Keywords: Modelling and Identification, Control Applications
Abstract: The most important and time consuming part of an industrial application of control is the modelling. It may take 50 per cent or more of the entire project. Therefore a major challenge for a control systems supplier like ABB is to constantly try to decrease the engineering effort for modelling. This talk discusses some different aspects of modelling and identification originating from application in many different industries such as pulp and paper, rolling mills, power plants and specialty chemicals.

Keywords: Inferential sensing, State Estimation and Sensor development, Control Applications
Abstract: The moving horizon estimator (MHE) formulation utilizes a window of measurements to compute the estimates of the states in that particular window. This approach leads to smoothing of the state estimates included in the window, since future information is used to compute the same. However, the effect of smoothing, in the MHE algorithm, on the state estimates has not been studied in the literature. In this work the performance of the MHE is compared with recursive Bayesian state estimators (such as UKF, EnKF) to study the effect of the moving window of the past data on the quality of state estimates, via an application on a benchmark pH simulation case study. The simulations are carried out for two scenarios– the ideal case and the case with a parametric model-plant mismatch. The results obtained indicate that the use of MHE results in improved state estimates when compared to the recursive Bayesian state estimators, but does not help compensate for model-plant-mismatch.

Keywords: Inferential sensing, State Estimation and Sensor development, Modelling and Identification, Control Applications
Abstract: This paper proposes an optimal parameter estimation method for a stochastic process involving monomolecular adsorption and desorption occurring at the nano-scale. Recently, several carbon nanotube sensors that can selectively detect target molecules at a trace concentration level have been developed. These sensors make use of light intensity changes mediated by the adsorption or desorption phenomena on their surfaces. However, the molecular-level events are inherently stochastic, posing a challenge for optimal estimation. The stochastic behavior is modeled by the chemical master equation (CME), which contains a set of ordinary differential equations describing the time evolution of probabilities for the possible adsorption states. Given the inherent stochastic nature of the underlying phenomena, rigorous stochastic estimation based on the CME should outperform deterministic parameter estimation formulated based on the continuum model. Motivated by this expectation, we formulate the maximum likelihood estimation (MLE) based on an analytical solution of the relevant CME for both the constant and time-varying parameter cases. The performance of the MLE and the deterministic least squares are compared using data generated by kinetic Monte Carlo (KMC) simulations of the stochastic process.

Keywords: Inferential sensing, State Estimation and Sensor development, Control Applications, Process and Performance Monitoring, Fault Detection, Supervision and Safety
Abstract: Unscented Kalman Filter (UKF) is a popular method for state and parameter estimation of nonlinear dynamic systems. An attractive feature of UKF is that it utilizes deterministically chosen points (called sigma points), and the number of such points depends linearly on the dimension of the state space. However, an implicit assumption in UKF is that the underlying probability densities are Gaussian. To mitigate the Gaussianity assumption, Gaussian Sum-UKF has been proposed in literature that approximates all underlying densities using a sum of Gaussians. For accurate approximation, the number of sigma points required in this approach is significantly higher than UKF, thereby making the Gaussian Sum-UKF computationally intensive. In this work, we propose an alternate approach labeled unscented Gaussian Sum Filter (UGSF) that leverages the ability of Sum of Gaussians to approximate an arbitrary density, while using the same number of sigma points as in UKF. This is achieved by making suitable design choices of the various parameters in the Gaussian Sum representation. Thus, our approach requires similar computational effort as in UKF and hence does not suffer from the curse of dimensionality. We implement the proposed approach on a nonlinear state estimation case study and demonstrate its superior performance over UKF.

Keywords: Inferential sensing, State Estimation and Sensor development, Control Applications, Process Optimization, Control
Abstract: The states and unknown parameters of a simplified non-linear grinding mill circuit model for process control was estimated from real plant data by means of an Extended Kalman Filter. The output of the model as calculated from the states and parameters estimated by the Extended Kalman Filter closely follow the actual output of the plant. The continuous estimate of states and parameters from plant data allows for the continuous update of a process model used for process control. This limits model-plant mismatch which deteriorates controller performance.

Keywords: Inferential sensing, State Estimation and Sensor development, Process and Performance Monitoring, Fault Detection, Supervision and Safety
Abstract: The occurrence of model-plant mismatch is a common problem in dynamic model based applications such as state estimation. The use of an inaccurate model results in biased estimates of the states. Hence, conventional state estimation algorithms are modified in various ways to compensate for model-plant mismatch. In this work, the performance of four adaptive state estimation algorithms is compared in the presence of a model plant mismatch arising due to random drifts in parameter values. The comparison is carried out through simulations on a benchmark non-isothermal CSTR problem. Simulation results demonstrate that online re-identification of the parameters susceptible to drift or change is the most effective approach to minimize the effect of model-plant mismatch on the state estimates.

