Corn fermentation in biorefineries produces residual biomass and by-products, particularly corn kernel fiber and outgassed carbon dioxide (CO2), that have value-added potential for improving sugar and bioethanol conversions. Recovered corn kernel fiber contains lignocellulosic components which can be made accessible by pretreating the biomass with an alkaline sodium carbonate solution made with captured CO2 and then used as supplemental biomass in corn ethanol production. In this work, different ratios of whole and degermed corn kernel fibers are pretreated and mixed with corn to be evaluated as beneficial ingredients in bioethanol co-fermentation. Sugar yields from enzymatic hydrolysis demonstrate the pretreatment promotes saccharification reaching over 70% total sugar conversion for the whole corn fibers. During co-fermentation, 10 and 20% corn solid loadings significantly increased ethanol yields while additional corn fiber loadings increased sugar yields. Conversion rates and yields were similar between the whole and degermed corn fibers supporting how a single recovery design can benefit multiple corn streams.

High value co-products generated from waste agricultural feedstocks are important components that can improve economic conditions for integrated biorefineries. Red pigmented carotenoids, such as astaxanthin, are one type of co-product that can add value to biorefineries based on its wide range of uses that include supplementing aquaculture feed, natural dyes for fabrics, food, and pharmaceuticals. In this work, corn stover was ammoniated using the low moisture anhydrous ammonia (LMAA) process. The pretreated corn stover was then deconstructed and converted to astaxanthin using simultaneous saccharification and co-fermentation (SSCF) with the yeast strain Phaffia rhodozyma. Four different commercially available cellulase enzyme formulations were used to release monomeric sugars during SSCF. Two different enzyme formulations used for SSCF processing allowed for 4 mg/L astaxanthin to be produced. One interesting finding was that total sugar consumption did not correlate to higher astaxanthin titers. This result provides more evidence that astaxanthin generation from yeasts is tightly controlled based upon the availability of both carbon and nitrogen in the fermentation media.

This article reports the development and utilisation of an adaptive design workflow methodology for use as a platform technology for the printing, testing, and optimisation of biopharmaceutical processing reactors. This design strategy was developed by application to the complex structure of the coiled flow inverter (CFI). In this way, the many possible physical parameters of the reactor were optimised, via a combination of experimental results, computational fluid dynamics and machine learning approaches, to find the CFI setups that provide the optimal flow properties for a particular application.Additively manufactured reactors are seeing increasing interest in the field of biopharmaceutical production. This is because the desired output volumes are typically small and there is an increasing move towards adopting continuous production, to replace traditional batch production. This approach allows for the tailoring of reactors for a specific reaction, i.e. attempting to maximise the desired aspects of the reaction through refinement of the physical parameters of the reactor, so creating a large possible parameter space to explore.Consequently, the holistic optimisation of CFI reactors and 3D printing is established as providing better plug flow mixing relative to traditional tube coiled reactors. In addition, a trained metamodel in combination with multilayer equations is demonstrated to predict reactor performance quickly and accurately.

In this study, a compartmental disintegration and dissolution model is proposed for the prediction and evaluation of the dissolution performance of directly compressed tablets. This dissolution model uses three compartments (Bound, Disintegrated, and Dissolved) to describe the state of each particle of active pharmaceutical ingredient. The disintegration of the tablet is captured by three fitting parameters. Two disintegration parameters, β0 and βt,0, describe the initial disintegration rate and the change in disintegration rate, respectively. A third parameter, α, describes the effect of the volume of dissolved drug on the disintegration process. As the tablet disintegrates, particles become available for dissolution. The dissolution rate is determined by the Nernst-Brunner equation, whilst taking into account the hydrodynamic effects within the vessel of a USP II (paddle) apparatus. This model uses the raw material properties of the active pharmaceutical ingredient (solubility, particle size distribution, true density), lending it towards early development activities during which time the amount of drug substance available may be limited. Additionally, the strong correlations between the fitting parameters and the tablet porosity indicate the potential to isolate the manufacturing effects and thus implement the model as part of a real-time release testing strategy for a continuous direct compression line.

