Process development for the production of mesenchymal stromal cell-derived extracellular vesicles in conventional 2D systems.

BACKGROUND: In recent years, the importance of extracellular vesicles (EVs) derived from mesenchymal stromal cells (MSCs) has increased significantly. For their widespread use, a standardized EV manufacturing is needed which often includes conventional, static 2D systems. For these system critical process parameters need to be determined. METHODS: We studied the impact of process parameters on MSC proliferation, MSC-derived particle production including EVs, EV- and MSC-specific marker expression, and particle functionality in a HaCaT cell migration assay. RESULTS: We found that cell culture growth surface and media affected MSCs and their secretory behavior. Interestingly, the materials that promoted MSC proliferation did not necessarily result in the most functional MSC-derived particles. In addition, we found that MSCs seeded at 4 × 103 cells cm-2 produced particles with improved functional properties compared to higher seeding densities. MSCs in a highly proliferative state did not produce the most particles, although these particles were significantly more effective in promoting HaCaT cell migration. The same correlation was found when investigating the cultivation temperature. A physiological temperature of 37°C was not optimal for particle yield, although it resulted in the most functional particles. We observed a proliferation-associated particle production and found potential correlations between particle production and glucose consumption, enabling the estimation of final particle yields. CONCLUSIONS: Our findings suggest that parameters, which must be defined prior to each individual cultivation and do not require complex and expensive equipment, can significantly increase MSC-derived particle production including EVs. Integrating these parameters into a standardized EV process development paves the way for robust and efficient EV manufacturing for early clinical phases.

Cloud-Based Machine Learning Methods for Parameter Prediction in Textile Manufacturing.

In traditional textile manufacturing, downstream manufacturers use raw materials, such as Nylon and cotton yarns, to produce textile products. The manufacturing process involves warping, sizing, beaming, weaving, and inspection. Staff members typically use a trial-and-error approach to adjust the appropriate production parameters in the manufacturing process, which can be time consuming and a waste of resources. To enhance the efficiency and effectiveness of textile manufacturing economically, this study proposes a query-based learning method in regression analytics using existing manufacturing data. Query-based learning allows the model training to evolve its decision-making process through dynamic interactions with its solution space. In this study, predefined target parameters of quality factors were first used to validate the training results and create new training patterns. These new patterns were then imported into the solution space of the training model. In predicting product quality, the results show that the proposed query-based regression algorithm has a mean squared error of 0.0153, which is better than those of the original regression-related methods (Avg. mean squared error = 0.020). The trained model was deployed as an application programing interface (API) for cloud-based analytics and an extensive auto-notification service.

A 4H-SiC CMOS Oscillator-Based Temperature Sensor Operating from 298 K up to 573 K.

In this paper, we propose a temperature sensor based on a 4H-SiC CMOS oscillator circuit and that is able to operate in the temperature range between 298 K and 573 K. The circuit is developed on Fraunhofer IISB's 2 µm 4H-SiC CMOS technology and is designed for a bias voltage of 20 V and an oscillation frequency of 90 kHz at room temperature. The possibility to relate the absolute temperature with the oscillation frequency is due to the temperature dependency of the threshold voltage and of the channel mobility of the transistors. An analytical model of the frequency-temperature dependency has been developed and is used as a starting point for the design of the circuit. Once the circuit has been designed, numerical simulations are performed with the Verilog-A BSIM4SiC model, which has been opportunely tuned on Fraunhofer IISB's 2 µm 4H-SiC CMOS technology, and their results showed almost linear frequency-temperature characteristics with a coefficient of determination that was higher than 0.9681 for all of the bias conditions, whose maximum is 0.9992 at a VDD = 12.5 V. Moreover, we considered the effects of the fabrication process through a Monte Carlo analysis, where we varied the threshold voltage and the channel mobility with different values of the Gaussian distribution variance. For example, at VDD = 20 V, a deviation of 17.4% from the nominal characteristic is obtained for a Gaussian distribution variance of 20%. Finally, we applied the one-point calibration procedure, and temperature errors of +8.8 K and -5.8 K were observed at VDD = 15 V.

Importance of Process Parameters Influencing the Mean Diameters of siRNA-Containing Lipid Nanoparticles (LNPs) on the in Vitro Activity of Prepared LNPs.

