Manufacturing components using thermoplastic composite materials necessitates a judicious balance among fabrication parameters, cost considerations and the ultimate quality of the elements produced. Continuous manufacturing technologies, exemplified by methods such as continuous compressing molding and glide forming, seek to revolutionize production through their continuous processing approach. This study aimed to investigate the effects different process parameters have on the final quality of the manufactured parts when a continuous manufacturing technology, such as glide forming, is applied to thermoplastic composite materials. An experimental rig was designed, and 19 samples were prepared using a unidirectional-carbon-fiber-reinforced LM-PAEK (low-melting polyaryletherketone) composite. The process parameters studied were temperature, pressure and forming speed. The quality of the final parts was evaluated based on their thickness and consolidation levels. The findings underscore the feasibility of leveraging continuous manufacturing technologies for producing components using thermoplastic composite materials, but the process parameters must be carefully controlled to ensure the quality of the final part. The models obtained could be used as a post-processing tool to predict thickness and consolidation levels when simulating the manufacture of a component on macroscale levels. Further research is needed to optimize the process parameters and study their effects on other thermoplastic composite materials. Thermoplastic composites are the combination of fibers  and a thermoplastic polymer matrix. The resultant is a lightweight material that combines the strength and resistance of the fibers with the malleability and chemical resistance of the polymer. Unlike thermoset polymers, thermoplastics consist of long chains of molecules that can be repeatedly softened and hardened through heating and cooling cycles without undergoing any chemical changes . Thus, tIn recent years, there has been a significant amount of research into the optimization of thermoplastic composite consolidation processes. For example, Aquier et al.  reviewedThe emergence of new CFRTP (carbon fiber reinforced thermoplastic polymers) manufacturing technologies makes it necessary to continue investigating consolidation parameters, especially when these technologies differ from the standard due to their continuous application—such as glide forming ,10,11,12GF (glide forming) is an advanced automated manufacturing technology currently under development and is specifically tailored for crafting slender structural components with intricate double curvature profiles using composite materials. This innovative technique addresses a critical industry need—the efficient production of complex geometries such as stringers, wing reinforcements and stabilizer components, all of which pose significant manufacturing challenges due to their lengthy and curved geometries. Traditional methods often fall short when it comes to achieving high production rates for these parts. What sets the GF process apart are its key advantages, not the least being its remarkable flexibility. By employing a flexible nozzle concept and localized heating, it can seamlessly adapt to create components with varying cross-sectional shapes, thus eliminating the need for extensive tooling changes and resulting in substantial time and cost savings. Furthermore, the process boasts high levels of automation, reducing labor demands, and its environmentally friendly approach, which eschews the necessity for heated tools, positions it as a cost-effective and sustainable manufacturing solution compared to alternatives.Likewise, CCM (continuous compression molding) is also a manufacturing technology currently undergoing development to produce CFRTP components. This process involves feeding a continuous laminate into a compression molding machine that contains a series of heated molds or dies. The material is heated above its melting point and then compressed consecutively in different stages as it passes through the machine until the final geometry is achieved. CCM is a highly efficient process for producing large quantities of constant section components with consistent quality and dimensional accuracy. This process has many advantages over other manufacturing processes for CFRTP, such as stamp forming or hot forming. CCM can produce large components, and its production rate is relatively high. Moreover, the process can be automated, making it cost-effective for high-volume production. Despite these advantages, CCM has some limitations, such as high initial tooling costs and limited flexibility for small batch production.To address the lack of studies around these emerging technologies, a statistical study was proposed aimed at identifying the optimal process parameters—temperature, pressure and time (forming speed)—for achieving optimal consolidation levels using continuous manufacturing technologies in LM-PAEK reinforced with carbon fibers.The experiment was based on the most commonly used manufacturing processes such as stamp forming ,17,18, hThe statistical analysis was conducted following an experimental sequence based on a 3-factor Box–Behnken design experiment ,22,23 thThe experiment was conducted using unidirectional-carbon-fiber-reinforced LM-PAEK composite from Toray . The key properties of the material are presented in 5]s, point-welded in the center ply by ply  ply see . Each laPre-heating and impregnation/forming phases;Consolidation phase;When manufacturing thermoplastic composite material components, three phases  are distinguished during the forming as described in reference . They prBased on this concept, a special testing rig involving two presses adapted to each defined stage was designed see . Stage 1The experiment started by introducing the laminate into stage 1 press and heating the material. The heating time is equivalent to the actual heating time of the forming process. Once the temperature on the thermocouple reached the target value, the impregnation pressure was then applied for the corresponding duration of the stage in the real process. After this period, the samples were rapidly transferred to stage 2 press, where they were subjected to consolidation pressure for the designated time in the experimental sequence. To deem the sample acceptable, the temperature drops during the transfer had to be less than 5 °C.Previous studies, such as those conducted by Khan M.A. et al.  and BrooForming temperature: The material supplier typically provided the recommended range for forming temperatures. For TC1225/T700 [Consolidation pressure: The consolidation pressure range could vary depending on the manufacturing process. From previous experiences with high-rate applications, good consolidation values were achieved in a range of pressures between 0.2 and 2 MPa .Time: Time is an indirect measure of the forming speed. It was feasible to approximate the forming speed by considering a standard length for the forming skids (counter molds) used in glide forming. Thus, for the impregnation/forming phase-assuming a skid length of 200 mm and forming speeds between 1 mm/s and 10 mm/s—forming equivalent time varied from 20 s to 200 s. For the consolidation phase, the equivalent time considering a 100 mm skid varied from 10 s to 100 s.Other parameters, such as the number of plies, impregnation pressure and tool temperature, may also impact the forming process but were kept constant in this study to reduce the number of variables. The number of plies was set to 20, the impregnation pressure to 4 kPa, and the tool temperature to 250 °C. These values were selected based on previous experiences with the glide forming process and material supplier indications.Number of plies: The number of plies in the laminate, a key parameter in the design of the component, is typically within a range of 5 to 20 for secondary structures. However, in certain cases, local reinforcements may increase the number of plies up to 50. For the purposes of this study, a laminate with 20 plies was chosen.Impregnation pressure: Impregnation pressure, applied during the forming phase, is utilized to secure the cohesion of the plies while the material is at the appropriate forming temperature. This results in a greater degree of intimate contact among the plies and ensures that each fiber is fully impregnated by the thermoplastic matrix. Additionally, impregnation pressure is employed in shaping the component. Based on previous research, an appropriate value for this pressure was determined to be 4 kPa.Tool temperature: Tool temperature, which is typically below melting point and above the crystallization temperature, was set to 250 °C for this study.The consolidation of the component, measured by the porosity level and thickness, was studied to evaluate the results and identify the optimal process settings.Porosity: Porosity was quantified using the A-scan method via MUPE . The transducer used was a mono-element straight beam transducer with a delay of 8.178 and a speed setting of 2713 m/s. The analysis involved comparing the sample attenuation to a reference standard. Micrographics were also utilized to provide a detailed examination of select samples. The methodology employed was in accordance with the Airbus AITM (Airbus Industries Test Methods) 6-4005 standard .Thickness: Thickness was measured using a micrometer and compared to the nominal thickness of a consolidated ply. The measured nominal thickness of the raw material was 0.170 mm.A total of 19 samples were tested in accordance with the randomized experimental design. The study utilized the 3-parameter Box–Behnken design for 12 of the samples, which were set with varying parameters. Three samples were designated as the central point  for the purpose of evaluating the normal dispersion and repeatability of the experimental data. Additionally, four extra points were randomly selected to validate the models.Randomization of the experiment design was crucial in ensuring the independence of sample conditions, as it prevented any potential influence on