A review of the Residence Time Distribution (RTD) applications in solid unit operations

Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing

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.

Distribution of residence time in rotary-drum composting and implications for hygienization

Rotary drums enable rapid composting compared to static systems. Residence times (RT) of 3-5 days are commonly applied to fulfill sanitary requirements and ensure the initial stabilization of organic matter. Practically, RT distribution (RTD) implies that a portion of the feed is discharged earlier than the mean RT, which may not guarantee safe application of the end product. This study assessed RTD and other physical-chemical and biological parameters of cattle manure and green waste composted in an EcodrumTM rotary drum (∼10 m3). Two types of tracers were used: pieces of plastic tubing and lumps of raw material in which plant seeds were buried, which were packed in nylon socks. A transient-state during which less than 50 % of the drum volume was occupied was distinguished from a steady-state stage, during which the drum operated with its optimal loading of about two-thirds of its volume. Starting temperatures inside the drum were close to ambient when the drum was mostly empty and then increased up to 60-65 °C as the occupied volume approached 50 %. The two types of tracers seemed to provide complementary measurements; under steady-state conditions, actual RTs were 60 % of the mean RT for 10 % of the feed material. The viability of plant seeds which were included in tracers was somewhat dependent on the specific RT. Under transient-state conditions, even shorter RTs (relative to the mean RT) are expected, coupled with non-thermophilic conditions, reducing the likelihood of adequate destruction of pathogens.

Performance evaluation of a flighted rotary dryer for lateritic ore in concurrent configuration

The lateritic ore drying in the Cuban nickel producing industry is realized within flighted rotary dryers. In this investigation, performance indicators in regards to transfer of momentum, heat and mass were evaluated. The dryers operate in a concurrent configuration with combustion gas, at a productivity between 40 t h-1 and 50 t h-1. The distribution function of the residence time (RTD) was best fitted to a model of a multi-branch tanks-in-series system, theoretical residence time was 51 ± 2 min and experimental mean residence time 61 min, at a rate of 45 t h-1 and hydraulic efficiency 1.23, due to the presence of dead-zoon. Mass and energy balance was made following a "black box" model, as results, the specific fuel consumption was 27.25 ± 0.25 kg fuel t-1 of wet ore, specific energy consumption 79.66 ± 0.95 kg fuel t-1 of H2O evaporated, energy efficiency 97.28 ± 0.01 %, thermal efficiency 66.88 ± 0.71 % and drying efficiency 98.77 ± 0.12 %. Mathematical modelling was made using a system of differential equations, the rate of drying in falling rate period was estimated by Arrhenius equation, then, temperature profile and ore moisture content along the dryer was simulated. The model provided a successful predictive performance; for an inlet gas temperature between 850 °C and 900 °C, the ore moisture was reduced form 33.0 % (wet basis) to a range depending on the dryer productivity, from 3.0 % to 7.1 %. Designing a computerized system that implements these algorithms can benefit on efficiency and productivity of the production plant.

Visualization of the granule temperature using thermal imaging to improve understanding of the granulation mechanism in continuous twin-screw melt granulation

The aim of this study is to increase process understanding of the granulation mechanism in twin-screw melt granulation by evaluating the influence of different screw configurations on granule formation and granule temperature via thermal imaging. The study used a Design of Experiments (DoE) to process a miscible and immiscible formulation (85% API/binder w/w) using a twin-screw extruder with varying screw configurations. The barrel temperature (°C), screw speed (rpm), throughput (kg/h), and kneading zone (direction and stagger angle) were varied. Granule and process properties were evaluated for samples collected at four different locations along the length of the granulation barrel to visualize the granule formation, and granule temperature was monitored by an infrared camera to measure heat transfer on the granules. The resulting temperature was linked to the granule properties and the granule formation along the length of the barrel. The most influencing factors on the granule temperature are the direction of the kneading zone and the set barrel temperature. It was observed that granule formation mainly occurred in the zones that apply more kneading on the granules. The highest temperature increase was observed when the smallest stagger angle in reverse configuration was used, and could be linked to better granule quality attributes.