Keywords: Inferential sensing, State Estimation and Sensor development
Abstract: The subject of compressed sensing, especially, the related concept of sparse representation has been growing into an exciting area with a diverse set of applications in the fields of image sensing and analysis, signal compression, network reconstruction, textit{etc}. The efficacy of the associated techniques depends on the ability to discover a suitable basis for a sparse representation of the underlying signal. This paper presents a method for discovering this basis adaptively from the data. Specifically, the method estimates the dictionary of basis functions that maps the sub-sampled signal to the sparse representation of the signal. We present an application of this technique to the reconstruction of missing data, which is an important problem in all data-driven methods. Two case studies, namely, the reconstruction of missing data in a liquid level system and missing pixels of a 2-D signal (image) are presented. Results show that the proposed algorithm outperforms the existing KSVD algorithm in terms of both accuracy and speed of the reconstruction.

Keywords: Control Applications, Process Optimization, Control, Process and Performance Monitoring, Fault Detection, Supervision and Safety
Abstract: In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or a pumped liquid oxygen (PLOX) cycle, the flowrate of the liquid nitrogen stream connecting the high- and low-pressure columns has a major impact on the total oxygen yield. It is shown that this yield reaches a maximum at a certain optimal flowrate of LN2 stream, creating a challenging feedback controller design problem. To dynamically maximize the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. It is shown that at any operating point of the ASU, the MMPC algorithm, through model-weight calculation based on plant measurements, naturally and continuously selects the dominant model(s) corresponding to the current plant state, while making control-move decisions that approach the maximum oxygen yield point. This dynamically facilitates less energy consumption in form of compressed feed-air compared to a simple ratio control during load-swings. In addition, since a linear optimization problem is solved at each time step, the approach involves much less computational cost than a model predictive controller (MPC) based on a first-principles model.

Keywords: Interaction Between Design and Control, Process Optimization, Control, Control Applications
Abstract: We derive global stability results for continuous crystallizers using convergence analysis. The sufficient condition for exponential stability is derived in the analytical expression. The analytical condition offers a lower bound of the nucleus size and an upper bound of the largest size that crystals can grow. These conditions ensure that all the trajectories of the crystallizer converge to a single trajectory which is not necessarily known beforehand.

Keywords: Process Optimization, Control
Abstract: In this work, two numerical solution methods are presented for discounted economic nonlinear model predictive control on infinite horizons without terminal constraints. While the first formulation simply replaces the infinite by a finite horizon, the second formulation uses a time transformation function to project the infinite to a finite horizon. For the first formulation, an algorithm is presented which heuristically determines a sufficiently long final time with the help of the turnpike property in order to ensure good closed-loop control performance. For the second formulation, a two-stage formulation is introduced to deal with large differences in the dynamics of the objective function and the states. The solution accuracy is improved for both formulations by using a control vector adaptation strategy such that an adequate number of decision variables is obtained. Both solution methods are compared in a case study.

Keywords: Process Optimization, Control, Control Applications
Abstract: In this paper, a coordination control algorithm based on hierarchical scheme is presented to coordinate several non-linear model predictive controllers (NMPC) working in parallel, with an upper layer, where a price-driven coordination technique is used to drive the controllers in such a way that some global constraints are satisfied. To coordinate the lower layers, it is used a price-adjustment algorithm based on Newton’s method, in which a reformulation of Fiacco’s work is used in order to obtain the sensitivity analysis for a nonlinear system no matter the set of active constraints. The efficiency of the scheme is evaluated using a simulation of a four-tank benchmark

Keywords: Process Optimization, Control, Control Applications
Abstract: The main objective of this work is to develop computationally efficient Global linearization based control (GLC) schemes, which are suitable for control of nonlinear processes with fast dynamics. Models used for the controller synthesis are discrete time block oriented nonlinear black box state space models identified directly from the input-output perturbation data. The chosen model structures facilitate construction of closed form solutions to the unconstrained GLC formulations. The efficacy of the proposed control formulations is evaluated by conducting simulation studies on a benchmark continuously stirred tank reactor (CSTR) system which exhibits input multiplicity behavior. Analysis of the simulation results reveals that the proposed GLC formulations are able to achieve a significant reduction in the average computation time without compromising the closed loop performance.

Keywords: Control Applications, Process Optimization, Control
Abstract: Shale gas reservoirs are classified as unconventional reservoirs. Their key features include low permeability, rapid decline in production rate, and liquid loading at the well-bottom. An industrial perspective towards automation in Shale gas is provided in this paper. Specifically, the challenges and opportunities in controlling the liquid loading problem and optimizing the production from shale gas wells are discussed. Automation systems and control hierarchy are discussed and parallels with the more familiar Process Industries are highlighted. The key components of reservoir modeling, well-bore modeling, feed-back control, model parameter update, multi-well optimization, and production management are presented. An example of periodic shut-in operation is used to underline the various concepts discussed in this paper.

Keywords: Control Applications
Abstract: Active control of the production choke valve is the recommended solution to prevent severe slugging flow at offshore oilfields. This requires operation in an open-loop unstable operating point. It is possible to use PI or PID controllers which are the preferred choice in the industry, but they need to be tuned appropriately for robustness against plant changes and large inflow disturbances. The focus of this paper is on finding tuning rules based on model identification from a closed-loop step test. We perform an IMC (Internal Model Control) design based on the identified model, and from this we obtain PID and PI tuning parameters. In addition, we find simple PI tuning rules for the whole operation range of the system considering the nonlinearity of the static gain. The proposed model identification and tuning rules show applicability and robustness in experiments on a test rigs as well as in simulations using the OLGA simulator.

