Journal of advanced manufacturing and processing最新文献

A custom design multi-flying jet plasma torches (MFJPT) reactor was tested for plasmolysis of water vapor and mixtures of water vapor-carbon dioxide in a series of experimental investigations at various reactor operational parameters. Experimentation plans were applied within the range of induced power (100–300 watts) and various vapor/gas throughputs. The produced gases were analyzed through online gas chromatography. The results of water vapor plasmolysis in two schemes demonstrated the production of 1337 ppm of hydrogen from water vapor/argon and 1665 ppm from applying a water vapor/argon/CO₂ combination. Valuable hydrocarbon gases (e.g., Ethane, Ethylene/Acetylene) were generated and detected at higher conversions upon introducing H₂O vapor, argon, and CO₂ mixtures. The experimental data were trained through machine learning and a Gaussian Process Regression (GPR) model has fitted the data quite well. Ultimately, optimization study outcomes are presented through a color heat-map for system scaling-up purposes.

Dry cooling, where forced air is the heat transfer medium, is a preferable cooling method in arid locations lacking readily available process water. However, such locations often experience high ambient temperatures that limit the effectiveness of air cooling. The objective of this study is to quantify the economic and energetic benefits of heat transfer intensification via the implementation of aeroelastically fluttering reeds to the air-cooled condenser of a methanol distillation column. Condenser size and performance, regarding recovered methanol and required fan power, is evaluated across condenser operating temperatures (T_cond) from 60 to 62°C and heat transfer coefficients (U) U_base–2U_base for a range of inlet air temperatures based on ambient temperature data from Yuma, Arizona. Under typical design sizing, condenser capital cost was reduced by 6%–35% (1.3U_base–2U_base) and nominal methanol recovery was increased from 0.26% to 0.38% (T_cond = 62–60°C). At optimized condenser size, all enhanced U and T_cond pairs increase methanol recovery and reduce fan power costs compared to the optimal U_base reference. Overall, using enhanced heat transfer to maintain condenser temperature under a wider range of inlet conditions, rather than to reduce operation temperature, produces more favorable performance. Methanol price is not a determining factor in which pairs are profitable. Analysis was repeated for a global warming scenario, revealing more valuable improvements under elevated temperatures. Energy savings from condenser improvement to a methanol production system are not significant with respect to an optimized conventional system. Unit economic and energetic incentives suggest implementation of fluttering reeds may be justified in other applications.

Vaccine packaging innovations have a potential impact on the supply chain, as well as acceptability and uptake by users. To better understand recent developments in packaging innovations, we assessed the benefits and challenges of compact pre-filled auto-disable devices (CPADs), polymer-based primary containers (or vials), and dual-chamber delivery devices. These packaging innovations have clear public health benefits related to ease of use, improved safety, supply logistics, and uptake enabling new vaccination opportunities. In addition, there seems to be high acceptability from users at country-level. However, uncertain costs including the impact on distribution and cold chain storage have hampered demand and consequently commercialization. For innovations to reach those who can benefit most, global stakeholders should place greater attention on new packaging technologies and assess the trade-offs between safety, coverage, uptake, ease of administration, wastage and costs.

Biological therapeutics are increasingly being formulated to high protein concentrations to decrease drug substance storage space and increase the flexibility of administration to patients. With the higher protein concentration targets comes added challenges to the downstream purification manufacturing process. Tangential flow filtration (TFF) operations are typically performed to reach target protein concentration. TFF operations for a given drug substance may include an Ultrafiltration and Diafiltration (UFDF) step, or a UFDF step followed by a single pass tangential flow filtration (SPTFF) step. Whether a TFF step achieves a target protein concentration is determined by at-line protein concentration measurements performed at the completion of a process step. If the measured protein concentration is outside the specified range, the unit operation may need to be restarted, reprocessing may need to be performed, or a batch may need to be terminated. Out of specification protein concentration measurements may be a result of the TFF operation or sample measurement. Increased viscosities associated with high concentration TFF operations pose added challenges to the TFF process and sample measurement. Implementation of an inline process analytical technology (PAT) to monitor real-time protein concentration during TFF operations has the potential to improve the accuracy of the operations in achieving target protein concentrations. This will result in improved process consistency and efficiency, increased operator confidence and decreased likelihood of batch failures. This paper studies the performance of a K-Patents PR-23 refractometer (Vaisala) as a PAT to monitor and control the UFDF and SPTFF unit operations of a commercial scale monoclonal antibody purification process.