Existing corn ethanol biorefineries produce about 94% of the ethanol capacity in the USA and currently have surplus production capacity. Expanding feedstocks for existing facilities rather than building new dedicated facilities could provide significant benefits and cost savings. Grain sorghum is a feedstock with a similar composition to corn, which could be utilized at significant incorporation levels in existing facilities with minimal or no modifications but it is currently only used minimally. To understand the impact of grain sorghum incorporation better we studied mixed corn and grain sorghum fermentation at the laboratory scale and utilized the data generated to develop technical models for the individual grains and for a 50/50 mixture at 119 million kg per year (40 million gal per year). Detailed processing and economic comparisons were developed to determine the overall impact. The results showed significant feedstock savings ($8 million per year) potential for utilization of sorghum relative to corn. Ethanol production cost was reduced by $0.07 per kg of ethanol using sorghum relative to corn. Other potential impacts on coproduct composition and values were also determined and discussed.

Background and Objectives: Fuel ethanol produced from corn requires proper yeast nutrition. Corn without supplementation provides inadequate nitrogen at high solids levels. Inorganic nitrogen is typically added; however, organic nitrogen could also be added. To evaluate the impact of supplemental nitrogen and compare it with protease addition, we developed a solids-free corn media to study and compare fermentations.Findings: Amino acid concentrations showed uptake occurred rapidly as fermentation started and reached near zero levels for most amino acids. After sugar utilization was complete, amino acids were released by the yeast into the media. Protease addition without supplemental nitrogen reached rates and yields equivalent to supplemented media.Conclusions: Free amino acids decrease significantly at the beginning of fermentation but, under some conditions, are released again. Protease addition increased certain amino acids and reached equivalent ethanol yields without the addition of urea. Urea allowed the yeast to produce significantly more amino acids during fermentation that were released into the media after all the available sugar was consumed.Significance and Novelty: Amino acid uptake and release in corn fermentations has not been previously reported with this detail, and the improved understanding could lead to performance benefits.

Performing large scale simulation analyses using complex process-driven models can be very time consuming and incur significant computational expense. These analyses involve generating synthetic datasets and include pro-cesses such as impacts analysis (IA) and variance-based sensitivity analysis (SA). Machine learning (ML) provides a potential alternative path to reduce computational costs incurred when generating output from large simu-lation experiments. We assessed the accuracy and computational efficiency of three ML-based emulators (MLEs): artificial neural networks, multivariate adaptive regression splines, and random forest algorithms, to replicate the outputs of the APSIM-NextGen chickpea crop model. The MLEs were trained to predict seven outputs of the process-driven model. All the MLEs performed well (R2 > 0.95) for predicting outputs for the training data set locations but did not perform well for previously unseen test locations. These findings indicate that modellers using process-driven models can benefit from using MLEs for efficient data generation, provided suitable training data is provided.

A digital-first approach to produce quality particles of an active pharmaceutical ingredient across crystallisation, washing and drying is presented, minimising material requirements and experi-mental burden during development. To demonstrate current predictive modelling capabilities, the production of two particle sizes (D90 = 42 and 120 & mu;m) via crystallisation was targeted to deliver a predicted, measurable difference in in vitro dissolution performance. A parameterised population balance model considering primary nucleation, secondary nucleation and crystal growth was used to select the modes of production for the different particle size batches. Solubility prediction aided solvent selection steps which also considered manufacturability and safety selection cri-teria. A wet milling model was parameterised and used to successfully produce a 90 g product batch with a particle size D90 of 49.3 & mu;m, which was then used as the seeds for cooling crystal-lisation. A rigorous approach to minimising physical phenomena observed experimentally was implemented, and successfully predicted the required conditions to produce material satisfying the particle size design objective of D90 of 120 & mu;m in a seeded cooling crystallisation using a 5 -stage MSMPR cascade. Product material was isolated using the filtration and washing processes designed, producing 71.2 g of agglomerated product with a primary particle D90 of 128 & mu;m. Based on experimental observations, the population balance model was reparametrised to increase accuracy by inclusion of an agglomeration term for the continuous cooling crystallisation. The dissolution performance for the two crystallised products is also demonstrated, and after 45 min 104.0 mg of the D90 of 49.3 & mu;m material had dissolved, compared with 90.5 mg of the agglom-erated material with D90 of 128 & mu;m. Overall, 1513 g of the model compound was used to develop and demonstrate two laboratory scale manufacturing processes with specific particle size targets. This work highlights the challenges associated with a digital-first approach and limitations in current first-principles models are discussed that include dealing ab initio with encrustation, fouling or factors that affect dissolution other than particle size. & COPY; 2023 The Author(s). Published by Elsevier Ltd on behalf of Institution of Chemical Engineers. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).

