robust optimization approximation methods for improving the quality of robust solution were investigated. The methods include the traditional a priori probability bound based solution method, the a posteriori probability bound based method, the iterative method, and the recently proposed optimal robust optimization approximation algorithm. Applications of the different methods were demonstrated in a process scheduling problem and a production planning problem. Solution quality and computational effectiveness were also compared for the various methods.

Keywords: Optimization and Scheduling, Process Applications, Process and Control Monitoring
Abstract: Responding to a scalable production system in a make-to-order environment requires increased effective decisions. This paper considers challenges brought by lead time existed in a multi-product multi-stage manufacturing system when making a short-term production planning. The order-based production routes enhance the production flexibility, and in the meanwhile complicate the decision-making for each production stage. We define a series of sets to aggregate the production route and then model the problem as a mixed integer linear program (MILP) problem. The model seeks to find optimal production lots with several practical extensions. To efficiently capture the production dynamics induced by the lead times and setup, the production planning is implemented on a rolling horizon basis. The model is divided into several sub-problems as the horizon is rolled forward, of which a fix-and-relax strategy is applied. A study is conducted using data from a real case to evaluate the performance.

Keywords: Modeling and Identification, Optimization and Scheduling, Process Applications
Abstract: Industrial scale process modelling and optimization of long chain branched polymer reaction network is currently an area of extensive research owing to the advantages and growing popularity of branched polymers. The highly complex nature of these reaction networks requires a large set of stiff ordinary differential equations to model them mathematically with adequate precision and accuracy. In such a scenario, where execution time of model is expensive, the idea of making the online optimization and control of these processes seems to be a near impossible task. Catering to these problems in the ongoing research, the authors presented a novel work where the kinetic model of long chain branched poly vinyl acetate has been utilized to find the optimum processing conditions of operation using Sobol sequence based ANN as meta models in a fast and highly efficient manner. The article presents a novel generic algorithm, which not only disables the heuristic approach of designing the ANN architecture but also allows the computationally expensive first principle model to determine the configuration of the ANN which can emulate it with maximum accuracy along with the size of training samples required. The use of such a fast and efficient Sobol based ANN as surrogate model obtained by the proposed algorithm makes the optimization process 10 times faster as compared to a case where optimization is carried out with the expensive first principle model.

Keywords: Model-based Control, Modeling and Identification, Optimization and Scheduling
Abstract: Verifying if a process controller achieves a desired goal regarding safety specifications or performance is an important task in practice. This work presents a method for controller verification and parametrization of uncertain polynomial discrete-time systems with closed-loop requirements. Apart from quantitative constraints, also qualitative requirements, which are not directly linked to a specific time or amplitude, are considered. For formalizing these constraints, we employ linear temporal logic formulas and polynomial inequalities. Uncertainties can be considered in the input, the output, the initial conditions and the model parameters to account e.g. for model plant mismatch and noise, described as unknown-but-bounded variables. We combine the requirements and the system dynamics into a nonlinear feasibility problem to verify the controller and determine admissible controller parametrization. This problem is solved by relaxing it to a mixed-integer linear program. The relaxation procedure guarantees that the derived set of possible parametrization fulfill the quantitative and qualitative requirements of the closed-loop behavior despite the present uncertainties. The proposed method is illustrated by verifying and parametrizing a controller for a two tank system.

Keywords: Model-based Control
Abstract: An iterative procedure is proposed to design decentralized PID controllers for multi-loop processes. Each SISO loop is designed at a time, the controller parameters are computed by a convex optimization problem with constraints on stability margins. Despite the SISO approach, loops interactions are taken into account by Gershgorin bands and Equivalent Open-loop Process (EOP). Two simulation examples are presented to compare the performance with related techniques.

Keywords: Model-based Control, Process and Control Monitoring, Process Applications
Abstract: Model reduction and tuning rules given in the SIMC (Simple Internal Model Control) method are very effective in tuning PI controllers. For some processes with large lead elements, control performances by the SIMC method are somewhat oscillatory or sluggish. To mitigate such drawbacks, additional tuning rules based on the second order plus time delay model with lead term are proposed. Improvements for certain types of models are critical. For such processes, besides the SIMC tuning rule, no PI controller tuning rules that are analytic and given in terms of process parameters are not available. Since the proposed tuning rules are very simple, they can be used in the field, effectively complementing the SIMC method.

Keywords: Optimization and Scheduling, Batch Process Modeling and Control, Energy Processes and Control
Abstract: Energy integration in batch reactors offers significant savings but at the cost of additional operational constraints. In this paper, optimal operation of a batch reactor-feed effluent heat exchanger system is pursued for a production campaign. The coupling between subsequent batches due to energy integration is exploited to predict and thus plan future batches to achieve desired product purity at the end of the production campaign in the presence of disturbances and time-dependent energy prices. The proposed solution leads to better operation compared to a controller with disturbance rejection.