Predicting sample injection profiles in liquid chromatography: A modelling approach based on residence time distributions

The pharmaceutical and bio-pharmaceutical industries rely on simulations of liquid chromatographic processes for method development and to reduce experimental cost. The use of incorrect injection profiles as inlet boundary condition for these simulations may, however, lead to inaccurate results. This study presents a novel modelling approach for accurate prediction of injection profiles for liquid chromatographic columns. The model uses the residence time distribution theory and accounts for the residence time of the sample through the injection loop, connecting tubes and heat exchangers that exist upstream of the actual chromatographic column, between the injection point and the column inlet. To validate the model, we compare simulation results with experimental injection profiles taken from the literature for 20 operating conditions. The average errors in the predictions of the mean and variance of the injection profiles result to be 8.98% and 8.52%, respectively. The model, which is based on fundamental equations and actual hardware details, accurately predicts the injection profile for a range of sample volumes and sample loop-filling levels without the need of calibration. The proposed modelling approach can help to improve the quality of in-silico simulation and optimization for analytical chromatography.

Flowsheet modelling of a powder continuous feeder-mixer system

In this study, an integrated flowsheet model of the continuous feeder-mixer system was calibrated, simulated and compared against experimental data. The feeding process was first investigated using two major components (ibuprofen and microcrystalline cellulose (MCC)), in a formulation comprised of: 30 wt% of ibuprofen, 67.5 wt% MCC, 2 wt% of sodium starch glycolate and 0.5 wt% of magnesium stearate. The impact of a refill on feeder performance was experimentally evaluated for different operating conditions. Results showed that it had no influence on feeder performance. While simulations with the feeder model fairly reproduced the material behaviour observed in the feeder, unintended disturbances were underpredicted due to the model's low complexity. Experimentally, mixer's efficiency was assessed based on ibuprofen residence time distribution. Mean residence time pointed to a higher mixer's efficiency at lower flow rates. Blend homogeneity results showed that for the entire set of experiments, ibuprofen RSD <5%, irrespective of process variables. A feeder-mixer flowsheet model was calibrated, after regressing the axial model coefficients. The regression curves exhibited a R² above 0.96, whereas the RMSE varied from 1.58x10^-4 to 1.06x10^-3 s^-1 across all fitted curves. Simulations confirmed that flowsheet model captured the powder dynamics inside the mixer and qualitatively predicted the mixer's filtering ability against feeding composition fluctuations, as well as ibuprofen RSD in blend, in line with real experiments.

Optimal quantification of residence time distribution profiles from a quality assurance perspective

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.

Elucidation of the powder flow pattern in a twin-screw LIW-feeder for various refill regimes

The importance of residence time distribution modeling is acknowledged as a tool for enabling material tracking and control within a continuous manufacturing line in order to safeguard both product quality and production efficiency. One of the first unit-operations into a continuous direct compression line (i.e. CDC-line) worthwhile doing extensive RTD-analysis upon are the LIW-feeders since they dose the ingredients in a controlled way following the label claim and hence can directly influence critical quality attributes like content uniformity. An NIR measurement method was developed determining the RTD of selected powders at specific feeder settings. Step-change experiments using sodium saccharin as a tracer were conducted. In order to gain and in depth understanding of the material flow, spatial samples throughout the hopper were taken at predefined timepoints during the step change experiments. This revealed the presence of a bypass trajectory along the edges of the agitator, while in the center of the agitator an inner mixing volume in which the tracer concentration lags behind seemed to be present. Finally, a model based on a plug flow and continuous stirred tank reactor was evaluated. The fitted model was not able to capture this complex flow behavior and shows the need for an extended compartmental model distinguishing between a bypass trajectory formed by the agitator and an inner mixing volume.

