Diverse Processing Conditions Research Articles

Artificial intelligence assisted prediction of optimum operating conditions of shell and tube heat exchangers: A grey‐box approach

AbstractIn this study, a Grey‐box (GB) model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger (STHE) under varying process conditions. Aspen Exchanger Design and Rating (Aspen‐EDR) was initially used to construct a first principle model (FP) of the STHE using industrial data. The Genetic Algorithm (GA) was incorporated into the FP model to attain the minimum exit temperature for the hot kerosene process stream under varying process conditions. A dataset comprised of optimum process conditions was generated through FP‐GA integration and was utilised to develop an Artificial Neural Networks (ANN) model. Subsequently, the ANN model was merged with the FP model by substituting the GA, to form a GB model. The developed GB model, that is, ANN and FP integration, achieved higher effectiveness and lower outlet temperature than those derived through the standalone FP model. Performance of the GB framework was also comparable to the FP‐GA approach but it significantly reduced the computation time required for estimating the optimum process conditions. The proposed GB‐based method improved the STHE's ability to extract energy from the process stream and strengthened its resilience to cope with diverse process conditions.

Early quality prediction of complex double-walled hollow turbine blades based on improved whale optimization algorithm

Abstract The precision in forming complex double-walled hollow turbine blades significantly influences their cooling efficiency, making the selection of appropriate casting process parameters critical for achieving fine-casting blade formation. However, the high cost associated with real blade casting necessitates strategies to enhance product formation rates and mitigate cost losses stemming from the overshoot phenomenon. To address this challenge, we propose a machine-learning (ML) data-driven framework leveraging an enhanced whale optimization algorithm (WOA) to estimate product formation under diverse process conditions. Complex double-walled hollow turbine blades serve as a representative case within our proposed framework. We constructed a database using simulation data, employed feature engineering to identify crucial features and streamline inputs, and utilized a whale optimization algorithm-back-propagation neural network (WOA-BP) as the foundational ML model. To enhance WOA-BP's performance, we introduce an optimization algorithm, ICWOA-BP, incorporating cubic chaotic mapping adaptation. Experimental evaluation of ICWOA-BP demonstrated an average mean absolute error (MAE) of 0.001995 mm, reflecting a 36.21% reduction in prediction error compared to conventional models, as well as two well-known optimization algorithms (PSO, QANA). Consequently, ICWOA-BP emerges as an effective tool for early prediction of dimensional quality in complex double-walled hollow turbine blades.

Deciphering the Morphology Evolution of Layer‐by‐Layer Processing Via In Situ Spectroscopy Measurement for High‐Performance Organic Photovoltaics

AbstractAlthough the layer‐by‐layer (LBL) processing can usually achieve an optimal bulk‐heterojunction morphology for high‐performance Organic photovoltaics (OPVs), the unambiguous working principles governing the morphology evolution are still lacking. To address this issue, here the phase‐separation kinetics of LBL processing are comprehensively studied using in situ spectroscopies, which are very sensitive to the intermolecular interactions, e.g. the molecular packing and D:A phase‐separation. Upon casting the nonfullerene acceptor (i.e., the BTP‐eC9) solution on top of the polymer donor, i.e., PM6, it is found that 1) the solvent will first swell the polymer and induce a polymer gel, 2) following the polymer gelation, the NFAs will immediately permeate into the cavities of polymer gel and form a molecular‐level D:A mixing, and 3) with the evaporation of the solvent, phase‐separation between the donor and acceptor occurs and results in a bulk‐heterojunction morphology as those achieved with blend‐cast processing. With these understandings, more diverse processing conditions have been purposely utilized to dictate the phase evolution process precisely. As a result, a high efficiency of 19.7% is reached, representing one of the best among binary OPVs. Therefore, this work deciphers phase‐separation kinetics of LBL processing and should pave the way toward advanced morphology control for high‐performance OPVs.