subsequent samples. This randomness provided a basis for drawing reliable conclusions from the test results, thus avoiding any ambiguities. The experimental factor ranges and the experimental sequence are presented in The results of the experiment were processed to determine the multiple response surface for each evaluation parameter. The multiple response surface provided a mathematical representation of the relationship between the various parameters and the evaluation criteria. While this approach alone may not provide highly accurate predictions, it serves as a useful and efficient means of obtaining a preliminary approximation. Further experimentation is required to validate the results and to enhance the level of accuracy of the predictions.The statistical significance of the models obtained from the experimental results were determined using Analysis of Variance (ANOVA) with a confidence level of 90%. ANOVA is a powerful tool for evaluating the relationship between control variables and the observable response.p-value of the coefficient must be less than the set confidence level, which was 0.1 in this case.As per references ,21,22, tThe following section presents the results of the 19 runs, along with the regression models, their ANOVAs and micrographics. A summary of the results is provided in The porosity results were obtained through the measurement of the amplitude of the backwall echo, using the protocol specified in the AITM-6-4005  regulatii are the model coefficients, T is the forming temperature, P is the consolidation pressure, and t is the equivalent time.The thickness response surface is represented by Equation (1), with parameters listed in p-value of each coefficient in the thickness model. The p-value of the overall model (0.0000) is below the confidence level (0.1), thereby enabling the rejection of the null hypothesis. The null hypothesis states that there is no relationship between the control variables  and the dependent variable (thickness). This result confirms that the proposed quadratic model with interaction and linear terms accurately explains the relationship between thickness, temperature, pressure and time.The effect of each coefficient in the equation was analyzed, and it was found that the squared term of temperature, linear term of temperature and the interaction term of pressure and time had a significant impact on the thickness results. On the other hand, the linear terms of pressure and time were found to be insignificant for the model but were retained because of the significant impact their interaction had on the final thickness of the component.The study of the influence of three parameters on component manufacturing was conducted by setting the equivalent time as a constant variable.The response surface results, shown in The relationship between temperature, pressure and the viscosity of the matrix  i2 (95.82%) and adjR2 (93.49%) values indicate that the model is able to predict the response with over 90% accuracy.A residual analysis, depicted in i are the model coefficients, T is the forming temperature, P is the consolidation pressure, and t is the equivalent time.The backwall echo response surface revealed a more complex relationship between the independent variables and the consolidation level of the final part, thus suggesting logarithmic dependencies. The model indicated that the interaction of pressure and time, as well as the linear terms of temperature and pressure, were the most significant parameters within the model. Equation (2) represents the model in its logarithmic and exponential forms.p-value (0.0003) is significantly lower than the confidence interval. This result supports the rejection of the null hypothesis, indicating the feasibility of a relationship between the amplitude backwall echo and the control parameters.The variance of the model was analyzed using a 90% confidence interval, following the methodology described in a previous section. The results of the ANOVA, summarized in p-value lower than the confidence interval, thus rendering it statistically insignificant.Upon analyzing the effect of each coefficient, the amplitude backwall echo was found to be highly sensitive to variations in temperature and pressure. In contrast, the interaction between the logarithm of pressure and temperature exhibited lower significance. The logarithm of pressure did not exhibit a The results show that the highest degree of consolidation occurs at the upper right corner of the surface, corresponding to the maximum temperature and pressure. Conversely, the minimum amplitude of backwall echo is observed at the lower left corner, where temperature and pressure are at their lowest values. As the equivalent time increases, the backwall amplitude becomes higher for lower temperatures and pressures.These findings suggest a correlation between the control variables of thickness and porosity. The highest amplitude of the backwall echo is recorded in the case of minimum thickness. This correlation may be attributed to the creep behavior of the material, which helps in evacuating the air trapped between the plies and molecules, leading to a more compact structure with reduced porosity. This relationship agrees with Darcy’s law that describes the flow of a fluid through a porous medium (applicable to laminar fluid flow through a porous media at low flow rates).2 (0.8548) and adjR2 (0.7968) values, which are lower than those of the thickness model. Despite this, the model still explains approximately 85% of the observed variation in the data. Further analysis of the residuals and the fitted values (The results of the model diagnosis a indicatd values b, as weld values c, failedAs previously reported, the thickness and porosity level of a manufactured part are strongly correlated. The observed effects are in line with expectations, as a reduced thickness leads to increased material flow, enabling the filling of all existing pores within the matrix, thereby reaching higher levels of porosity. This effect underscores the significance of controlling the parameters, as conditions favoring the nominal thickness are found to decrease the amplitude of the backwall echo.The superimposed graphs of the thickness and amplitude of the backwall echo are presented in Conversely, the other end of the spectrum is represented by the figure corresponding to 100 s, i.e., equivalent to a velocity of 1 mm/s. As indicated by the outcomes, the slower material flow due to the reduced velocity leads to lower temperature and pressure requirements as the material has more time to flow. Surprisingly, this prolonged flow time demonstrates a counterproductive impact, resulting in higher porosity compared to the other two time frames tested.Another noteworthy trend that is discernible from the figures is that lower pressure expands the range of temperatures that yield thickness values within tolerance. On the other hand, as pressure increases, temperature control becomes increasingly critical, given the reduced thermal range.In light of these findings, it is evident that the consolidation, thickness and forming conditions wield a substantial influence on the resulting composite material properties. The interaction between parameters such as consolidation time, temperature and pressure plays a decisive role in determining the porosity and thickness outcomes of the manufactured parts. The delicate balance between these parameters is crucial to striking the right balance between sufficient material flow for pore filling and avoiding excessive porosity.Furthermore, the relationship between temperature, pressure and time underscores the trade-offs inherent in the forming process. While higher velocities facilitated quicker production and reduced cycle times, they necessitated higher temperatures and pressures to compensate for the limited material flow duration. On the other hand, lower velocities offer extended flow periods, allowing for decreased temperature and pressure requirements, but at the cost of potentially reduced material quality due to increased porosity.One of the objectives of the present study was to establish the parameters for the glide forming process adapted to thermoplastic composite materials. Similar to other manufacturing processes, lower values of the controllable parameters are typically preferred due to ease and cost considerations. High-temperature and high-pressure processes pose challenges because of the lack of auxiliary materials and complex heating systems, respectively. Thus, the aim was to find the minimum pressure and temperature that can meet the quality requirements of the parts being manufactured while also maintaining a high rate of production to remain commercially competitive.The presented models can assist this determination by considering the limitations of tooling and auxiliary materials. An example of the use of these models is illustrated in With this information, the models were used to set the production parameters focused on production rate and thickness. The 10 s section plots were selected to meet the manufacturing rate. This rate requires a forming temperature of 359 °C and a pressure of 1 MPa, resulting in a normalized thickness of 1.0 and an amplitude of backwall echo of 68%.In conclusion, the study suggested that continuous consolidation manufacturing technologies, such as glide forming, could be adapted to thermoplastic composite materials, but the process parameters must be carefully controlled to ensure the quality of the final part. The results of this study provide important information for the industrial application of the glide forming technology to thermoplastic composite materials. The ANOVA analysis allowed us to establish the significant manufacturing parameters affecting the quality of the laminate, leading to two response surface models. These models could be used as a post-processing tool to predict the thickness and consolidation level when simulating the manufacture of a component on macroscale levels. Further research is needed to optimize the process parameters and to study the effects for other thermoplastic composite materials.