Keywords: Control Applications, Process Optimization, Control
Abstract: Proportional-Integral-Derivative (PID) controllers are used extensively in the process industries for regulating single-input, single output (SISO) processes, with Model Predictive Controllers (MPC) typically being reserved for use on large scale systems. However, in recent years there has been suggestions that MPC may offer benefits when applied to SISO systems at the regulatory level. This paper compares the performance of PID and MPC when they are both applied to first and second order, SISO systems that contain a time delay. From the comparison it can be concluded that improved performance can be achieved by using MPC for, in some cases, very small time delays. Both PID and MPC are shown to be robust to plant-model mismatch.

Keywords: Control Applications, Modelling and Identification
Abstract: The control of paste thickener is important because underflow solids concentration has to be maintained within a certain operating window to maximize water recovery while avoiding operational difficulties, e.g. pump bogging. Paste thickener dynamics is complicated due to the interactions between key process variables, uncertainties in operating conditions and large time constant. In this paper, a dynamic model of the paste thickener which addresses varying coal parameters were developed and a model predictive control (MPC) was formulated based on the model. The simulated MPC results based on actual coal processing plant data show that a significant improvement can be achieved in terms of the ability to control the underflow solids concentration as well as conforming to the constraints imposed by the physical limitations of the process, e.g. pump speed and solids amount inside the thickener.

Keywords: Control Applications, Modelling and Identification
Abstract: Falling film evaporators are widely used in the dairy industry to pre-concentrate milk for powder production. FFE control is, however, not performed well with many plants still under operator or proportional and integral (PI) control. Several authors have created fundamental models to use for controller development, yet these models have various differences in structure and span feed flow rates ranging from a laboratory scale (2 500kg/h) to industrial (27 000kg/h). This paper used a single semi-empirical model developed by Haasbroek (2013) to offer a sensible comparison the most often seen dairy FFE controllers. Disturbance rejection was tested by introducing a feed dry mass (WF) step and then comparing the product dry mass (WP) increase as percentage (∆WP/∆WF x 100). It was found that linear quadratic (LQR) control (Haasbroek et al., 2013) and fuzzy predictive controllers showed the best performance (70% and 69% respectively), followed by cascade control (77%) and lastly PI control (123%). The fuzzy controller does, however, struggle with disturbances it has not been tuned for, while cascade and LQR controllers still perform well. Taking into account the involved design required for LQR control, cascade control offers a well balanced approach to FFE disturbance rejection.

Keywords: Control Applications
Abstract: Baker’s yeast fermentation is influenced by the relative concentration of glucose and dissolved oxygen (DO) in the reactor. The process is sensitive to the oxidative capacity of the cells and exhibits a range of metabolic regimes depending on available glucose and oxygen. The time profiles of cell mass, glucose, ethanol and dissolved oxygen concentrations possess strong nonlinear characteristics. A decoupled input-output linearizing controller (DIOLC), which exhibited satisfactory performance in simulation experiments, was implemented online for validation. Our resulted showed that the DIOLC was capable of negating the interaction between glucose and dissolved oxygen, and execute satisfactory control action in experiments carried out in a 5 liter bioreactor. The performance of DIOLC was better compared to that of a PID controller implemented under identical test conditions.

Keywords: Process Optimization, Control
Abstract: Optimal PID-settings are found for first-order with delay processes for specified levels of robustness (Ms -value) and compared with a extended SIMC-rule. Optimality (performance) is defined in terms of the integrated absolute error (IAE) for combined step changes in load output and input disturbances. The SIMC-rules gives a PI-controller for first order systems and no recommendation is given for tuning the derivative part. We propose to counteracting time delay introducing derivative action with tau_D=theta/3. With the extended SIMC, the robustness level is adjusted by changing the tuning parameter tau_c, and the modification was found to give surprisingly good settings with near Pareto-optimal performance. However, to take advantage of this performance increase the tuning parameter tau_c should be reduced to about half.

Keywords: Process Optimization, Control, Modelling and Identification, Control Applications
Abstract: Output distribution control is required in many industrial processes mainly for the purpose of improving product qualities. Dierent from the traditional mean and variance control of stochastic processes, the probability density function (PDF) control provides a comprehensive solution to deal with outputs with general distributions. Various models based on B-splines have been developed to approximate the output PDF required for closed-loop control, among them the rational square-root (RSR) B-spline model can guarantee the nonnegativeness and the integration constraint of a PDF. In this paper, the relationship between the so-called actual weights and pseudo weights of the RSR B-spline PDF model is investigated so as explore a simplified modelling algorithm for the very complex nonlinear PDF modelling problem. Based on the proposed modelling algorithm, a predictive PDF control strategy has been established and applied to an exemplar system of closed-loop molecular weight distribution (MWD) control in a polymerisation process. The merit of predictive control over conventional PDF control is clearly demonstrated through the simulation study.