Genetic drugs have the potential to treat a variety of diseases. Recently, lipid nanoparticles (LNPs) have attracted much attention among drug delivery systems for genetic drugs. LNPs have been practically used in small interfering RNA (siRNA) drugs and mRNA vaccines. Although LNPs are generally prepared by mixing nucleic acids in acidic aqueous buffer and lipid excipients in alcohol (i.e., ethanol), it is not well understood which process parameters in the LNPs formation affect the physicochemical properties and the functionality of LNPs. In this study, we used siRNA-containing LNPs as a model, and evaluated the effect that aqueous solution parameters (buffering agent type, salt concentration, and pH) and mixing parameters (ratio, speed, and temperature) exert on the physicochemical properties and in vitro gene-knockdown activity of LNPs. Among such parameters, the type of buffering agent, salt concentration (ionic strength), pH in acidic aqueous buffer, as well as the mixing ratio and speed significantly affected the mean particle diameter and in vitro gene-knockdown activity of LNPs. A strong correlation between the mean particle diameters and their in vitro gene-knockdown activities was observed. These observations suggest that the process parameters influencing the mean LNPs diameter are likely to be important in the formation of LNPs and also that these correlate with in vitro gene-knockdown activity. Because LNP systems are being further developed for future clinical applications of genetic drugs, information regarding the LNPs manufacturing process is of utmost importance. The results observed in this study will be useful for the manufacturing of optimal LNPs.

Prediction Performance and Economic Efficiency of Soft Sensors for in-Line Water Content Monitoring in Fluidized Bed Granulation: PP-Based Model vs. NIRS-Based Model.

Soft sensors play a crucial role as process analytical technology (PAT) tools. They are classified into physical models, statistical models, and their hybrid models. In general, statistical models are better estimators than physical models. In this study, two types of standard statistical models using process parameters (PPs) and near-infrared spectroscopy (NIRS) were investigated in terms of prediction accuracy and development cost. Locally weighted partial least squares regression (LW-PLSR), a type of nonlinear regression method, was utilized. Development cost was defined as the cost of goods required to construct an accurate model of commercial-scale equipment. Eleven granulation lots consisting of three laboratory-scale, two pilot-scale, and six commercial-scale lots were prepared. Three commercial-scale granulation lots were selected as a validation dataset, and the remaining eight granulation lots were utilized as calibration datasets. The results demonstrated that the PP-based and NIRS-based LW-PLSR models achieved high prediction accuracy without using the commercial-scale data in the calibration dataset. This practical case study clarified that the construction of accurate LW-PLSR models requires the calibration samples with the following two features: 1) located near the validation samples on the subspace spanned by principal components (PCs), and 2) having a wide range of variations in PC scores. In addition, it was confirmed that the reduction in cost and mass fraction of active pharmaceutical ingredient (API) made the PP-based models more cost-effective than the NIRS-based models. The present work supports to build accurate models efficiently and save the development cost of PAT.

[Study on extraction kinetics of Salviae Miltiorrhizae Radix et Rhizoma: model establishment and investigation of critical process parameters].

In this study, Fick's first law and partition equilibrium were used to represent the internal and external mass transfer processes of Salviae Miltiorrhizae Radix et Rhizoma at the macroscopic level, and a mass transfer model was established. The specific surface area was integrated into the mass transfer resistance, which effectively avoided the irregular shape of medicinal materials and expanded the application scope of the model. Meanwhile, the mass transfer model was further combined with the kinetic model of salvia-nolic acid degradation to establish the extraction kinetic models of salvianolic acid B, lithospermic acid and Danshensu. The model was applied to study the extraction process of Salviae Miltiorrhizae Radix et Rhizoma. According to the sensitivity analysis results, the relative error of the model prediction was within 5% near the maximum extraction rate(320 min), and the prediction performance of the model was good. According to the investigation results of different process parameters, stirring could significantly accelerate the mass transfer rate of salvianolic acid B, while the mass transfer resistance and degradation rate constant were not affected by solvent-to-solid ratio. The linear relationship between the reciprocal of temperature and the logarithm of mass transfer resistance was good(R~2=0.996), indicating that the temperature and mass transfer resistance conformed to Arrhenius formula. In addition, we also found that the concentration changes of lithospermic acid and Danshensu were weakly affected by mass transferwhen the extraction temperature was higher than 358 K. This study has provided the basis for the process optimization and quality control of traditional Chinese medicine extraction.

A cross-industry forum on benchmarking critical quality attribute identification and linkage to process characterization studies.

