The molecule 2,2 '-bifuran-5,5 '-dicarboxylic acid (BFDCA) has attracted significant attention due to its potential as a building block to form biorenewable polymers. However, there is no information available on the solubility of BFDCA in pure solvents, hindering the effective design for reaction, separation, and crystallization processes. The present work evaluated the solubility of BFDCA in eight different solvents at temperatures from 298.15 K to 363.15 K. On a mole fraction basis, the BFDCA solubility from high to low at 298.15 K followed the order: DMAc > DMSO > MeOH > 1-pentanol > 2-propanol > acetic acid > acetonitrile > H2O. Additional solubility tests were performed in cyclohexane, acetone, and dichloromethane, but the equilibrium solute concentration was below the detection limit of the instrument in the range of temperatures studied. The sigma (sigma) profile of BFDCA shows that this molecule is strongly polar and can behave as a donor and acceptor of hydrogen bonds. A higher screening charge density was found in the hydrogen bonding (HB) donor regions in BFDCA, which would favor the interaction with the solvents' HB acceptor regions. Correlations between BFDCA solubility and solvent property parameters show that solvents' HB acceptor propensity and total basicity play an important role in the solute solubility, which agrees with the findings from the sigma profile of BFDCA and solvents and linear energy solvation relationships (LSER). However, the dipole moment, dielectric constant, cohesive energy density, and surface tension also influence the BFDCA solubility since the evaluated molecular descriptors are insufficient to explain the solubility behavior in some cases. The Apelblat equation and the NRTL activity coefficient model aptly described the experimentally observed solubility with model fitting, whereas the COnductor-like Screening MOdel for Real Solvents (COSMO-RS) method predicted qualitative trends.

Globally, less than 0.5% of postconsumer textile waste is recycled, with the majority incinerated or ending up in landfills. Most postconsumer textiles are mixed fibers, complicating mechanical recycling due to material blends and contaminants. Here, we demonstrate the chemical conversion of postconsumer mixed textile waste using microwave-assisted glycolysis over a ZnO catalyst followed by solvent dissolution. This approach electrifies the process heat while allowing rapid depolymerization of polyester and spandex to their monomers in 15 minutes. A simple solvent dissolution enables the separation of cotton and nylon. We assess the quality of all components through extensive material characterization, discuss their potential for sustainable recycling, and provide a techno-economic analysis of the economic feasibility of the process.

N-(1,3-Dimethylbutyl)-N '-phenyl-p-phenylenediamine (6PPD) is a ubiquitous rubber antioxidant and antiozonant that extends the lifetime of common rubber products, such as those found in tires. It transforms into a quinone derivative following certain environmental conditions. 6PPD and the quinone can leach into the environment and cause severe morbidity to aquatic life at diminutive concentrations, with health effects on humans still not fully understood. With legislation on the horizon to ban 6PPD entirely, developing effective methods for its removal and conversion to safe compounds is essential. Here we show that 6PPD survives microwave-assisted pyrolysis and escapes in the oil product, rendering decontamination essential. We introduce a decontamination strategy that removes 6PPD from end-of-life tires before it enters the broader ecosystem. We demonstrate the catalytic upgrade of 6PPD to safe chemicals and the valorization of crumb rubber to aromatics and carbon black using microwave-assisted pyrolysis.

Residence time distribution (RTD) method has been widely used in the pharmaceutical manufacturing for understanding powder dynamics within unit operations and continuous integrated manufacturing lines. The dynamics thus captured is then used to develop predictive models for unit operations and important RTD-based applications ensuring product quality assurance. Despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Savitsky Golay filtering turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications.

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors (Sampat et al., 2022), this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. The results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

Residence time distributions (RTDs) are quickly becoming an important component of the quality control strategy for continuous manufacturing (CM) of pharmaceutical products. However, methodologies for determining RTDs, including appropriate selection of tracers and proper processing of RTD data, have not been fully developed, and there is potential to improve current methods. This work aims to implement protocols for tracer selection and establish a methodology for the determination and quantitative comparison of RTD curves obtained by the pulse method. We used an extensive material property data library and multivariate analyses to select appropriate tracers for active pharmaceutical ingredient (API) of interest. Tracer performance was evaluated in two continuous blenders using pulse input experiments. The equivalence of RTDs obtained from the API and the tracer was examined for formulations with widely different blend flow properties. For the most suitable tracer candidate, material properties were determined for mixtures with different API concentrations to ensure that blend properties containing varying amounts of tracer along the RTD profile remained similar to those of the target formulation. It was observed that even small changes in the physical properties of the target formulation would result in a different tracer RTD. This observation emphasized the importance of preserving the physical properties of the blend when characterizing the dynamical behavior of a unit operation. Further, the RTD of the tracer and the API in the blender were characterized under three different base flow properties. The outcome of these experiments enabled a fair side-by-side comparison of RTDs obtained using pulses of either API or tracer for powders with a varying range of properties. In finding an appropriate tracer, RTD profiles were compared quantitively using different methods. Multivariate analysis of variance (MANOVA) was shown to be a suitable tool for RTD profile comparisons.