Keywords: Energy Processes and Control, Process Applications
Abstract: The paper presents basic principles of RF heating and describe its possible applications for oil extraction and soil remediation, based on the experience obtained by several years of work at Consorzio Polo Tecnologico Magona (CPTM). Activities include a complete approach, which goes through the steps of problem formulation, modeling, and experimentation. It is shown that RF heating can represent a valid alternative to more consolidated techniques for application to oil extraction from oil sand or heavy oil reservoirs and for organic polluted soil remediation, in terms of performances and operational flexibility. In particular, radiofrequency heating can be used in several scenarios where the use of alternative methods (such as steam injection) is not possible or strongly limited by geological or logistic constraints.

Keywords: Energy Processes and Control, Optimization and Scheduling, Process Applications
Abstract: Biobutanol has received significant attention as a renewable gasoline substitute and as a chemical feedstock owing to its high energy content, low volatility, and low water solubility. Low volumetric productivity caused by the toxicity of butanol in batch fermentation stands as one of the major obstacles to the commercialization. In this paper, continuous biobutanol fermentation with ex-situ adsorption recovery of butanol is investigated as a way to overcome this limitation. In this integrated system, the spatial segregation of the adsorption system and the fermentation process enables continuous biobutanol production without the need to stop the fermentation. Since the adsorption column needs to be switched periodically owing to the limited capacity of the adsorbent, the overall operation follows a cyclic pattern and the fermentation process converges to a cyclic steady state (CSS). In this study, a dynamic model of the integrated process is constructed and used for dynamic simulation to determine the system. Major operating variables are optimized through grid search for given feed concentrations based on the predicted CSS behavior in order to design an operation strategy that satisfies given requirements.

Keywords: Optimization and Scheduling, Batch Process Modeling and Control, Energy Processes and Control
Abstract: This paper deals with the dynamic real time optimization of a benchmark model that represents a genuine problem found at the crystallization section of sugar factories. A most relevant characteristic of the control problem is given by its hybrid nature, mixing continuous control and the scheduling of the crystallizer batch units. The plant-wide optimal control task is approached in a non-centralized hierarchical way with two-layers. The lower layer consists of a set of MPCs, one for each crystallizer, and is in charge of taking the local decisions that minimize an index related with the economic behavior of each unit. The higher level coordinator layer drives the state of the whole plant near the overall economic optimum, while respecting the constraints imposed by the existence of shared resources.

Keywords: Energy Processes and Control, Model-based Control, Batch Process Modeling and Control
Abstract: A lithium-plating side reaction at the lithiated graphite (LiC6) anode leads to poor safety of the lithium-ion battery. Faster charging at normal temperature may lead to a plating side reaction during the end of charging at the anode-separator interface. At lower temperature, the lithium-plating side reaction may become thermodynamically favorable during almost the entire charging period, even at low rates. This paper uses an electrochemical engineering model and dynamic optimization framework to derive charging profiles to minimize lithium plating at low temperatures. Transport parameters for lithium-ion battery are very sensitive at low temperatures. This paper shows the derivation of the optimal charging profile considering strict lower bounds on the plating reaction depending on various thermal insulation conditions (adiabatic, isothermal, and normal heat transfer coefficient) surrounding the battery.

Keywords: Optimization and Scheduling
Abstract: Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. This paper develops a robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply our approach to address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply.

Keywords: Modeling and Identification, Optimization and Scheduling
Abstract: In biological systems, reactions on different time scales exist and need to be considered for the analysis of physiological reactions such as proliferation. In this contribution we describe a two-level approach to decode biological reactions on a short term scale, e. g. signaling into long term cellular responses, e. g. proliferation. First, we derive a valid and parametrized dynamic model for events on the early signaling scale using set-based estimation methods allowing to take uncertainties into account. Second, the derived model candidate for early signaling events is fused with a model for long term proliferation using shape-based signaling properties. This approach is realized in the specific case study of Interleukin-6-induced signaling and proliferation. Our modeling approach enables us to consider both, dynamic early signaling events and static long term events. Furthermore, it allows a deeper understanding of how cells process information from early signaling events to long term cellular responses.