Non‐Ionic Perylene‐Diimide Polymer as Universal Cathode Interlayer for Conventional, Inverted, and Blade‐Coated Organic Solar Cells

AbstractAs a class of predominantly used cathode interlayers (CILs) in organic solar cells (OSCs), perylene‐diimide (PDI)‐based polymers exhibit intriguing characteristics of excellent charge transporting capacity and suitable energy levels. Despite that, PDI‐based CILs with satisfied film‐forming ability and adequate solvent resistance are rather rare, which not only limits the further advance of OSC performances but also hinders the practical use of PDI CILs. Herein, we designed and synthesized two non‐conjugated PDI polymers for achieving high power conversion efficiency (PCE) in diverse types of OSCs. The utilization of oligo (ethylene glycol) (OEG) linkage enhanced the n‐doping effect of PDI polymers, leading to an improved ability of the CIL to reduce work function and improve electron transporting capability. Moreover, the introduction of the non‐ionic OEG chain effectively improve the wetting property and solvent resistance of PDI polymers, so the PPDINN CIL can withstand diverse processing conditions in fabricating different OSCs, including conventional, inverted and blade‐coated devices. The binary OSC with conventional structure using PPDINN CIL showed a PCE of 18.6 %, along with an improved device stability. Besides, PPDINN is compatible with the large‐area blade‐coating technique, and a PCE of 16.6 % was achieved in the 1‐cm2 OSC where a blade‐coated PPDINN was used.

Non-Ionic Perylene-Diimide Polymer as Universal Cathode Interlayer for Conventional, Inverted, and Blade-Coated Organic Solar Cells.

As a class of predominantly used cathode interlayers (CILs) in organic solar cells (OSCs), perylene-diimide (PDI)-based polymers exhibit intriguing characteristics of excellent charge transporting capacity and suitable energy levels. Despite that, PDI-based CILs with satisfied film-forming ability and adequate solvent resistance are rather rare, which not only limits the further advance of OSC performances but also hinders the practical use of PDI CILs. Herein, we designed and synthesized two non-conjugated PDI polymers for achieving high power conversion efficiency (PCE) in diverse types of OSCs. The utilization of oligo (ethylene glycol) (OEG) linkage enhanced the n-doping effect of PDI polymers, leading to an improved ability of the CIL to reduce work function and improve electron transporting capability. Moreover, the introduction of the non-ionic OEG chain effectively improve the wetting property and solvent resistance of PDI polymers, so the PPDINN CIL can withstand diverse processing conditions in fabricating different OSCs, including conventional, inverted and blade-coated devices. The binary OSC with conventional structure using PPDINN CIL showed a PCE of 18.6 %, along with an improved device stability. Besides, PPDINN is compatible with the large-area blade-coating technique, and a PCE of 16.6 % was achieved in the 1-cm2 OSC where a blade-coated PPDINN was used.

Energy consumption assessment and economic analysis of a novel sustainable electro-machining auxiliary system

In contrast to traditional machining methods, electro-machining presents a new opportunity for treating materials that are problematic for conventional approaches. While gaining increasing prominence in the manufacturing industry, electro-machining is marked by heightened energy consumption and reduced operational efficiency. A comprehensive survey of electro-machining manufacturing facilities has identified two pivotal factors influencing energy consumption and efficiency enhancements: 1) determining optimal electrical parameters for electro-machining during the production planning stage, and 2) timely adjustment of electro-machining parameters throughout the production stage. To address these two challenges, an innovative model-based electro-machining auxiliary system has been introduced for the surveillance and optimization of the machining process. The system aims to curtail energy consumption, enhance machining efficiency, and fortify economic viability, all while maintaining machining quality. Notably, the system demonstrates adaptability in deriving optimized electrical parameters tailored to diverse processing conditions. Energy consumption evaluation and techno-economic analysis, employing primary industry data, were conducted to compare the energy efficiency and economic performance of the machining process with the new auxiliary system against the conventional approach. The foremost outcome of adopting the new auxiliary system is a substantial reduction in energy usage; from an economic standpoint, the analysis shows that the cost of machining a single workpiece is reduced by 27.75%; the energy efficiency analysis reveals a 27.75% decrease in energy consumption per unit area, accompanied by an approximately 30% increase in machining efficiency. The proposed approach has demonstrated significant practical value for the sustainable development of the electro-machining process.

Design and Research of a Field Bus Control System Laboratory for Metal Mining, Beneficiation and Metallurgy

Fieldbus control systems play a pivotal role in industries such as mining, beneficiation, and metallurgy, facilitating precise process control. However, diverse process conditions and applications often lead to challenges during system implementation. The prevalence of process control projects underscores the need for dedicated control system laboratories to address these problems. Our research delves into the complexities of process control systems, focusing on mainstream brands such as Siemens, Rockwell, and Emerson, involving analysis of network architectures, software, and hardware configurations. Through rigorous testing of real equipment systems, we uncover prevalent issues in practical control system applications. These findings guide the resolution of technical challenges faced in project control, concurrently enhancing the design and debugging prowess of engineering professionals. We also anticipate the trajectory of intelligent manufacturing, embracing collaborative manufacturing aspects in networked environments. This research establishs a robust foundation for the forthcoming generation of control network technologies specific to metal mining, beneficiation, and metallurgy.