Powder deposition and accumulation on walls of spray drying chamber has been known to impact spray drying processes, resulting in lower yield, frequent shutdowns, and downtimes. Critical factors that impact the extent and rate of wall deposition have been studied extensively in the chemical and food industry. In this paper, we present an atypical process yield issue wherein acceptable yield is obtained during the first batch of spray drying but undergoes significant yield loss in consecutive batches. Through understanding the interplay of the process, material properties, and equipment, we identify key mechanisms that are playing a role in causing the process yield issue. These mechanisms include surface roughness of the inner wall of the spray dryer, variation in gas flow due to the introduction of process analytical technology, start-up and shutdown operating parameters that expose the wall deposited powder from the prior batch to temperatures close to the onset of glass transition temperature and cause depression of its glass transition temperature. These factors result in more wall accumulation and impact the yield in subsequent batches. By correcting for most of these factors, the yield reduction issue was mitigated, and processing efficiency was improved.  Spray drying is commonly used commercially across several industries to make spray-dried food products, chemicals, pharmaceutical drug products, and many more. Spray drying processes are used to remove liquid from a suspension, emulsion, or solution (feed) to generate dried solids, by spraying the feed into a hot drying medium . During To understand the driving forces leading to wall deposition during spray drying, experimental studies and modeling ,3,4,5,6 In the pharmaceutical industry, the spray drying process optimization studies often follow the formulation development studies. The process optimization experiments focus on designing wide enough process ranges such that there is no impact on the critical quality attributes such as stability, dissolution, etc., in addition to ensuring adequate process efficiency of the process. Significant attention in the pharmaceutical literature is focused on process aspects such as modeling and scale-up of the spray drying process ,18,19 asIn this paper, we present an experimental study involving a root cause investigation of an atypical yield behavior involving wall deposition during spray drying of a model compound on a pharmaceutical spray dryer. The spray drying process for the compound was established after confirming the acceptability of attributes of the spray-dried powder over a range of process parameters such as liquid-to-gas flow rates and process outlet temperature of the spray dryer. The spray drying process was established to operate below the glass transition temperature of the wet spray-dried powder. Atypical yield behavior was noticed when spray drying consecutive batches. The yield of the second batch was significantly lower whereas the yield of the first batch was high. Our results showcase an experimental solution built based on the understanding gained through the root cause investigation. The solution involved key process modifications that mitigated the yield issue, such as revising the process start-up and shutdown conditions that minimize the depression of the wet spray-dried powder’s glass transition temperature coupled with eliminating factors that contribute to gas flow variability. We conclude with a discussion focusing on the successes and limitations of the approach and recommendations for future products to address the yield challenge.Hydroxy propyl methyl cellulose acetate succinate (HPMCAS) was purchased from Shin-Etsu . The crystalline drug substance was supplied by Merck & Co., Inc., Rahway, NJ, USA. Acetone was purchased from Sasol Solvents .® Type PSD-2 spray dryer (referred to as Dryer 1) modified to run at a nominal drying gas flow rate of 400 kg/h in a closed cycle, as depicted in the schematic in The crystalline drug substance and HPMCAS are dissolved in acetone and the resulting solution was spray-dried on a co-current PharmaSDPrior to spray drying a given batch, a startup procedure is conducted. The drying chamber is heated using nitrogen gas to an inlet temperature of 100 °C for a minimum of 2 h. The condenser is set at 5 °C during this period. Next, acetone is sprayed at 39 kg/h with the condenser setting revised to −15 °C to reach the steady state outlet temperature of 43 °C. Thereafter, the feed solution is processed. At the end of a batch, a shutdown procedure is conducted. After the feed solution is processed, acetone is spray-dried at 39 kg/h for approximately 1 h. Thereafter the inlet process temperature is ramped down to room temperature.® Type PSD-2 dryer in a closed cycle of similar but not identical design (Dryer 2). The startup and shutdown conditions used on dryer 2 were at the same set points for temperature and flow rates but for a much shorter duration. An additional difference was that during the heat-up phase, the condenser was set at −15 °C and maintained throughout the run.A second spray drying experiment involving spray drying two consecutive batches  of the same drug-polymer solution was also conducted on a second modified Pharma-SDThe methods used for the characterization of the spray-dried powders are provided in the An atypical process yield behavior was observed during the spray-drying process development of the spray-dried powder of the model compound on dryer 1. The initial batch processed on a fully cleaned spray dryer achieved a wet yield of 98%. A typical acceptable wet yield for a spray-dried polymer product is >80% since some powder deposition on the wall is common and cannot be recovered. Additionally, wet yields above 100% can sometimes be observed because the drying solvent has not been completely removed from the powder. Most often, the spray drying process with yield issues shows the risk of yield loss in the first batch. Others might have a decreasing trend in yield in subsequent batches. However, in this experiment, the entire second batch was plagued with less than expected collection of spray dried powder with a significant yield reduction and net wet yield of 59% .After the completion of the batch, the inner chamber walls were noted to have significant atypical powder deposition on the roof and near the top regions of the dryer as shown in g in Samples of the powder collected from the cyclone during steady state operation and wall deposited powder were characterized and confirmed to have similar properties as seen by the morphology SEM images in At the outset of troubleshooting the atypical yield problem, we established an experiment to test the occurrence of yield reduction problems during two-batch manufacture on a second Pharma Spray Dryer-2. Same operating conditions, namely, same set of inlet conditions  were used and these resulted in a similar outlet temperature at the exit of the spray dryer. However, the two spray dryers and their operation had some potential and some known differences, namely, (i) potential differences in the design of gas dispersers (proprietary knowledge), (ii) differences in specifications on the surface finish of inner walls, (iii) the second spray-dryer did not include the in-situ particle size analyzer, and (iv) differences in start-up and shut-down conditions.A comparison of in-process or running yields was conducted in the two spray drying experiments. Running wet yield during the spray drying batch is calculated as the ratio of the amount of wet powder collected to the amount of solids sprayed from the beginning of the batch up until the given time point in the batch and is expressed in percentage. Since the wet spray dried powder contains approximately 3–4.5% of solvent, the wet yield can exceed 100%. The in-process running wet solids collected against the amount of solids sprayed over two consecutive batches of spray drying is shown in Since dryer 2 did not show significant wall deposition and the resulting yield reduction, we focused our attention on the known differences between the two spray dryers and assessed each of our hypotheses systematically.An initial representation of the root causes for yield reduction based on our observations and our review of spray drying literature. We established three major categories: equipment, process, and material properties of the product, or an interplay of some or all these factors as shown in Equipment design elements, such as the surface roughness of the inner walls and gas disperser design could be responsible for the wall adhesion of partially dried spray-dried powder. Since gas disperser design is proprietary knowledge, the focus of the investigation was on surface roughness and gas flow variability.The atypical wall deposition noted on the roof and upper walls of the drying chamber A–C of a Further, an understanding of the dependence of glass transition temperature on processing parameters such as outlet temperature and the concentration of the solvent in the drying gas could provide insight into factors leading to the adherence of spray-dried powder to the walls.In our approach to understanding and overcoming yield reduction, we critically analyzed process parameters for unexpected behaviors and attempted to correlate process observations and our potential root causes. We will study testable hypotheses through targeted experiments at the laboratory scale when appropriate, and finally, implement a combination of our learnings on the spray dryer towards mitigating the yield reduction problem.The differences in the surface roughness of the two dryers were as follows: Dryer 1 has a specification for the inner chamber surface to be mechanically polished to ≤0.5 µm Ra followed by electropolishing, whereas Dryer 2 has no electropolishing, and chamber surface is specified to ≤0.8 µm Ra. The impact of surface roughness on the adherence of the spray-dried powder to the wall was studied using the angle of wall friction between the wall and the powder.Powder wall friction experiments were conducted on three different stainless-steel coupons shown in  at 0.87 During a typical operation of the spray drying process, the swirling gas flow patterns introduced into the spray dryer by the gas disperser results in drying the sprayed droplets and entrainment of the spray-dried powder to the cyclone with some wall deposition on the lower conical portion of the dryer. Due to the unexpected wall deposition of spray-dried powder on the roof and the upper cylindrical section of dryer 1 while operating at the same target conditions as dryer 2, a review of instrument data on gas flowrate, exhaust valve, and bag