<p>Developed countries have built wealth and prosperity on the strength of their manufacturing sectors, with China’s success story of lifting 800 million people out of extreme poverty in 30 years a sterling and most recent example of how manufacturing-led industrialization can foster economic development. Sub-Saharan Africa, unfortunately, find itself today in a similar situation as China did in 1990, with over 50% of the world’s desperately poor 719 million people living in the region. But unlike China, Sub-Saharan Africa is faced with the additional challenge of overcoming poverty in a world with stricter constraints to global trade and climate change limitations to modern-day industrialization. Compounding the challenges further is the region's limited know-how and human capital — a consequence of years of underdevelopment, creating a classic chicken and egg dilemma where the lack of industrialization perpetuates the dearth of know-how and human capital, and vice versa.</p><p>Considering these challenges, we investigate how chemical manufacturing and what chemical manufacturing approaches can be leveraged to effectively drive industrialization and economic development in Sub-Saharan Africa. We propose chemicals manufacturing using prefabricated modules – which are constructed offsite in places with available human capital and transported to be assembled in places where they are needed – as a flexible and needed approach. However, Economy of Scale, which generally favors large-scale chemical manufacturing, poses as a major constraint to such modularization approach, especially given the presently small serviceable market sizes in Sub-Saharan Africa due to low purchasing power parity. We thus utilize mathematical modeling techniques to determine and establish scenarios for economic viability of the proposed approach, providing modeling frameworks and introducing measures for further studies in the process. We also provide and analyze exemplary flowsheets synthesized for a net-zero carbon emissions chemical manufacturing paradigm in the region.</p><p>This work concludes with a prefeasibility study of a chemical manufacturing project in Nigeria, as part of the author’s quest to build prefabricated modular plants across Africa. <i>Modular plants are attractive as they can be tuned to market demand of a developing market and region that needs them, putting less capital at risk.</i></p><p>This thesis is intended to be a vanguard of potential solutions to the complex challenges to industrialization in Sub-Saharan Africa. It endeavors to pave the way for addressing these issues through chemical manufacturing, offering valuable insights for sustainable progress.</p>

<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>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 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>

Orientador: Rubens Maciel Filho / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química / Made available in DSpace on 2018-08-23T11:34:50Z (GMT). No. of bitstreams: 1 Maia_JulioPereira_D.pdf: 8705466 bytes, checksum: e604d7a471ca19eb99492912d431174b (MD5) Previous issue date: 2013 / Resumo: Esta tese apresenta um estudo de estratégias de esquemas de controle em unidades de destilação a vácuo de refinarias de petróleo, com o uso de dados e informações de uma refinaria brasileira, de modo a se desenvolver uma simulação representativa do processo, onde uma diferença global máxima de 5% entre os resultados de simulação e os dados de saída reais foi obtida. A simulação foi executada com alto nível de detalhamento, com cálculos de queda de pressão, dimensionamento de sistemas de bombeamento e uso de internos de coluna comerciais. Uma análise paramétrica foi executada para a verificação das variáveis mais influentes do processo. A simulação em estado estacionário resultante foi então convertida para o regime dinâmico, onde um esquema de controle equivalente ao esquema de controle da planta real foi implementado. Este esquema de controle foi submetido a um conjunto de perturbações usuais ao processo real, produzindo respostas dinâmicas do processo para cada perturbação aplicada. Pela análise das dinâmicas destas respostas e das respostas do sistema em malha aberta, um esquema de controle alternativo foi proposto e verificado da mesma maneira que o esquema de controle equivalente. Malhas de controle específicas para quantificar a qualidade dos produtos, tendo por base o índice ASTM D86 foram inseridas. A comparação entre os dois esquemas de controle por meio das respostas dinâmicas na qualidade dos produtos, considerando como parâmetro o ISE (Integral Squared Error) das malhas de cada esquema para comparação, apresentou uma redução média do erro em 70% na qualidade dos produtos principais / Abstract: A petroleum vacuum distillation unit study on control scheme strategies is developed in this work. Real plant data and information is gathered from a Brazilian Refinery to develop a representative simulation of the process, which had achieved a maximum 5% overall difference from the plant results. The simulation was set to be highly detailed, including pressure drop calculations, pumping system and the use of commercial column internals (packing and plates) in it. A parametric analysis was carried in order to verify the most influent variables in the process, with respect to temperature profiles, product flows and product qualities. The resultant steady state simulation was then converted into dynamic regime, when a control scheme equivalent to the real plant control scheme was implemented. This control scheme was then subjected to a set of common perturbations that occur in the real process, producing the dynamic response of the process to each perturbation applied. By analyzing the dynamics of these responses and the open loop responses, an alternative control scheme is proposed and verified in the same manner the later one was. A specific control loop was proposed to account a petroleum product quality index, such as ASTM D86 95% recovery. The comparison of the control schemes by means of the dynamic responses considering the correlated ISE (integral squared error) of each scheme has shown an average error reduction of 70% in the main products quality / Doutorado / Desenvolvimento de Processos Químicos / Doutor em Engenharia Química