Realization of Al:ZnO (AZO)-Ag nanocomposite as a novel photocatalyst capable of broadband photon harnessing driven by near-infrared plasmonics

The rising level of environmental pollution is a global concern and towards its mitigation, infrared photocatalysis emerges as a new concept, as the solar radiation at the earth’s surface has over 50% energy distribution in the near infrared (NIR) region. Al-doped ZnO (AZO) is a non-toxic congener of indium tin oxide (ITO) as transparent conducting oxide (TCO). A TCO-metal framework is developed as AZO-Ag@AZO nanocomposite by pulsed DC/DC magnetron sputtering. The consequences of diverse process conditions, viz, doping level, sputtering pressure, deposition time, and Ag/AZO co-sputtering time are noteworthy as the morphological, compositional, optical, and electrical properties are critically dependent on the process conditions. The structural and compositional analyses confirm wurtzite AZO and f.c.c. Ag with nanosized crystalline domains. A series of photocatalysis experiments reveal the significance of NIR plasmonics of AZO, realized by nanostructuring and generating optimum electron density (∼1020 cm−3). Notably, the optimal degradation rate (95%) of R6G is attained with tailored AZO-Ag@AZO nanocomposite. The gravity of visible surface plasmon resonance (SPR) due to Ag nanocrystallites and electron-hole pair creation in AZO cannot be undermined due to the remarkable participation of energetic electrons, and holes in redox reactions in harmony with actions of NIR SPR driven lower energy electrons.

Highly Durable Membrane Electrode Assembly with Multiwalled Carbon Nanotubes/CeO2‐Reinforced Nafion Composite Membrane by Spraying Method for Fuel Cell Applications

AbstractFor achieving economically viable polymer electrolyte membrane fuel cells (PEMFCs) in the commercial market, simplifying the fabrication process and manufacturing of robust membrane electrode assembly (MEA) are important. Herein, a single spray‐coating method for developing a robust and free‐standing reinforced Nafion composite membrane and electrodes is reported. By comparing the fabricated membrane's morphology under diverse process conditions including the nozzle‐to‐substrate distance and solution‐loading rates, the optimized spray‐coating for uniformly deposited spray‐based membrane without any defects has been determined. To explain this optimized condition, a simple theoretical model based on the concept of the surface coverage of sprayed droplets is proposed. The fabricated multiwalled carbon nanotubes/CeO2‐reinforced Nafion composite membrane shows excellent mechanical properties with minimal proton‐conducting loss despite using additional fillers of total 4 wt% in the Nafion matrix. By successively depositing the catalyst layer on that membrane using the same spray‐coating device, an MEA with the reinforced spray‐based membrane has been successfully fabricated; moreover, diverse electrochemical measurements and durability tests have been conducted. The MEA with the reinforced membrane shows comparable initial performance and considerably higher performance after the durability test compared to the MEA with commercial Nafion membrane.

A high solids field-to-fuel research pipeline to identify interactions between feedstocks and biofuel production

BackgroundEnvironmental factors, such as weather extremes, have the potential to cause adverse effects on plant biomass quality and quantity. Beyond adversely affecting feedstock yield and composition, which have been extensively studied, environmental factors can have detrimental effects on saccharification and fermentation processes in biofuel production. Only a few studies have evaluated the effect of these factors on biomass deconstruction into biofuel and resulting fuel yields. This field-to-fuel evaluation of various feedstocks requires rigorous coordination of pretreatment, enzymatic hydrolysis, and fermentation experiments. A large number of biomass samples, often in limited quantity, are needed to thoroughly understand the effect of environmental conditions on biofuel production. This requires greater processing and analytical throughput of industrially relevant, high solids loading hydrolysates for fermentation, and led to the need for a laboratory-scale high solids experimentation platform.ResultsA field-to-fuel platform was developed to provide sufficient volumes of high solids loading enzymatic hydrolysate for fermentation. AFEX pretreatment was conducted in custom pretreatment reactors, followed by high solids enzymatic hydrolysis. To accommodate enzymatic hydrolysis of multiple samples, roller bottles were used to overcome the bottlenecks of mixing and reduced sugar yields at high solids loading, while allowing greater sample throughput than possible in bioreactors. The roller bottle method provided 42–47% greater liquefaction compared to the batch shake flask method for the same solids loading. In fermentation experiments, hydrolysates from roller bottles were fermented more rapidly, with greater xylose consumption, but lower final ethanol yields and CO2 production than hydrolysates generated with shake flasks. The entire platform was tested and was able to replicate patterns of fermentation inhibition previously observed for experiments conducted in larger-scale reactors and bioreactors, showing divergent fermentation patterns for drought and normal year switchgrass hydrolysates.ConclusionA pipeline of small-scale AFEX pretreatment and roller bottle enzymatic hydrolysis was able to provide adequate quantities of hydrolysate for respirometer fermentation experiments and was able to overcome hydrolysis bottlenecks at high solids loading by obtaining greater liquefaction compared to batch shake flask hydrolysis. Thus, the roller bottle method can be effectively utilized to compare divergent feedstocks and diverse process conditions.

Exploring the Link Between the Geographical Origin of European Fermented Foods and the Diversity of Their Bacterial Communities: The Case of Fermented Meats.

European fermented meat products are prepared according to a wide variety of different recipes and processing conditions, which can influence their fermentative microbiota. However, due to the diverse processing conditions applied across Europe, it remained unclear to which degree bacterial heterogeneity can be encountered in commercially available fermented meat products and whether this is linked to their geographical origin. Therefore, the bacterial species diversity of 80 fermented meat products available in the Belgian retail, coming from five different countries, was investigated. It was also assessed how this related to the country of origin and the key processing parameters pH and salt concentration. The samples originated from Belgium, France, Germany, Italy, and Spain. In general, Southern European fermented meat products commonly had a higher pH, with their lactic acid bacteria (LAB) communities being represented by Lactobacillus sakei and with mostly Staphylococcus xylosus and Staphylococcus equorum governing over the coagulase-negative staphylococci (CNS) communities. Among these products, the Spanish variants showed a higher prevalence of S. equorum, whereas S. xylosus was the prevailing CNS species in most French and Italian fermented meat products. In contrast, Northern European fermented meat products were generally more acidified and showed a higher prevalence of Pediococcus pentosaceus in their LAB communities, whereas Staphylococcus carnosus represented the CNS communities. Non-parametric statistical tests indicated the impact of the geographical origin on the prevalence of the LAB and CNS species. The latter was likely due to the combination of differences in process technology as well as starter culture use.

Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat

AbstractThis study highlighted the potential of integrating texture and spectra information of hyperspectral imaging to predict the evolution of 2‐thiobarbituric acid reactive substances (TBARS) and peroxide value (PV) as lipid oxidation attributes in pork muscles under diverse processing conditions. Partial least square regression (PLSR) models were established on data fusion information (image and optimal spectra) and compared with other three PLSR models built on spectra (full range and optimal), and image information. Regarding TBARS measurement, model based on optimal spectra was superior with determination coefficient of prediction (Rp2) of .896, root mean square error of prediction (RMSEP) of 1.034, and residual predictive deviation (RPD) of 2.311 as compared to the data fusion model (Rp2 = .865, RMSEP = 0.994, RPD = 2.280). For PV, model on data fusion yielded the best (Rp2 = .899, RMSEP = 0.966, RPD = 2.314) followed by that on optimal spectra (Rp2 = .883, RMSEP = 1.073, RPD = 2.281). Then prediction maps were drawn to visualize the distribution of TBARS and PV in the samples using models built on optimal spectra. The maps revealed intrinsic differences in samples which are not readily perceptible by visual observation.Practical ApplicationsModels based on spectra and spatial information of hyperspectral imaging have been widely used to predict and visualize food quality attributes. However, these are not enough to elucidate the impact of processing on the meat samples as it may not be able to directly measure attributes related to external properties such as texture properties. The present study investigated the feasibility of integrating texture features obtained from gray‐level co‐occurrence matrix and spectra data to predict quality changes associated with pork meat subjected to diverse processing conditions. The results obtained proved that the methods presented can be useful for designing future experiments in a bid to establishing robust models for food quality assessment.

A generic concept for Helium purification and liquefaction plant

This study describes and evaluates the performance of producing a pure Helium fraction from Helium extraction facility designed for cryogenic natural gas plants. A generic concept for obtaining a Helium pure fraction, which has relatively lower capital and operating costs should be provided. In order to achieve this objective, a new concept for obtaining a Helium pure fraction from a crude Helium fraction, is proposed based on simulations run under diverse process conditions regarding crude Helium gas’ temperature, pressure and composition. This concept is characterized by; reducing the plant safety requirements due to the extensive separation of combustible components, and compact layout of Helium extraction plant. Further re-purification is included in the subsequent Helium liquefaction step through selective adsorption, hence then increasing the purity of the Helium product and reducing the plant energy consumption required for liquefying Helium-rich fraction and the valuable Helium boil-off routed from the storage facility. The Nitrogen-rich fraction is routed to Nitrogen liquefaction installation. Liquid Nitrogen is generated within Helium recovery facility for liquid Helium shielding and container cooling. Surplus gaseous Nitrogen either can be liquefied and used within cryogenic natural gas plant as process coolant or be vented to atmosphere.

Development of online soft sensors and dynamic fundamental model-based process monitoring for complex sulfide ore flotation

Complex sulfide ores are difficult to process and often require multi-stage sequential flotation. Process outputs such as grade and recovery in each stage are affected by various sub-processes in the system, and it is crucial to monitor the performance in order to maximize the production. In this work, we have proposed and implemented a dynamic monitoring scheme using fundamental modeling and an online soft sensor network for real-time measurements of grade and recovery. Dynamic fundamental models for lead and zinc recovery were developed to represent the multi-stage rougher flotation for lead-zinc sulfide ores. A soft sensor network was built to measure the grade and recovery in real-time using support vector machine classification and regression on multivariate image data. A factorial design with feed particle size, collector dosage in the lead rougher flotation stage, and collector dosage in the zinc rougher flotation stage as the design variables was used to obtain diverse process conditions for validation. Successful validation at the entire range of process conditions demonstrates the potential of the technique for use in process control and monitoring applications. Changes in the collector dosage were monitored in the lead and zinc rougher flotation stages using state and parameter estimates of the fundamental model structure. The process monitoring framework can be extended to monitor other key variables in the process.

Redesigning pH optimum of Geobacillus sp. TF16 endoxylanase through in silico designed DNA swapping strategy

Thermoalkaliphilic xylanases are highly desired and of great importance due to their vast potential in paper pulp and bleaching processes. Here, we report rapid, cost-effective, and result-oriented combinatorial potential of in silico DNA swapping strategy to engineer the pH optimum of industrially crucial enzymes, particularly engineering of Geobacillus sp. TF16 endoxylanase for alkaline environments. The 3D structures of Geobacillus sp. TF16 and donor Bacillus halodurans C-125 endoxylanases were firstly predicted, analyzed, and compared for their similarities before any in silico design of mutants. Reasonably, to improve its alkaline pH tolerance, the corresponding regions in Geobacillus sp.TF16 endoxylanase were further engineered by swapping with negatively-charged amino acid-rich regions from B. halodurans C-125 endoxylanase. Through only two of four in silico-designed mutants, the optimum pH of GeoTF16 endoxylanase was improved from 8.5 to 10.0. Moreover, as compared to GeoTF16 parental enzyme, both GeoInt3 and GeoInt4 mutants revealed (i) enhanced biobleaching performance, (ii) improved adaptability to alkaline conditions, and (iii) better activity for broader pH range. Unlike GeoTF16 losing activity at pH 11.0 completely, GeoInt4 retained 60% and 40% of its activity at pH 11.0 and 12.0, respectively. Thus, GeoInt4 stands out as a more competent biocatalyst that is suitable for alkaline environments of diverse industrial applications.The current study represents an efficient protein engineering strategy to adapt industrial catalysts to diverse processing conditions. Further comprehensive and fine-tuned research efforts may result in biotechnologically more promising outcomes.

Model improvement for predicting moisture content (MC) in pork longissimus dorsi muscles under diverse processing conditions by hyperspectral imaging

This study investigated the feasibility of hyperspectral imaging (HSI) to predict moisture content (MC) values in heated - dehydrated (H-D) and cool - air - dehydrated (C-A-D) pork samples using the calibrated models established based on fresh (F), frozen - thawed (F-T), dry - salting - dehydrated (D-S-D) and wet - salting - dehydrated (W-S-D) pork samples. The full spectra were extracted from the region of interests (ROIs) in the spectral range of 400–1000 nm and the textural variables were extracted by gray-level gradient co-occurrence matrix (GLGCM) method from the first two PC images accounting for 98.73% of the total variance. Moreover, the optimal wavelengths were selected by regression coefficients (RC). Partial least-squares regression (PLSR) predictive model was developed based on the above extracted data and their mutual combination, in which the changes can be correlated with MC - related attributes. The results demonstrated that the PLSR model based on the incorporation of the optimal wavelengths and the textures (OW-T) from F, F-T, D- S -D and W-S-D samples were the best to predict MC in H-D and C-A-D samples with Rp2 of 0.9489 and RMSEP of 1.4736. Moreover, the generated visualization maps provided a rapid way to screen the MC values unequally distributed in the whole pork samples after diverse processing conditions. Therefore, it is feasible and promising to improve the applicability of the existing MC predictive models for MC prediction in the pork samples by supplementing more samples under different treatments as another prediction sets.