Keywords: Inferential sensing, State Estimation and Sensor development, Process and Performance Monitoring, Fault Detection, Supervision and Safety, Modelling and Identification
Abstract: A data-driven Bayesian framework for real-time performance assessment of inferential sensors is proposed. The application of the proposed Bayesian solution does not depend on the identification techniques employed for inferential model development. The effectiveness of the proposed method is demonstrated through a simulation case study.

Keywords: Batch Process Modelling, Optimization and Control, Modelling and Identification
Abstract: This paper describes the application of batch trajectory alignment, outlier detection, and multiblock multiway principal component analysis (MPCA) to data from an industrial active pharmaceutical ingredient manufacturing process. The process data routinely collected from historical batches, including temperatures, pressures, and controller outputs, has been used to improve process operation and understanding. MPCA highlighted questionable batches from which plant issues were identified. Variable contributions to the MPCA scores were used to identify the process variables potentially causing the variation in batch drying time.

Keywords: Batch Process Modelling, Optimization and Control, Control Applications
Abstract: Multivariate statistical process control (MSPC) techniques play an important role in industrial batch process monitoring and control. One particularly popular approach to MSPC is partial least squares (PLS), which has been successfully applied many times in the modelling, estimation and control of batch processes. However, the nonlinear nature of many real, complex chemical systems means that traditional linear PLS is not always suitable. In this paper, the use of a nonlinear multi-way PLS is proposed to address the issues of non-linearity in batch processes. By analysing and comparing linear multi-way PLS, Neural network multi-way PLS, Type I and Type II nonlinear multi-way PLS models, the advantages and limitations of these methods are identified and summarised.

Keywords: Batch Process Modelling, Optimization and Control, Modelling and Identification
Abstract: Most of the microalgae growth models are based on modified Monod kinetics, which often involve many parameters to identify. Some fundamental questions about the validity of such empirical growth rate still remain. On the other hand, flux balance analysis (FBA) can compute a steady-state flux distribution of metabolic networks within a feasible flux space constrained by fundamental laws including mass balances. This work proposes how to set up various constraints as boundary conditions in FBA, relate the resulting flux distribution to the growth rate, and dynamically simulate the microalgae growth kinetics. In order to relate mass balances of the bioreactor to the FBA solution, accumulation rates as well as uptake and production rates are used. Dynamic simulations were performed by modifying pseudo-steady state assumption for FBA and integrating the ordinary differential equations for bioreactor model over time, leading to a two-time scale description. The proposed scheme can reduce the number of parameters and explain adaptation to the changing environment. A Chlamydomonas reinhardtii culture system is illustrated to present the applicability of the proposed scheme.

Keywords: Batch Process Modelling, Optimization and Control, Inferential sensing, State Estimation and Sensor development, Control Applications
Abstract: The temperature control of a semi-batch polymerization reactor described by Chylla and Haase, a control engineering benchmark problem is considered. The process is nonlinear and time varying in nature. There is significant change in heat transfer coefficient, during a batch due to changes in the viscosity of the polymer and also from batch to batch due to surface fouling. Also change in ambient temperature during summer and winter makes the temperature control of this process a challenging task. The conventional cascade control provides robust operation, but often lacks in control performance concerning the required strict temperature tolerances. Hence an adaptive control design by employing a self tuning cascade with feed forward control concept is proposed in this paper to provide excellent control. This design calculates a trajectory for the cooling jacket temperature in order to follow a predefined trajectory of the reactor temperature. The reaction heat as well as the heat transfer coefficient required for the proposed controller is estimated online by using Unscented Kalman Filter (UKF). Simulation results under model uncertainties show the effectiveness of the proposed adaptive control concept.

Keywords: Control Applications, Process Optimization, Control, Modelling and Identification
Abstract: This paper presents a novel application of linear model predictive control (MPC) for pressure and flow control in underbalanced drilling operations. Coordinated control of pump flow and choke pressure is used to control the return flow rate, and the well pressure profile. The control system is verified using a high fidelity drilling simulator for some common drilling operations. The proposed solution shows promising results for operations close to the selected set-points, but the simple models employed have distinct limitations. The control solution is easily extendable to larger control problems, and can be augmented with better models.

Keywords: Batch Process Modelling, Optimization and Control, Modelling and Identification
Abstract: A new methodology to identify models in a pseudo-state space form for batch/fed- batch processes is proposed. The methodology employs historical data from previous batch runs, where a few intermittent measurements of product quality were made, and multivariate statistical methods in order to identify data-based models. Multivariate statistical methods, such as principal components analysis (PCA) and partial least squares (PLS), are being increasingly employed for batch processes model identification due to the advantages they offer over more difficult and time-consuming first-principle modelling techniques. In the proposed model identification approach, predictors are obtained employing PCA and PLS algorithms. Then, after a new vector of pseudo-states is defined, a pseudo-state space model is identified by performing an algebraic manipulation of the PCA and PLS statistical models. The ability of the pseudo-state space models to accurately predict future process variable trajectories is demonstrated by means of a simulation benchmark for penicillin production.