Identification of Critical Quality Attributes (CQAs) and subsequent characterization in process development studies are the key elements of quality by design (QbD) for biopharmaceutical products. Since the inception of ICH Q8R2, several articles have been published on approaches to conducting CQA risk assessments as well as the application to process understanding. A survey was conducted by multiple companies participating in an International Consortium working group on the best practices for identifying CQAs with linkages to process characterization (PC) studies. The results indicate that the companies surveyed are using similar approaches/timing to identify CQAs during process development. Consensus was also observed among the companies surveyed with approaches to linkage of CQAs to process characterization studies leading to impact to control strategies and lifecycle management.

[Identification of critical process parameters of Jinqing alcohol precipitation of Reduning Injection by big data].

Lonicerae Japonicae Flos and Artemisiae Annuae Herba(LA or Jinqing) alcohol precipitation has various process parameters and complex process mechanism, and is one of the key units for manufacturing Reduning Injection. In order to identify the critical process parameters(CPPs) affecting the weight of the extract produced from the alcohol precipitation process, 259 batches of historical production data from 2017 to 2018 were collected, with a total of 829 318 data points. These data showed characteristics of large data, such as a large data volume, a low value density, and diverse sources. The data cleaning and feature extraction were first performed, and 48 feature variables were selected. The original data points were reduced to 9 936. Then, a combination of Pearson correlation analysis and grey correlation analysis were used to screen out 15 potential critical process parameters(pCPPs). After that, the partial least squares(PLS) was used in prediction of the weight of the extract, proving that the performance of predictive model based on 15 pCMAs is equivalent to that of predictive model based on 48 feature variables. The variable importance in projection(VIP) index was used to identify 9 CPPs, including 2 alcohol precipitation supernatant volume parameters, 4 initial extract weight parameters and 3 added alcohol volume parameters. As a result, the number of data points was 1 863, accounting for 0.28% of the original data. The big data analysis approach from a holistic point of view can effectively increase the value density of the original data. The critical process parameters obtained can help to accurately describe the quality transfer mechanism of the Jinqing alcohol precipitation process.

Defining Processing Times for Accelerator Produced 225Ac and Other Isotopes from Proton Irradiated Thorium.

During the purification of radioisotopes, decay periods or time dependent purification steps may be required to achieve a certain level of radiopurity in the final product. Actinum-225 (Ac-225), Silver-111 (Ag-111), Astatine-211 (At-211), Ruthenium-105 (Ru-105), and Rhodium-105 (Rh-105) are produced in a high energy proton irradiated thorium target. Experimentally measured cross sections, along with MCNP6-generated cross sections, were used to determine the quantities of Ac-225, Ag-111, At-211, Ru-105, Rh-105, and other co-produced radioactive impurities produced in a proton irradiated thorium target at Brookhaven Linac Isotope Producer (BLIP). Ac-225 and Ag-111 can be produced with high radiopurity by the proton irradiation of a thorium target at BLIP.

High-shear granulation of high-molecular weight hypromellose: effects of scale-up and process parameters on flow and compaction properties.

CONTEXT: Knowledge of the effects of high-shear granulation process parameters and scale-up on the properties of the produced granules is essential for formulators who face challenges regarding poor flow and compaction during development of modified release tablets based on high-molecular weight hypromellose (hydroxypropylmethylcellulose (HPMC)) polymers. Almost none of the existing studies deal with realistic industrial formulation. OBJECTIVE: The aim was to investigate the effects of scale-up and critical process parameters (CPPs) of high-shear granulation on the quality attributes of the granules, particularly in terms of the flow and compaction, using a realistic industrial formulation based on HPMC K100M polymer. METHODS: The flow properties were determined using flow time, Carr index, tablet mass, and crushing strength variations. The compaction properties were quantified using the 'out-of-die' Heckel and modified Walker models, as well as the tensile strength profile and elastic recovery. High-shear granulation was performed at different scales: 4 L, 300 L, and 600 L. RESULTS AND CONCLUSION: The scale itself had larger effects on the granule properties than the CPPs, which demonstrated high robustness of formulation on the individual scale level. Nevertheless, to achieve the desired flow and compaction, the values of the CPPs need to be precisely selected to fine-tune the process conditions. The best flow was achieved at high volumes of water addition, where larger and more spherical granules were obtained. The CPPs showed negligible influence on the compaction with no practical implications, however, the volume of water addition volume was identified as having the largest effects on compaction.