Ni-W Alloys for Hydrogen Evolution

Ni-W alloys were prepared by electrodeposition at diverse processing conditions. The Ni-W alloys were studied by SEM, EDX and XRD analysis to determine composition and morphology of the surface in dependence on electrodeposition conditions. Focus was put on surface with electroactive sites for hydrogen evolution. Stability of the alloys in chloride medium was determined applying chronopotentiometry and potentiodynamic polarization. Electrochemical behavior of the alloys was tested in alkaline solution by cyclic voltammetry. It was found that processing conditions directly influence quality of the Ni-W alloys concerning phase, morphology and composition. Prevailing amorphous phase of Ni-W alloys supports corrosion rate growth.

Comparison of Microstructure and Properties of Ti-6Al-7Nb Alloy Processed by Different Powder Metallurgy Routes

The Ti-6Al-7Nb alloy was specially developed to replace the well-known Ti-6Al-4V alloy in biomedical applications due to supposed cytotoxicity of vanadium in the human body. This alloy is normally fabricated by conventional ingot metallurgy by forging bulk material. Nevertheless, powder metallurgy techniques could be used to obtain this alloy with specific properties. This is because by changing the processing parameters, such as the sintering temperature, it is possible to vary the porosity level and to tailor the final properties. This work deals with the production of the Ti-6Al-7Nb alloy by means of the master alloy addition variant of the blending elemental approach. The powder is processed by means of different powder metallurgy routes considering diverse processing conditions for each method. The materials are characterised in terms of microstructural features, relative density and hardness. Homogeneous microstructures as well as properties comparable to those of the wrought alloy are generally obtained.

Glycoside Hydrolases from a targeted Compost Metagenome, activity-screening and functional characterization

BackgroundMetagenomics approaches provide access to environmental genetic diversity for biotechnology applications, enabling the discovery of new enzymes and pathways for numerous catalytic processes. Discovery of new glycoside hydrolases with improved biocatalytic properties for the efficient conversion of lignocellulosic material to biofuels is a critical challenge in the development of economically viable routes from biomass to fuels and chemicals.ResultsTwenty-two putative ORFs (open reading frames) were identified from a switchgrass-adapted compost community based on sequence homology to related gene families. These ORFs were expressed in E. coli and assayed for predicted activities. Seven of the ORFs were demonstrated to encode active enzymes, encompassing five classes of hemicellulases. Four enzymes were over expressed in vivo, purified to homogeneity and subjected to detailed biochemical characterization. Their pH optima ranged between 5.5 - 7.5 and they exhibit moderate thermostability up to ~60-70°C.ConclusionsSeven active enzymes were identified from this set of ORFs comprising five different hemicellulose activities. These enzymes have been shown to have useful properties, such as moderate thermal stability and broad pH optima, and may serve as the starting points for future protein engineering towards the goal of developing efficient enzyme cocktails for biomass degradation under diverse process conditions.

Modeling the behavior of fiber suspensions in the molding of polymer composites

Mechanistic simulations of fiber suspensions to predict final fiber orientation distribution in the molding of fiber-reinforced composites have been developed. The mathematical algorithm models fibers as chains of connected rigid beads. Parameters such as fiber volume fraction, number of beads per fiber, and fiber flexibility can be modified to model diverse processing conditions. This article focuses on the extensional flow of short fiber suspensions at high fiber concentrations. Simulation results were compared with predictions obtained with the Folgar—Tucker model. It was found that for the case of short fiber suspensions, fiber orientation is well predicted by the Folgar—Tucker model with the interaction constant CI in close agreement with a model proposed by Phan-Thien et al. It was also found that fiber—matrix separation increases with the increase of fiber aspect ratio. The mathematical model presented in this study provides a tool to predict defects and to model orientation distribution in the processing of fiber-reinforced composites.