Residence time distribution (RTD) has been widely applied across various fields of chemical engineering, including pharmaceutical manufacturing, for applications such as material traceability, quality assurance, system health monitoring, and fault detection. Determination of a representative RTD, in principle, requires an accurate process analytical technology (PAT) procedure capturing the entire range of tracer concentrations from zero to maximum. Such a wide concentration range creates at least two problems: i) decreased accuracy of the model across the entire range of concentrations, relating to limit of quantification, and ii) ambiguity associated with the detection of the tracer for low concentration levels, relating to limit of detection (LOD). These problems affect not only the RTD profile itself, but also RTD-based applications, which can potentially lead to erroneous conclusions. This article seeks to minimize the impact of these problems by understanding the relative importance of different features of RTD on the detection of out-of-specification (OOS) products. In this work, the RTD obtained experimentally was truncated at different levels, to investigate the impact of the truncation of RTD on funnel plots for OOS detection. The main finding is that the tail of the RTD can be truncated with no loss of accuracy in the determination of exclusion intervals. This enables the manufacturing scientist to focus entirely on the peak region, maximizing the accuracy of chemometric models.

Microwaves (MWs) are a powerful technology for electrifying chemical processing and can enable selective heating, which is impossible in conventional systems. This effect was recently ascribed to enhancing liquid-liquid reactive extractions in biomass derivative chemistry. Expanding upon prior research that showcased MWinduced temperature gradients and their optimization, this study delves into the impact of these gradients on extraction using a combination of experiments and simulations. The findings illustrate that the colder organic phase enforced by the MW heating leads to alterations in solute partitioning and phase behavior. Partitioning can be further tuned by adding salts or by carefully selecting solvents, as demonstrated via COSMO-RS calculations. The colder organic phase also leads to Rayleigh instabilities and active buoyancy-driven mixing. These effects, measured experimentally and simulated in COMSOL, can greatly enhance mass transfer rates in liquid-liquid systems. Under conditions reported in the literature, the MW enhancement to reactive extractions is minimal. Conditions that may lead to more impressive extractions and reactive extractions are suggested. Additionally, it's observed that temperature gradients retard byproduct formation.

AbstractThe global pharmaceutical industry is a trillion‐dollar market. However, the pharmaceutical sector often lags in manufacturing innovation and automation which limits its potential to maximize energy efficiency. The integration of techno‐economic analysis (TEA) with advanced process models as part of an overarching smart manufacturing platform, can help industries create business models, which can be adapted for manufacturing to reduce energy consumption and operating costs while ensuring product quality which can further enable a more sustainable process operation. In this study, a rational design of experiment on three unit‐operations (wet granulation, drying, and milling) was performed on a batch (case 1) and continuous (case 2) pharmaceutical process to obtain experimental data. Process models for predicting product quality and energy efficiency of each of the three‐unit operations were developed. The experimental data were used to validate the models and good agreement was observed. The energy consumption of each unit operation was calculated using statistical models relating the power consumption and the process parameters. The developed process models and energy models were further integrated into a TEA framework, which quantified the energy and monetary cost of manufacturing for both batch and continuous manufacturing cases. With this integrated framework, energy costs savings of ~33% was obtained in the continuous manufacturing process (case 2) over the batch process (case 1).

Residence time distribution (RTD) is a probability density function that describes the time materials spend inside a system. It is a promising tool for mixing behavior characterization, material traceability, and real-time quality control in pharmaceutical manufacturing. However, RTD measurements are accompanied with some degree of uncertainties because of process fluctuation and variation, measurement error, and experimental variation among different replicates. Due to the strict quality control requirements of drug manufacturing, it is essential to consider RTD uncertainty and characterize its effects on RTD-based predictions and applications. Towards this end, two approaches were developed in this work, namely model-based and data-based approaches. The model-based approach characterizes the RTD uncertainty via RTD model parameters and uses Monte Carlo sampling to propagate and analyze the effects on downstream processes. To avoid bias and possible reduction of uncertainty during model fitting, the data-based approach characterizes RTD uncertainty using the raw experimental data and utilizes interval arithmetic for uncertainty propagation. A constrained optimization approach was also proposed to overcome the drawback of interval arithmetic in the data-based approach. Results depict probability intervals around the upstream disturbance tracking profile and the funnel plot, facilitating better decision-making for quality control under uncertainty.

Detailed process understanding of powder systems is important for ensuring regulation compliant manufacturing of pharmaceutical solid-based drug products. However, this can get challenging due to the current limitations in experimental and computational approaches, constraining the available knowledge of critical quality attributes. To address this drawback, we propose the use of computationally efficient and mechanistically informed surrogate models developed using multi-zonal compartmentalization methodology. In this work, the proposed modelling strategy is demonstrated for continuous powder blenders, wherein, first principle-based discrete element simulation is used to obtain an inter-connected network of zones for detailed evaluation of mixing within the system. The obtained surrogate models are further validated using the original simulation, illustrating the accuracy of the proposed methodology.