Investigation of the interactions of critical scale-up parameters (pH, pO2 and pCO2) on CHO batch performance and critical quality attributes.

Understanding process parameter interactions and their effects on mammalian cell cultivations is an essential requirement for robust process scale-up. Furthermore, knowledge of the relationship between the process parameters and the product critical quality attributes (CQAs) is necessary to satisfy quality by design guidelines. So far, mainly the effect of single parameters on CQAs was investigated. Here, we present a comprehensive study to investigate the interactions of scale-up relevant parameters as pH, pO2 and pCO2 on CHO cell physiology, process performance and CQAs, which was based on design of experiments and extended product quality analytics. The study used a novel control strategy in which process parameters were decoupled from each other, and thus allowed their individual control at defined set points. Besides having identified the impact of single parameters on process performance and product quality, further significant interaction effects of process parameters on specific cell growth, specific productivity and amino acid metabolism could be derived using this method. Concerning single parameter effects, several monoclonal antibody (mAb) charge variants were affected by process pCO2 and pH. N-glycosylation analysis showed positive correlations between mAb sialylation and high pH values as well as a relationship between high mannose variants and process pH. This study additionally revealed several interaction effects as process pH and pCO2 interactions on mAb charge variants and N-glycosylation pattern. Hence, through our process control strategy and multivariate investigation, novel significant process parameter interactions and single effects were identified which have to be taken into account especially for process scale-up.

[Application of quality by design in granulation process for Ginkgo leaf tablet (⠢)： process control strategy based on design space].

In this paper, under the guidance of quality by design (QbD) concept, the control strategy of the high shear wet granulation process of the ginkgo leaf tablet based on the design space was established to improve the process controllability and product quality consistency. The median granule size (D50) and bulk density (Da) of granules were identified as critical quality attributes (CQAs) and potential critical process parameters (pCPPs) were determined by the failure modes and effect analysis (FMEA). The Plackeet-Burmann experimental design was used to screen pCPPs and the results demonstrated that the binder amount, the wet massing time and the wet mixing impeller speed were critical process parameters (CPPs). The design space of the high shear wet granulation process was developed within pCPPs range based on the Box-Behnken design and quadratic polynomial regression models. ANOVA analysis showed that the P-values of model were less than 0.05 and the values of lack of fit test were more than 0.1, indicating that the relationship between CQAs and CPPs could be well described by the mathematical models. D50 could be controlled within 170 to 500 µm, and the bulk density could be controlled within 0.30 to 0.44 g•cm⁻³ by using any CPPs combination within the scope of design space. Besides, granules produced by process parameters within the design space region could also meet the requirement of tensile strength of the ginkgo leaf tablet..

The effect of spray-drying parameters on the flavor of nonfat dry milk and milk protein concentrate 70.

Unit operations during production influence the sensory properties of nonfat dry milk (NFDM) and milk protein concentrate (MPC). Off-flavors in dried dairy ingredients decrease consumer acceptance of ingredient applications. Previous work has shown that spray-drying parameters affect physical and sensory properties of whole milk powder and whey protein concentrate. The objective of this study was to determine the effect of inlet temperature and feed solids concentration on the flavor of NFDM and MPC 70% (MPC70). Condensed skim milk (50% solids) and condensed liquid MPC70 (32% solids) were produced using pilot-scale dairy processing equipment. The condensed products were then spray dried at either 160, 210, or 260°C inlet temperature and 30, 40, or 50% total solids for NFDM and 12, 22, or 32% for MPC70 in a randomized order. The entire experiment was replicated 3 times. Flavor of the NFDM and MPC70 was evaluated by sensory and instrumental volatile compound analyses. Surface free fat, particle size, and furosine were also analyzed. Both main effects (30, 40, and 50% solids and 160, 210, and 260°C inlet temperature) and interactions between solids concentration and inlet temperature were investigated. Interactions were not significant. In general, results were consistent for NFDM and MPC70. Increasing inlet temperature and feed solids concentration increased sweet aromatic flavor and decreased cardboard flavor and associated lipid oxidation products. Increases in furosine with increased inlet temperature and solids concentration indicated increased Maillard reactions during drying. Particle size increased and surface free fat decreased with increasing inlet temperature and solids concentration. These results demonstrate that increasing inlet temperatures and solids concentration during spray drying decrease off-flavor intensities in NFDM and MPC70 even though the heat treatment is greater compared with low temperature and low solids.

Prediction and optimization method for welding quality of components in ship construction.