2,3-butanediol (2,3-BDO) is a platform chemical that can be converted to a wide array of products ranging from bio-based materials to sustainable aviation fuel. This chemical can be produced by a variety of microorganisms in fermentation processes. Challenges remain for high titer 2,3-BDO production during fermentation due to several parameters, but controlling oxygen is one of the most relevant processing parameters to ensure viable product output. This work investigated the fermentation of plant biomass sugars by the 2,3-BDO producer Paenibacillus polymyxa . Aerobic and oxygen limited fermentation conditions were initially evaluated using molasses-based media to determine cell growth and 2,3-BDO output. Similar conditions were then evaluated on hydrolysate from pretreated sweet sorghum bagasse (SSB) that contained fermentable sugars from structural polysaccharides. Fermentations in molasses media under aerobic conditions found that 2,3-BDO could be generated, but over time the amount of 2,3-BDO decreased due to conversion back into acetoin. Oxygen limited fermentation conditions exhibited improved biomass growth, but only limited suppression of 2,3-BDO conversion to acetoin occurred. Glucose depletion appeared to have a greater role influencing 2,3-BDO conversion back into acetoin. Further improvements in 2,3-BDO yields were found by utilizing detoxified SSB hydrolysate.

Population balancemodel is a valuable modelling tool which facilitates the optimization andunderstanding of crystallization processes. However, in order to use this tool,it is necessary to have previous knowledge of the crystallization kinetics,specifically crystal growth and nucleation. The majority of approaches toachieve proper estimations of kinetic parameters required experimental data.Across time, a vast literature about the estimation of kinetic parameters andpopulation balances have been published. Considering the availability of data,this work built a database with information on solute, solvent, kineticexpression, parameters, crystallization method and seeding. Correlations wereassessed and clusters structures identified by hierarchical clusteringanalysis. The final database contains 336 data of kinetic parameters from 185different sources. The data were analysed using kinetic parameters of the mostcommon expressions. Subsequently, clusters were identified for each kineticmodel. With these clusters, classification random forest models were made usingsolute descriptors, seeding, solvent, and crystallization methods asclassifiers. Random forest models had an overall classification accuracy higherthan 70% whereby they were useful to provide rough estimates of kineticparameters, although these methods have some limitations.

The plastic disposal problem has been augmented over the years due to the surge in utilization of single-use plastics, the indiscriminate discarding of plastics, and space limitations associated with landfill sites. Therefore, in this study the biodegradable, biorenewable, and biocompatible plastic substitutes, poly(hydroxyalkanoate) (PHA) biopolymers were synthesized from low-value and high-volume corn stover in combination with levulinic acid (LevA). Acid hydrolysis liberated the fermentable sugars from the cellulosic and hemicellulosic fractions of the corn stover. This hy-drolysate was used as a feedstock in combination with LevA to produce a terpolyester composed of 3-hydroxybutyric acid (3-HB), 3-hydroxyvaleric acid (3-HV), and 4-hydroxyvaleric acid (4-HV) by the bacterium Azohydromonas lata, and a copolymer of 3-HB and 3-HV by the bacterium Burkholderia sacchari. Detoxification of the hydrolysate through 'overliming' was necessary to induce bacterial growth and PHA production in A. lata, while B. sacchari was able to utilize the hydrolysate without detoxification achieving a maximum PHA titer of 1.2 g PHA/L. The inclusion of higher LevA in the medium affected the monomeric composition of the copolymers produced by B. sacchari resulting in a 3-HB:3-HV ratio of 39:61 when grown in the presence of 0.4% (w/v) LevA. In contrast, the monomeric compositions were relatively unaffected by the LevA media concentration for the PHA biopolymers produced by A. lata. The results of this study should help reduce the overall costs to synthesize bacterially derived PHA biopolymers and increase their applicability, thus reducing our dependence on recalcitrant petroleum-based plastics.