Keywords: Modelling and Identification
Abstract: This paper is concerned with identification of nonlinear systems with multiple and correlated scheduling variables. Multiple auto regressive exogenous (ARX) models are identified on different process operating conditions, and a normalized exponential function as the probability density function associated with each of the local ARX models taking effect is then used to combine all the local models to represent the complete dynamics of a nonlinear system. The parameters of the local ARX models and the exponential functions are estimated simultaneously under the framework of the expectation maximization (EM) algorithm. A numerical example is applied to demonstrate the proposed identification method.

Keywords: Modelling and Identification
Abstract: Performance of any clustering algorithm depends critically on the number of clusters that are initialized. A practitioner might not know, a priori, the number of partitions into which his data should be divided; to address this issue many cluster validity indices have been proposed for finding the optimal number of partitions. In this paper, we propose a new “Graded Distance index” (GD_index) for computing optimal number of fuzzy clusters for a given data set. The efficiency of this index is compared with well-known existing indices and tested on several data sets. It is observed that the “GD_index” is able to correctly compute the optimal number of partitions in most of the data sets that are tested.

Keywords: Process and Performance Monitoring, Fault Detection, Supervision and Safety
Abstract: The use of multivariate image and texture analysis is proposed in this study to quantitatively characterize the elegance of film-coated tablets. Four unsupervised metrics are developed to quantify both the color uniformity of tablet faces/bands and the erosion level inside and outside the tablet logo. Latent variable modeling is used to regress the measured elegance against coating operating conditions in order to investigate the driving forces acting on the system, consistently with the quality-by-design framework promoted by the Food and Drug Administration.

Keywords: Control Applications, Interaction Between Design and Control
Abstract: Gas metal arc welding (GMAW) process is one of the most popular manufacturing processes in industries such as automotive, aerospace, ship buildings, and boiler. To increase the consistency in welding quality, the automation of the welding process with feedback controllers is inevitable. This paper presents an Integral Sliding Mode (ISM) controller, a robust controller, to control the welding current and arc voltage of a GMAW system. The concept of ISM controller design technique is to combine a discontinuous control with a nominal control to achieve robustness against matched perturbations. The proportional plus integral (PI) and composite nonlinear feedback (CNF) controllers are used as nominal controllers. It is shown in the paper that the CNF controller performs better than PI controller. A linear multi input multi output (MIMO) system model has been considered here for the design. The performance of the controller is analyzed in the presence of model parameter uncertainties and external disturbances and the simulation results are presented and discussed in order to establish a comparison framework.

Keywords: Process Optimization, Control
Abstract: Refinery is a multiproduct manufacturing plant. Crude oil is processed to get intermediate products which are blended to meet quantity, quality and schedule specification of the final products. Refinery optimization is a complex problem therefore it is broken down into sub-problems which are solved independently. The solution obtained using this approach can be improved by integrating different sub-problems for real-time optimization. Refinery works on very small Gross Refinery Margin (GRM); off specification product blends results in considerable cost overhead because product blending is the last operation in the refinery process. Therefore we propose a method for real time integration of product blending with secondary process units to significantly improve the GRM. This method is designed and implemented in the present work. A case study is included to demonstrate the benefits of the proposed method.

Keywords: Control Applications, Interaction Between Design and Control, Modelling and Identification
Abstract: In this work, the design and evaluation of a fuzzy logic control of liquid flow process is analyzed experimentally using MATLAB package. MATLAB is a widely used software environment for research and teaching applications on control and automation. The interface is a collection of hardware and software modules used to flexibly connect a plant, process or instrument (etc.) to a digital computer. The experimental performance of proposed fuzzy logic control is carried out on existing computer control of flow process. The program of Real-time data acquisition and control has been developed using modules called, "To Instrument" and "Query Instrument" of MATLAB for experimental work. Thus, The present implementation of intelligent fuzzy logic control on real-time basis is a pioneering work at laboratory scale. It is considered to be a great contribution in area of advanced process control systems. The simulation and experimental results clearly shows that the Intelligent Fuzzy Logic Controller gives a better control without overshoots of liquid flow rate in comparison with conventional PID controller.

Keywords: Process Optimization, Control, Process Scheduling and Decision support, Integration between Scheduling and Control, Control Applications
Abstract: A robust model predictive control (RMPC) using polyhedral invariant sets for linear parameter varying (LPV) systems is presented in this work. A sequence of state feedback gains associated with a sequence of nested polyhedral invariant sets is constructed off-line in order to reduce the computational burdens. At each control iteration, when the measured state lies between any two adjacent polyhedral invariant sets constructed, a state feedback gain is determined by interpolation of two pre-computed state feedback gains incorporated with scheduling parameters. Three interpolation algorithms are proposed. In the first algorithm, the real-time state feedback gain is determined by maximizing the state feedback gain with subjected to a set of constraints associated with current invariant set. In the second algorithm, the real-time state feedback gain is calculated by minimizing the violation of the constraints of the adjacent inner invariant set with subjected to a set of constraints associated with current invariant set. In the last algorithm, the real-time state feedback gain is obtaned by minimizing the upper bound of infinite horizon worst case performance cost, which is estimated by Lyapunov function at current state, with subjected to a set of constraints associated with current invariant set. The controller design is illustrated with a case study of nonlinear two-tank system. The simulation results showed that the proposed RMPC with interpolation provides a better control performance while on-line computation is still tractable as compared to previously reported algorithms.