An intelligent dynamic setting control framework for a multimode impurity removal process

The main task of the impurity removal process is to control the oxidation reduction potential (ORP) within the range of the optimized set value. The impurity removal process is essentially an oxidation-reduction process. Oxidation reduction potential (ORP) is an external reflection of reaction state inside the impurity removal reactor. However, actual industry is time-varying, nonlinear and multimode. It is difficult to determine the appropriate dosage of impurity remover in practice. This will lead to large fluctuations in the operation mode, affecting the safety and stability of the process and the final product quality. To solve these problems, an intelligent dynamic setting control framework (IDSCF) for the multimode impurity removal process is proposed in this paper. It includes a preset module of the dosage of impurity remover based on impurity remover utilization (IRU) estimation, an operation mode detection module based on autoencoder, a normal mode adjustment module based on fuzzy logic, and an unsteady mode adjustment module based on case-based reasoning (CBR). The framework can determine the reasonable preset dosage of impurity remover and adjust the dosage according to the current operation mode of the impurity removal process. Because the operation mode is related to the residual dosage of impurity remover added over a period of time, that is, the accumulative effect of the large-scale metallurgical reactor. When calculating the preset dosage of impurity remover, the reactant accumulation ratio (RAR) is calculated, which makes the calculation of the preset value more reasonable. In addition, it can detect the unsteady modes causing large fluctuations in the process and adjust them in time. Experiments are carried out in accordance with the data of an actual cobalt removal process. The results show that this method can effectively improve the stability of the impurity removal process, control the ORP within the set range and cope with complex mode changes.

Soft sensor for content prediction in an integrated continuous pharmaceutical formulation line based on the residence time distribution of unit operations

In this study, a concentration predicting soft sensor was achieved based on the Residence Time Distribution (RTD) of an integrated, three-step pharmaceutical formulation line. The RTD was investigated with color-based tracer experiments using image analysis. Twin-screw wet granulation (TSWG) was directly coupled with a horizontal fluid bed dryer and an oscillating mill. Based on integrated measurement, we proved that it is also possible to couple the unit operations in silico. Three surrogate tracers were produced with a coloring agent to investigate the separated unit operations and the solid and liquid inputs of the TSWG. The soft sensor's prediction was compared to validating experiments of a 0.05 mg/g (15% of the nominal) concentration change with High-Performance Liquid Chromatography (HPLC) reference measurements of the active ingredient proving the adequacy of the soft sensor (RMSE < 4%).

Residence Time Distribution of Non-Spherical Particles in a Continuous Rotary Drum

The motion of non-spherical particles with sharp edges, as they are commonly involved in practice, was characterized by residence time distribution (RTD) measurement in a continuous drum. Particles with two sizes, 6 and 10 mm, and two densities, 750 and 2085 kg/m3, were used in the experiments. The effects of rotation speed (3–11 rpm), incline angle (2–4°), feed rate, and mixture composition were investigated and compared to the results of other researchers on particles without sharp edges. We also fitted the RTD with an axial dispersion model to obtain a better insight into the flow behavior. MRT of non-spherical particles with sharp edges depends on ω−α similar to other shapes, while the value of alpha is higher for particles with sharp edges (0.9 < α < 1.24), especially at high incline angles. The MRT depends on incline angle, β−b, where b varies between 0.81 (at low ω) and 1.34 (at high ω), while it is close to 1 for other shapes. Feed rate has a slight effect on the MRT of particles with sharp edges and the effect of particle size diminishes when rotation speed increases. The MRT linearly increases with volume fraction of light particles in a mixture of light and heavy particles (from pure heavy to pure light particles).

Continuous air purification by front flow photocatalytic reactor: Modelling of the influence of mass transfer step under simulated real conditions

In this work, a solution for the treatment of toxic gases based on a photocatalytic process using TiO₂ coated on a cellulosic support, has been investigated. Here, cyclohexane was chosen as the reference for testing its removal efficiency via a continuous front flow reactor as type A anti-gas filters. The photocatalytic support was firstly characterized by EDX, to confirm its elemental composition. Then, the experiments were carried out, starting with a batch reactor in order to evaluate the degradation efficiency of the photocatalytic media, as well as the monitoring of the photocatalytic process which allowed the establishing of a carbon mass balance corresponding to the stoichiometric number of our target pollutant. The transition to a continuous treatment with a front flow reactor aims to highlight the influence of the input concentration (0.29-1.78 mM m^-3) under different flow rates (12, 18 and 36 L min^-1). The relative humidity effect was also investigated (from 5 to 90% of humidity) where an optimum rate was obtained around 35-45%. In addition, the mineralization rate was monitored. The major rates obtained were for a cyclohexane input concentration of 0.29 mM m^-3 in wet condition (38%) at an air flow rate of 18 L min^-1, where the CO₂ selectivity reached 77% for an abatement of 62%. In order to understand the limiting steps of the photocatalytic process, a model considering the reactor geometry and the hydraulic flow was developed. The obtained results showed that the mass transfer must be considered in the photocatalytic process for a continuous treatment. The Langmuir-Hinshelwood bimolecular model was also developed to represent the influence of the humidity.