Quality drives customer satisfaction, improved business performance, and safer products. Reducing waste and variation is critical to the financial success of organizations. Today, it is common to see Lean and Six Sigma used as the two main strategies in improving Quality. As advancements in information technologies enable the use of big data, defect reduction and continuous improvement philosophies will benefit and even prosper. Predictive Quality Analytics (PQA) is a framework where risk assessment and Machine Learning technology can help detect anomalies in the entire ecosystem, and not just in the manufacturing facility. PQA serves as an early warning system that directs resources to where help and mitigation actions are most needed. In a world where limited resources are the norm, focused actions on the significant few defect drivers can be the difference between success and failure

<p>To apply Industry 4.0 technologies and accelerate the modernization of continuous pharmaceutical manufacturing, digital twin (DT) and advanced process control (APC) strategies are indispensable. The DT serves as a virtual representation that mirrors the behavior of the physical process system, enabling real-time monitoring and predictive capabilities. Consequently, this facilitates the feasibility of real-time release testing (RTRT) and enhances drug product development and manufacturing efficiency by reducing the need for extensive sampling and testing. Moreover, APC strategies are required to address variations in raw material properties and process uncertainties while ensuring that desired critical quality attributes (CQAs) of in-process materials and final products are maintained. When deviations from quality targets are detected, APC must provide optimal real-time corrective actions, offering better control performance than the traditional open loop-control method. The progress in DT and APC is beneficial in shifting from the paradigm of Quality-by-Test (QbT) to that of Quality-by-Design (QbD) and Quality-by-Control (QbC), which emphasize the importance of process knowledge and real-time information to ensure product quality.</p> <p><br></p> <p>This study focuses on four key elements and their applications in a continuous dry granulation tableting process, including feeding, blending, roll compaction, ribbon milling and tableting unit operations. Firstly, the necessity of a digital infrastructure for data collection and integration is emphasized. An ISA-95-based hierarchical automation framework is implemented for continuous pharmaceutical manufacturing, with each level serving specific purposes related to production, sensing, process control, manufacturing operations, and business planning. Secondly, investigation of process analytical technology (PAT) tools for real-time measurements is highlighted as a prerequisite for effective real-time process management. For instance, the measurement of mass flow rate, a critical process parameter (CPP) in continuous manufacturing, was previously limited to loss-in-weight (LIW) feeders. To overcome this limitation, a novel capacitance-based mass flow sensor, the ECVT sensor, has been integrated into the continuous direct compaction process to capture real-time powder flow rates downstream of the LIW feeders. Additionally, the use of near-infrared (NIR)-based sensor for real-time measurement of ribbon solid fraction in dry granulation processes is explored. Proper spectra selection and pre-processing techniques are employed to transform the spectra into useful real-time information. Thirdly, the development of quantitative models that establish a link between CPPs and CQAs is addressed, enabling effective product design and process control. Mechanistic models and hybrid models are employed to describe the continuous direct compaction (DC) and dry granulation (DG) processes. Finally, applying APC strategies becomes feasible with the aid of real-time measurements and model predictions. Real-time optimization techniques are used to combine measurements and model predictions to infer unmeasured states or mitigate the impact of measurement noise. In this work, the moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework is utilized. It leverages the capabilities of MHE for parameter updates and state estimation to enable adaptive models using data from the past time window. Simultaneously, NMPC ensures satisfactory setpoint tracking and disturbance rejection by minimizing the error between the model predictions and setpoint in the future time window. The MHE-NMPC framework has been implemented in the tableting process and demonstrated satisfactory control performance even when plant model mismatch exists. In addition, the application of MHE enables the sensor fusion framework, where at-line measurements and online measurements can be integrated if the past time window length is sufficient. The sensor fusion framework proves to be beneficial in extending the at-line measurement application from just validation to real-time decision-making.</p>