Keywords: Process Applications, Model-based Control
Abstract: This paper presents the application of a plant-wide model-based methodology to wastewater treatment plants. The focus is on a tertiary activated sludge plant with anaerobic sludge treatment, owned and operated by Sydney Water. A dynamic plant-wide model is first developed and calibrated using historical data. A scenario-based optimization procedure is then applied for computing the effect of key discharge constraints on the minimal net power consumption, via the repeated solution of a dynamic optimization problem. The results show a potential for reduction of the energy consumption by about 20%, through operational changes only, without compromising the current effluent quality. It is also found that nitrate (and hence total nitrogen) discharge could be reduced from its current level around 22 mg(N)/L to less than 15 mg(N)/L with no increase in net power consumption, and could be further reduced to <10 mg(N)/L subject to a 15% increase in net power consumption upon diverting part of the primary sludge to the secondary treatment stage. This improved understanding of the relationship between nutrient removal and energy use will feed into discussions with environmental regulators regarding nutrient discharge licensing.

Keywords: Modeling and Identification, Optimization and Scheduling
Abstract: An agent-based model is developed to simulate the vascular growth in engineered biomaterials. This study investigates the influence of growth factor release rate on rapid and stable vascularization. Different growth factor release profiles are generated and tested using the agent-based model. The simulation results are verified with experimental studies. Effective release policies are identified; microsphere properties for promoting angiogenesis are suggested.

Keywords: Model-based Control, Optimization and Scheduling
Abstract: Submerged membrane bioreactors are increasingly applied for wastewater treatment but requires a tight control of the membrane fouling so as to ensure safe and efficient operation. The objective of this paper is to design a nonlinear model predictive control to minimize the irreversible resistance while keeping the trans-membrane pressure, which is a good indicator of membrane fouling, at an acceptable level. To this end, the manipulated variables are the permeate flow and the air scouring flow, which allows the material layer formed on the membrane (in short the "cake") to be detached. The NMPC structure is tested in simulation considering a detailed simulator as the reference process, and a reduced-order model as the predictor. The results show that the process can be regulated until the irreversible resistance takes the main role in the fouling resistance. When this state is reached, a chemical cleaning is required, or a larger trans-membrane pressure has to be accommodated.

Keywords: Batch Process Modeling and Control, Modeling and Identification
Abstract: A hybrid simulation framework was proposed to predict the dynamics in cell culture bioreactors. The model is based on a multi-agent approach where CHO cells are considered as individuals (agents) following a rule base governing their behavior, while a flux balance model is embedded in agents to predict quantitative changes in nutrient and metabolite concentrations. The model takes the measured dissolved oxygen, and sodium data as input along with initial cell culture conditions and predicts the dynamics of viable cell density, viability, concentrations of glucose and lactate. The model showed good agreement with the experimental findings from our laboratory for two sets of cell culture experiments.

Keywords: Modeling and Identification, Optimization and Scheduling, Process Applications
Abstract: Two optimal experimental design (OED) problems for an enzymatic biodiesel production system are investigated to improve parameter estimation quality. An orthogonalized sensitivity analysis method is firstly implemented to select important parameters. Next the design of measurement set and sampling strategy is developed in the form of two convex optimization problems which are solved by the interior-point algorithm and the Powell’s method, respectively. Simulation results demonstrate the function of OED in reducing parameter estimation errors. The biodiesel concentration is identified to be the most valuable state variable observation, and the parameter estimation accuracy can be improved through optimal sampling design.

Keywords: Process and Control Monitoring, Process Applications
Abstract: The water supply network has a complex structure especially in cities with high population density. A damage to the water pipe can occur in the form of a leakage or a burst and the technique for early detection of the occurrence and for the exact determination of the location is required. In this paper, we propose a novel method that can detect the leakage of the water supply network using the pressure data. After the noise is eliminated using the Kalman Filter, the mean of steady state pressure is calculated and deviation with the mean is obtained. By calculating the cumulative integral of the pretreated data and applying a floor function, the leakage can be detected. Once the leakage is detected, the time of occurrence is refined by radius of curvature and the location is estimated by using that time and a statistical method. The verification test is conducted with respect to the two different field data. It is found that the suggested method is more robust and practical to implement and shows a higher precision compared to the previous methods.

Keywords: Process and Control Monitoring, Modeling and Identification
Abstract: A novel approach has been developed to diagnose intermittent stochastic faults by combining a generalized polynomial chaos (gPC) method with maximum likelihood estimation. The gPC is used to propagate stochastic changes in an input variable to a measured output variable from which the fault is to be inferred. The fault detection and diagnosis (FDD) problem is formulated as an inverse problem of identifying the unknown input from a maximum likelihood based fitting of the predicted and measured output variables. Simulation studies compare the proposed method with a Particle Filter (PF) to estimate the value of an unknown feed mass fraction of a chemical process. The proposed method is shown to be significantly more computational efficient and less sensitive to user defined tuning parameters than PF.

Keywords: Process and Control Monitoring
Abstract: Recently, a new concurrent projection to latent structures (CPLS) for multivariate statistical process was proposed. In this paper, we discuss a new fault diagnosis approach based on CPLS. Five monitoring indices used in CPLS are unified into two general forms. Based on these general forms, we define their complete decomposition contributions (CDC) and reconstruction-based contributions (RBC). The diagnosability of these two contribution methods are further analyzed. At the end, synthetic case studies are presented to demonstrate the results.

Keywords: Batch Process Modeling and Control
Abstract: In statistical process monitoring, contribution plots are commonly used by operators and experts to identify the root cause of abnormal events. Because contribution plots suffer from fault smearing - an effect that possibly masks the cause of an upset - this paper investigates whether automated fault identification can be improved by using process data instead of contributions. Hereto, both approaches (i.e., using either the sensor measurements or their contributions as inputs for a classification model) are tested on the benchmark penicillin fermentation process Pensim, implemented in RAYMOND. To optimize the performance of each approach, different manipulations of both the process data and the variable contributions are introduced based on the nature of the occurring faults. It is observed that these manipulations have a large influence on the classification performance. Furthermore, this paper demonstrates that fault smearing negatively affects the classification based on the variable contributions. It is concluded that automated fault identification is improved by using the process data rather than the variable contributions as model inputs for the investigated case study.

Keywords: Process and Control Monitoring, Process Applications
Abstract: It is very important to diagnose abnormal events in industrial processes. Based on normal operating data in a dynamic process, dynamic latent variable model provides a clear view of separating dynamic and static variations. Recent work has shown an effective diagnosis in faulty variables with multidirectional reconstruction based contributions. Furthermore, their work took causality analysis into accounts to explore the casual relations instead of only correlations. Although Granger causality is a widely used method for many applications, it needs time series to be stable to calculate the causality index, which is not available for nonstationary fault processes. In this paper, a new causality analysis method based on dynamic time warping technology is proposed to determine the causality direction between pairs of faulty variables. The case study on Tennessee Eastman process with a step fault shows the effectiveness of the proposed approach.

Keywords: Process and Control Monitoring
Abstract: A recursive probabilistic principal component analysis (PPCA) based data-driven fault identification method is proposed to handle the missing data samples and the mode transition in multi-mode process. This model is recursively obtained by using the increasing number of normal observations with partly missing data. First, based on the singular value of historic data matrix, the whole process is divided into different steady modes and mode transitions. For steady modes, the conventional PPCA is used to obtain the principal components, and to impute the missing data. When the mode is a mode transition, the proposed recursive PPCA is applied, which can actually reveal the between-mode dynamics for process monitoring and fault detection. After that, in order to identify the faults, a contribution analysis method is developed and used to identify the variables which make the major contributions to the occurrence of faults. The effectiveness of the proposed approach is demonstrated by the Tennessee Eastman chemical process. The results show that the presented approach can accurately detect abnormal events, identify the faults, and it is also robust to mode transitions.

Keywords: Model-based Control, Optimization and Scheduling
Abstract: Type 1 diabetes is a disease caused by an autoimmune reaction. The Artificial Pancreas (AP) is an automatic closed-loop system composed of a subcutaneous glucose sensor, a subcutaneous insulin pump and a device on which a control algorithm and a human interface are implemented. The last years have seen an accelerated improvement of these three components that became more reliable and compact, making the system safer, wearable, and usable in real life. An overview on AP and its components is presented together with an introduction on the in-silico tools used to develop and tune the control algorithm and to make pre-clinical tests. Particular attention is devoted to the design of a Model Predictive Control, to the choice of the model and of the constraints, and to the definition of the most relevant performance indices. Most of the choices have been driven by the experience gained by both in-silico and in-vivo trials. In-silico experiments involved thousand of hours of simulations on the Food and Drug Administration accepted simulator equipped with 100 adult virtual patients. In-vivo experiments, of which a complete list is presented, involved about forty thousand hours of trials, first, conducted in a clinical environment and, then, at home.

Keywords: Process and Control Monitoring
Abstract: In industrial processes, it is necessary to maintain the user-specified control performance in order to achieve desired productivity. This paper describes a design scheme of smart adaptive controller based on mentioned strategy. In our proposed method, variance of control error and input are evaluated on-line. Moreover, control parameters are adjusted only when the user-specified control performance is not obtained. Control parameters are calculated directly from closed-loop data and they are adjusted by 1-parameter tuning. The effectiveness of the proposed method is verified by using a simulation example and experiment of temperature control system.