The separation of four or more components traditionally requires the use of three or more distillation columns. Due to the associated high costs, process intensification techniques have been studied. Semicontinuous separation is one method that allows multiple separations using one column integrated with middle vessels. This thesis aims to develop a new semicontinuous separation process that can separate a mixture with four or more components into high purity products with one column and two or more middle vessels. It is an extension of the conventional ternary semicontinuous process, which has been repeatedly shown to be profitable at intermediate throughputs when compared to continuous systems. The semicontinuous process operates in a forced cycle, with three operating modes that ensure separation objectives are met. The performance of the proposed quaternary semicontinuous separation is analyzed through rigorous dynamic simulations over a range of production capacities. To determine the feasibility, operability, and applicability to non-ideal mixtures, three case studies were considered: 1. Equimolar mixture of alkanes (n-hexane; n-heptane; n-octane; n-nonane). 2. Equimolar mixture of aromatics (benzene; toluene; ethyl-benzene; and o-xylene). 3. Non-ideal mixture of mixed-alcohols (methanol, ethanol, and water; propanol; isobutanol; pentanol and hexanol) The extendibility of the quaternary semicontinuous separation process, referred to as quintenary semicontinuous separation, is then evaluated on a five-component alkane mixture (n-hexane; n-heptane; n-octane; n-nonane; n-decane), via three case studies: 1. Equimolar mixture 2. Non-equimolar mixture, rich in light and heavy components. 3. Non-equimolar mixture, rich in intermediate components. The results for both the quaternary and quintenary semicontinuous processes indicate that this new technique is successful at achieving separation objectives while staying within safe operating limits. Comparison of both equimolar mixtures of alkanes for quaternary and quintenary semicontinuous processes with continuous systems indicates that the proposed system is profitable for intermediate flow rates. / Thesis / Master of Applied Science (MASc) / Traditionally, several large distillation columns (that can be hundreds of feet tall) are required to split a mixture of liquid chemicals into its individual components. Distillation is the separation of mixtures due to differences in boiling points. When the mixture is heated, the vapour phase will contain the components with lower boiling points, which can be separated once the vapour phase is cooled and condensed. The main goal of this research is to create a new system that can carry out the same separation, but using complex techniques that require only one column and a few extra storage tanks that are much cheaper and smaller than a distillation column. Different liquid mixtures were used to show how well the new process is able to separate the liquid into its individual components, while remaining in safe operating limits.

This thesis considers the problem of control of nonlinear systems subject to limited availability of measurements and uncertainty in model parameters. To address this problem, first a linear offset free MPC is designed. Subsequently, a Lyapunov-based offset free MPC design is presented to handle structured uncertainty subject to constant disturbances. The controller's ability to handle unstructured uncertainty and measurement noise is demonstrated through simulation examples. Next, the problem of handling lack of state measurements as well as uncertainty is considered. To achieve simultaneous state and disturbance parameter estimation, a Lyapunov-based model predictive controller (MPC) is integrated with a moving horizon based mechanism, to achieve (where possible) offset elimination in the unmeasured states as well. A chemical reaction process example is presented to illustrate the key points. Finally its efficacy is demonstrated through a polymerization process example. / Thesis / Doctor of Philosophy (PhD)