A predictive tool was developed to aid process design and to rationally select optimal solvents for isolation of active pharmaceutical ingredients. The objective was to minimize the experimental work required to design a purification process by (i) starting from a rationally selected crystallization solvent based on maximizing yield and minimizing solvent consumption (with the constraint of maintaining a suspension density which allows crystal suspension); (ii) for the crystallization solvent identified from step 1, a list of potential isolation solvents (selected based on a series of constraints) is ranked, based on thermodynamic consideration of yield and predicted purity using a mass balance model; and (iii) the most promising of the predicted combinations is verified experimentally, and the process conditions are adjusted to maximize impurity removal and maximize yield, taking into account mass transport and kinetic considerations. Here, we present a solvent selection workflow based on logical solvent ranking supported by solubility predictions, coupled with digital tools to transfer material property information between operations to predict the optimal purification strategy. This approach addresses isolation, preserving the particle attributes generated during crystallization, taking account of the risks of product precipitation and particle dissolution during washing, and the selection of solvents, which are favorable for drying.

Amylose lipid complexes (AMLs) are likely to form during liquefaction of ground corn in the dry grind process. AML will form under high temperature (>= 85 degrees C) and excess water conditions, due to interaction of gelatinized starch with corn lipids. AMLs are resistant to alpha-amylase action, resulting in a decrease in starch available for enzymatic hydrolysis. This affects sugar available for fermentation and the final ethanol yield. In this study, the effects of liquefaction temperature, corn particle size, slurry solids content, and different commercial alpha-amylases on AML formation are evaluated. AML content in post liquefaction solids (liquefact) is found to decrease from 3.46 to 1.00% as corn grind size is increased from 0.5 to 2.5 mm. Across all slurry solids contents tested (25, 32, and 34%), the mean difference in AML content for all three solids contents is 0.61% when liquefaction temperature is increased to 105 from 85 degrees C. At 85 degrees C, liquefact from all three alpha-amylases used, had similar AML content. However, when liquefaction temperature is increased to 105 degrees C, enzyme AA2 had lower AML production compared to other amylases. Overall, increasing liquefaction temperature to above 100 degrees C had the most predominant effect on reducing AML formation. Optimizing liquefaction parameters can help reduce AML formation and may improve profitability of the dry grind ethanol process.

Distillers grains and solubles generated from the ethanol fermentation of grains contain acylglycerols (AG) that can be successfully converted to fatty acid methyl esters (FAME) and fatty acid ethyl esters (FAEE), commonly known as biodiesel. However, when grain sorghum (milo) DDGS were used as a feedstock for in situ transesterification (IST) under the previously established optimal conditions for other AG-bearing substrates, the yield plateaued at only 32.2% (corrected in this study to 24.2%). Several IST studies have reported significantly higher conversions of AG-bearing substrates to FAME. Therefore, the goal of this IST study was to improve the conversion of the AG in milo DDGS to FAME and FAEE by varying the temperature of reaction, the concentrations of the base (sodium methylate, NaOMe), volume of methanol and ethanol, and the amount of moisture in DDGS. Methyl tert-butyl ester was also evaluated as a co-solvent intended to improve miscibility and reaction rate. Among these variables, the most effective change was an increase in temperature from 40 to 65 degrees C. The most successful reaction used a AG:NaOMe:MeOH molar ratio of 1.0:2.6:168.9. Those reaction conditions used 4.8 mmol NaOMe dissolved in 12.6 mL MeOH and resulted in a 79.8% conversion of AG to FAME.