Scaling-down biopharmaceutical production processes via a single multi-compartment bioreactor (SMCB)

Biopharmaceutical production processes often use mammalian cells in bioreactors larger than 10,000 L, where gradients of shear stress, substrate, dissolved oxygen and carbon dioxide, and pH are likely to occur. As former tissue cells, producer cell lines such as Chinese hamster ovary (CHO) cells sensitively respond to these mixing heterogeneities, resulting in related scenarios being mimicked in scale-down reactors. However, commonly applied multi-compartment approaches comprising multiple reactors impose a biasing shear stress caused by pumping. The latter can be prevented using the single multi-compartment bioreactor (SMCB) presented here. The exchange area provided by a disc mounted between the upper and lower compartments in a stirred bioreactor was found to be an essential design parameter. Mimicking the mixing power input at a large scale on a small scale allowed the installation of similar mixing times in the SMCB. The particularities of the disc geometry may also be considered, finally leading to a converged decision tree. The work flow identifies a sharply contoured operational field comprising disc designs and power input to install the same mixing times on a large scale in the SMCB without the additional shear stress caused by pumping. The design principle holds true for both nongassed and gassed systems.

Development and Use of a Residence Time Distribution (RTD) Model Control Strategy for a Continuous Manufacturing Drug Product Pharmaceutical Process

Residence-time-distribution (RTD)-based models are key to understanding the mixing dynamics of continuous manufacturing systems. Such models can allow for material traceability throughout the process and can provide the ability for removal of non-conforming material from the finished product. These models have been implemented in continuous pharmaceutical manufacturing mainly for monitoring purposes, not as an integral part of the control strategy and in-process specifications. This paper discusses the steps taken to develop an RTD model design space and how the model was statistically incorporated into the product’s control strategy. To develop the model, experiments were conducted at a range of blender impeller speeds and total system mass flow rates. RTD parameters were optimized for each condition tested using a tank-in-series-type model with a delay. Using the experimental RTD parameters, an equation was derived relating the mean residence time to the operating conditions (i.e., blender impeller speed and mass flow rate). The RTD parameters were used in combination with real-time upstream process data to predict downstream API concentration, where these predictions allowed validation across the entire operating range of the process by comparison to measured tablet assay. The standard in-process control limits for the product were statistically tightened using the validation acceptance criteria. Ultimately, this model and strategy were accepted by regulatory authorities.

Challenges in tracing material flow passing a loss-in-weight feeder in continuous manufacturing processes

Loss-in-weight feeders are an integral part of most continuous manufacturing processes, ensuring a constant mass flow. The feeders cause a significant degree of back-mixing in such lines. Understanding back-mixing is essential for the treatment of disturbances. However, feeders refilled semi-continuously contradict the common theory assuming steady-state. This study aims at understanding dynamic back-mixing and related phenomena. Low filling levels of a feeder are investigated using a fluorescent tracer. These investigations prove an impact of the addition of material probably caused by a non-uniform draw-in of the screws and dead material in the hopper. In turn, the dead material accounts for up to 50 % of the material in the hopper. Possible evidence of dead zones at higher filling levels and in feeders from literature are discussed additionally. Steady-state models from literature are extended to represent the observations and back-mixing at all filling levels. This extension reduces the root-mean-squared deviation of the model from the experimental data by 41%. The model predicts different responses to similar disturbances depending on the filling. This state-dependent back-mixing and the observed dead zones are challenging for diverting non-conforming material and material traceability. Therefore, these phenomena should be considered in selecting and operating feeders.