house filter differential pressure was assessed on a representative batch from the data historian on dryer 1.The results of the data analysis in Potential sources of pressure variation are gas purges from the sealing shaft of the blower and the inline particle size analyzer, gas supply to differential pressure instruments on the cyclone and the baghouse filters, and pulsing of the bag house filters. One or a combination of these sources could trigger the pressure control loop to cause the observed gas flow variability. We found that modifications could not be made to the sealing shaft of the blower or the differential pressure instruments on the cyclone to ensure adequate operation of the dryer. Further, dryer 2, which does not have yield reduction issues, also does not have the inline particle size analyzer. Therefore, our approach to reducing the gas flow variability involved the removal of the inline particle size analyzer.The removal of the inline particle size analyzer and the associated nitrogen purge removed the high-frequency periodic  variation in gas flow rate as seen in To further reduce this observed variability, we turned our attention to optimizing the pulsing operation of baghouse filters. The pulse frequency of the bag house filter was revised from 120 to 900 s thereby reducing the rate of nitrogen supply to the system for pulsing. The effect of this additional change on the gas flow rate-time graph is depicted in These approaches to reduce gas flow variability were implemented along with other mitigations from other root causes of the spray drying process in later sections and the results of the experiment are as discussed in While previous factors such as surface finish and gas flow variability could create conditions that support the wall deposition of the spray-dried powder, it is the material properties of the powder at specific process conditions that cause the powder adhesion to the wall. Therefore, to understand the stickiness of the powder, studies of glass transition temperature dependence on the level of spray-solvent in the powder and sorption studies were conducted. Thereafter, the operating conditions of the spray dryer were critically evaluated against this material property behavior to postulate potential root causes for the increased wall deposition during the second batch of spray drying.g, onset) and mid-point glass transition temperature (Tg), respectively. g, onset, and Tg on the weight percent of acetone in the spray-dried powder. Independently, the amount of acetone in the wet spray dried powder when operating at the process outlet temperature range of 38–48 °C was measured to be 3.4–4.6% (w/w). As shown in the shaded region in g, onset of the wet spray dried powder, and spans to the mid-point Tg line potentially making the powder prone to stick to walls.Modulated DSC experiments were conducted in hermetically sealed pans of spray-dried powder pre-equilibrated with different amounts of acetone. The glass transition of the spray dried powder was detected by heat capacity variations with temperature and reported as the onset of glass transition temperature  heat-up phase, (ii) solvent start-up phase, (iii) solution phase (spray dried powder is produced in this phase), (iv) solvent shutdown followed by (v) cool-down phase and (vi) gap period before the start of next batch.w/w), as noted in g, onset temperature of wet spray dried powder exiting the spray dryer. Therefore, the powders deposited on the wall are exposed to conditions that could cause it to stick to the wall. Switch from solution to solvent shutdown phase (iv) maintains a similar process temperature and partial pressure of solvent, and therefore no significant change in behavior is expected.Wall deposits are expected to be first formed during the solution phase (iii) when the first batch of the spray-dried solution is sprayed. Noting that the residual solvent concentration in the wet spray dried powder leaving the spray dryer is between 3.4–4.6%  of the first batch, the solvent spray is turned off, and shortly afterwards, the inlet temperature is slowly ramped down as shown in g of the wall deposits. Thus, the spray, solvent shutdown, cool down and gap phases of the first batch seem to ensure that a small fraction of wall-deposited powders remain stuck to the wall even as we get an acceptable yield of 98% for this batch.The next phase is the gap between two batches (vi), wherein the spray dryer remains shut down until the start of the next batch, and the spray dryer is not maintained in an inert state. Therefore, the wall deposits are equilibrated with room moisture. The presence of moisture could result in plasticization and reduction of Tg, onset and rendering the material more adhesive.During the gas flow Heat-Up phase of the second batch, heated gas is blown through the drying chamber to warm up the equipment. The chamber is maintained at an outlet temperature of 65 °C for approximately 2 h, once again exposing the wall deposits from batch 1 to thermal conditions at or above Tg reduction. These characteristics coupled with other root causes could make the wall deposits more likely to accumulate more particles during the solution spray phase of the second batch.During the solvent startup phase of the second batch, the condenser set point changes from −5 °C to −15 °C at the same time when the solvent spray is initiated. Therefore, for a short 15–20 min until the condenser stabilizes to −15 °C, the inlet gas to the chamber carried a higher partial pressure of acetone leading to elevated acetone concentration in the resulting outlet gas. At 5 °C, thermodynamic modeling and estimation of saturation pressure via Antoine’s equation suggests the chamber outlet gas could go as high as 26% P/Po whereas it will drop to ~14% at steady-state operation at −15 °C. Acetone diffuses into the wall deposits, with the surface of wall deposits prone to higher concentrations of acetone and therefore further Tg, onset and exposure to conditions that lower the Tg of wall deposits were identified as root causes after critical assessment of the interplay of material properties and process conditions. The specific process conditions identified were specific to the long duration of the gas flow start-up and shutdown period, and specific settings to avoid temperature or acetone partial pressure spikes during start-up and shutdown. Additionally, we postulate that maintaining the inertness of the chamber between batches could also help avoid depression in the glass transition temperature of wall deposits.In contrast, dryer 2 with shorter start-up and short down periods, and different temperature control which led to fewer temperature spikes, may have avoided priming the wall deposits for future accumulation and therefore maintained the yields during the second batch. Thus, two mechanisms, exposure of wall deposits to temperatures higher than Tg, onset and exposure to conditions that lower the Tg of wall deposits. Based on the root cause investigation, we identified process parameters to address the mechanism that impacts the yield. A summary of the mechanism and the process/equipment parameter to be revised are provided in Based on our understanding of the root causes, and hypotheses leading to increased wall deposition, we assimilated the various mechanisms, namely gas flow stabilization, exposure of wall deposits to temperatures higher than TThe resulting yield improvement on a series of four consecutive batches is shown in Furthermore, this improvement was achieved without any significant changes to the target operation of the spray drying of the product. This approach was critical for pharmaceutical spray drying processing because if significant process changes to the target spray drying process are required then it could impact the performance of the tablet which contains the spray-dried powder. The impact of such changes, especially during later stages of drug product development may require additional studies to confirm the acceptability of such changes. In worst cases, such changes could impact the shelf life of the product and therefore, may not be feasible for the product.g, onset temperature. However, from a practical perspective, when 5–10 batches are required to be sprayed consecutively, it is common practice to scale up to the next larger scale to reduce the operational burden.A closer look at the running yield performance for the 4th batch indicates a marginal decrease in the final product yield compared to the previous batches. Thus, while the approaches identified above have been effective in mitigating yield reduction for a reasonable number of batches, the risk of wall deposition leading to reduced yields increases forlarger number of consecutively run batches. This behavior could be because, during the steady state operation of the dryer, the gas temperature and acetone partial pressure could bring the wall deposited spray dried powder close to the Tg [g) may exhibit atypically strong adhesion to the wall leading to reduced yield and delayed recovery.Applying the learnings to the development of future amorphous solid dispersions especially during early stages of drug development, we highlight general and targeted approaches identified here to avoid the occurrence of such issues proactively. It is well-known that the process conditions of the spray drying process (such as the liquid: gas flow rate and process temperature) need to be chosen such that the product experiences temperatures below its wet glass transition temperature. It is also known through the review of the literature on sticking and wall deposition ,9, that g . Howeverg as well as Tg, onset) on the residual solvent levels relative to the operating conditions are critical. If the intended processing condition overlaps with wet Tg,onset region as such in Therefore, during the early development of the spray drying process, characterization of the dependence of wet glass transition temperature (Tg, onset curve (A process-centric approach involves moving away from and below the wet Tet curve A by redug, onset. Modification of solvent systems could also be considered if the new solvents can adequately modify the glass transition temperature dependence on the residual solvent level of the new solvents such that T-Tg, onset is high. Modification of solvent systems could also be considered if the new solvents can modify the glass transition temperature dependence on the residual solvent level of the new solvents.Formulation-centric approaches include a selection of polymeric stabilizers or modification of the ratio of drug to polymer to ensure that the resulting amorphous solid dispersion has sufficiently high T-TWhen