The term “Advanced Pharmaceutical Manufacturing” (APM) has become an ubiquitous buzzword with deep potential policy implications. There is a real danger that APM will be seen as a general panacea for solving economic woes and drug shortages, devoiding it from specific meaning, and depriving the technical community of focus much needed for its development and implementation. This paper first discusses several semantic definitions of APM that have been proposed in the field. A functional definition of APM is then presented as: A system that is designed using predictive models, where automation minimizes human intervention while enabling closed loop process control and real time quality assurance, where performance has been optimized to maximize desired process goals, where flexible amounts of product with equivalent attributes can be manufactured, and where equivalent processes can be implemented at multiple locations to manufacture products with equivalent critical quality attributes. The proposed definition is presented to motivate discussion and elicit contributions from other stakeholders, and to contribute to the development of a consensus framework for design, development, implementation, and evaluation of APM.

Plug-screw feeders are critical in many industrial processes for compressing slurry materials via a rotating plug-screw feeder. Over time, increasing plug-screw feeder wear will eventually lead to catastrophic mechanical failure. Early detection of the wear state can prevent unplanned catastrophic failures resulting in operational shut-downs, costly repairs, and most importantly the health and safety of workers. We present a theoretical basis for a noninvasive, in operando acoustic resonant technique to monitor the wear state of plug-screw feeders. The technique is based on tracking the resonant acoustic modes of the plug-screw feeder, which are sensitive to the plug-screw feeder geometry, material, and operating conditions. We implemented a multivariate polynomial model to estimate the plug-screw feeder wear state using multiple acoustic resonances by simulating the acoustic resonant modes for two categories of wear (tip and thread damage) that are common in plug-screw feeders. Fitting multiple resonances to the polynomial model, we demonstrate accurate estimation of the total mass loss, as well as characterization of the type of damage (i.e., tip vs. thread). Current approaches for monitoring plug-screw feeder wear rely on shutting down the operation and visually inspecting the plug-screw feeder. The presented acoustic technique offers a noninvasive, in operando measurement approach that mitigates unplanned catastrophic failures. The acoustic resonance technique presented in the paper has a broad range of industrial applications including the Pharmaceutical, Mining, Integrated Biorefineries (IBR), and Additive Manufacturing industries, to name a few.

This study employed machine learning (ML) models to predict the cardiomyocyte (CM) content following differentiation of human induced pluripotent stem cells (hiPSCs) encapsulated in hydrogel microspheroids and to identify the main experimental variables affecting the CM yield. Understanding how to enhance CM generation using hiPSCs is critical in moving toward large-scale production and implementing their use in developing therapeutic drugs and regenerative treatments. Cardiomyocyte production has entered a new era with improvements in the differentiation process. However, existing processes are not sufficiently robust for reliable CM manufacturing. Using ML techniques to correlate the initial, experimentally specified stem cell microenvironment's impact on cardiac differentiation could identify important process features. The initial tunable (controlled) input features for training ML models were extracted from 85 individual experiments. Subsets of the controlled input features were selected using feature selection and used for model construction. Random forests, Gaussian process, and support vector machines were employed as the ML models. The models were built to predict two classes of sufficient and insufficient for CM content on differentiation day 10. The best model predicted the sufficient class with an accuracy of 75% and a precision of 71%. The identified key features including post-freeze passage number, media type, PF fibrinogen concentration, CHIR/S/V, axial ratio, and cell concentration provided insight into the significant experimental conditions. This study showed that we can extract information from the experiments and build predictive models that could enhance the cell production process by using ML techniques.

As side products of concern, glycidyl fatty acid esters (GE) are an unwanted impurity in food emulsifier production. This study investigates the reduction of the GE content in such monoglycerides and their blends with diglycerides (mono-/diglycerides) by thermal post-treatment. The results indicate that the kinetics of GE reduction and equilibrium concentrations largely depend on the specific temperatures applied. For instance, while reaching equilibrium levels of GE can take several weeks at 80°C, they can be reached within hours at 150°C. The thermal treatment can have, however, also negative impact on other quality parameters, like the content of monoglycerides in both, distilled monoglycerides and mono-/diglycerides. Based on 47 laboratory and pilot trials the influence of post-treatment temperatures between 80 and 200°C on reducing GE levels has been systematically investigated. The obtained data were analyzed by mathematical modeling.