<p>  </p> <p>In the pharmaceutical industry, special care must be taken by companies to guarantee high quality medications that are free from byproducts and impurities. The development process involves various considerations including solvent selection, solubility screening, unit operation selection, environmental, and health impact evaluations. Traditionally, pharmaceutical manufacturing consisted of large, centralized facilities to meet pharmaceutical demands; however, there has been a recent shift toward distributed manufacturing. With distributed manufacturing platforms, rapidly changing supply chain needs can be met regionally in addition to supplying small-volume medications and personalized medicines to hospitals and pharmacies. To produce quality pharmaceuticals, distributed manufacturing platforms should integrate digital design, novel unit operations, and process analytical technology (PAT) tools for quality monitoring and control. In this dissertation, a process design and development framework is proposed and implemented for a small-scale pharmaceutical manufacturing platform: MiniPharm.</p> <p>Various approaches to process design are detailed in this dissertation, which include heuristic-based and digital or simulation-based design. For heuristic-based design, the knowledge of the researchers was utilized to provide unit operation evaluation and screening of process alternatives. In cases when unit operations were highly complex, digital or simulation-based design was utilized to conduct sensitivity analyses and simulation-based design of experiments. With the implementation of simulation-based design, material and time needs were reduced while gaining knowledge about the system. The integration of various unit operations comes with increased understanding of start-up dynamics and operational constraints. What was found to be the most successful approach was the combination of heuristics and digital design to combine researcher knowledge and experience with the information gained from process modeling and simulation to create process alternatives that utilized system dynamics to reach desired process outcomes. </p> <p>Additionally, MiniPharm was used for process model development at the small-scale. Various software packages have been made commercially available that focus on production scale; however, models for small-scale operations are not typically implemented in these packages. Models for unit operations were fit with collected experimental data to estimate model parameters for small-scale synthesis, liquid-liquid extraction, and crystallization unit operations. The models were implemented to better capture the heat and mass transfer of the milli-fluidic scale platform, which consist of unit operations housed within microchannels. MATLAB was utilized for estimation of parameters such as kinetic rate constants and overall mass transfer coefficients. These parameters were used for design space determination and process disturbance simulation. The exploration of the impact of various process parameters on quality attributes helps researchers gain a deeper understanding about the manufacturing process and helps to demonstrate how to control the process. </p> <p>An important aspect of MiniPharm is the process development progress that has been demonstrated. With the construction of a modular and reconfigurable platform, various process alternatives can now be experimentally validated. The integration of unit operations operated at a decreased scale makes MiniPharm an example of process intensification. The implementation of integrated unit operations decreases handling time of intermediates and reduces the overall footprint for manufacturing. Designed to allow for increased flexibility of operation, perfluoroalkoxy alkane (PFA) tubing was used for synthesis and purification. With PFA tubing clean in place procedures can be implemented using continuous solvent flow or the low cost, PFA tubing can be replaced. The modular nature of the platform also allows for the investigation of individual unit operations for performance evaluation. </p> <p>Finally, a novel continuous solvent switch distillation unit operation was designed and constructed along with customized reactor and crystallizers for process alternative screening for the synthesis and purification of two compounds: Diphenhydramine hydrochloride and Lomustine. Diphenhydramine hydrochloride is a low-value, high volume allergy medication commonly found in Benadryl and Lomustine is a high-value, low volume cancer medication used to treat glioblastoma and Hodgkin Lymphoma. The production of the compounds demonstrated the flexibility of the manufacturing platform to produce both a generic and a specialty medication. A versatile platform is needed for distributed manufacturing because of quickly changing supply chain needs. Overall, this dissertation successfully demonstrates the process design, development, and simulation for small-scale manufacturing.</p>