applying our learnings to the occurrence of reduced yield behaviors later in drug development as in our case, we recommend an assessment of the mechanisms. At the first observation of yield reduction, it is important to confirm or rule out common failure modes leading to wall deposition, such as atypical or incomplete or significant differences of atomization arising from nozzle variability or poor droplet drying leading to agglomeration on walls. By comparing the attributes of wall-deposited material against the steady-state material, we confirmed that these failure modes were not contributing to this specific problem as significantly larger and agglomerated particles would be expected and associated potential shifts in bulk density properties. If the attributes are different , a thoroIf the mechanisms point to the interplay of glass transition thermodynamics with processing space, employing practical approaches demonstrated here to avoid conditions prone to depression of glass transition temperature and exposures to a temperature above glass transition temperature could be useful. It is important to note that the changes to start and shutdown conditions in addition to a reduction of gas flow variability arising from inline analytical equipment were adequate to improve the yield in our case because the overall target processing was not at or above the wet glass transition temperature of the powder. We also note that although this study has been effective in mitigating yield reduction for a few batches, wall-build up can impact yields when running a longer number of batches. Therefore, if the implementation of these changes does not result in significant improvement in yield, then approaches such as process or formulation changes proposed for the early stage of drug development may be necessary.Further, during the technical transfer of the spray drying process to different spray drying units, a critical assessment of gas flow variability must be conducted in addition to other process fit considerations. In our case, while the variations in gas flow were noted, they were attributed to normal process variability until a targeted in-depth understanding of the gas flow and the feedback control clarified that the source of near periodic variability was due to the presence of the inline analytical equipment. Therefore, a thorough assessment studying the impact of additional inline equipment on the gas flow variability during qualification is important for the successful operation of the spray drying.An atypical yield issue during pharmaceutical spray drying is described and practical solutions to effectively mitigate the wall deposition-related yield issues are demonstrated in this paper. After identification of the yield reduction problem, moving the target process further away from and below the onset of glass transition of the powder through simple mass and energy balance in the drier is a simple, useful, and possibly common solution. However, noting that dryer 2 can be operated under similar conditions without yield reduction, this investigation puts a spotlight on hypothesis-based understanding of the driving forces for the reduction in yield and translating into approaches to overcome the yield reduction problem on dryer 1. While in-depth understanding requires additional experimentation varying these factors one at a time, the evidence of improvement in yields offers strength to these reasoned and mechanism-based solutions.As with most real-world problems, our root causes revealed more than one factor that could be contributing to the wall deposition. Gas flow variability introduced by the use of an inline particle size analyzer resulted in partially dried particles being forced to the roof and upper wall regions of the drying chamber. Start-up and shutdown conditions that exposed wall-deposited material to temperatures near or above the onset of glass transition, and conditions that suppressed its glass transition temperature coupled with the gas flow variability increased the extent of wall deposition during the subsequent batches. Incorporating changes that addressed these root causes reduced the extent of wall deposition and improved the recovery rate of re-entrained powder during production.Understanding the gas flow variability due to purges and pulsing using computational fluid dynamics of transient flow in the spray-dryer could be considered to further optimize the performance of the spray dryer. The internal chamber surface finish remains an interesting factor for further research to evaluate as a separate factor. However, predictive models of stickiness and wall deposition in a spray drying operation are very difficult since the dynamics of sticking are more complex than what glass transition theory and computational fluid dynamics can offer .The root cause analysis presented in the paper illustrates a hypothesis-driven approach to troubleshooting a complex problem such as yield issue during spray drying. We have further distilled our learnings into recommendations for early and late-stage drug development approaches. In addition, we have highlighted the importance of a thorough assessment of process parameters such as gas flow variability during the implementation of in-line analytical equipment in the spray dryer during qualification. Such assessments would enable seamless technical transfer of processes between different dryers. We hope this work provides useful ideas and concepts for process engineers to study when dealing with a yield challenge in their spray drying process.


Continuous monitoring and recording of the type and caloric content of ingested foods with a minimum of user intervention is very useful in preventing metabolic diseases and obesity. In this paper, automatic recognition of food type and caloric content was achieved via the use of multi-spectral images. A method of fusing the RGB image and the images captured at ultra violet, visible, and near-infrared regions at center wavelengths of 385, 405, 430, 470, 490, 510, 560, 590, 625, 645, 660, 810, 850, 870, 890, 910, 950, 970, and 1020 nm was adopted to improve the accuracy. A convolutional neural network (CNN) was adopted to classify food items and estimate the caloric amounts. The CNN was trained using 10,909 images acquired from 101 types. The objective functions including classification accuracy and mean absolute percentage error (MAPE) were investigated according to wavelength numbers. The optimal combinations of wavelengths (including/excluding the RGB image) were determined by using a piecewise selection method. Validation tests were carried out on 3636 images of the food types that were used in training the CNN. As a result of the experiments, the accuracy of food classification was increased from 88.9 to 97.1% and MAPEs were decreased from 41.97 to 18.97 even when one kind of NIR image was added to the RGB image. The highest accuracy for food type classification was 99.81% when using 19 images and the lowest MAPE for caloric content was 10.56 when using 14 images. These results demonstrated that the use of the images captured at various wavelengths in the UV and NIR bands was very helpful for improving the accuracy of food classification and caloric estimation. Precise and continuous monitoring of the types and amounts of foods consumed is very helpful for the maintenance of good health. For health professionals, being aware of the nutritional content of ingested food plays an important role in the proper treatment of patients with weight-related diseases and gastrointestinal diseases as well as those at high risk for metabolic diseases such as obesity . For peoSeveral automatic food recognizers (AFRs) are available to continuously recognize the types and amounts of consumed food with minimum user intervention required. Wearable sensing and digital signal-processing technologies are key factors in the implementation of AFRs which are divided into several categories according to the adopted sensing method. In acoustic-based methods, classifying the types of food is achieved via chewing and swallowing sounds. The underlying principle is that chewing sounds vary depending on the physical characteristics of the food, which includes shape, hardness, and moisture content. In-ear microphone ,6,7 and A variety of sensors are used in non-acoustic methods. These include an imaging sensor , a visibSince types of food are easily distinguished according to their shape, texture, and color, visual cues have been used for the classification of food types and estimation of the food amount ,21,22,23Multispectral analysis has been widely adopted in food analysis ,37,38,39The multispectral approach has also been implemented by using a number of narrow-bandwidth light sources, such as light emitting diodes (LEDs), and a digital camera ,31,35,39In the present study, multi-wavelength imaging techniques were applied to classifying food items and estimating caloric content. Compared with conventional RGB image-based methods, the usefulness of NIR/UV images was experimentally verified for food classification and predictions of caloric content. The optimal number and combination of the wavelengths was determined to maximize the estimation accuracy where a piecewise selection method was adopted.This paper is organized as follows: In The list of the food items used in this study is presented in A custom-made image acquisition system was employed to obtain the multispectral images. A schematic of the image acquisition system appears in The number of control commands was predefined both in the control module of the acquisition system and in the host desktop PC. Hence, the task of acquisition was achieved entirely by constructing the sequence of the individual commands. The acquisition code was written in Python (version 3.6.11). The communication between the desktop PC and the control module was achieved using Bluetooth technology. Image acquisition was carried out in a dark chamber  where external light was blocked. The angular resolution of the rotating table was set at 10Y is the set of possible brightness values, and y included in image I at a wavelength of The main objective of this study was to improve the accuracy of food classification and calorie estimation by using UV/NIR images. Prior to construction of the classification/estimation rules, the use of UV/NIR images was experimentally verified as adequate for this purpose. The white LED used for capturing RGB images was experimentally measured as emitting light with a wavelength that ranged from 430 to 640 nm. Hence, images acquired under a light source with a wavelength shorter than 430 nm were considered UV images, and the images acquired