Keywords: Inferential sensing, State Estimation and Sensor development, Modelling and Identification, Process and Performance Monitoring, Fault Detection, Supervision and Safety
Abstract: One significant challenge in nonlinear system identification development for industrial processes is that the modeling samples often contain outliers and unknown noise. In this paper, a novel Correntropy-based Kernel Learning (CKL) method is proposed for identification of nonlinear systems with such uncertainty. Without resort to unnecessary efforts, the CKL identification method can reduce the effects of outliers by the use of a robust nonlinear estimator that maximizes correntropy. The superiority of the proposed CKL method is demonstrated through identification of an industrial process in Taiwan. The benefit of its more accurate and reliable performance indicates that CKL is promising in practice for identification of nonlinear systems with unknown noise.

Keywords: Modelling and Identification
Abstract: We give a simple and accurate method for estimating the paramters of continuous time systems under the constraint that all the poles of the system lie to the left of the line s = -1. The method relies on the simple solution of a linear system of equations in the complex domain. We demonstrate by the use of simulation that the proposed methods gives accruate estimates when compared to existing methods. Methods for obtaining sparse solutions, which help in determining the order of the system are also given.

Keywords: Modelling and Identification, Control Applications
Abstract: In this paper, a generic, modular model framework for describing fluid separation by distillation is presented. At present, the framework is able to describe a conventional distillation column and a heat-integrated distillation column, but due to a modular structure the database can be further extended by additional configurations. The framework provides the basis for fair comparison of both steady state and dynamic performance of the different column configurations for a given binary or multicomponent separation.

Keywords: Process and Performance Monitoring, Fault Detection, Supervision and Safety
Abstract: This paper focuses on the problem of handling sensor faults in controlled particulate processes with multi-rate sampled-data measurements. The problem is addressed on the basis of an approximate finite-dimensional system that captures the dominant dynamics of the infinite-dimensional particulate process system. An observer-based output feedback controller with an inter-sample model predictor is initially designed. The inter-sample model predictor provides the observer with estimates of the unavailable outputs, and its predictions are corrected each time that a measurement becomes available. Owing to the different sampling rates of the measurement sensors, the model update is performed using different outputs, or combinations of outputs, at each update time. The combined discrete-continuous closed-loop system is analyzed, and an explicit characterization of the feasible combinations of output sampling rates, model uncertainty, as well as controller and observer design parameters is obtained. This characterization is used as the basis for the development of both passive and active fault-tolerant control strategies that preserve closed-loop stability in the presence of sensor faults. The results are illustrated using a simulated model of a non-isothermal continuous crystallizer.

Keywords: Process Optimization, Control, Batch Process Modelling, Optimization and Control, Interaction Between Design and Control
Abstract: In this paper a design method for proportional-integral-derivative (PID) controller based on internal model control (IMC) principle is proposed. A feedback controller equivalent to internal model control is obtained and then PID controller is derived by an approximate frequency response matching at two low frequency points. A simple and meaningful criterion is provided to choose such low frequency points. The method is illustrated through examples taken from literature.

Keywords: Inferential sensing, State Estimation and Sensor development, Modelling and Identification, Process and Performance Monitoring, Fault Detection, Supervision and Safety
Abstract: The problem of on-line estimating on the basis of temperature measurements the distillate NC4 impurity in an industrial IC4-NC4 splitter is addressed within an adjustable-structure Geometric Estimation approach, yielding: (i) suggestive sensor location guidelines drawn from detectability measures, and (ii) conclusive results obtained from estimator functioning assessment with simulated and experimental data. The resulting estimator performs the estimation task within an admissible error tolerance, and considerably less ODEs than the ones of the standard EKF technique employed in the majority of related previous studies.

Keywords: Inferential sensing, State Estimation and Sensor development
Abstract: A run-of-mine (ROM) ore milling circuit poses many difficulties in terms of measuring process variables and determining accurate models. Control of the ROM circuit is therefore not a trivial task to achieve. An example of a ROM circuit model with reduced complexity that works well for control purposes is discussed. The mill model is discussed in detail, as this model is used for state estimation. A neural network is trained with three disturbance parameters and used to estimate the internal states of the mill, and the results are compared with those of particle filter implementation. A novel combined neural network and particle filter state estimator is presented. The estimation performance of the neural network is promising when the disturbance magnitude used is smaller than that used to train the network.

Keywords: Inferential sensing, State Estimation and Sensor development, Control Applications
Abstract: In this paper, we study dierent possibilities to overcome the inverse response problem which is caused by combining dierent measurements with fast and slow dynamics to form an estimator. Our goal is to obtain a response with "No inherent limitation".