Tracking raw material flow through a continuous direct compression line Part I of II: Residence time distribution modeling and sensitivity analysis enabling increased process yield

Continuous manufacturing (CM) offers advantages in quality and space-time yield compared to common batch manufacturing. However, higher yield losses due to the start-up procedure make a broad application uneconomical. This work discusses the possibility of reducing yield losses by adjusting the degree of back-mixing. Back-mixing of nonconforming material from disturbances or start-up will result in the contamination of subsequent material. Therefore, higher degrees of back-mixing cause the discharge of additional material. Choosing an advantageous setting of operational parameters may be a simple way to change the degree of back-mixing. Based on direct compression, this work demonstrates the identification of promising parameters. Therefore, step-change experiments using color-marked material in the feeder, blender, and tablet press quantify the impact of three operational parameters per device. Models for the devices and the entire process result from those measurements. Subsequently, a global variance-based sensitivity analysis identifies the most influential parameters. As a result, adjusting the minimal filling level of the feeder and the rotational feed frame speed of the tablet press reduces back-mixing by more than 30%. At high costs of the raw materials, the resulting savings can significantly improve the economic performance of CM compared to batch manufacturing.

Using residence time distribution in pharmaceutical solid dose manufacturing - A critical review

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.

A systematic techno-economic approach to decide between continuous and batch operation modes for injectable manufacturing

A comprehensive approach is proposed to systematically determine the optimal mode of operation between continuous and batch injectable manufacturing considering product and market conditions. At the core of this approach are two integrated complete mathematical modules for discrete and continuous injectable manufacturing, which are supplemented with an economic evaluation module that can then be used to explore the impact of all relevant process parameters (e.g., lot-size, number of operators, solubility, product demand, raw material costs). When the developed approach was applied to two case studies, it was found that batch production was preferred at low to moderate solution (raw material) costs. In contrast, at higher solution costs, the preference for batch and continuous production processes changed back and forth as the annual product demand changed. The study also found that continuous production processes became increasingly preferred at medium to large final dosage volumes and a competitive alternative even at moderate solution costs. From a decision-making point of view, batch injectable manufacturing will be preferred over the novel continuous manufacturing technology unless there is a significant economic incentive to overcome the perceived technology risk. The proposed approach is intended as a decision-support tool for pharmaceutical process engineers.

Hybrid Chemomechanical Plastics Recycling: Solvent-free, High-Speed Reactive Extrusion of Low-Density Polyethylene

Low-density polyethylene (LDPE) is ubiquitous in the packaging industry owing to its flexibility, toughness, and low cost. However, it is typically contaminated with other materials, seriously limiting options for mechanical recycling. Interest in chemical recycling techniques such as pyrolysis and hydrothermal liquefaction is growing, but most of these processes face technoeconomic challenges that have limited commercial deployment. This study concerns a hybrid chemomechanical approach using reactive twin-screw extrusion (TSE) for tailoring the molecular weight and chain structure of reclaimed LDPE. Two types of zeolite catalysts at several loading levels are evaluated over a range of processing conditions. Structural, thermal, and rheological properties of the extruded samples are investigated and compared to virgin LDPE and LDPE extruded without the catalyst. NMR spectroscopy is used to investigate changes in the structure of the polymer. LDPE extruded with microporous Y zeolite shows lower degradation temperature and increased short chain branching. Mesoporous MCM-41 also induces increased branching but has no effect on the degradation temperature. The theoretical mechanical energy input for the chemical modification is calculated by using process modeling. The demonstrated hybrid reactive extrusion process provides a potential low-cost, simple approach for repurposing LDPE-based flexible packaging as coatings and adhesives.