The presence of fouling in evaporators can increase energy consumption as well as capital and labor costs. During corn ethanol production, fouling occurs when thin stillage is concentrated in multiple effect evaporators to form condensed distillers solubles. Limited studies have been conducted on fouling of corn ethanol processing. Process streams are biological in origin and have variable compositions. The objective of this study was to develop an improved understanding of components that accelerate fouling of thin stillage evaporators. An annular fouling probe was used to evaluate compositional variables on fouling behavior of thy grind corn thin stillage. Three experiments were performed with commercial processing streams. In the first experiment, the effects of carbohydrate materials in thin stillage on evaporator fouling were investigated by adding starch and sucrose. In the second experiment, commercial thin stillage samples were treated by adding wet cake. The third experiment was designed to observe if the age of thin stillage sample would affect fouling. The results indicate that fouling resistances increased with starch addition, as well as with wet cake addition, at equal total solids contents. Insoluble starch addition had larger effects than soluble sucrose addition. Sucrose alone did not cause increased rapid fouling. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Background and objectives Fuel ethanol is almost exclusively produced from corn with minor contributions from other cereal grains. Cellulosic ethanol production has significant potential as well as serious limitations. This study proposed that pretreated corn stover could be simultaneously converted and fermented with corn in existing ethanol facilities. Findings Mixed fermentation ethanol yields increased significantly relative to the control fermentations. Incorporation of 5% pretreated corn stover increased ethanol yields by 12.7% with 10% corn mash and 11.9% with 20% corn mash over corn only control fermentations. Using 20% corn, residual xylose and arabinose were 0.072 and 0.023 grams/gram biomass, respectively, indicating addition ethanol potential with a C5 fermenting yeast. DDGS color and lipid contents were negatively impacted; however, the nutrient value of the corn portion is unlikely to be altered. Conclusions Direct incorporation of pretreated corn stover into the corn ethanol process can be done with little or no reduction in corn or stover conversion efficiency. Significance and novelty This study demonstrates clearly that pretreated biomass can be efficiently co-fermented with corn to produce cellulosic ethanol in yields approximating those from biomass alone and eliminates many of the significant economic and processing issues that stand-alone cellulosic ethanol facilities face.

Particle swelling is a crucial component in the disintegration of a pharmaceutical tablet. The swelling of particles in a tablet creates stress inside the tablet and thereby pushes apart adjoining particles, eventually causing the tablet to break-up. This work focused on quantifying the swelling of single particles to identify the swelling-limited mechanisms in a particle, i.e. diffusion- or absorption capacity-limited. This was studied for three different disintegrants (sodium starch glycolate/SSG, croscarmellose sodium/CCS, and low-substituted hydroxypropyl cellulose/L-HPC) and five grades of microcrystalline cellulose (MCC) using an optical microscope coupled with a bespoke flow cell and utilising a single particle swelling model. Fundamental swelling characteristics, such as diffusion coefficient, maximum liquid absorption ratio and swelling capacity (maximum swelling of a particle) were determined for each material. The results clearly highlighted the different swelling behaviour for the various materials, where CCS has the highest diffusion coefficient with 739.70 μm2/s and SSG has the highest maximum absorption ratio of 10.04 g/g. For the disintegrants, the swelling performance of SSG is diffusion-limited, whereas it is absorption capacity-limited for CCS. L-HPC is both diffusion- and absorption capacity-limited. This work also reveals an anisotropic, particle facet dependant, swelling behaviour, which is particularly strong for the liquid uptake ability of two MCC grades (PH101 and PH102) and for the absorption capacity of CCS. Having a better understanding of swelling characteristics of single particles will contribute to improving the rational design of a formulation for oral solid dosage forms.

Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a "nano" extension to the InChI standard.

Materials at the nanoscale exhibit specific physicochemical interactions with their environment. Therefore, evaluating their toxic potential is a primary requirement for regulatory purposes and for the safer development of nanomedicines. In this review, to aid the understanding of nano–bio interactions from environmental and health and safety perspectives, the potential, reality, challenges, and future advances that artificial intelligence (AI) and machine learning (ML) present are described. Herein, AI and ML algorithms that assist in the reporting of the minimum information required for biomaterial characterization and aid in the development and establishment of standard operating procedures are focused. ML tools and ab initio simulations adopted to improve the reproducibility of data for robust quantitative comparisons and to facilitate in silico modeling and meta‐analyses leading to a substantial contribution to safe‐by‐design development in nanotoxicology/nanomedicine are mainly focused. In addition, future opportunities and challenges in the application of ML in nanoinformatics, which is particularly well‐suited for the clinical translation of nanotherapeutics, are highlighted. This comprehensive review is believed that it will promote an unprecedented involvement of AI research in improvements in the field of nanotoxicology and nanomedicine.