<p dir="ltr">Condition-based maintenance is a proactive maintenance strategy that prevents failures or diminished functionality in process systems through proper monitoring and management of process conditions. Despite being considered a mature maintenance management strategy in various industries, condition-based maintenance remains underutilized in pharmaceutical manufacturing. This situation needs to change, especially as the pharmaceutical industry continues to shift from batch to continuous manufacturing, where the implementation of CBM as a maintenance strategy assumes a greater importance.</p><p dir="ltr">This dissertation focused on addressing the challenges of implementing CBM in a continuous drug product manufacturing system. These challenges stem from the unique aspects of pharmaceutical drug product manufacturing, which includes the peculiar behavior of particulate materials and the evolutionary nature of pharmaceutical process development. The proposed solutions to address these challenges revolve around an innovative framework for the practical development of condition monitoring systems. Overall, this framework enables the incorporation of limited process knowledge in creating condition monitoring systems, which has the desired effect of empowering data-driven machine learning models.</p><p dir="ltr">A key feature of this framework is a formalized method to represent the process condition, which is usually vaguely defined in literature. This representation allows the proper mapping of preexisting condition monitoring systems, and the segmentation of the entire process condition model into smaller modules that have more manageable condition monitoring problems. Because this representation methodology is based on probabilistic graphical modelling, the smaller modules can then be holistically integrated via their probabilistic relationships, allowing the robust operation of the resulting condition monitoring system and the process it monitors.</p><p dir="ltr">Breaking down the process condition model into smaller segments is crucial for introducing novel fault detection capabilities, which enhances model prediction transparency and ensures prediction acceptance by a human operator. In this work, a methodology based on prediction probabilities was introduced for developing condition monitoring systems with novel fault detection capabilities. This approach relies on high-performing machine learning models capable of consistently classifying all the initially known conditions in the fault library with a high degree of certainty. Simplifying the condition monitoring problem through modularization facilitates this, as machine learning models tend to perform better on simpler systems. Performance indices were proposed to evaluate the novel fault detection capabilities of machine learning models, and a formal approach to managing novel faults was introduced.</p><p dir="ltr">Another benefit of modularization is the identification of condition monitoring blind spots. Applying it to the RC led to sensor development projects such as the virtual sensor for measuring granule flowability. This sensor concept was demonstrated successfully by using a data-driven model to predict granule flowability based on size and shape distribution measurements. With proper model selection and feature extraction guided by domain expertise, the resulting sensor achieved the best prediction performance reported in literature for granule flowability.</p><p dir="ltr">As a demonstration exercise in examining newly discovered faults, this work investigated a roll compaction phenomenon that is usually concealed from observation due to equipment design. This phenomenon results in the ribbon splitting along its thickness as it comes out of the rolls. In this work, important aspects of ribbon splitting were elucidated, particularly its predictability based on RC parameters and the composition of the powder blend used to form the ribbon. These findings have positive ramifications for the condition monitoring of the RC, as correspondence with industrial practitioners suggests that a split ribbon is desirable in some cases, despite being generally regarded as undesirable in the limited literature available on the subject.</p><p dir="ltr">Finally, this framework was primarily developed for the pharmaceutical dry granulation line, which consists of particle-based systems with a moderate level of complexity. However, it was also demonstrated to be feasible for the Tennessee Eastman Process (TEP), a more complex liquid-gas process system with a greater number of process faults, variables, and unit operations. Applying the framework resulted in machine learning models that yielded one of the best fault detection performances reported in literature for the TEP, while also introducing additional capabilities not yet normally reported in literature, such as fault diagnosis and novel fault detection.</p>

<div>The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in therms of energy consumption. Therefore, it becomes one of the primary target when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. </div><div><br></div><div>System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.</div><div><br></div>