A formulation of a subspace identification method for multi-phase processes with applications to rotational moulding and suggestions for improvements and experimental applications. / This thesis focuses on the implementation of subspace identification (SSID) for nonlinear, chemical batch processes by introducing a model identification method for multi-phase processes. In this thesis, a multi-phase process refers to chemical or biological batch-like processes with properties that cause a change in the dynamics during the evolution of the process. This can occur, for example, when a process undergoes a change of state upon reaching a melting point. Existing SSID techniques are not designed to utilize any known, multiphase nature of a process in the model identification stage. The proposed approach, Multiphase Subspace Identification (MPSSID), is conducted by first splitting historical data into phases during the identification step and then building a subspace model for each phase. The phases are then connected via a partial least squares (PLS) model that transforms the states from one phase to the next. This approach makes use of existing SSID techniques that allow for model construction using batches of nonunifrom length. Here, MPSSID is applied to a uniaxial rotational moulding process. In rotational moulding, the dynamics switch as the process undergoes heating, melting, and sintering stages that are visibly distinct and recognizable upon a certain temperature (not time) being reached. Results demonstrate the ability of multiphase models to better predict the temperature trajectories and final product quality of validation batches. As an extension to this rotational moulding analysis, additional MPSSID methods of implementation are proposed and the results are compared. A MPSSID mixed integer linear program is then introduced for implementation within model predictive control. The applications to rotational moulding are presented within the context of plastics manufacturing and the impact of plastic on the global climate crisis, with suggestions for future work. / Thesis / Master of Applied Science (MASc) / The control of chemical processes is an important factor in achieving high quality products. To control a process well, the mathematical model of the system must be accurate. In the past, mathematical models for process control were designed based on engineering approximations. Now, with major advances in computing and sensor technology, it is possible to design a simulation of the entire process. These simulations can be designed using first-principles or black box approaches. First-principles approaches utilize rigorous models that are based on the complex chemical and physical formulas that govern a system. Black box approaches do not look at the first-principles dynamics. They only utilize the measured process inputs and outputs to form a model of the system. They are widely used because of their ease of implementation in comparison to first-principles approaches. In this thesis, a new black box process control model is proposed and is found to yield better theoretical results than existing techniques. This model is tested on data from a plastics manufacturing process called rotational moulding, which involves loading polymer powders into a mould that is simultaneously rotated and heated to yield seamless plastic parts. Lastly, a control framework that is compatible with the new black box model is proposed to be used for future experimental tests.

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<p>This thesis presents a method for the design of a predictive controller with variable step sizes.Predictive methods such as receding horizon control (or model predictive control) use aa fixed sampling frequency when updating the inputs. In the proposed method, the switchingtimes are incorporated into an optimization problem, thus resulting in anadaptive step-size control process. The controller with variable timesteps is shown to require less tuning and to reduce the number of expensive model evaluations.An alternate solution approach had to be developed to accommodate the new problem formulation.The controller's stability is proven in a context that does not require terminal cost or constraints.The thesis presents examples that compare the performance of the variable switching time controllerwith the receding horizon method with a fixed step size. This research opens many roads for futureextension of the theoretical work and practical applications of the controller.</p> / Doctor of Science (PhD)

This thesis focuses on data-driven modeling and control of batch and batch-like processes. These processes are highly nonlinear and time-varying which, unlike continuous operations, are characterized by the finite duration of operation and absence of equilibrium conditions. This makes the modeling and control approaches available for continuous processes not readily applicable and requires appropriate adaptations of the available approaches to handle a) batch data structure for modeling and b) a control objective different than that of maintaining a steady-state operation as often encountered in a continuous process. With these considerations, this work adapted the batch subspace identification for modeling and control of a variety of batch and batch-like processes. A particular focus of this work was on the application of the proposed ideas on real engineering systems along with simulated case studies. The applications considered in this work are batch crystallization, a hydrogen plant startup dynamics in a collaboration with Praxair Inc. and a rotational molding process in collaboration with the polymer research group at McMaster University. For the seeded batch crystallization process, subspace identification techniques are adapted to identify a linear time invariant model for the, otherwise, infinite dimensional process. The identified model is then deployed in a linear model predictive control (MPC) strategy to achieve crystal size distribution (CSD) with desired characteristics subject to both manipulated input and product quality constraints. The proposed MPC is shown to achieve superior performance and the ability to respect tighter product quality constraints as well as robustness to uncertainty in comparison to an open loop policy as well as a traditional trajectory tracking policy using classical control. In another contribution, merits of handling data variety in a subspace identification framework was demonstrated on the crystallization process. The proposed approach facilitates the specification of a desired shape of the particle size distribution required at the termination of the batch process. Further, novel model validity constraints are proposed for the subspace identification based control framework. In the collaborative work on hydrogen plant startup, it is recognized as a batch-like process due to its similarity to batch processes. Firstly, in this work a high fidelity model of the Hydrogen unit was developed with relevant startup and shutdown mechanisms. This setup is used to mimic the trends in the key process variables during the startup/shutdown operation. The simulated data is used to identify a state-space model of the process and validated on new simulated startup. Further, the approach was demonstrated on real plant data from one of the Praxair's plants. The predictive capabilities of the model provide ample handle for the plant operator for averting failures and abrupt shutdown of the entire plant. This is expected to have immense economic advantages. Finally, the subspace identification based modeling and control approach was applied to a lab-scale rotational modeling (RM) process. It is a polymer processing technique that is characterized by the placement of a polymer resin inside a mold, subsequent closure of the mold, followed by the simultaneous application of uni-axial (as is the case in the present work) or bi-axial rotation and heat. The resin is deposited on the mold wall where it forms a dense unified layer following which, the mold is cooled while still rotating the mold. Once demolding temperatures are achieved, the finished part is removed from the mold. Its potential as a manufacturing process for polymeric components is limited by a number of concerns including difficulties in process control, in particular, determining efficiently the process operation to yield the desired product consistently, and produce new products. This work has contributed by developing optimal control strategies for the process to achieve user-specified product quality and reject variability across batches. The results obtained demonstrate the merits of the proposed approach. / Thesis / Doctor of Philosophy (PhD)

