This report details the work which has been done to redesign an older process control lab setup, in order for it to become fully modular and wirelessly controllable.The process control lab setup of interest was the quadruple water tank process. Asa proof of concept, a single prototype tank module of the quadruple tank setup wasto be redesigned, where this new design would use entirely modular and wirelesslycontrollable sensors and actuators. The design of these wireless devices was donefrom scratch, the end result was that a set of micro-controller based devices whichcommunicate over Xbee radios were manufactured. Control of this wireless tanksystem was achieved through a custom Simulink interface, said interface was alsocreated during this project. The finalized tank prototype system worked as intendedand it fulfilled all of the relevant system requirements regarding the sensor and actuator network design. There was originally a mechanical design component in thisproject which had to be left after the initial research stages due to time constraints,which concerned the construction of a new tank system. This prototype system wasa proof of concept, which proved the feasibility of building larger scale wirelesslycontrollable process control systems, such as the quadruple water tank process.

<p>Crystallization is a key solid-liquid separation and purification technique used in pharmaceutical industry. Some of the critical quality attributes (CQAs) of a product from crystallization process include crystal size distribution (CSD), purity, polymorphic form, morphology, etc. Different size and polymorphs of a drug substance may have different dissolution profiles and different bioavailability, which can have adverse effect on human health. Therefore, it is important to design and control crystallization process to meet product CQAs. In recent years, drug substances are becoming more complex, often being heat sensitive, which may limit the temperature that can be used in the crystallization step. Consequently, the traditional cooling only crystallization may not be well suited to recover the high value drug substances. For these systems, antisolvent crystallization is typically employed to improve the yield. On the other hand, the solvent composition can significantly impact the polymorphic outcome. Therefore, designing combined cooling and antisolvent crystallization (CCAC) processes to solve the challenges of active pharmaceutical ingredient (API) crystallization in a highly regulated environment is a complex engineering problem. </p> <p>With rising energy costs and intense price competition from generic pharmaceutical companies, the pharmaceutical industry is looking for ways to reduce the cost of manufacturing via process intensification (PI). This thesis focuses on different PI techniques for CCAC of drug substances. Continuous or smart manufacturing is gaining popularity due to its potential to lower the cost of manufacturing while maintaining consistent quality. Continuous crystallization is an important link in the continuous manufacturing process. The first part of the thesis shows PI of a commercial drug substance, Atorvastatin calcium (ASC) for target polymorph development via continuous CCAC using an oscillatory baffled crystallizer (OBC). An existing batch CCAC process for ASC was compared with the continuous CCAC in OBC and it was found the continuous process 30-fold more productive compared to the batch process. An array of process analytical technology (PAT) tools was used in this work to assess key process parameters that affect the polymorphic outcome and CSD. The desired narrower CSD product was obtained in the OBC compared to that from a batch crystallizer.</p> <p>The next part of the thesis focused on model-based PI technique for efficient determination of crystallization kinetics of a polymorphic system in CCAC. A novel experimental design was proposed which significantly reduced the number of experiments required to determine crystallization kinetics in a CCAC process. The kinetic parameters were validated, and a validated polymorphic model was used to perform an in-silico design of experiment (DoE) to develop a design space that can be used to identify operating conditions to achieve a desired crystal size and polymorphic form. </p> <p>The final part of the thesis combines the experimental and model-based approach for designing a continuous CCAC process for ASC in a cascade of Coflore agitated cell reactor (ACR) and three-stage mixed suspension mixed product removal (MSMPR). A combined artificial neural network (ANN) and principal component analysis (PCA) method was used to calibrate an ultraviolet (UV) probe which was used to monitor ASC solute concentration in the cascade process. The crystallization kinetic parameters were estimated in ACR and MSMPR which was used to build a digital model of the cascade process. The digital model was then used to obtain a design space with different temperature profile in the three-stage MSMPR that yielded narrow CSD of ASC form I. Overall, this thesis demonstrates the benefits of applying PI in the CCAC of drug substances using a holistic approach including novel equipment, application of an array of PAT tools, and model-based digital design to achieve desired CQAs of the product.</p>