under a light source with a wavelength longer than 640 nm were considered NIR images. Two food items could not be well-distinguished visually under a visible light source, which necessitated confirming whether the corresponding images acquired under UV or NIR light sources could be relatively well-distinguished. Food images are visually distinguished according to shape, texture, and the distribution of brightness values (histogram). The shapes and textures of foods, however, are uniquely determined independent of the light source, whereas the distribution of brightness values is affected not only by food types but also by the wavelength of the light source. Accordingly, brightness distribution was used to measure the difference between two food images that are affected by the wavelength of the light source. A histogram was obtained from the image in which the non-food portion was masked. In the present study, the Bhattacharyya distance was employed to measure the differences between two food images. The Bhattacharyya distance for two images m and n, and S. In this study, the maximum The usefulness of UV/NIR images in terms of food classification was verified by examining the proportion of food pairs that were visually similar under visible light but visually distinct under UV or NIR light. To this end, a cumulative distribution function was defined; it can be heuristically computed as follows:The cumulative distributions for the various thresholds are plotted in n and m is given by the following absolute relative difference:n and m, respectively. The Pearson correlation for the k-th wavelength images is given byx and y and x. In terms of caloric estimation, the usefulness of a specific wavelength image was determined by examining whether differences in the amount of calories between two food items were significantly correlated with differences between the two corresponding images. The underlying assumption is that if the difference between the two food images is large, their caloric difference will also be large and vice-versa. The caloric count was computed via the measured weight and the nutrition facts for each food. The Bhattacharyya distances were also adopted to measure the differences between the two food items. The caloric difference between the two food items p = 0.7). From such results, it can be reasonably assumed that VIS- and UV/NIR-images are equally useful in terms of caloric estimation.The correlations are presented across the wavelengths of each light source in i are the indices of the wavelength and shot, respectively, and Although a highly stable current source was adopted to drive the LEDs, there was some variation in the intensity of the light from shot to shot. This caused unwanted changes in the acquired images and resulted in degradation of the estimation accuracy. A simple way to compensate for variations in the intensity of light sources is to adjust the intensity of the incident light so that the average intensity approximates that of the reference intensity. A typical scale factor is given byThere are many machine learning schemes, such as random forest (RF), support vector regression (SVR), partial least squares regression (PLSR) and artificial neural networks (ANNs), that can be applied to recognition of the types, amounts, and nutritional content of food. Among them, the ANN-based approaches have an advantage wherein nonlinear relationships between the input  and the output  can be taken into consideration in constructing the estimation rules. This leads to higher performance in terms of classification and regression. Accordingly, a supervised learning approach that employs convolutional neural networks (CNNs) was adopted in this study. The architecture of the CNN adopted in this study is shown in We tested the performance in terms of food classification accuracy and estimation error for target values according to different sizes of CNN input (input image sizes). The results showed that sizes of 64 × 64 yielded the highest performance for both classification and estimation accuracies. Accordingly, all images from the camera were reduced to 64 × 64 by using interpolation. Before reducing the size, no cropping was carried out on the acquired images, and the entire image size (640 × 480) was used.The performance according to the hyperparameters of CNN was also investigated using a validation dataset. This was performed separately for each task . The resultant CNN was composed of two convolution/max pooling layers and a fully connected multi-layer perceptron (MLP) with a recognition output, as shown in A rectified linear unit (ReLU) was adopted as an activation function for all hidden layers. A soft-max function and linear combination function were employed for the output layer for classification and regression CNNs, respectively. Accordingly, the loss functions were given by the cross-entropy and the mean absolute percentage error (MAPE) in the cases of food classification and caloric estimation, respectively. A total of 1000 epochs resulted in a trained CNN with sufficient performance in terms of food recognition accuracy. It is noteworthy that the accuracy of classification/estimation was strongly affected by the mini-batch size. The experimental results showed that a mini-batch size of 32 gave the best performance for all cases. Since the MAPE is given by dividing the absolute error value by its ground truth, the loss value cannot be calculated when the given target value is zero. As shown in N is the total number of the wavelengths. The set of the wavelengths was gradually constructed by adding and removing the wavelength either to or from the previously constructed set. The overall procedure is as follows:Although evaluation of all possible wavelength combinations was performed via off-line processing, it was desirable to avoid an enormous amount of computational time for a brute-force grid search. In the present study, a piecewise selection method was adopted to select the set of optimal wavelengths. Let i-th forward step, all combinations Step-1. Forward selection: Let i-th backward step is then given byS that is given by the final loss of the learned CNN.Step-2. Backward elimination: The element (wavelength) that minimizes the loss function is removed from Step-3. Final forward selection: The final set is built by the forward selection step wherein the optimal wavelength The procedure Steps-1∼3 was iteratively performed until A large number of the wavelengths in The food classification accuracy according to the number of input images is presented in Thus far, the results were obtained by excluding RGB images. The food classification accuracy is presented in As noted in the previous section, a high level of accuracy is paramount when using a small number of images. From this point of view, it is meaningful to examine how the correct recognition rate changes according to the food item when recognition is performed by adding only one type of NIR or UV image to an RGB image. The experimental results on two input images (including the RGB image) showed that the highest classification accuracy was achieved when an image from a wavelength of 890 nm was added to the RGB image. Accordingly, the change in the recognition rate for each food item was examined when adding only one NIR image from a wavelength of 890nm to the RGB image. From among 101 food items, 62 items showed an improved recognition rate by adding only one type of NIR image, and 35 items showed the same recognition rate. A decrease in the recognition rate was observed for only four food items, but the level of decrease was generally small (<5%). The list of food items that resulted in a significant level of improvement in the recognition rate by adding one type of NIR image is presented in The results for caloric estimation are presented in  . Overall, the MAPEs take the form of decreasing curves with an increasing number of input images . This indicates that the accuracy of caloric estimation for many food items (>78%) can be improved by including only one type of NIR image. This is also confirmed by the fact that most of the MAPE reduction (74.8%) was achieved when only one NIR image (at 970 nm) was added to the RGB image, as shown in In conclusion, the use of UV/NIR images in additionj to RGB images increases the accuracy of caloric estimation. Even with a smaller number of UV/NIR images, the performance in terms of caloric estimation was significantly improved compared with conventional RGB-based estimation.The wavelengths selected for food classification appear in When the RGB image was included, the ratio of wavelengths in the NIR band being selected was further increased to 50.53% This is because the RGB image actually includes three visible light images (RGB images), so the NIR band images are likely to be selected. Whether the RGB images were included or excluded, frequent wavelength selections were 385, 870, 890, and 970 nm. This also indicates that the wavelengths corresponding to the invisible band were more frequently selected for food classification.The wavelength selection results for caloric estimation are shown in RGB images are mostly used in image-based food analysis. The proposed approach assumes that the types of food and the caloric count in them could partially be determined by the morphological characteristics and wavelength distributions of food images. A multi-spectral analysis technique using NIR and UV light along with VIS light was employed in the analysis of various foods and the results approximated those of conventional chemical analysis techniques. The present study was motivated by such a multi-spectral approach, and the procedure for food classification and caloric estimation adopted a multi-spectral analysis technique. Automated equipment capable of acquiring images of up to 20 wavelengths was devised, and approximately 15,000 images were acquired per wavelength from 101 types of food. The types of food and caloric content were estimated using a CNN. A CNN was used so that the relevant features for estimating the target variables could be automatically derived from the images, and a feature extraction step was unnecessary. The experimental results showed that the performance in terms of the accuracy for food item classification and caloric estimation was notably superior to previous methods. This was due mainly to the usage of various light sources in the UV/VIS/NIR band, unlike conventional methods which use only RGB images. It would be interesting to connect the multi-spectral imaging techniques with the quantification of ingredients such as proteins, fats, carbohydrates, sugars, and sodium. Future work will focus on this issue.


