Conventional Process Research Articles

Photocatalytic Bleaching Process of Cotton Fabric with H2O2 and H2O2 /n-TiO2

This study delves into the exploration of an alternative bleaching method as a substitute for hydrogen peroxide, addressing concerns related to its excessive chemical usage, high waste load, and elevated energy and water consumption. Photocatalytic bleaching was implemented on 100% cotton fabrics. Initially, hydrogen peroxide served as a catalyst, and the photocatalytic effect was meticulously examined. Subsequently, nano-sized TiO2 was introduced into the photocatalytic recipe, and operational conditions were fine-tuned to determine the optimal bleaching process for cotton fabrics. All photocatalytic experiments were systematically compared with conventional hydrojen peroxide bleaching method. A comprehensive analysis, encompassing color spectrum values, SEM, SEM-EDX, XRD, and FTIR-ATR tests, was conducted. The results revealed a notable 13.36% enhancement in whiteness performance during bleaching when subjected to photocatalytic treatment with nano TiO2 and H2O2 in tandem. This study posits itself as an environmentally conscious alternative to the long-standing conventional bleaching processes prevalent in the textile industry.

Realization of global audio telepresence via a learning-based model-matching approach with an acoustic array system

A Global Audio Telepresence (GOAT) system requires a microphone array to capture the spatial audio signals in the far end and a loudspeaker array to reconstruct the sound field in the near end. This seamlessly immerses near-end users in remote audio scenes with full ambience. In this paper, we use a learning-based GOAT system (L-GOAT) based on the model-matching principle, where a deep neural network (DNN) acts as non-linear filters for the GOAT system. The network training attempts to minimize the matching error between the signals reproduced by the DNN and the desired signals filtered by the far-end acoustic transfer functions (ATFs). Extensive simulations were carried out for multi-source scenarios in two different rooms with different reverberation times. To implement the L-GOAT system, a five-microphone linear array was adopted in the far-end room, while a six-loudspeaker array was utilized in the near-end room. The objective evaluation matrices, including the Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), and the matching errors, were conducted to validate the efficacy of the GOAT systems. The proposed learning-based approach has demonstrated superior performance compared to a conventional digital signal processing (DSP)-based method.

Numerical and experimental studies on the effect of cryogenic treatment on welding residual stress of TIG welded EN8 steel plate

Most of the conventional welding process induces Welding Residual Stress (WRS), hydrogen embrittlement and distortions to welded components. The post-weld mechanical behaviour has an impact on both structural integrity and operational effectiveness of the welded component. Traditional Post Weld Heat Treatment (PWHT) is capable of relieving WRS to some extent. Based on recent research, Heat Treatment (HT) cycles are comprised of conventional HT and Cryogenic Treatment (CT) gives better fatigue life to welded components by redistributing the residual stress field. There is a need for further research to explore the impact of different parameters of CT affects the mechanical behaviour of welded specimens. In this study, numerical and experimental investigations are conducted to understand the impact of HT cycles comprised of austenitizing, quenching, CT and tempering process, over the TIG welded EN8 plates. Numerical model is developed for predicting the variation in WRS with each stage of HT cycles and observed a considerable redistribution of WRS after the completion of proposed HT cycle. Using the proposed HT cycle, the influence of different soaking periods of CT over the welded components are studied experimentally and shows that 36 hours soaking period have higher impact on the welded component’s mechanical behaviour.

Efficient catalysis for acidic methanol oxidation: Exploration of a Low-Platinum quaternary alloy catalyst via a Two-Step method