Keywords: Inferential sensing, State Estimation and Sensor development
Abstract: In most of the industries the two-phase flow pattern is obtained when gas and liquid flow simultaneously in a pipe. These two phase flows are complex, dynamic and are difficult to measure. An approach for identifying the flow pattern using Neural Network and Support vector machine is developed. Flow images are captured using high speed SLR camera and are preprocessed. After preprocessing the images, the textural features such as entropy, homogeneity, contrast, correlation and energy are extracted. The textural features extracted are given as input to the neural network and support vector machine. Four typical flow regimes such as bubbly flow, slug flow, stratified flow and annular flow are captured from the experimental set-up. The results obtained shows that support vector machine method of classification is very effective with accuracy of 98.03 percent and hence higher recognition is done.

Keywords: Inferential sensing, State Estimation and Sensor development
Abstract: Local learning based soft sensing methods are effective in dealing with process nonlinearities as well as time varying characteristics. In this paper, an anti-over-fitting method is proposed for appropriate online local model adaptation. The proposed method is based on the weighted sum of the predicted errors for the newest few samples, the weights of which are determined adaptively. Moreover, to reduce the online computational load and memory cost, we propose two adaptive process states division schemes which consider the influence of both the variance and mean value of the predicted residual. Two case studies on continuous stirred tank reactor and debutanizer column demonstrate the effectiveness of the proposed soft sensing scheme.

Keywords: Process Optimization, Control, Control Applications, Interaction Between Design and Control
Abstract: Typical production objectives in lift gas assisted oil wells include stable and optimal production. These objectives are normally daunted by the presence of unstable dynamic behavior resulting from interplay between the energies in the casing head and tubing head of the wells. Moreover, realistic constraints on compressor power and sufficient lift gas availability need to be considered to determine the optimal production from the wells. This paper proposes an alternate optimization formulation to reflect these realistic constraints and exploit the additional degree of freedom associated with the production choke opening. It is demonstrated that a co-ordinated functioning of the choke with the lift gas flow can result in improved and stable production. To overcome the limitations of corrupt measurements in the uncertain downhole environment, we propose the use of a statistical estimator. Validation results involving the simulation model of Jahanshahi et al., (2012) point to the efficacy of the proposed optimization model as well as the soft-sensing approach for estimating downhole pressures.

Keywords: Process Optimization, Control, Control Applications, Batch Process Modelling, Optimization and Control
Abstract: This paper proposes an ecient algorithm for automatic loop shaping in Quantitative Feedback Theory(QFT). The proposed method uses hybrid optimization and consistency techniques. Hull consistency is used to prune the input domain by removing the inconsistent values which are not a part of the solution. The hybrid optimization part combines interval global optimization and nonlinear local optimization methods. The proposed method is demonstrated on uncertain DC motor plant model and performance is compared with those of existing interval methods.

Keywords: Process Optimization, Control, Control Applications, Interaction Between Design and Control
Abstract: We demonstrate that the normal vector method for robust optimization of nonlinear systems with uncertain parameters can be extended to systems with delays. A first-order exothermic irreversible reaction carried out in a CSTR with recycle stream serves as example for the broad class of nonlinear delay differential equations (DDE) with uncertain parameters. The stability boundaries that must be taken into account in the robust optimization consist of Hopf bifurcation points in this case. We show that (i) an unstable steady state of operation results if stability boundaries are neglected and (ii) a conservative optimal steady state results if the delay is ignored.

Keywords: Process Scheduling and Decision support, Integration between Scheduling and Control
Abstract: The present work is concerned with the development of alternative optimization models which address the scheduling problem of diesel blending and distribution in oil-renery operations. The problem involves intermediate products stored in dedicated tanks that are blended to produce diesel with three dierent grades. The nal products are then shipped to nal destination through pipelines. Our study starts by revisiting a model originally proposed in the literature by Pinto et al. (2000). Next, improvements mainly concerning with the interface identication constraints are proposed and evaluated with the intention to extend the model applicability. Three dierent approaches were proposed. Results demonstrate that the introduction of penalties as to pumping interruptions produce good results enabling the formulations to be applied in cases where the time horizon is extended.

Keywords: Process Optimization, Control, Control Applications
Abstract: In recent literature, explicit model predictive control (e-MPC) has been proposed to facilitate implementation of the popular model predictive control (MPC) approach to fast dynamical systems. e-MPC is based on multi-parametric programming. The key idea in e-MPC is to replace the online optimization problem in MPC by a point location problem. After locating the current point, the control law is simply computed as an appropriate linear function of the states. A variety of approaches have been proposed in literature for the point location problem. In this work, we present a novel approach based on linear machines for solving this problem. Linear machines are widely used in multi-category pattern classification literature for developing linear classifiers given representative data from various classes. The idea in linear machines is to associate a linear discriminant function with each class. A given point is then assigned to the class with the largest discriminant function value. In this work, we develop an approach for identifying such discriminant functions from the hyperplanes characterizing the given regions as in multi-parametric programming. Apart from being an elegant solution to the point location problem as required in e-MPC, the proposed approach also links two apparently diverse fields namely e-MPC and multi-category pattern classification. To illustrate the utility of the approach, it is implemented on a hypothetical example as well as on a quadruple tank benchmark system taken from literature.