<p>Accommodating changing consumer tastes, nutritional targets, competitive pressures and government regulations is an ongoing task in the food industry. Product development projects tend to have competing goals and more potential solutions than can be examined efficiently. However, existing databases or spreadsheets containing formulas, ingredient properties, and product characteristics can be exploited using latent variable methods to confront difficult formulation issues. Using these methods, a product developer can target specific final product properties and systematically determine new recipes that will best meet the development objectives.</p> <p>Latent variable methods in reformulation are demonstrated for a product line of frozen muffin batters used in the food service industry. A particular attribute is to be minimized while maintaining the taste, texture, and appearance of the original products, but the minimization is difficult because the attribute in question is not well understood. Initially, existing data is used to develop a partial least squares (PLS) model, which identifies areas for further testing. Design of experiments (DOE) in the latent variable space generates new data that is used to augment the model. An optimization algorithm makes use of the updated model to produce recipes for four different products, and a significant reduction of the target attribute is achieved in all cases.</p> <p>Latent variable methods are also applied to a difficult classification problem in oat milling. Process monitoring involves manually classifying and counting the oats and hulls in the product streams of groats; a task that is time-consuming and therefore infrequent. A solution based on near infrared (NIR) imaging and PLS-discriminant analysis (PLS-DA) is investigated and found to be feasible. The PLS-DA model, built using mixed-cultivar samples, effectively separates the oats and groats into two classes. The model is validated using samples of three pure cultivars with varying moistures and growing conditions.</p> / Master of Applied Science (MASc)

<p>This thesis provides new regression methods for the removal of structured noise in datasets. With multivariable data, the variables and the noise can be both temporally correlated (i.e. auto correlated in time) and contemporaneously correlated (i.e. cross-correlated at the same time). In many occasions it is possible to acquire measurements of the noise, or some function of it, during the data collection. Several new constrained latent variable methods (LVM) that are built upon previous LVM regression frameworks are introduced. These methods make use of the additional information available about the noise to decompose a dataset into basis for the noise and signal. The properties of these methods are investigated mathematically, and through both simulation and application to actual biomedical data.</p> <p>In Chapter Two, linear, constrained LVM methods are introduced. The performance of these methods are compared to the other similar LVM methods as well as ordinary PLS throughout several simulation studies. In Chapter Three, a NIPALS type algorithm is developed for the soft constrained PLS method which is also able to account for missing data as well as datasets with large covariance matrices. Chapter Four introduces the nonlinear-kernelized constrained LVM methods. These methods are capable of handling severe nonlinearities in the datasets. The performance of these methods are compared to nonlinear kernel PLS method. In Chapter Five the constrained methods are used to remove ballistocardiographic and muscle artifacts from EEG datasets in combined EEG-fMRI as well as single EEG experiments on patients. The results are shown and compared to the standard noise removal methods used in the field. Finally in Chapter Six, the overall conclusion and scope of the future work is laid out.</p> / Doctor of Philosophy (PhD)

Statistical quality control is the application of statistical methods to problems for which it is of interest to evaluate, establish, or verify the quality of a product. The two basic areas of statistical quality control that have received both the greatest attention in the literature and the widest acceptance in industry are acceptance sampling and statistical process control. In the majority of such techniques, a single characteristic of an item is used to describe its quality. In such cases, one of two basic types of product quality measures is typically used: attributes product quality measures and variables product quality measures. Variables product quality measures evaluate an item’s quality by measuring its quality characteristic on a continuous scale. Attributes product quality measures assign a 0 to an item if its characteristic is conforming to some specification, and 1 if its characteristic is nonconforming. Although attributes and variables product quality measures have many appropriate applications, there are many situations m which product quality is best described by classifying a single characteristic of the item using three or more discrete levels. A multilevel discrete product quality measure is a function that assigns a numerical value to such an item corresponding to the level in which it is classified. Several acceptance sampling plans and control charts that incorporate the use of multilevel discrete product quality measures are defined here. In addition to the multilevel discrete product quality measure, each of the defined methods utilizes a quality value function. A quality value function assigns a numerical value to an item based on the classification it receives from the multilevel discrete product quality measure. Each of the defined multilevel acceptance sampling plans and multilevel control charts is evaluated with respect to its probabilistic behavior. In addition, the problem of parameter selection and quality value function specification is addressed for each of the defined techniques. The cases considered are the 3-level case, the 4-level case, and the general j-level case. / Ph. D.

Motivation is not something that we can take for granted. Some would say that motivation in the workplace is key for optimal performance and production. This master thesis looks into how shift-based operators in industrial work environments can sustain motivation, with the help of Self-Determination Theory, looking deeper into Autonomous motivation and how this can be a change in how operators perform, even in the long shift hours that are common in process control industries. This thesis aims to evaluate the current motivational drivers with operators. What keeps operators motivated in their work today? What type of motivation is lacking in their current workplace? And, how can they keep their motivation for a longer time? The method includes observations, surveying, and interviews. The results showed that there is a lower motivation in three subcategories of intrinsic behavior; Pressure/Tension, Perceived Choice, and Value/Usefulness. This thesis work is part of the research project Interactive Process Control, at Umeå University. By using these insights there is an opportunity to target these motivations in the future development of the IPC interactive tool.

No / Experimental observations of melt temperature profiles and melting performance of extruder screws are reported. A novel temperature sensor consisting of a grid of thermocouple junctions was used to take multiple temperature readings in real time across melt flow in a single screw extruder. Melt pressure in the die and power consumption were also monitored. Three extruder screws at a range of screw speeds were examined for a commercial grade of low density polyethylene. Results showed melt temperature fields at low throughputs to be relatively independent of screw geometry with a flat-shaped temperature profile dominated by conduction. At high throughputs, melting performance and measured temperature fields were highly dependent upon screw geometry. A barrier-flighted screw with Maddock mixer achieved significantly better melting than single flighted screws. Low temperature "shoulder" regions were observed in the temperature profiles of single-flighted screws at high throughput, due to late melting of the solid bed. Stability of the melt flow was also dependent upon screw geometry and the barrier-flighted screw achieving flow with lower variation in melt pressure and temperature. Dimensionless numbers were used to analyze the relative importance of conduction, convection, and viscous shear to the state of the melt at a range of extrusion conditions.

<p dir="ltr">Annual rates for global polystyrene (PS) waste accumulation have reached 28 million tons, yet recycling rates remain around 1%. Conventional waste treatment methods have proven largely ineffective in reducing PS waste accumulation. As PS waste degrades, it generates microplastics and releases harmful chemicals that impact human health and ecosystems. This study developed batch and continuous low-pressure hydrothermal processing (LP-HTP) methods to convert PS into oils. In the batch LP-HTP study, the effects of temperature, time, and water loading on oil yield and composition were evaluated. The process converted PS to 96-99% oils with minimal char formation (1-2%) while requiring no catalyst, outperforming traditional pyrolysis. The LP-HTP methods also require lower energy inputs and pressures than supercritical water liquefaction. Co-processing PS with polyolefins resulted in oil yields of 87% and higher aromatic contents compared to polyolefin-only oils. Mono-aromatic (C₆-C₉) yields were limited by reversible reactions with poly-aromatics (C₁₀-C₂₄₊). Efficient continuous LP-HTP methods were then developed, achieving 95-99% oil yields at 0.2-1.2 kg/hr under atmospheric pressure. The oil contained styrene monomers, dimers, and trimers with a total yield of 88.5% at 391°C. A detailed kinetic model was constructed, with intrinsic parameters estimated from continuous conversion data to enable process optimization and scale-up. These LP-HTP methods show potential for reducing environmental impacts and achieving up to 4.7 times higher energy recovery than incineration. The resulting hydrocarbons, if separated into pure monomers, can be used as chemical feedstocks, supporting a circular hydrocarbon economy that incentivizes plastic waste conversion.</p>

No description available.

A dynamic job shop control system with a combined input/output control mechanism is developed for achieving improved shop performance. The problem is modelled such that at periodic intervals, the best combination of input and output variables for the forthcoming period are identified. The purpose of the control system is to determine the set of jobs to be released into the shop (input variables) and the capacity levels of machines (output variables) for a planning period such that a composite cost function is minimized. The problem is mathematically formulated as a 0-1 linear mixed integer program (MIP). An iterative based heuristic optimizing algorithm incorporating the MIP is developed. The control algorithm is compared with another job shop control system where only the input is assumed variable. The two systems are compared by computer simulation and results indicate significant improvements for most of the performance measures evaluated. Significant reductions in the mean and variance of the manufacturing lead time with a better distribution among its parts were also achieved. / Ph. D.