Welding process, as one of the crucial industrial technologies in ship construction, accounts for approximately 70% of the workload and costs account for approximately 40% of the total cost. The existing welding quality prediction methods have hypothetical premises and subjective factors, which cannot meet the dynamic control requirements of intelligent welding for processing quality. Aiming at the low efficiency of quality prediction problems poor timeliness and unpredictability of quality control in ship assembly-welding process, a data and model driven welding quality prediction method is proposed. Firstly, the influence factors of welding quality are analyzed and the correlation mechanism between process parameters and quality is determined. According to the analysis results, a stable and reliable data collection architecture is established. The elements of welding process monitoring are also determined based on the feature dimensionality reduction method. To improve the accuracy of welding quality prediction, the prediction model is constructed by fusing the adaptive simulated annealing, the particle swarm optimization, and the back propagation neural network algorithms. Finally, the effectiveness of the prediction method is verified through 74 sets of plate welding experiments, the prediction accuracy reaches over 90%.

Effect of Laser Parameters on Fracture Properties of Laser-Repaired Cracks with Micro/NanoMaterial Addition: Multiscale Analysis.

In laser crack repair processes, laser parameters have significant influence on repair quality. Improper combination of laser process parameters may result in defects-such as porosity, ablation, and coarse grain size-in remelted zones. A trans-scale computational model is established by combining crystal plasticity finite elements and variable-node finite elements. The influence of microstructure characteristics such as grain size and porosity of the repair layer on the cumulative plastic slip (CPS) on the dominant slip system at the meso-scale and the J-integral at the macro-scale is studied to explore the effect of laser process parameters on repair quality. The results show that when the laser power is 1800 W and the heating time is 0.5 s, the grain size and porosity of the repaired specimen are the smallest. The J-integral of the repaired specimen is more than 8% smaller than that of the unrepaired specimen and about 3% smaller than that of the repaired specimen, with a laser power of 2000 W and a heating time of 1 s. Pores increase the CPS of the crystal around the pores, especially when a pore have sharp corners. Selecting appropriate laser process parameters can not only refine grain size but also reduce the volume fraction of pores and thus reduce the J-integral and eventually improve repair quality of repaired specimens. The study investigates the relationship of process parameter-microstructure-repair quality in the laser repair process and provides a method for studying the mechanical behavior of materials at macro and micro scales.

Effects of Thermoforming Parameters on Woven Carbon Fiber Thermoplastic Composites.

The quality of woven carbon fiber fabric/polycarbonate thermoplastic composites after thermoforming and demolding was investigated using finite element simulation and the Taguchi orthogonal array. The simulation utilized a discrete approach with a micro-mechanical model to describe the deformation of woven carbon fabric, combined with a resin model. This simulation was validated with bias extension tests at five temperatures. The thermoforming process parameters considered were blank temperature, mold temperature, and blank holding pressure, with three levels for each factor. Optimal values for the fiber-enclosed angle, spring-back angle, mold shape fitness, and the strain of the U-shaped workpiece were desired. The results indicated that the comparison of the stress-displacement curve of bias extension tests verified the application of the discrete finite element method. Results from the Taguchi array indicated that blank holding pressure was the dominant parameter, with the optimal value being 1.18 kPa. Blank temperature was the second most significant factor, effective in the range of 160 °C to 230 °C, while mold temperature had a minor effect. Furthermore, the four quality values are dependent and have a similar trend. The best combination was identified as a blank holding press of 1.18 kPa, a blank temperature of 230 °C, and a mold temperature of 190 °C.

Influences of Composite Electrodeposition Parameters on the Properties of Ni-Doped Co-Mn Composite Spinel Coatings.

To enhance the comprehensive performance of solid oxide fuel cells (SOFCs) ferritic stainless steel (FSS) interconnectors, a novel approach involving composite electrodeposition and thermal conversion is proposed to prepare Ni-doped Co-Mn composite spinel protective coatings on FSS surfaces. The process involves the composite electrodeposition of a Ni-doped Co-Mn precursor coating, followed by thermal conversion to obtain the Co-Mn-Ni composite spinel coating. Crofer 22H was used as the substrate and orthogonal experiments were designed to investigate the influences of deposition solution pH, stirring rate, cathode current density, and the element content of Mn and Ni on the surface morphology and properties of the composite coatings, respectively. The characterization of the prepared coatings was conducted through macroscopic and microscopic morphology observations of the component surface, energy dispersive spectroscopy (EDS) analysis, and area specific resistance (ASR) testing, etc. Finally, the optimized composite electrodeposition parameters and the Mn-Ni content ratio in the solution were obtained. Experimental results indicated that the composite spinel coating prepared with the optimized process parameters exhibited excellent adhesion to the substrate, and the diffusion and migration of Cr element has been effectively inhibited. Compared with the substrate, the ASR of the coated components has also been decreased simultaneously, which provided an effective method for the surface modification of SOFC FSS interconnectors.

