Journal of advanced manufacturing and processing最新文献

{"title":"Refractive index as a process analytical technology to measure real time protein concentration for monitoring and control of commercial scale tangential flow filtration operations","authors":"Yik Lam,&nbsp;Devin McCann,&nbsp;Abdullah Loman,&nbsp;John Paul Smelko,&nbsp;Alex Brinkmann","doi":"10.1002/amp2.10151","DOIUrl":"10.1002/amp2.10151","url":null,"abstract":"<p>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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44020692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced pharmaceutical manufacturing: A functional definition","authors":"Rodolfo J. Romañach,&nbsp;Torsten Stelzer,&nbsp;Eric Sanchez,&nbsp;Fernando Muzzio","doi":"10.1002/amp2.10150","DOIUrl":"10.1002/amp2.10150","url":null,"abstract":"<p>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: <i>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.</i> 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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45944325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resonant acoustic monitoring of damage in plug-screw feeders","authors":"John Greenhall,&nbsp;Cristian Pantea,&nbsp;Troy Allen Semelsberger","doi":"10.1002/amp2.10149","DOIUrl":"10.1002/amp2.10149","url":null,"abstract":"<p>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, <i>in operando</i> 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, <i>in operando</i> 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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48140677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of cardiac differentiation outcome, percentage of cardiomyocytes on day 10 of differentiation, for hydrogel-encapsulated hiPSCs","authors":"Samira Mohammadi,&nbsp;Mohammadjafar Hashemi,&nbsp;Ferdous Finklea,&nbsp;Bianca Williams,&nbsp;Elizabeth Lipke,&nbsp;Selen Cremaschi","doi":"10.1002/amp2.10148","DOIUrl":"10.1002/amp2.10148","url":null,"abstract":"<p>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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43085907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clean Energy Smart Manufacturing Innovation Institute (CESMII) Special Issue","authors":"Haresh Malkani,&nbsp;Prakashan Korambath","doi":"10.1002/amp2.10146","DOIUrl":"10.1002/amp2.10146","url":null,"abstract":"","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41877770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal degradation of glycidyl esters in monoglycerides and mono- and diglyceride blends","authors":"Thomas Schmidt,&nbsp;Flemming Vang Sparsoe,&nbsp;Rico Bagger-Jørgensen,&nbsp;Thomas Norup From,&nbsp;Anna Kjaer Keskin,&nbsp;Rasmus Leth Miller","doi":"10.1002/amp2.10147","DOIUrl":"10.1002/amp2.10147","url":null,"abstract":"<p>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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45382734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and estimating kinetic parameters for CO2 methanation from fixed bed reactor experiments","authors":"Toshiki Tsuboi,&nbsp;Shoya Yasuda,&nbsp;Cheolyong Choi,&nbsp;Wei Zhang,&nbsp;Hiroshi Machida,&nbsp;Koyo Norinaga,&nbsp;Tomoyuki Yajima,&nbsp;Yoshiaki Kawajiri","doi":"10.1002/amp2.10145","DOIUrl":"10.1002/amp2.10145","url":null,"abstract":"<p>CO₂ methanation, which converts CO₂ and hydrogen into methane as fuel, is one of the promising candidates for the development of CO₂ utilization technologies. Recently, a highly active catalyst made of Ni/ZrO₂ for methanation has been developed, and is currently investigated as a potential use in a high-performance reactor. However, design of reactor must be carried out carefully, since this reaction is highly exothermic, which may cause reactor runaway and deterioration of catalysts. For this problem, a mathematical model that can predict the behavior inside the reactor is necessary. In this work, we consider the methanation reaction of CO₂ in a reactor model and estimate the kinetic parameters in the reaction rate model from experimental data. In the parameter estimation using literature values and Tikhonov regularization, eight kinetic parameters in the rate equations were identified from 64 data points with a wide range of conditions. We confirm that molar fractions at the reactor exit predicted by this reactor model are in good agreement with the experimental results. Furthermore, the developed model was validated to predict the compositions and temperature that were not used in the estimation. We expect the developed model will be a powerful tool for the reactor design.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48655463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the lower operation limit and the gain of flexibility of an innovative segmented tray column","authors":"Henrik Fasel,&nbsp;Marcus Grünewald,&nbsp;Julia Riese","doi":"10.1002/amp2.10144","DOIUrl":"10.1002/amp2.10144","url":null,"abstract":"<p>Operating windows of conventional tray columns may not be large enough to ensure a sufficient separation with the current or future challenges of a volatile energy and raw material supply. Therefore, an innovative, segmented separation tray has been developed, which enlarges the operation window and thus leads to a higher flexibility, managing the challenges of higher volatility in downstream processes. In this contribution, a hydrodynamic characterization and the resulting lower operation limits of this segmented tray are presented. Furthermore, an approach to obtain shorter start-up times for the column and a resulting faster response time to changes in the process are presented. The feasibility of this type of operation is evaluated by the investigation of the stability of the tray under new operation conditions.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49632113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensor selection and tool wear prediction with data-driven models for precision machining","authors":"Seulki Han,&nbsp;Qian Yang,&nbsp;Krishna R. Pattipati,&nbsp;George M. Bollas","doi":"10.1002/amp2.10143","DOIUrl":"10.1002/amp2.10143","url":null,"abstract":"<p>Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency-domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data-driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time-domain signal statistics, frequency-domain analysis, and time-frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least-Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time-frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47529471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engineering a ceramic piston pump to minimize particle formation for a therapeutic immunoglobulin: A combined factorial and modeling approach","authors":"Kirk Roffi,&nbsp;Israel B. Sebastião,&nbsp;Alexandre Morel","doi":"10.1002/amp2.10142","DOIUrl":"https://doi.org/10.1002/amp2.10142","url":null,"abstract":"<p>During fill-finish manufacturing, protein-pump surface interactions can induce subvisible particle (SVP) formation which poses a risk to drug product quality and patient safety. Despite this risk, there have been no concerted efforts to understand the effects of piston pump design on SVP formation. We've systematically varied the design of the piston-cylinder interface to minimize SVP formation for a therapeutic immunoglobulin. The clearance factor, surface roughness factor, and their combined interaction significantly affected particle concentrations, quantitated by light obscuration and microflow imaging. Optimized pump designs reduced particle levels by 1–2 orders of magnitude compared to the off-the-shelf equipment. At the piston surface, scanning electron microscopy revealed evidence of protein film abrasion, a process which ejects SVPs from the piston-cylinder interface as wear debris. Computational fluid dynamics and quartz crystal microbalance were applied to simulate fluid flow and protein adsorption phenomena in the pump respectively. The risk of protein film abrasion was modeled along a hypothetical Stribeck curve, thereby interconnecting design parameters, lubrication conditions, and SVP formation. Our findings support implementation of a modular pump platform with interchangeable pistons; this approach would enable the pump design to be customized based on each protein's propensity to form SVPs. This flexible approach can benefit pharmaceutical manufacturers and patients alike by accelerating tech transfer and improving process control.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50147966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}