While continuous manufacturing (CM) of pharmaceutical solid-based drug products has been shown to be advantageous for improving the product quality and process efficiency in alignment with FDA's support of the quality-by-design paradigm (Lee, 2015; Ierapetritou et al., 2016; Plumb, 2005; Schaber, 2011), it is critical to enable full utilization of CM technology for robust production and commercialization (Schaber, 2011; Byrn, 2015). To do so, an important prerequisite is to obtain a detailed understanding of overall process characteristics to develop cost-effective and accurate predictive models for unit operations and process flowsheets. These models are utilized to predict product quality and maintain desired manufacturing efficiency (Ierapetritou et al., 2016). Residence time distribution (RTD) has been a widely used tool to characterize the extent of mixing in pharmaceutical unit operations (Vanhoorne, 2020; Rogers and Ierapetritou, 2015; Teżyk et al., 2015) and manufacturing lines and develop computationally cheap predictive models. These models developed using RTD have been demonstrated to be crucial for various flowsheet applications (Kruisz, 2017; Martinetz, 2018; Tian, 2021). Though extensively used in the literature (Gao et al., 2012), the implementation, execution, evaluation, and assessment of RTD studies has not been standardized by regulatory agencies and can thus lead to ambiguity regarding their accurate implementation. To address this issue and subsequently prevent unforeseen errors in RTD implementation, the presented article aims to aid in developing standardized guidelines through a detailed review and critical discussion of RTD studies in the pharmaceutical manufacturing literature. The review article is divided into two main sections - 1) determination of RTD including different steps for RTD evaluation including experimental approach, data acquisition and pre-treatment, RTD modeling, and RTD metrics and, 2) applications of RTD for solid dose manufacturing. Critical considerations, pertaining to the limitations of RTDs for solid dose manufacturing, are also examined along with a perspective discussion of future avenues of improvement.

To modernize drug manufacturing, the pharmaceutical industry has been moving towards implementing emerging technologies to enhance manufacturing robustness and process reliability for production of regulation compliant drug products. Although different science and risk based technologies, like Quality-by-Design, have been used to illustrate their potential, there still exist some underlying obstacles. Specifically, for the production of oral solid drug products, an in-depth process understanding, and predictive modeling of powder mixing in continuous powder blenders is one such major obstacle and originates from the current limitations of the experimental and modeling approaches. Though first principle based discrete element modeling (DEM) approach can address the above issues, it can get very computationally intensive which limits its applications for predictive modeling. In the proposed work, we aim to address this limitation using a multi-zonal compartment modeling approach, which is constructed from DEM. The approach provides a computationally efficient and mechanistically informed hybrid model. The application of the proposed approach is first demonstrated for a periodic section of the blender, followed by its extension for the entire continuous powder blender and the obtained model predictions are validated. The proposed approach provides an overall assessment of powder mixing along axial and radial directions, which is an important requirement for the quantification of blend uniformity. Given the low computational cost, the developed model can further be integrated within the predictive flowsheet model of the manufacturing line.

Research emphases on extensive experimental studies and modeling efforts have been on the rise for the development of accurate predictive models of pharmaceutical unit operations and ‘digital-twin’ framework for continuous manufacturing lines. These exhaustive studies have been conducted at different process conditions to acquire comprehensive knowledge of effects of process parameters on the overall process dynamics. However, there still lacks a detailed understanding of material property effects of pharmaceutical powders on process operation. To address this issue, a discrete element modeling (DEM) approach combined with material calibration is applied for simulation of feeder unit to obtain particle-level insight into effects of material properties on feeder performance with focus on particle flow and powder mixing within the feeder unit. Bulk calibration is implemented to accurately represent powder material properties within the DEM framework. Different refill situations are simulated using DEM to observe powder mixing, measured at the outlet. Feeder DEM simulations are further applied to understand correlations of material properties on feeder operation. These studies provide a detailed physical insight and particle-scale information into the powder mechanics during powder feeding operation.

Blend uniformity is a critical quality attribute in continuous pharmaceutical manufacturing. However, its measurement is challenging due to the difficulty and inaccessibility of sampling locations in a continuous blender. Though computational approaches like Discrete Element Method (DEM) can address this, one of the underlying challenges is the appropriate selection of mixing index for measurement of blend uniformity. Given the wide plethora of literature implementing different indices, it becomes cumbersome to identify suitable index for specific blending application. The proposed article aims to address this concern using a detailed review, classification and comparison of existing mixing indices. These indices are used to evaluate mixing in a periodic section of blender using DEM. The effects of process parameters, like blade speed, on mixing are further investigated. This review article, thus, contributes towards devising a systematic strategy for selection of appropriate mixing index based on the powder system and its application.

Accurate process predictability and efficient flowsheet analysis has been the focus of research in pharmaceutical manufacturing. Currently available mechanistic models, being unable to account for all complex powder variabilities, need to be improved for accurate digital twin framework of the manufacturing plant. In this work, we propose a novel methodology of integrated plant data for model enhancement. These enhanced models are applied for flowsheet analyses focusing on design space identification. Surrogate-based feasibility analysis is implemented using artificial neural network with adaptive sampling techniques. This strategy is demonstrated for a direct compaction line capturing realistic scenarios observed in the manufacturing plant.

The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed.