Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks.

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producing clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

Membrane-based inverse-transition purification facilitates a rapid isolation of various spider-silk elastin-like polypeptide fusion proteins from extracts of transgenic tobacco.

Plants can produce complex pharmaceutical and technical proteins. Spider silk proteins are one example of the latter and can be used, for example, as compounds for high-performance textiles or wound dressings. If genetically fused to elastin-like polypeptides (ELPs), the silk proteins can be reversibly precipitated from clarified plant extracts at moderate temperatures of ~ 30 °C together with salt concentrations > 1.5 M, which simplifies purification and thus reduces costs. However, the technologies developed around this mechanism rely on a repeated cycling between soluble and aggregated state to remove plant host cell impurities, which increase process time and buffer consumption. Additionally, ELPs are difficult to detect using conventional staining methods, which hinders the analysis of unit operation performance and process development. Here, we have first developed a surface plasmon resonance (SPR) spectroscopy-based assay to quantity ELP fusion proteins. Then we tested different filters to prepare clarified plant extract with > 50% recovery of spider silk ELP fusion proteins. Finally, we established a membrane-based purification method that does not require cycling between soluble and aggregated ELP state but operates similar to an ultrafiltration/diafiltration device. Using a data-driven design of experiments (DoE) approach to characterize the system of reversible ELP precipitation we found that membranes with pore sizes up to 1.2 µm and concentrations of 2-3 M sodium chloride facilitate step a recovery close to 100% and purities of > 90%. The system can thus be useful for the purification of ELP-tagged proteins produced in plants and other hosts.

Bayesian cell therapy process optimization.

Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.

Optimization of biotransformation processes of Camarosporium laburnicola to improve production yields of potent telomerase activators.

BACKGROUND: Telomerase activators are promising agents for the healthy aging process and the treatment/prevention of short telomere-related and age-related diseases. The discovery of new telomerase activators and later optimizing their activities through chemical and biological transformations are crucial for the pharmaceutical sector. In our previous studies, several potent telomerase activators were discovered via fungal biotransformation, which in turn necessitated optimization of their production. It is practical to improve the production processes by implementing the design of experiment (DoE) strategy, leading to increased yield and productivity. In this study, we focused on optimizing biotransformation conditions utilizing Camarosporium laburnicola, a recently discovered filamentous fungus, to afford the target telomerase activators (E-CG-01, E-AG-01, and E-AG-02). RESULTS: DoE approaches were used to optimize the microbial biotransformation processes of C. laburnicola. Nine parameters were screened by Plackett-Burman Design, and three significant parameters (biotransformation time, temperature, shaking speed) were optimized using Central Composite Design. After conducting validation experiments, we were able to further enhance the production yield of target metabolites through scale-up studies in shake flasks (55.3-fold for E-AG-01, 13-fold for E-AG-02, and 1.96-fold for E-CG-01). CONCLUSION: Following a process optimization study using C. laburnicola, a significant increase was achieved in the production yields. Thus, the present study demonstrates a promising methodology to increase the production yield of potent telomerase activators. Furthermore, C. laburnicola is identified as a potential biocatalyst for further industrial utilization.

Low-coordinated Mn-N2 sites in graphene oxide induce peroxydisulfate activation for tetracycline degradation: Process optimization and theoretical calculation.

Atom-dispersed low-coordinated transition metal-Nx catalysts exhibit excellent efficiency in activating peroxydisulfate (PDS) for environmental remediation. However, their catalytic performance is limited due to metal-N coordination number and single-atom loading amount. In this study, low-coordinated nitrogen-doped graphene oxide (GO) confined single-atom Mn catalyst (Mn-SA/NGO) was synthesized by molten salt-assisted pyrolysis and coupled to PDS for degradation of tetracycline (TC) in water. Aberration-corrected high-angle annular dark-field scanning transmission electron microscopy (AC-HAADF-STEM) and X-ray absorption fine structure spectroscopy (XAFS) analysis showed the successful doping of single-atom Mn (weight percentage 1.6%) onto GO and the formation of low-coordinated Mn-N2 sites. The optimized parameters obtained by Box-Behnken Design achieved 100% TC removal in both prediction and experimental results. The Mn-SA/NGO + PDS system had strong anti-interference ability for TC removal in the presence of anions. Besides, Mn-SA/NGO possessed good reusability and stability. O2•-, •OH, and 1O2 were the main active species for TC degradation, and the TC mineralization reached 85.1%. Density functional theory (DFT) calculations confirmed that the introduction of single atoms Mn could effectively enhance adsorption and activation of PDS. The findings provide a reference for the synthesis of high-performance single-atom catalysts for effective removal of antibiotics.

Optimization of lactic acid bacterial starter culture to improve the quality and flavor characteristics of traditional Hurood.

Hurood is a traditional fermented milk product prepared by traditional Mongolian techniques of fermenting raw milk, partial degreasing, heating, whey drainage, emulsification of curd, and molding. Currently, Hurood available in the market is generally prepared by small-scale enterprises at home or in open air. Therefore, lack of standardization of bacterial starter culture leads to variation in the flavor and sensory properties of Hurood from batch to batch. In this study, we aimed to assess the best starter culture combination obtained from 37 lactic acid bacterial strains isolated from traditional Hurood. The solidification state and sensory quality were used as indexes for determining the fermentation efficiency of the bacterial starter culture combinations. The yield and texture characteristics were used to determine the optimal ratio of bacterial strains in a combination and the processing conditions for traditional Hurood production. The most optimal bacterial culture combination was observed to be NF 9-3:NF 10-4:CH 3-1 in 5:4:1 ratio and in 3% amount. The most optimal whey temperature and heating-stirring temperature were observed to be 55°C to 60°C and 85°C to 90°C, respectively. Hurood prepared with the optimal combination of bacterial strains exhibited significantly enhanced sensory quality, flavor, and contents of AA and fatty acids. Therefore, the use of optimal starter culture of lactic acid bacteria could produce Hurood with significantly superior sensory qualities, making the product more acceptable to consumers.

Trichoderma koningiopsis fermentation in airlift bioreactor for bioherbicide production.

During scaling of fermentations, choosing a bioreactor is fundamental to ensure the product's quality. This study aims to produce bioherbicides using Trichoderma koningiopsis fermentation, evaluating process parameters in an Airlift bioreactor. As a response, we quantified the production of enzymes involved in the bioherbicide activity (amylase, cellulase, laccase, lipase, and peroxidase). In addition, it evaluated the agronomic efficiency of the fermented extract optimized through tests that promoted soybean growth and nodulation, soybean seed germination, and in vitro phytopathogen control. As a result of optimizing the scaling bioprocess, it was possible to obtain an adequate fermentation condition, which, when applied to soybean seeds, had beneficial effects on their growth. It allowed the production of an enzyme cocktail. These results add a crucial biotechnological potential factor for the success of the optimized formulation in the Airlift bioreactor, in addition to presenting relevant results for the scientific community.

Quantification and discovery of quality markers from Toddalia asiatica by UHPLC-MS/MS coupled with chemometrics.

INTRODUCTION: Toddalia asiatica (TA) is a classical traditional Chinese medicine used to treat rheumatoid arthritis and contusions. However, research regarding TA quality control is currently limited. OBJECTIVE: We aimed to establish a strategy for identifying quality markers that can be used for the evaluation of the quality of TA. METHOD: A rapid and efficient ultra-high-performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (UHPLC-MS/MS) method was developed for the quantitative determination of 19 compounds in TA from different regions. Then, the extraction process of TA was successively optimized by single-factor optimization and response surface methodology. Moreover, chemometrics was employed to confirm the correlation between quality and target compounds. RESULTS: Utilizing the UHPLC-MS/MS method, separation of the 19 bioactive compounds was achieved within 14 min. The method was validated in terms of linearity (r2 > 0.9982), precision (0.08%-3.70%), repeatability (0.50%-2.54%), stability (2.26%-5.46%), and recovery (95.8%-113%). The optimal extraction process (extraction solvent, 65% ethanol aqueous solution; solid-liquid ratio, 1:20; extraction time, 25 min) was determined with the total content of 19 bioactive compounds as indicator. Significant disparities were observed in the contents of target compounds across different batches of TA. Besides, all samples could be categorized into two distinct groups, and magnoflorine, (-)-lyoniresinol, nitidine chloride, norbraylin, skimmianine, and decarine were identified as quality markers. CONCLUSION: In the present study, we developed a strategy to improve the quality control of TA. In consideration of the pharmacodynamic activity and statistical differences, six compounds are proposed as quality markers for TA.

Enhancing Hospital Efficiency and Patient Care: Real-Time Tracking and Data-Driven Dispatch in Patient Transport.

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals.

Cyber-Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era-A Review.

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber-physical optimization system.

A Direct Comparison of rAAV5 Variants Derived from the Baculovirus Expression System Using LC-MS Workflows Demonstrates Key Differences in Overall Production Yield, Product Quality and Vector Efficiency.

Gene therapy holds great promise for the treatment of severe diseases, and adeno-associated virus (AAV) vectors have emerged as valuable tools in this field. However, challenges such as immunogenicity and high production costs complicate the commercial viability of AAV-based therapies. To overcome these barriers, improvements in production yield, driven through the availability of robust and sensitive characterization techniques that allow for the monitoring of critical quality attributes to deepen product and process understanding are crucial. Among the main attributes affecting viral production and performance, the ratio between empty and full capsids along with capsid protein stoichiometry are emerging as potential parameters affecting product quality and safety. This study focused on the production of AAV vectors using the baculovirus expression vector system (BEVS) in Sf9 cells and the complete characterization of AAV5 variants using novel liquid chromatography and mass spectrometry techniques (LC-MS) that, up to this point, had only been applied to reference commercially produced virions. When comparing virions produced using ATG, CTG or ACG start codons of the cap gene, we determined that although ACG was the most productive in terms of virus yield, it was also the least effective in transducing mammalian cells. This correlated with a low VP1/VP2 ratio and a higher percentage of empty capsids. Overall, this study provides insights into the impact of translational start codon modifications during rAAV5 production using the BEVS, the associated relationship with capsid packaging, capsid protein stoichiometry and potency. The developed characterization workflow using LC-MS offers a comprehensive and transferable analysis of AAV-based gene therapies, with the potential to aid in process optimization and facilitate the large-scale commercial manufacturing of these promising treatments.

Alternative sequential combinations of electrocoagulation with electrooxidation and peroxi-coagulation for effective treatment of adhesive production industry wastewater.

Adhesive production industry wastewater can be characterized by high chemical oxygen demand (COD) sourced from high refractory organic contaminants and high total suspended solids (TSS) concentration. Biodegradability of the wastewater is low and wastewater quality is unstable. Various treatment processes have limited applicability in such characterized wastewater. In this study, the treatment performance of electrochemical processes was investigated. Because it is not possible to meet the discharge standards by application of only one process for high refractory organic content, sequential electrochemical processes were studied in this work. In the first step of the sequential process, electrocoagulation (EC) using Al electrodes by which better performance was achieved was applied. In the second step, electrooxidation (EO) and peroxi-coagulation (PC) processes were applied to the EC effluent. In EO, Ti/MMO was selected as the most effective anode whereas in PC, Fe was used as the anode, and graphite was used as the cathode. Box-Behnken Design was applied to optimize the operating conditions of EO and PC processes and to obtain mathematical model equations. In the EC process, 77% COD, 78.5% TSS, and 85% UV254 removal efficiency were obtained under the optimum conditions (pH 7.2, reaction time 35 min, and current density 0.5 mA/cm2). With the EO and PC processes applied to the effluent of EC, 68.5% COD, 77% TSS, and 83% UV254 removal and 77.5% COD, 87% TSS, and 86.5% UV254 removal were obtained, respectively. The specific energy consumption of EC-EO and EC-PC processes was 16.08 kWh/kg COD and 15.06 kWh/kg COD, respectively. Considering the treatment targets and process operating costs, it was concluded that both sequential electrochemical systems could be promising alternative systems for the treatment of adhesive production industry wastewater.

An in-depth study of the oxidative liquefaction process for polymeric waste reduction and chemical production from wind turbine blades and personal protective equipment used in the medical field.

An in-depth study of the oxidative liquefaction process has been provided to degrade the polymeric waste from personal protective equipment (PPEs) and wind turbine blades (WTBs). Thermogravimetric investigations demonstrate that WTBs have three prominent peaks throughout the degradation, whereas PPEs display solitary peak features. Experiments are carried out employing specific experimental design approaches, namely the Central Composite Face-Centered Plan (CCF) for WTBs and the Central Composition Design with Fractional Factorial Design for PPEs in a batch-type reactor at temperature ranges of 250-350 °C, pressures of 20-40 bar, residence times of 30-90 min, H2O2 concentrations of 15-45 %, and waste/liquid ratios of 5-25 % for WTBs. These values were 200-300 °C, 30 bar, 45 min, 30-60 % and 5-7 % for PPE. A detailed comparison has been provided in the context of total polymer degradation (TPD) for PPE and WTBs. Liquid products from both types of wastes after the oxidative liquefaction process are subjected to gas chromatography with flame ionization detection (GC-FID) to identify the existence of oxygenated chemical compounds (OCCs). For WTBs, TPD was 20-49 % and this value was 55-96 % for PPE while the OCC yield for WTBs (36.31 g/kg - 210.59 g/kg) and PPEs (39.93 g/kg - 212.66 g/kg) was also calculated. Detailed optimization of experimental plans was carried out by performing the analysis of variance (ANOVA) and optimization goals were maximum TPD and OCCs yields against the minimum energy consumption, though a considerable amount of complex polymer waste can be reduced and high concentrations of OCC can be achieved, which could be applied for commercial and environmental benefits.

Optimizing hydrothermal treatment for sustainable valorization and fatty acid recovery from food waste.

This study employs response surface methodology and a central composite design (CCD) to optimize hydrothermal treatment (HTT) conditions for the valorization of food waste (FW). Lab-scale pressure reactor-based HTT processes are investigated to detect the effects of temperature (220-340 °C) and resident time (90-260 min) on elemental composition and fatty acid recovery in the hydrothermal liquid. Central to the study is the identification of temperature as the primary factor influencing food waste conversion during the HTT process, showcasing its impact on HTT product yields. The liquid fraction, rich in saturated fatty acids (SFA), demonstrates a temperature-dependent trend, with higher temperatures favoring SFA recovery. Specifically, HTT at 340 °C in 180 min exhibits the highest SFA percentages, reaching up to 52.5 wt%. The study establishes HTT as a promising avenue for nutrient recovery, with the liquid fraction yielding approximately 95% at optimized conditions. Furthermore, statistical analysis using response surface methodology predicts the optimal achievable yields for hydrochar and hydrothermal liquid at 6.15% and 93.85%, respectively, obtained at 320 °C for 200 min.

Optimization of process parameters and kinetics of fluoride extraction from spent potlining using response surface methodology.

Over the years, spent potlining (SPL) treatment has only focused on the extraction of its hazardous compounds, especially fluorides and cyanides. The literature has not sufficiently addressed the optimization and kinetics of fluoride extraction using statistical modeling to determine relevant factors for efficient, cost-effective, and sustainable SPL treatment. Hence, this study is focused on response surface methodology (RSM) combined with central composite design (CCD) to statistically model fluoride extraction of SPL behaviour in acidic environments. Shrinkage core model (SCM) was used to investigate the kinetics of fluoride extraction. The RSM analyses suggested a second-order quadratic model with outstanding accuracy, statistically supported by R2 and adjusted R2 values of 0.986 and 0.973, respectively. The quadratic model indicates the main factors influencing fluoride extraction, showing the complex interactions of temperature, particle size, acid concentration, and leaching time. These main factors were observed to have significant effects on fluoride extraction, except for particle sizes of the SPL. The optimization process, a key success of this study, achieved fluoride extraction of 87.49% at specific factor levels of 48.43 °C, 0.752 mm, 1.2 M, and 10 min. Subsequently, the SCM investigations suggested that diffusion through a liquid film mechanism best approximates the fluoride extraction kinetic behaviour with R2 > 0.80 across varying temperatures. Investigations into temperature dependence with the Arrhenius plot further validated that the reaction kinetics were principally controlled by diffusion through liquid film, with an activation energy of 36.26 kJ/mol. Integrating these kinetic frameworks provides a novel approach to analyzing and optimizing SPL fluoride extraction. Overall, adopting the present study in the industrial settings with the optimized parameters will ensure efficient, sustainable, and cost-effective treatment of SPL.

Production of Omega-3 Fatty Acid Concentrates from Common Kilka Oil: Optimization of the Urea Complexation Process.

There has been an increase in interest in the application of ω-3 PUFAs, especially EPA and DHA, in the development of various food products owing to their myriad health benefits. However, most fish oils do not contain more than 30% combined levels of EPA and DHA. In this study, through the urea complexation procedure, the production of EPA and DHA concentrate in their free fatty acids (FFAs) form was achieved from an enzymatic oil extracted from common kilka (Clupeonella cultriventris caspia). To gain the maximum value of EPA and DHA, the response surface methodology (RSM), which is an effective tool to categorize the level of independent variables onto the responses of an experimental process, was also used. Different variables including the urea-fatty acids (FAs) ratio (in the range of 2-6, w/w), the temperature of crystallization (in the range of -24-8 °C), and the time of crystallization (in the range of 8-40 h) were investigated by response surface methodology (RSM) for maximizing the EPA and DHA contents. Following the model validation, the levels of the variables at which the maximum desirability function (0.907 score) was obtained for response variables were 5:1 (urea-FAs ratio), -9 °C (the temperature of crystallization), and 24 h (the time of crystallization). Under these optimal conditions, increases of 2.2 and 4.4 times in the EPA and DHA concentrations were observed, respectively, and an increase in the concentrations of EPA and DHA from 5.39 and 13.32% in the crude oil to 12.07 and 58.36% in the ω-3 PUFA concentrates were observed, respectively. These findings indicate that the urea complexation process is efficient at optimizated conditions.

Optimization of cold plasma combined treatment process and its effect on the quality of Asian sea bass (Lates calcarifer) during refrigerated storage.

BACKGROUND: Cold plasma exhibits broad applicability in the realm of fish sterilization and preservation. The combination process of plasma-activated water and dielectric barrier discharge (PAW-DBD) was optimized, and its disinfection effects on bass fillets were studied. RESULTS: The best conditions for disinfection of PAW-DBD were as follows. Bass fillets were soaked in PAW for 150 s, and then treated by DBD system at 160 kV for 180 s. The total viable count (TVC) reduced by 1.68 log CFU g-1 . On the 15th day of refrigerated storage, TVC of PAW-DBD group was 7.01 log CFU g-1 , while the PAW and DBD group exhibited a TVC of 7.02 and 7.01 log CFU g-1 on day 12; the TVC of the control group was 7.13 log CFU g-1 on day 6. The sensory score, water-holding capacity, and 2-thiobarbituric acid reactive substance values of the PAW-DBD group were significantly higher than those of PAW and DBD group (P < 0.05), whereas the TVC, Pseudomonas spp. count, and pH of the group were significantly lower (P < 0.05) during refrigerated storage. CONCLUSION: PAW-DBD treatment can enhance the disinfection effect, maintain good quality, and extend the storage period of bass fillets. © 2023 Society of Chemical Industry.

Expanding the horizons of levan: from microbial biosynthesis to applications and advanced detection methods.

Levan, a ß-(2,6)-linked fructose polymer, exhibits diverse properties that impart versatility, rendering it a highly sought-after biopolymer with various industrial applications. Levan can be produced by various microorganisms using sucrose, food industry byproducts and agricultural wastes. Microbial levan represents the most potent cost-effective process for commercial-scale levan production. This study reviews the optimization of levan production by understanding its biosynthesis, physicochemical properties and the fermentation process. In addition, genetic and protein engineering for its increased production and emerging methods for its detection are introduced and discussed. All of these comprehensive studies could serve as powerful tools to optimize levan production and broaden its applications across various industries.

Transition Metal-Promoted Carbon-Silicon Solid Acid Catalysts for the Synthesis and Process Optimization of Benzaldehyde Glycol Acetal.

A series of transition metal (M)-promoted carbon-silicon (C-M-Si; M=Mn, Fe, Co, Ni, Cu, Zn, Zr) solid acid catalysts with designated molar ratio of M/Si=1 : 8 were fabricated and exploited for acetalization of benzaldehyde (BzH) with ethylene glycol (EG). The physical and chemical properties of these C-M-Si catalysts prepared by sol-gel method were characterized by various techniques, namely, SEM, EDS, TGA-DTG, BET, XRD, FTIR, XPS, and NH3 -TPD. Among various examined acidic C-M-Si catalysts, the C-Fe-Si catalyst exhibited the optimal catalytic activity with the benzaldehyde glycol acetal (BEGA) yield of 97.67 %, in excellent agreement with the value (97.88 %) predicted by the response surface methodology (RSM) based on a Box-Behnken design (BBD). C-Fe-Si catalyst with the high catalytic activities and excellent stability and reusability may be ascribed to the suitable acidity and uniform surface distribution of active sites requisite for the acid-catalyzed acetalization reaction.

Using artificial neural networks to accelerate flowsheet optimization for downstream process development.

An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches enable to screen a broad range of the design-space, in contrast to traditional statistical or heuristic-based approaches. Though, chromatographic mechanistic modeling (MM), one of the advanced model-based approaches, can be speed-limiting for flowsheet optimization, which evaluates every purification possibility (e.g., type and order of purification techniques, and their operating conditions). Therefore, we propose to use artificial neural networks (ANNs) during global optimization to select the most optimal flowsheets. So, the number of flowsheets for final local optimization is reduced and consequently the overall optimization time. Employing ANNs during global optimization proved to reduce the number of flowsheets from 15 to only 3. From these three, one flowsheet was optimized locally and similar final results were found when using the global outcome of either the ANN or MM as starting condition. Moreover, the overall flowsheet optimization time was reduced by 50% when using ANNs during global optimization. This approach accelerates the early purification process design; moreover, it is generic, flexible, and regardless of sample material's type.