We consider a principal components based decomposition of the expected value of the multivariate quadratic loss function, i.e., MQL. The principal components are formed by scaling the original data by the contents of the loss constant matrix, which defines the economic penalty associated with specific variables being off their desired target values. We demonstrate the extent to which a subset of these ``loss-scaled principal components", i.e., LSPC, accounts for the two components of expected MQL, namely the trace-covariance term and the off-target vector product. We employ the LSPC to solve a robust design problem of full and reduced dimensionality with deterministic models that approximate the true solution and demonstrate comparable results in less computational time. We also employ the LSPC to construct a test statistic called loss-scaled T^2 for multivariate statistical process control. We show for one case how the proposed test statistic has faster detection than Hotelling's T^2 of shifts in location for variables with high weighting in the MQL. In addition we introduce a principal component based decomposition of Hotelling's T^2 to diagnose the variables responsible for driving the location and/or dispersion of a subgroup of multivariate observations out of statistical control. We demonstrate the accuracy of this diagnostic technique on a data set from the literature and show its potential for diagnosing the loss-scaled T^2 statistic as well.

This dissertation is composed of three major parts, each studying a problem related to semiconductor manufacturing. The first part of the dissertation proposes a high-level scheduling model that serves as an intermediate stage between planning and detailed scheduling in the usual planning hierarchy. The high-level scheduling model explicitly controls the WIP over time in the system and provides a more specific guide to detailed scheduling. WIP control is used to balance the WIP (Work In Process) level and to keep the bottleneck station busy to maintain a high throughput rate. A mini-fab simulation model is used to evaluate the benefits of different approaches to implementing such a high-level scheduling model, and to compare different WIP control policies. Extensive numerical studies show that the proposed approaches can achieve much shorter cycle times than the traditional planning-scheduling approach, with only a small increase in inventory and backorder costs. With increasing worldwide competition, high technology product manufacturing companies have to pay great attention to lower their production costs and guarantee high quality at the same time. Advanced process control (APC) is widely used in semiconductor manufacturing to adjust machine parameters so as to achieve satisfactory product quality. The interaction between scheduling and APC motivates the second part of this dissertation. First, a single-machine makespan problem with APC constraints is proved to be NPcomplete. For some special cases, an optimal solution is obtained analytically. In more general cases, the structure of optimal solutions is explored. An efficient heuristic algorithm based on these structural results is proposed and compared to an integer programming approach. Another important issue in manufacturing system is maintenance, which affects cycle time and yield management. Although there is extensive literature regarding maintenance policies, the analysis in most papers is restricted to conventional preventive maintenance (PM) policies, i.e., calendar-based or jobbased PM policies. With the rapid development of new technology, predictive maintenance has become more feasible, and has attracted more and more attention from semiconductor manufacturing companies in recent years. Thus, the third problem considered in this dissertation is predictive maintenance in an M/G/1 queueing environment. One-recipe and two-recipe problems are studied through semi-Markov decision processes (SMDP), and structural properties are obtained. Discounted SMDP problems are solved by linear programming and expected machine availabilities are calculated to evaluate different PM policies. The optimal policy can maintain a high machine availability with low long-run cost. The structures of the optimal PM policies show that it is necessary to consider multiple recipes explicitly in predictive maintenance models. / text