National strategies for preparedness for future outbreaks of COVID-19 often include timely preparedness with vaccines. Fiscal health modelling (FHM) has recently been brought forward as an additional analysis by defining the public economic impact from a governmental perspective. As governments are the main decision-makers concerning pandemic preparedness, this study aimed to develop an FHM framework for infectious diseases in the Netherlands. Based on the Dutch COVID-19 outbreak of 2020 and 2021 and publicly available data on tax income and gross domestic product (GDP), the fiscal impact of COVID-19 was assessed using two approaches. Approach I: Prospective modelling of future fiscal impact based on publicly available laboratory-confirmed COVID-19 cases; and Approach II: Retrospective assessment of the extrapolated tax and benefit income and GDP. Approach I estimated the consequences that can be causally linked to the population counts reducing income taxes by EUR 266 million. The total fiscal loss amounted to EUR 164 million over 2 years (excluding pension payments averted). The total losses in terms of tax income (2020 and 2021) and GDP (2020) (Approach II), were estimated at, respectively, EUR 13.58 billion and EUR 96.3 billion. This study analysed different aspects of a communicable disease outbreak and its influence on government public accounts. The choice of the two presented approaches depends on the perspective of the analysis, the time horizon of the analysis and the availability of data. The outbreak of a communicable disease (CD) can have large consequences on the general health and daily life of societies. The global SARS-CoV-2 COVID-19) pandemic has been an extreme example of this. At the pandemic’s peak, societies and governments struggled to cope with large infectious waves, placing strains on national healthcare systems and public finances 9 pandemi,4.Given the extent of the impact of the COVID-19 pandemic and counteracting measures—in lives lost, reduced population health and economically—the necessity to examine CD-counteracting healthcare interventions with a broad perspective, reaching beyond the current health technology assessment (HTA) practice, is highlighted. Even additional scenario analyses, such as future unrelated healthcare costs which sometimes are added to conventional cost-effectiveness analyses in HTA procedures, do not fully capture the broader economic consequences of a CD outbreak such as COVID-19. Governments around the world have now stressed the importance of preparedness for future CD outbreaks through the means of the timely availability and executability of vaccination campaigns requiring upfront investment. Consequently, this will create the need for public decision-makers to assess different vaccines and (emergency) policies for their respective benefits and costs, incorporating the broad perspective described above . The panIn the past, vaccination campaigns have been investigated regarding the added health benefit relative to the added costs for society . However, in the field of health economics, fiscal health modelling (FHM) has recently been brought forward as an estimation technique specifically focused on quantifying the consequences of health policies from a public finance perspective . MultiplThis study aims to determine such scope by defining an FHM analytic framework for vaccination strategies as part of societal and governmental pandemic preparedness policies. Secondly, using the real-world example case of COVID-19 in the Netherlands, we aim to retrospectively quantify the fiscal impact of the COVID-19 pandemic on the Dutch government. The goal of this manuscript is to develop a fiscal health modelling framework for the Netherlands to calculate the fiscal impact of CD based on previously published literature.FHM estimates the effects of a health condition on governmental net lifetime tax value and average government transfer payments following changes in morbidity and mortality rates due to a disease and/or intervention. As a result of the significant cross-sectoral impact of a CD and the public urgency of an outbreak, interventions aiming to counteract often fall under the responsibility of national governments. Yet, these might be outside the funding responsibilities of national health authorities generally covering health care. Hence, FHM is suited to policymakers and budgetary holders outside the health sector that requires a different set of economic data to inform policy.Health economics is characterized by two perspectives that are often used to inform decision-makers regarding budget allocation in health care. The payer and societal perspectives are most commonly used. In the Netherlands, the societal perspective is the primary perspective as prescribed by the Dutch guidelines for economic evaluations . In the This study was performed in the context of previously published literature regarding FHM, the governmental perspective on health technology assessment (HTA) and the impact of COVID-19 in the Netherlands. The COVID-19 pandemic has highlighted the importance of a structured and improved assessment of responses to CDs from the governmental perspective. These methods, while previously described, have not yet been quantified for the Netherlands and could substantially impact and improve the decision-making regarding CDs in the Netherlands. A scoping literature review from the core literature identified and supplemented with internal expert opinion meetings regarding methodology and fiscal categories was performed.The fiscal health modelling framework was developed based on previously published available literature. The studies by Mauskopf et al., Connolly et al. and Kotsopoules et al. provided the basis for the fiscal health modelling framework and were expanded through a scoping literature search from the previously mentioned relevant papers ,12,13,14Based on the literature search and expert opinions, we identified two possible approaches to estimate the fiscal impact of CD. Under the first approach, direct consequences which can be causally linked to an infectious case should be determined and summed up. This procedure is followed by more uncertain or indirect consequences, which should then be estimated. The second approach to estimating the fiscal impact we propose is to use simple regression analysis to compare an estimated change in fiscal categories with the realized value observed in 2021. A visual representation of the proposed framework is depicted in For the first approach to fiscally analysing a CD outbreak, the absolute amount of disease effects should be collected in the form of population data, reporting the number of individual laboratory-confirmed registered cases of COVID-19 within a period.Given the potentially broad health impact of a CD outbreak within society, we propose this analysis to assess disease effects which are reported with conventional population data or population data from HTA models. Data on the cases of infections, hospitalizations and mortality are expected to be the most readily available form of reliable data. Following the fiscal profiles of different age groups, count data should additionally be categorized into age cohorts. Analysts should determine the number of age cohorts by trading off/weighting the difficulty of obtaining data on smaller age cohorts as well as the consequent model complexity against the achieved incremental model accuracy.COVID-19-specific population data on laboratory-confirmed infections, hospital admissions, intensive care unit (ICU) admissions and mortality, were collected for the pre-specified timeframe using multiple publicly available data files of the Dutch Institute of Public Health (RIVM) [COVID-19 cases in the Netherlands. Infection and hospitalization data were processed using R (version 4.0.3) to group individuals into five age cohorts: 0 years–19 years; 20 years–39 years; 40 years–59 years; 60 years–79 years; 80+ years [Dutch h (RIVM) . The pop0+ years .The collected population data can be connected to fiscal consequences for which there has been a proven causal relationship, or which can be logically inferred. For example, a deceased person cannot pay income tax anymore, which consequently represents a loss to the government. Aiming to capture all influences of the CD outbreak, researchers should use the accounting principle of receivables and payables to assess how a health condition influences tax revenue and government payments in relation to specific health events. Preferably, fiscal values on different payables and receivables should be specific to the previously determined age cohorts to enable a more detailed analysis of the final fiscal impact. In addition, the mentioned fiscal values should be adjusted for the employment status of the included individuals .To translate the disease effects collected above, the Dutch social system was the basis for the determination of the direct fiscal consequences. The fiscal inputs were recalculated to 2020. The receivables of the state in this analysis consisted of income tax on salary, value-added tax (VAT) revenue and inheritance tax revenue due to mortality. The framework allows the inclusion of payables in the form of social benefits due to newly incurred work disability.As described above, the FHM analysis in Approach I sums up the fiscal results per health state as a result of mortality and morbidity. The collected patient data per specified age cohort were combined with the quantified estimates of the receivables and payables using the specified equations in To calculate the income tax per capita, the gross income per age group was collected from the Central Bureau of Statistics and tax authorities . The incIn the Dutch social security system, salaries are continued to be paid out for a minimum of 70% for up to 2 years in the case of work disability by the employer ,31. AfteAnother possible payment of social benefits is the WLZ (‘wet langdurige zorg’). The WLZ is between 85% and 100% of the income of someone who requires (intensive) care and is fully unable to work . There aIn the particular case of COVID-19 in the Netherlands, no data on the utilization of long-term care and work disability benefits were yet available at the population level, which is why the WLZ and WIA have not yet been quantified. Further financial and other economic support measures were excluded from these equations, as these focus only on the epidemiological impact of the virus. The average state pension payments were calculated to be EUR 11.686 annually per capita . To captAs many implications and consequences of the COVID-19 outbreak are uncertain, this study quantifies exemplary long-term consequences, as mentioned and qualified in the framework above. An infection of a CD can also have consequences which are more long-term and uncertain in their nature than the outbreak itself. Hence, consequences not specifically quantified in the literature should be included separately from the direct consequences in the form of hypotheses or value ranges. We propose to categorize these consequences into two areas:An example of a long-term change in population health is the novel incidence of self-reported long-lasting symptoms after a COVID-19 infection (Long-Covid). As an example in this category, published estimates regarding Long-Covid were sourced from a population survey by the British Office of National Statistics . From thThe cost of emergency