The quest for platinum-based catalysts with enhanced catalytic efficacy and durability during the methanol oxidation reaction (MOR) has been a focal point in energy conversion science. Traditional platinum-based catalysts are often constrained by their high platinum content, which escalates economic costs and complicates experimental protocols. In this study, we introduce a low-platinum-loaded catalyst synthesized via a two-step method, which incorporates a four-component alloy. This innovative approach significantly reduces platinum content while maintaining superior catalytic performance through precise control over the alloy’s composition and structure. The two-step synthesis methodology streamlines the conventional multi-step processes, enhancing the catalyst preparation’s efficiency and reproducibility. The resulting PtCoSnCu/N-C alloy catalyst exhibits remarkable catalytic activity in methanol solution, with mass activity and specific activity that are 9.73 and 9.75 times higher, respectively, than those of commercial platinum on carbon (Pt/C) catalysts. Notably, the optimized PtCoSnCu/N-C alloy catalyst demonstrates exceptional long-term stability, outperforming Pt/C in aging and extended stability tests. Density functional theory (DFT) calculations elucidate the optimization of the electronic structure and the synergistic effect of surface atoms in the PtCoSnCu/N-C catalyst, which mitigates the adsorption energy of CO molecules and enhances the utilization rate of active sites during the oxidation process. This research presents a paradigm-shifting low-platinum catalyst strategy for methanol fuel cells, establishing a theoretical foundation for catalyst design and optimization.

Hybrid solvent loop CO2 capture process for zero-emission hydrogen production

Natural gas reforming with CO2 capture is expected to play a crucial role in producing low-carbon hydrogen under the net-zero emission scenario. This work focuses on optimizing the CO2 capture processes for zero-emission hydrogen production, targeting an ultra-high CO2 capture ratio of 99.9 % from the shifted syngas. We present a novel hybrid solvent loop CO2 capture process that enhances capture efficiency by overcoming the inherent trade-off between CO2 absorption kinetics and capacity in conventional processes. This is achieved by utilizing two independent solvent loops, each operating within its optimal performance range. In the lower section of the absorber, a high-capacity solvent, aqueous potassium carbonate, is used to leverage the high CO2 partial pressure. In the upper section, a fast-kinetic solvent, aqueous monoethanolamine, is employed to reduce CO2 concentration to 100 ppm. The conventional process, split-flow process with a multi-pressure stripper, and the hybrid solvent loop process are modeled in Aspen Plus® and compared. Our results demonstrate that the proposed hybrid solvent loop configuration outperforms conventional methods, significantly reducing the CO2 regeneration duty by 25 % and total equivalent work by 16 %. This process enhances the feasibility of low-carbon hydrogen production and supports the goal of achieving net-zero emissions.

Numerical simulation of the first layer printing in electric field-assisted fused filament fabrication for robust unconventionally oriented additive manufacturing

Fused filament fabrication (FFF) stands as a prominent thermoplastic additive manufacturing technique that has a wide range of potential applications. However, the conventional FFF process encounters limitations in unconventional environments, particularly in environments devoid of gravity, such as in extraterrestrial environments or in limited manufacturing conditions where maintaining consistent nozzle standoff distance becomes a challenging task. Fluctuations in nozzle standoff distance lead to inadequate material adhesion and dimensional errors, which results in a high failure rate and prolonged completion time. Thus, the conventional FFF process requires continuous monitoring or human intervention to ensure successful operation. To overcome the shortcomings of the traditional FFF process, an electric field-assisted FFF (E-FFF) process can be utilized to reduce the need for constant supervision and enable reliable production of parts. The E-FFF process integrates a high-voltage power supply unit with a standard FFF printer, generating an electrostatic force between the nozzle and the build plate. The generated electrostatic force can effectively attract the extruded molten polymer toward the build plate, even at a larger z-height of up to 1200 μm in unconventional build platform orientations such as vertical or reverse printing orientation. In this paper, finite element analysis is used to simulate the FFF and E-FFF process. The simulation focuses on the adhesion between extruded material and build plate with extended nozzle standoff distances and predicts the minimum voltage required to achieve a good material adhesion in unconventional orientations for the E-FFF process. The simulation model demonstrated its efficacy by estimating the minimum required voltage levels necessary for successful printing within the E-FFF process across a range of nozzle standoff distances in unconventional orientations.