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-06-22T18:25:08Z No. of bitstreams: 1 accacioferreiradossantosneto.pdf: 2827890 bytes, checksum: 63ffac13e0cef4ee756375e990b7ba3d (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-08-07T19:24:53Z (GMT) No. of bitstreams: 1 accacioferreiradossantosneto.pdf: 2827890 bytes, checksum: 63ffac13e0cef4ee756375e990b7ba3d (MD5) / Made available in DSpace on 2017-08-07T19:24:54Z (GMT). No. of bitstreams: 1 accacioferreiradossantosneto.pdf: 2827890 bytes, checksum: 63ffac13e0cef4ee756375e990b7ba3d (MD5) Previous issue date: 2013-05-10 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Face aos desafios atuais dos sistemas industriais, que necessitam operar de forma econômica, eficiente e sustentável, novos procedimentos e estratégias para o controle destes processos estão sempre em busca de inovações. Esta situação motivou e direcionou o objetivo do presente trabalho que foi utilizar ferramentas abertas, inovadoras, de utilização atual na indústria, para desenvolver um módulo laboratorial, com características multivariáveis, emulando a ambiência industrial, no que tange às dinâmicas das malhas selecionadas. Foram utilizados o protocolo de comunicação digital "OLE for Process Control - OPC" e software "Free and Open Source Software - FOSS" Scilab, ambas ferramentas de código aberto. O protocolo OPC é específico para ambientes industriais, agregando qualidades que propiciam o gerenciamento e controle de um sistema em tempo real com eficiência e qualidade. E o software Scilab, uma ferramenta FOSS que, dentre suas diversas funcionalidades, permite o desenvolvimento e a comunicação de um supervisório para um sistema físico, interagindo através do protocolo de comunicação OPC. Para o alcance da proposta foi desenvolvido um módulo laboratorial multivariável para controle de processos, que permitiu a modelagem e o controle de um sistema composto por malhas de nível e temperatura, comuns no meio industrial. Foi ainda desenvolvido um ambiente supervisório amigável que contempla técnicas diferenciadas de controle para o controlador "Proportional Integral Derivative - PID", além de análise e controle de dinâmicas monovariáveis e multivariáveis do sistema. / In face of the challenges of the current industrial systems, which needs to operate in an economic, efficient and sustainable forms, new procedures and strategies for control these processes are always in search of innovation. This situation motivated and directed the aim of this work that was to use open tools, innovative, current use in industry, to develop a laboratory module, with characteristics multivariable, emulating the industrial ambience, with respect to the dynamics of the selected loops. We used the digital communication protocol " OLE for Process Control - OPC software" and " Free and Open Source Software - FOSS" Scilab, both open source tools. The OPC protocol is specific to industrial environments, adding qualities that enable the management and control of a real-time system with efficiency and quality. And the Scilab software, FOSS tool that, among its many features, allows the development and communication of a supervisory for a physical system, interacting throught the OPC communication protocol. For the scope of the proposal we developed a laboratory module for multivariable process control, allowing the modeling and control of a system composed of meshes of level and temperature, common in industry. It was also developed an environment friendly supervisory techniques which includes different control for the "Proportional Integral Derivative - PID" controller, as well as analysis and control of dynamic SISO and multivariable of the system.

The thesis is focused on the production management company XY. In the first part, there is the theoretical knowledge required for the understanding of this problem and diploma thesis. In the second part there is the analysis of company processes. Based on the identified problems the methodology of improvement is designed in this section.

<p>  </p> <p>Crystallization is a purity and particle control unit operation commonly used in industries such as pharmaceuticals, agrochemicals, and energetics. Often, the active ingredient’s crystal mean size, polymorphic form, morphology, and distribution can impact the critical quality attributes of the final product. The active ingredient typically goes through a series of process development iterations to optimize and scale-up production to reach production scale. Guided by the FDA, the paradigm shift towards continuous processing and crystallization has shown benefits in introducing cheaper and greener technologies and relieving drawbacks of batch processing. To achieve successful batch scale-up or robust continuous crystallization design, process intensification of unit operations, crystallization techniques, and utilizing data driven approaches are effective in designing optimal process parameters and achieving target quality attributes. </p> <p>In this thesis, a collection or toolbox of various process intensification techniques was developed to aid in control, optimization, and digitalization of crystallization processes. The first technique involves developing a novel control algorithm to control agrochemical crystals of high aspect ratio to improve the efficiency of downstream processes (filtration, washing, and drying). The second technique involves the further improvement of the first technique through digitalization of the crystallization process to perform simulated optimization and obtain a more nominal operating profile while reducing material consumption and experimentation time. The third method involves developing a calibration procedure and framework for in-line video microscopy. After a quick calibration, the in-line video microscopy can provide accurate real-time measurements to allow for future control capabilities and improve data scarcity in crystallization processes. The last technique addresses the need for polymorphic control and process longevity for continuous tubular crystallizers. Through a sequential stirred tank and tubular crystallizer experimental setup, the control of polymorphism, particle mean size, and size distribution were characterized. Each part of this thesis highlights the importance and benefits of process intensification by creating a wholistic process intensification framework coupled with novel equipment, array of PAT tools, feedback control, and model-based digital design.</p>

<p>Owing to the rise in fossil fuel prices, overall energy security concerns, and the current push towards green engineering; renewable and green fuels have seen an increase in interest in recent years. Two notable technologies in this green movement are the production of biodiesel from microalgae and the production of biogas from anaerobic digestion of waste biomass. Production of biodiesel from microalgae was studied extensively in the 80s through the early 90s and found to be economically infeasible given the technology of the time. However, recent literature has suggested that one possible method to improve the feasability of the process would be to combine it with an anaerobic digestor to provide nutrient and biomass recycling. For such a system, having accurate models of each process would be highly advantageous for optimal design and control. To this end this thesis moves towards this overall goal by examining and modelling the anaerobic digestion of the microalgae <em>Chlorella vulgaris</em>.</p> <p>Starting with a set of experimental data (anaerobic digestion of <em>Chlorella vulgaris</em>) provided by LBE-INRA, the minimum number of kinetic equations needed to predict the data are found using principal component style analysis. This number is found to be two to three reactions. Using this as a basis for model development, a mass balance model is developed around both two and three reaction cases. To date there is very little literature on the modelling of anaerobic digestion of microalgae and so kinetic laws are selected from the general anaerobic digestion models ``Anaerobic Digestor Model 1'' (ADM1) and ``Acidogenesis/Methanogenisis Model'' (AM2). Given that the kinetic laws were derived from general literature, model fitting is a must. To faciliate this process a novel systematic parameter identification procedure to locate identifiable parameter subsets within each model is presented. Applying this novel procedure to the provided data is seen to lead to promising identification results. Through these identification trials it is shown that the three reaction model best captures the dynamics of the system. This three reaction model serves as the basis for subsequent steady state optimality and sensitivity analysis. From these efforts it is shown that the predicted optimal curves match literature data very well but uncertainty in certain key parameter estimates lead to highly sensitive model predictions (and therefore low confidence). This leads to the conclusion that the developed model is capable of predicting the kinetics of <em>Chlorella</em> digestion but additional trials are needed to further refine the model fitting results.</p> <p>Coupling an anaerobic digester to a microalgal culture is currently considered one of the most promising avenues towards the production of renewable bioenergy, either in the form of biodiesel or biogas. Accurate mathematical models are crucial tools to assess the potential of such coupled biotechnological processes and help optimize their design, operation and control. This paper focuses on the compartment of anaerobic digestion of microalgae. Using experimental data for the anaerobic digestion of <em>Chlorella vulgaris</em>, a grey-box model is developed that allows good prediction capabilities and retains low complexity. The proposed methodology proceeds in two steps, namely a structural and a parametric identification steps. The fitted model is then used to conduct preliminary optimization for the production of biogas from <em>Chlorella vulgaris</em>. The results provide some insight into the potential for bioenergy production from the digestion of microalgae and, more generally, the coupled process.</p> / Master of Applied Science (MASc)

<p>This thesis considers the problem of fault diagnosis and fault-tolerant control (FTC) for chemical process systems with nonlinear dynamics. The primary objective of fault diagnosis discussed in this work is to identify the failed actuator or sensor by using the information embodied in a process model, as well as input and output data. To this end, an active fault isolation method is first proposed to identify actuator faults and process disturbances by utilizing control action and process nonlinearity. The key idea is to move the process to a region upon fault detection where the effect of each fault can be differentiated from others. The proposed method enables isolation of faults that may not be achievable under nominal operation. This work then investigates the problem of sensor fault isolation by exploiting model-based sensor redundancy through state observer design. Specifically, a high-gain observer is presented and the stability property of the closed-loop system is rigorously established. A method that uses a bank of high-gain observers is then proposed to isolate sensor faults, which explicitly accounts for process nonlinearity, and to continue nominal operation upon fault isolation. In addition to fault diagnosis, this work addresses the problem of handling severe actuator faults using a safe-parking approach and integrating fault diagnosis and safe-parking techniques in a unified fault-handling framework. In particular, several practical issues are considered for the design and implementation of safe-parking techniques, including changes in process dynamics, the network structure of a chemical plant, and actuators frozen at arbitrary positions. The advantage of this approach is that it enables stable process operation under faulty conditions, avoiding the partial or entire shutdown of a chemical plant and resulting economic losses. The efficacy of the proposed fault diagnosis and FTC methods is demonstrated through numerous simulations of chemical process examples.</p> / Doctor of Philosophy (PhD)

<p>Process operability represents the ability of a process plant to operate satisfactorily away from the nominal operating or design condition, where flexibility and dynamic operability are two important attributes of operability considered in this thesis. Today's companies are facing numerous challenges, many as a result of volatile market conditions. Key to sustainable profitable operation is a robust process supply chain. Within a wider business context, flexibility and responsiveness, i.e. dynamic operability, are regarded as key qualifications of a robust process supply chain.</p> <p>The first part of this thesis develops methodologies to rigorously evaluate the dynamic operability and flexibility of a process supply chain. A model is developed which describes the response dynamics of a multi-product, multi-echelon supply chain system. Its incorporation within a dynamic operability analysis framework is shown, where a bi-criterion, two-stage stochastic programming approach is applied for the treatment of demand uncertainty, and for estimating the Pareto frontier between an economic and responsiveness criterion. Two case studies are presented to demonstrate the effect of supply chain design features on responsiveness. This thesis has also extended current paradigms for process flexibility analysis to supply chains. The flexibility analysis framework, where a steady-state supply chain model is considered, evaluates the ability to sustain feasible steady-state operation for a range of demand uncertainty.</p> <p>The second part of this thesis develops a decision-support tool for supply chain management (SCM), by means of a robust model predictive control (MPC) strategy. An effective decision-support tool can fully leverage the qualifications from the operability analysis. The MPC formulation proposed in this thesis: (i) captures uncertainty in model parameters and demand by stochastic programming, (ii) accommodates hybrid process systems with decisions governed by logical conditions/rulesets, (iii) addresses multiple supply chain performance metrics including customer service and economics, and (iv) considers both open-loop and closed-loop prediction of uncertainty propagation. The developed robust framework is applied for the control of a multi-echelon, multi-product supply chain, and provides a substantial reduction in the occurrence of back orders when compared with a nominal MPC framework.</p> / Master of Applied Science (MASc)

Complexity of production processes, high computing capabilities, and massive datasets characterize today’s manufacturing environments, such as those of continuous andbatch production industries. Continuous production has spread gradually acrossdifferent industries, covering a significant part of today’s production. Commonconsumer goods such as food, drugs, and cosmetics, and industrial goods such as iron,chemicals, oil, and ore come from continuous processes. To stay competitive intoday’s market requires constant process improvements in terms of both effectivenessand efficiency. Statistical process control (SPC) and design of experiments (DoE)techniques can play an important role in this improvement strategy. SPC attempts toreduce process variation by eliminating assignable causes, while DoE is used toimprove products and processes by systematic experimentation and analysis. However,special issues emerge when applying these methods in continuous process settings.Highly automated and computerized processes provide an exorbitant amount ofserially dependent and cross-correlated data, which may be difficult to analyzesimultaneously. Time series data, transition times, and closed-loop operation areexamples of additional challenges that the analyst faces.The overall objective of this thesis is to contribute to using of statisticalmethods, namely SPC and DoE methods, to improve continuous production.Specifically, this research serves two aims: [1] to explore, identify, and outlinepotential challenges when applying SPC and DoE in continuous processes, and [2] topropose simulation tools and new or adapted methods to overcome the identifiedchallenges.The results are summarized in three appended papers. Through a literaturereview, Paper A outlines SPC and DoE implementation challenges for managers,researchers, and practitioners. For example, problems due to process transitions, themultivariate nature of data, serial correlation, and the presence of engineering processcontrol (EPC) are discussed. Paper B further explores one of the DoE challengesidentified in Paper A. Specifically, Paper B describes issues and potential strategieswhen designing and analyzing experiments in processes operating under closed-loopcontrol. Two simulated examples in the Tennessee Eastman (TE) process simulatorshow the benefits of using DoE techniques to improve and optimize such industrialprocesses. Finally, Paper C provides guidelines, using flow charts, on how to use thecontinuous process simulator, “The revised TE process simulator,” run with adecentralized control strategy as a test bed for developing SPC and DoE methods incontinuous processes. Simulated SPC and DoE examples are also discussed.

<p dir="ltr">The purification of most active pharmaceutical ingredients (APIs) is primarily achieved through crystallization, conducted in batch, semi-batch, or continuous modes. Recently, continuous crystallization has gained interest in the pharmaceutical industry for its potential to reduce manufacturing costs and maintenance. Crystal characteristics such as size, purity, and polymorphism significantly affect downstream processes like filtration and tableting, as well as physicochemical properties like bioavailability, flowability, and compressibility. Developing an optimal operation that meets the critical quality attributes (CQAs) of these crystal properties is essential.</p><p dir="ltr">This dissertation begins by focusing on designing an innovative integrated crystallization system to enhance control over crystalline material properties. The system expands the attainable region of crystal size distribution (CSD) by incorporating multiple Mixed-Suspension Mixed-Product Removal (MSMPR) units and integrating wet milling, classification, and a recycle loop, enhancing robustness and performance. Extensive simulations and experimental data validate the framework, demonstrating significant improvements in efficiency and quality. The framework is further generalized to optimize crystallizer networks for controlling critical quality attributes such as mean size, yield, and CSD by evaluating various network configurations to identify optimal operating parameters.</p><p dir="ltr">The final part of this work concentrates on using the framework to improve continuous production of a commercial API, Atorvastatin calcium (ASC), aiming for higher yield and lower costs. This approach establishes an attainable region to increase crystal sizes and productivity. Due to ASC’s nucleation-dominated nature, the multi-stage system could not grow the crystals sufficiently to bypass granulation, the bottleneck process in ASC manufacturing. Therefore, spherical agglomeration was proposed as an intensification process within an integrated two-stage crystallization spherical agglomeration system to control the size and morphology of ASC crystals and improve downstream processing and tableting. This method proved highly successful, leading to the development of an end-to-end continuous manufacturing process integrating reaction, crystallization, spherical agglomeration, filtration, and drying. This modular system effectively addressed challenges in integrating various unit operations into a coherent continuous process with high production rates.</p>