policies are all expenses that can be identified from a governmental perspective as a response to the COVID-19 pandemic. The costs are collected from reports of the Dutch court of audit . They inAlternatively, fiscal categories in their entirety can be identified for the respective research setting and extrapolated. Generally, big fiscal ledgers can be considered, such as the general governmental tax or premium income, i.e., national accounts or the development of the national GDP in the pre-determined period. Following this approach, a simple regression fit should be conducted on the historical data per category to enable the  comparison of a situation with and without an outbreak. A type of consequence which was explicitly not included in the framework was absenteeism and presenteeism losses because this mostly impacts employers and rarely the direct financial detriment of the government.Another possibility to express the fiscal impact of the COVID-19 pandemic is the incremental change to governmental general tax revenue when comparing the pre- and post-COVID-19 years . Fiscal To assess the lost gross domestic product (GDP) due to the pandemic as a form of opportunity cost, reported GDP figures in the time frame of 1990–2019 were extrapolated using a linear function . The Dut® following the FAST financial modelling principles for financial modelling [As a case example, this study aimed to quantify the fiscal impact of the COVID-19 pandemic in the Netherlands between the 1 January 2020 and the 31 December 2021 following the framework described above. The FHM model was quantified using Excel 2016odelling , a commoBecause, in the Netherlands, multiple forms of interventions were introduced during the above-mentioned period, these were compared to the infections, hospitalization, ICU admissions and mortality and their respective effect on receivables and payables . InterveIn 2020, the total number of COVID-19 infections was 826,086, followed by 35,920 hospitalizations and 6694 ICU admissions, and the overall mortality was 15,102. In 2021, the total number of infections was 2,356,933, the number of hospitalizations was 52,678, ICU admissions happened 9805 times and mortality was 9493. Between 2020 and 2021, the number of infections, hospitalisations and ICU admissions shifted towards the lower age groups in attribution to the total in each category. No such dynamic was observed with mortality. The population per age group and category is shown in Based on the population data described above and the connected direct fiscal effects per subject highlighted earlier, the total loss of income tax attributed to COVID-19 mortality was equal to EUR 266 million. The total lost VAT income was EUR 230 million, whereas the total inheritance taxes gained was estimated to be EUR 332 million. The total state pension payments averted were EUR 953 million due to mortality. This resulted in lost receivables of EUR 164 million combining both 2020 and 2021. As no direct consequences of morbidity  were included in the direct consequences, the outcomes for payables were equal to a reduction of EUR 953 million. Thus, the total direct fiscal impact between 2020 and 2021 was, therefore, EUR 789 million. However, excluding pension payments averted, the total direct fiscal impact was equal to EUR –164 million. The receivables and payables as a result of two years of the COVID-19 pandemic per cost category are shown below in The total number of people estimated to be limited in their daily lives through Long-Covid was 42,434 and 112,907 for 2020 and 2021, respectively. Furthermore, the number of individuals to be severely limited was equal to 18,648 in 2020 and 47,988 in 2021. The population data per age group are shown in The expenditure of emergency policies in response to the COVID-19 pandemic in the Netherlands accounted for EUR 29 billion in the year 2020 and EUR 40 billion in 2021. These for the most part entailed liquidity measures for the private sector, subsidies and costs of emergency healthcare capacity. The ten largest expenses of emergency policies are shown in Using an exponential function for extrapolation, the expected tax income values for 2020 and 2021 were equal to EUR 214,781 million and EUR 228,291 million. Comparing these expected values to the realized values for 2020 and 2021, respectively, EUR 205,671 million and EUR 223,819 million, showed a difference of EUR −9110 million and EUR −4472 million. The largest difference between expected and realized value before 2020 was in 2019 (EUR 4248 million) and in 2016 (EUR −3239 million) The total tax income over time between 2012 and 2019 is shown in The fitted linear equation is used to calculate the expected values of 2020 equal to EUR 1.130 trillion compared to the realized value of EUR 1.034 trillion. This results in a difference between these two of EUR −96.296 billion. This was the largest difference observed, except for 2012. Comparable differences were seen in 2017. The mentioned data points, as well as the fitted function, are shown in The societal consequences of a CD outbreak are extensive. The disease itself, as well as potential emergency measures, affect every aspect of society, the impact of which is difficult to fully capture. However, the COVID-19 pandemic can be a learning case for future  CD outbreaks, as it highlights areas where society and government were affected the most. This framework—to our knowledge—is the first that utilizes these insights to allow analysis in the future for a more structured categorization of the specific impact areas of future outbreaks and policy interventions. By proposing two alternative approaches to the quantification of the fiscal impact, it is an intuitive orientation for researchers to analyse and communicate certain and uncertain aspects of the fiscal impact to decision-makers, while keeping the framework adaptable to specific disease contexts.The approaches differ, however, in their value for analysing CD impact and policy interventions. Approach I, using detailed consequence data or assumptions, allows for prospective modelling of population data and thus a fiscal impact beyond the timeframe of the outbreak itself, yet might be restricted in its accuracy due to the limited information on impact categories before an outbreak occurred. On the other hand, Approach II, based on historical observations, can include all dynamics on state receivables and payables but can only be implemented retrospectively and limited to a specified timeframe because of its need for broad reported data. When comparing Approach I to Approach II, we see that the impact is much lower because this is only based on the disease data and the expected effects of this. Because Approach II assessed the total fiscal impact based on the expected and observed tax income, the observed losses are much higher than those calculated for the predetermined categories in Approach I. The difference between the two approaches is likely governed by the interaction of economic domains that give rise to multiplier effects. This is also consistent with previous observations noting that micro and macro models do not always corroborate . Even thProspective disease modelling exercises used in health technology assessment together with an FHM analysis approach, as shown in Approach I, can be implemented to estimate the net value of that intervention in an already existing health economic methodology as part of an additional perspective. Through this approach, one can prospectively compare the damage averted and the needed investment by the state, especially relevant for decision-makers. FHM is an addition to the standard of HTA, valuing health units such as life-years or quality-adjusted life-years (QALYs) compared to costs . Which aAlthough COVID-19 has gained a significant amount of public attention, other CDs also affect daily life, especially that of individuals in vulnerable subpopulations. Children and the elderly affected by rotavirus, herpes zoster or influenza are well-known examples, yet societies oftentimes struggle to prevent these outbreaks. This is also partly because vaccination efforts within those populations have been deemed not to be cost-effective interventions; however, they may be justifiable on fiscal grounds. Regarding the fiscal impact, the direct consequences are expected to be minor for those groups as they are not part of the labour force. However, the indirect consequences could be notable, because their health does have repercussive effects on friends and family members who offer informal care or other support activities . These dIn addition, the fiscal impact of CD outbreaks, as shown in this study, and the response of governments to the COVID-19 pandemic could affect both the economic as well as the physiological implications of a disease outbreak . ImposinFuture research should focus on quantifying the presented framework for other CDs and countries. Transferability of consequence categories should be the main priority, as countries do have different designs of tax and social welfare systems. In this context, researchers should be careful to not use double-count costs, especially in the indirect effect areas. Lastly, future research should consider the prospective quantification of an outbreak by linking the epidemiological outcomes of a disease modelling exercise to the framework.The impact of the COVID-19 pandemic has shown that better preparation and response in the Netherlands could potentially save millions. The FHM framework presented in this study is the first that proposes two fiscal approaches to quantify the fiscal impact of a CD outbreak to inform decision-making regarding prevention, public health and social services. The choice of approach is dependent on the goal as well as available data. Prospective analysis can be used in future investment decisions and health economic analysis, while retrospective identification is a valuable addition in the assessment of historic pandemics or CD outbreaks. Using the example case of COVID-19 in the Netherlands, it was shown that the cross-sectorial is extensive and can go far beyond only the fiscal consequences of infected and/or deceased individuals. The substantial fiscal impact as a result of mortality shows that investing in prevention can save substantial costs. While not all CDs would become a pandemic, a more proactive position from the government is warranted based on the potential fiscal impact.The FHM framework for the Netherlands, as proposed in this manuscript, can be used by decision-makers and  economists to assess the fiscal impact and value of investing in prevention and/or interventions in health. The framework as presented in this manuscript could be translated to other disease areas or population data in order the calculate the fiscal impact. This could lead to a better substantiated investment decision and response to CD outbreaks. For society, this could mean that investments in public health, such as pandemic preparedness and prevention, have more value than previously acknowledged in economic analysis.Our study adds a method for the assessment of health and policy interventions in the Netherlands. This creates an opportunity for decision-makers to invest in pandemic preparedness, public health and prevention. While we focused on the Dutch settings, some interventions might be better assessed at an international level, such as the European procurement of vaccines . We show