The initial portion of this research focuses on appropriate parameter estimators within a general context of multivariate quality control. The goal of Phase I analysis of multivariate quality control data is to identify multivariate outliers and step changes so that the estimated control limits are sufficiently accurate for Phase II monitoring. High breakdown estimation methods based on the minimum volume ellipsoid (MVE) or the minimum covariance determinant (MCD) are well suited to detecting multivariate outliers in data. Because of the inherent difficulties in computation many algorithms have been proposed to obtain them. We consider the subsampling algorithm to obtain the MVE estimators and the FAST-MCD algorithm to obtain the MCD estimators. Previous studies have not clearly determined which of these two estimation methods is best for control chart applications. The comprehensive simulation study here gives guidance for when to use which estimator. Control limits are provided. High breakdown estimation methods such as MCD and MVE can be applied to a wide variety of multivariate quality control data. The final, lengthier portion of this research considers profile monitoring. Profile monitoring is a relatively new technique in quality control used when the product or process quality is best represented by a profile (or a curve) at each time period. The essential idea is often to model the profile via some parametric method and then monitor the estimated parameters over time to determine if there have been changes in the profiles. Because the estimated parameters may be correlated, it is convenient to monitor them using a multivariate control method such as the T-squared statistic. Previous modeling methods have not incorporated the correlation structure within the profiles. We propose the use of mixed models (both linear and nonlinear) to monitor linear and nonlinear profiles in order to account for the correlation structure within a profile. We consider various data scenarios and show using simulation when the mixed model approach is preferable to an approach that ignores the correlation structure. Our focus is on Phase I control chart applications. / Ph. D.

The Phase I analysis of data when the quality of a process or product is characterized by a linear function is studied in this dissertation. It is assumed that each sample collected over time in the historical data set consists of several bivariate observations for which a simple linear regression model is appropriate, a situation common in calibration applications. Using a simulation study, the researcher compares the performance of some of the recommended approaches used to assess the stability of the process. Also in this dissertation, a method based on using indicator variables in a multiple regression model is proposed. This dissertation also proposes a change point approach based on the segmented regression technique for testing the constancy of the regression parameters in a linear profile data set. The performance of the proposed change point method is compared to that of the most effective Phase I linear profile control chart approaches using a simulation study. The advantage of the proposed change point method over the existing methods is greatly improved detection of sustained step changes in the process parameters. Any control chart that combines sample information over time, e.g., the cumulative sum (CUSUM) chart and the exponentially weighted moving average (EWMA) chart, has an ability to detect process changes that varies over time depending on the past data observed. The chart statistics can take values such that some shifts in the parameters of the underlying probability distribution of the quality characteristic are more difficult to detect. This is referred to as the "inertia problem" in the literature. This dissertation shows under realistic assumptions that the worst-case run length performance of control charts becomes as informative as the steady-state performance. Also this study proposes a simple new measure of the inertial properties of control charts, namely the signal resistance. The conclusions of this study support the recommendation that Shewhart limits should be used with EWMA charts, especially when the smoothing parameter is small. This study also shows that some charts proposed by Pignatiello and Runger (1990) and Domangue and Patch (1991) have serious disadvantages with respect to inertial properties. / Ph. D.