An Innovative Approach in Oat Milk Production: Ohmic Heating

This study aimed to produce oat-milk by using the ohmic heating (OH) system and to compare some quality properties of this milk with the milk produced by conventional methods. The solution containing oats was heated from 20 °C to 75 °C using 3 different voltage-gradients, and then oat-milk was obtained by applying a series of processes. It has been determined that as the voltage gradient increased during heating in oat milk production, the processing time shortens, and this time was 482seconds in the conventional heating process. The effective electrical conductivity values increased as the temperature increased in the OH process during the production of oat-milk and these values vary between 0.13-0.41S/m. Also, some quality characteristics of oat milk such as color and total phenolic content were better preserved in milk obtained by the OH process than conventional heating process. In addition, oat milk obtained by OH process had higher apparent viscosity at all temperatures.

Energy-efficient optimization design of bio-butanol fermentation broth purification process

To address the downstream processing challenges of the IBE (isopropanol-butanol-ethanol) system and obtain biobutanol products with a mass fraction of 0.9999, as well as obtain a mass fraction of 0.99975 for the IE (isopropanol-Ethanol) mixture product as a gasoline additive, this study proposes a four-column distillation process termed "Dehydration-Butanol-Extractive Four Column Distillation" (DBE-4CD). With the heat load as the optimization target, the DBE-4CD process was optimized to determine the optimal operating parameters. Based on the optimized process and considering the energy-saving potential of the dividing wall column, an “Azeotropic Dividing Wall-Extractive Three Column Distillation” (ADE-3CD) process was subsequently proposed to further enhance energy efficiency and reduce consumption. Compared to both conventional literature process and the DBE-4CD process, the total load of the ADE-3CD process decreased to 7433.5 kW, representing reductions of 20.35% and 10.11%, respectively. Additionally, the mass recovery rates of butanol and the IE mixture reached 99.90% and 99.64%, respectively, exceeding those of the conventional literature process, which were 99.11% and 99.18%.

An Overview of H2 and CH4 as Environmentally Sustainable Alternative Reductants to C for Chromite Smelting

The application of hydrogen (H2) and methane (CH4) as gaseous reductants for pure chromite (FeCr2O4) is reviewed in four theoretical approaches. These approaches are evaluated against the conventional process, where the sole reductant is a solid carbon (C) source. The sustainability is measured by gaseous carbon monoxide (CO(g)) formation, determined by the reaction stoichiometry of each theoretical approach. Decreased CO(g) formation is critical for alleviating the adverse environmental impact of ferroalloy production. The prereduction of FeCr2O4 by H2, followed by reduction by CH4 shows the largest decrease in CO(g) formation, i.e., a 75% decrease, compared to the conventional process. Furthermore, the H2‐based prereduction and CH4‐based primary reduction occur at lower temperatures than C‐based reduction, due to kinetic advantages, and thus decrease energy consumption. The overview discusses the environmental impact of substituting C with H2 and CH4 and briefly discusses how it can be implemented in industry.

Hydrogen Separation Membranes: A Material Perspective

The global energy market is shifting toward renewable, sustainable, and low-carbon hydrogen energy due to global environmental issues, such as rising carbon dioxide emissions, climate change, and global warming. Currently, a majority of hydrogen demands are achieved by steam methane reforming and other conventional processes, which, again, are very carbon-intensive methods, and the hydrogen produced by them needs to be purified prior to their application. Hence, researchers are continuously endeavoring to develop sustainable and efficient methods for hydrogen generation and purification. Membrane-based gas-separation technologies were proven to be more efficient than conventional technologies. This review explores the transition from conventional separation techniques, such as pressure swing adsorption and cryogenic distillation, to advanced membrane-based technologies with high selectivity and efficiency for hydrogen purification. Major emphasis is placed on various membrane materials and their corresponding membrane performance. First, we discuss various metal membranes, including dense, alloyed, and amorphous metal membranes, which exhibit high hydrogen solubility and selectivity. Further, various inorganic membranes, such as zeolites, silica, and CMSMs, are also discussed. Major emphasis is placed on the development of polymeric materials and membranes for the selective separation of hydrogen from CH4, CO2, and N2. In addition, cutting-edge mixed-matrix membranes are also delineated, which involve the incorporation of inorganic fillers to improve performance. This review provides a comprehensive overview of advancements in gas-separation membranes and membrane materials in terms of hydrogen selectivity, permeability, and durability in practical applications. By analyzing various conventional and advanced technologies, this review provides a comprehensive material perspective on hydrogen separation membranes, thereby endorsing hydrogen energy for a sustainable future.