Keywords: Process Optimization, Control
Abstract: In the process industry it is often not known how well a process is operated, and without a good model it is difficult to tell if operation can be further improved. We present a data-based method for finding a combination of measurements which can be used for obtaining an estimate of how well the process is operated, and which can be used in feedback as a controlled variable. To find the variable combination, we use past measurement data and fit a quadratic cost function to the data. Using the parameters of this cost function, we then calculate a linear combination of measurements, which when held constant, gives near-optimal operation. Unlike previously published methods for finding self-optimizing controlled variables, this method relies only on past plant measurements and a few plant experiments to obtain the process gain. It does not require a model which is optimized off-line to find the controlled variable.

Keywords: Process Optimization, Control
Abstract: Simulated Moving Bed Chromatography (SMBC) is a technical realization of the counter current adsorption process approximate by sequentially switching the inlet and outlet valves of interconnected columns in the direction of fluid flow. In this work, a systematic, simulation based optimization study is carried out for different operational goals in SMBC to enhance the performance of an existing laboratory setup. The optimization work involves Pareto optimal solution for two different conflicting objective functions. Optimal transition between such operating conditions is a challenging task. The quantitative results obtained by comparing the optimal transition method with a non-optimal, step change method shows that the optimal transition requires less time to achieve the new reference cyclic steady state. All of the above methods are studied on a SMBC process for separation of glucose and fructose using Ca^{++} exchange resin.

Keywords: Control Applications, Process Optimization, Control, Process Scheduling and Decision support, Integration between Scheduling and Control
Abstract: We consider dynamic optimization of the energy consumption in a building with energy storage capabilities. The goal is to find optimal policies which minimize the cost of heating and respect operational constraints. The main complication in this problem is the time-varying nature of the main disturbances, which are the energy price and outdoor temperature. To find the optimal operable policies, we solve a moving horizon optimal control problem assuming known disturbances. Next, we proposed simple implementation based on feedback control, which gives a near-optimal operation for a range of disturbances. The methods were successfully tested in simulations, which show that there is a great economical gain in using dynamic optimization for the case of variable energy price.

Keywords: Interaction Between Design and Control, Control Applications
Abstract: In this paper design of reduced order switching function for a discrete-time uncertain nonminimum phase system in special coordinate basis form is proposed. The sliding mode control with this method guarantees the asymptotic stability of all states of system in presence matched disturbance. This problem is further extended to the tracking problem of discrete time uncertain nonminimum phase systems. The results obtained with reduced order sliding surface design are compared with the results of full order sliding surface.

Keywords: Control Applications, Modelling and Identification, Process Optimization, Control
Abstract: In this paper, we develop a novel Model Predictive Controller (MPC) based on soft output constraints for regulation of a cement mill circuit. The MPC is first tested using cement mill simulation software and then on a real plant. The model for the MPC is obtained from step response experiments in the real plant. Based on the experimental step responses an approximate transfer function model for the system is identified. The performance of the MPC in the real plant compares favorably to the existing control system based on fuzzy logic. Compared to the other controllers, soft MPC handles the real time uncertainties effectively. It also regulates the cement mill circuits better and in a plant friendly way by using less variation in the manipulated variables (MVs).

Keywords: Process Optimization, Control, Control Applications
Abstract: This paper addresses the problem of the multivariable control of the quadruple-tank process via Iterative Linear Matrix Inequality (ILMI) approaches. Three methods are revised and compared to design the feedback PID gain controllers. To evaluate the performances of each approach, we consider the two case studies of the minimum and non-minimum phase linear time invariant models. We also examine the feasibility, the stabilization problem resolution and the complexity of each ILMI algorithm.

Keywords: Process Optimization, Control, Control Applications
Abstract: This research presents a control scheme for a gas-fired ethylene dichloride (EDC) cracking furnace to handle a cracked gas temperature at the coil outlet. Input-output (I/O) linearizing control scheme is applied to the furnace model of which interaction between a lumped temperature of gas-fire radiating wall and spatially distributed dynamics of cracking coil is considered. In the proposed method, the feedback I/O linearizing controller for coupled PDE-ODE system is applied to force the cracked gas temperature to follow a desired trajectory by manipulation of a fuel gas flow rate. Simulation results showed that the proposed controller successfully forced the controlled output to a desired setpoint without off-set.

Keywords: Process Optimization, Control, Modelling and Identification, Control Applications
Abstract: Good control of plastic melt temperature for injection molding is very important in reducing operator setup time, ensuring product quality. Motivated by the practical temperature control of injection molding proposes a variety of controllers such as PID controllers, FLC and ANFIS based controllers in a multi model fashion .The injection molding process consists of three zones and the mathematical model for each zone is different. The control output for each zone controller is assigned a weight based on the computed probability of each model and the resulting action is the weighted average of the control moves of the individual zone controllers. The performance criteria of the different controllers are compared, and the advantages, limitations of different implementation methods are also discussed. The proposed real-time Fuzzy control for the injection molding process mainly contributes the barrel temperature control.