Parametric investigation of ultrashort pulsed laser surface texturing on aluminium alloy 7075 for hydrophobicity enhancement.

Hydrophobicity plays a pivotal role in mitigating surface fouling, corrosion, and icing in critical marine and aerospace environments. By employing ultrafast laser texturing, the characteristic properties of a material's surface can be modified. This work investigates the potential of an advanced ultrafast laser texturing manufacturing process to enhance the hydrophobicity of aluminium alloy 7075. The surface properties were characterized using goniometry, 3D profilometry, SEM, and XPS analysis. The findings from this study show that the laser process parameters play a crucial role in the manufacturing of the required surface structures. Numerical optimization with response surface optimization was conducted to maximize the contact angle on these surfaces. The maximum water contact angle achieved was 142º, with an average height roughness (Sa) of 0.87 ± 0.075 µm, maximum height roughness (Sz) of 19.4 ± 2.12 µm, and texture aspect ratio of 0.042. This sample was manufactured with the process parameters of 3W laser power, 0.08 mm hatch distance, and a 3 mm/s scan speed. This study highlights the importance of laser process parameters in the manufacturing of the required surface structures and presents a parametric modeling approach that can be used to optimize the laser process parameters to obtain a specific surface morphology and hydrophobicity. Supplementary Information: The online version contains supplementary material available at 10.1007/s00170-024-12971-8.

Selection and Optimization of Carbon-Reinforced Polyether Ether Ketone Process Parameters in 3D Printing-A Rotating Component Application.

The selection of process parameters is crucial in 3D printing for product manufacturing. These parameters govern the operation of production machinery and influence the mechanical properties, production time, and other aspects of the final product. The optimal process parameter settings vary depending on the product and printing application. This study identifies the most suitable cluster of process parameters for producing rotating components, specifically impellers, using carbon-reinforced Polyether Ether Ketone (CF-PEEK) thermoplastic filament. A mathematical programming technique using a rating method was employed to select the appropriate process parameters. The research concludes that an infill density of 70%, a layer height of 0.15 mm, a printing speed of 60 mm/s, a platform temperature of 195 °C, an extruder temperature of 445 °C, and an extruder travel speed of 95 mm/s are optimal process parameters for manufacturing rotating components using carbon-reinforced PEEK material.

Development of temperature-controlled batch and 3-column counter-current protein A system for improved therapeutic purification.

Maximizing product quality attributes by optimizing process parameters and performance attributes is a crucial aspect of bioprocess chromatography process design. Process parameters include but are not limited to bed height, eluate cut points, and elution pH. An under-characterized chromatography process parameter for protein A chromatography is process temperature. Here, we present a mechanistic understanding of the effects of temperature on the protein A purification of a monoclonal antibody (mAb) using a commercial chromatography resin for batch and continuous counter-current systems. A self-designed 3D-printed heating jacket controlled the 1 mL chromatography process temperature during the loading, wash, elution, and cleaning-in-place (CIP) steps. Batch loading experiments at 10, 20, and 30 °C demonstrated increased dynamic binding capacity (DBC) with temperature. The experimental data were fit to mechanistic and correlation-based models that predicted the optimal operating conditions over a range of temperatures. These model-based predictions optimized the development of a 3-column temperature-controlled periodic counter-current chromatography (TCPCC) and were validated experimentally. Operating a 3-column TCPCC at 30 °C led to a 47% increase in DBC relative to 20 °C batch chromatography. The DBC increase resulted in a two-fold increase in productivity relative to 20 °C batch. Increasing the number of columns to the TCPCC to optimize for increasing feed concentration resulted in further improvements to productivity. The feed-optimized TCPCC showed a respective two, three, and four-fold increase in productivity at feed concentrations of 1, 5, and 15 mg/mL mAb, respectively. The derived and experimentally validated temperature-dependent models offer a valuable tool for optimizing both batch and continuous chromatography systems under various operating conditions.