Digital twin of low dosage continuous powder blending - Artificial neural networks and residence time distribution models

In this paper we present a thorough description of the digital twin development for a continuous pharmaceutical powder blending process in accordance with the Process Analytical Technologies (PAT) and Quality by Design (QbD) guidelines. A low-dosage system of caffeine active pharmaceutical ingredient (API) and dextrose excipient was examined via continuous blending experiments. Near infrared (NIR) spectroscopy-based process analytics were applied; quantitative evaluation of spectra was achieved using multivariate data analysis. The blending system was represented with mechanistic residence time distribution (RTD) models and two types of recurrent artificial neural networks (ANN), experimental datasets were used for model training or fitting and validation. Detailed comparison of the two modelling approaches, the optimization of the model-based digital twin, and efficiency of the soft sensor-based process monitoring is presented through several validating simulations. Both RTD models and nonlinear autoregressive neural networks demonstrated excellent predictive power for the low dosage blending process. RTD models can prove to be more advantageous in industrial development as they are less resource-intensive to develop and prediction accuracy on low concentration levels lacks dependency from the precision of chemometric calibration. Reduced material costs and limited development timeframe render the digital twin an efficient tool in technological development.

A chemical engineer's take of COVID-19 epidemiology

SARS-CoV-2, a novel coronavirus spreading worldwide, was declared a pandemic by the World Health Organization 3 months after the outbreak. Termed as COVID-19, airborne or surface transmission occurs as droplets/aerosols and seems to be reduced by social distancing and wearing mask. Demographic and geo-temporal factors like population density, temperature, healthcare system efficiency index and lockdown stringency index also influence the COVID-19 epidemiological curve. In the present study, an attempt is made to relate these factors with curve characteristics (mean and variance) using the classical residence time distribution analysis. An analogy is drawn between the continuous stirred tank reactor and infection in a given country. The 435 days dataset for 15 countries, where the first wave of epidemic is almost ending, have been considered in this study. Using method of moments technique, dispersion coefficient has been calculated. Regression analysis has been conducted to relate parameters with the curve characteristics.

Review: Continuous Manufacturing of Small Molecule Solid Oral Dosage Forms

Continuous manufacturing (CM) is defined as a process in which the input material(s) are continuously fed into and transformed, and the processed output materials are continuously removed from the system. CM can be considered as matching the FDA's so-called 'Desired State' of pharmaceutical manufacturing in the twenty-first century as discussed in their 2004 publication on 'Innovation and Continuous Improvement in Pharmaceutical Manufacturing'. Yet, focused attention on CM did not really start until 2014, and the first product manufactured by CM was only approved in 2015. This review describes some of the benefits and challenges of introducing a CM process with a particular focus on small molecule solid oral dosage forms. The review is a useful introduction for individuals wishing to learn more about CM.

Characteristics of residence time distribution in a continuous high shear mixer granulation using scraper rotation

Characteristics of residence time distribution (RTD) in a continuous high shear mixer granulation were investigated to promote the development of a continuous manufacturing process in the pharmaceutical industry. A continuous granulator with an impeller and a scraper was utilized. The tracer behavior in the continuous wet granulation was verified in impulse-response experiments with acetaminophen. The RTD of acetaminophen changed depending on the scraper speed (15-50 rpm), and the mean residence time could be adjusted by the scraper speed in the wet granulation. The impact of changes in the liquid-to-solid ratio (0.10-0.20) and the addition of binder were also examined, and the variance of RTD was influenced by both. The degree of axial mixing was quantitatively evaluated with a dimensionless index, the Peclet number (Pe). Higher scraper speed was found to suppress fluctuations of the axial mixing that occurred with changes in the liquid feed. Moreover, the transition of granule size distribution with the change in liquid feed reached a steady state more quickly under the higher scraper speed. These results show that scraper rotation can help to adjust the RTD and the axial mixing, leading to a more robust continuous granulation.

Hybrid multi-zonal compartment modeling for continuous powder blending processes

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.

Rapid Prototyping of Miniaturized Powder Mixing Geometries

Continuous manufacturing is an important element of future manufacturing solutions enabling for both high product quality and streamlined development process. The increasing possibilities with computer simulations allow for innovating novel mixing principles applicable for continuous manufacturing. However, these innovative ideas based on simulations need experimental validation. The use of rapid prototyping based on additive manufacturing opens a possibility to evaluate these ideas at a low cost. In this study, a novel powder mixing geometry was prototyped using additive manufacturing and further, interfaced with an in-line near-IR spectrometer allowing for investigating the residence time distribution (RTD) in this geometry.

Continuous Manufacture and Scale-Up of Theophylline-Nicotinamide Cocrystals

The aim of the study was the manufacturing and scale-up of theophylline-nicotinamide (THL-NIC) pharmaceutical cocrystals processed by hot-melt extrusion (HME). The barrel temperature profile, feed rate and screw speed were found to be the critical processing parameters with a residence time of approximately 47 s for the scaled-up batches. Physicochemical characterization using scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and X-ray diffraction of bulk and extruded materials revealed the formation of high purity cocrystals (98.6%). The quality of THL-NIC remained unchanged under accelerated stability conditions.

Integrating sensors for monitoring blend content in a pharmaceutical continuous manufacturing plant

In a pharmaceutical manufacturing process, Critical Quality Attributes (CQAs) need to be monitored not only for the final product but also for intermediates. Blend uniformity of powders is one such attribute that needs to be measured to ensure the quality of the final product. Multiple in-line sensors were implemented within a Direct Compaction (DC) continuous tablet manufacturing line to monitor the blend content of the powders. In most cases, since the primary ingredient of interest is the active pharmaceutical ingredient (API), the concentration (potency) of the API was monitored/predicted over the course of manufacturing. For the calibration model building process, a unique setup involving dynamic powder spectral acquisition method was used. This setup was aimed at mimicking the powder flow characteristics within the manufacturing line, while at the same time utilizing a relatively small amount of powder. A Raman probe and a portable NIR were used concurrently at the exit of the blending process before the tableting stage. The performance of the two sensors and their respective models were evaluated in terms of accuracy, precision, operating range, measurement frequency, placement, reliability, robustness, and compared to predictions using gravimetric feed rates. Additionally, their performances were validated by off-line traditional analytical measurements.

Continuous Mixing Technology: Characterization of a Vertical Mixer Using Residence Time Distribution

Continuous powder mixing technology (CMT) application during continuous direct compression has emerged as a leading technology used in the development and manufacture of solid oral dosage forms. The critical quality attributes of the final product are heavily dependent on the performance of the mixing step as the quality of mixing directly influences the drug product quality attributes. This study investigates the impact of blend material properties (bulk density, API particle size distribution) and process parameters (process throughput, hold up mass and impeller speed) on the mixing performance. Mixing of the blend was characterized using the Residence Time Distribution (RTD) of the process by trending the outlet stream of the mixer using a near-infrared (NIR) probe after the injection of a small mass of tracer at the inlet stream. The outcomes of this study show that the RTDs of the mixer with throughput ranging between 15 and 30 kg/h; impeller speed ranging between 400 and 600 rpm and hold up mass (HUM) ranging between 500 and 850 g can be described by a series of two ideal Continuous Stirred Tank Reactors (CSTRs) with different volumes, and correspondingly, different mean residence times. It is also observed that the mixing is mainly occurring in the lower chamber of the CMT and the normalized RTDs of the mixer are similar across the range of process conditions and material attributes studied. The results also showed that the formulation blend with different API particle sizes and bulk properties, like bulk density and flowability, provide insignificant impact on the mixing performance. The CMT allows independent selection of target set points for HUM, impeller rotational speed and line throughput and it shows great robustness and flexibility for continuous blending in solid oral dose manufacturing.

Residence time distribution (RTD) revisited

Residence Time Distribution (RTD) theory is revisited and tracer technology discussed. The background of RTD following Danckwerts ideas is presented by introducing "distribution" functions for residence time, internal age and intensity function and how to experimentally obtain them with tracer techniques (curves C and F of Danckwerts). Compartment models to describe fluid flow in real reactors are reviewed and progressive modeling of chromatographic processes discussed in some detail. The shortcomings of Standard Dispersion Model (SDM) are addressed, the Taylor-Aris model discussed and the Wave Model of Westerterp's group introduced. The contribution of Computational Fluid Dynamics (CFD) is highlighted to calculate RTD from momentum and mass transport equations and to access spatial age distribution and degree of mixing. Finally smart RTD and future challenges are discussed.

Compact reactor architectures designed with fractals

A new methodology for digital production of fractal reactors was developed and implemented. Reactors manufactured with 3D fractals offer enhanced vorticity and compactness that can lead to reduction of material used for production.