CO2-Selective Membrane Reactor Process for Water-Gas-Shift Reaction with CO2 Capture in a Coal-Based IGCC Power Plant

Integrated gasification combined cycle (IGCC) power plants enable pre-combustion carbon capture to reduce CO2 emissions. Membrane water gas shift reactors can intensify these processes by converting raw syngas to H2 with simultaneous CO2 capture in one unit. This paper reports process design and techno-economic analysis (TEA) for a membrane reactor (MR) process with a CO2 selective ceramic-carbonate dual-phase (CCDP) membrane for water-gas shift (WGS) reaction with CO2 capture for a IGCC power plant. The target performance includes CO conversion >95%, hydrogen purity >90%, CO2 purity >95%, and carbon capture >90%. Using a commercial catalyst and a CCDP membrane, the MR can achieve the performance target at 750oC and space velocity of 250h-1. The outcome of the process design and TEA shows that the CCDP MR has an operating cost of $24M/year, significantly lower than that for the conventional processes (40M$/year). However, the MR process has a higher capital cost ($1,007M) than the conventional process ($527M) because of the higher cost of the CCDP MRs. Modeling analysis shows a MR with higher CO2 permeance can deliver the target at a higher space velocity and lower membrane surface area to catalyst volume ratio, leading to a significantly reduced MR capital costs.

A bibliometric analysis of ohmic heating on food processing in the last two decades

Novel food processing technologies have been devised to cater to the specific requirements of consumer products and tackle the challenges associated with conventional food processing technologies. Ohmic heating is a contemporary thermal-process technology with advantages for time efficiency, improved sensory and functional quality, and enhanced energy efficiency. This bibliometric investigation aimed to analyze the level of scholarly research on ohmic heating within the field of food research over the past twenty years (2003–2023). The findings indicate an upward annual growth rate of 11.09 % in the subject of food-related ohmic heating research. A total of 769 publications have been published, involving 1841 authors. Brazil is recognized as the nation with the most research contributions while Sastry, S.K., is the most productive author, and Teixeira, J.A. is the most collaborative author. Review studies examining the impact of ohmic processes on the nutritional composition of fruits, vegetables, and grains have garnered the highest number of citations. Innovative Food Science and Emerging Technologies and the Journal of Food Engineering have emerged as the most influential journals in this field. Keywords such as “ohmic heating,” “electroconductive heating,” and ”joule heating” are commonly used in academic publications on the application of ohmic heating in the food industry. Recent trends in this field focus on aspects such as extraction procedures, pasteurization, physicochemical components, and energy usage. Ohmic heating has a bright future. Rapidly growing research shows strong interest, especially recently, because of attractive advantages such as energy efficiency and sustainability. International collaborations will expand its applications in the food industry. Challenges include high capital costs, maintenance, and unclear regulations. Future research should focus on cost-effective materials, thermal stability, food safety, and broader applications. With a clear path, the food industry can adopt ohmic heating technology as affordable and ecofriendly technology more efficiently.

Effect of pulse current on the creep ageing behavior of pre-strained 2195 Al-Li alloy

Pre-strained aluminum lithium (Al-Li) alloy plates have ultra-high strength and comprehensive performance after aging treatments, and are widely used in the aerospace industry. The initial increase in dislocation density caused by these pre-strains will significantly affect the evolution of deformation, microstructure, and mechanical properties during the pulse current assisted creep ageing forming process. Therefore, the effect of pulse current on the creep ageing behavior of 2195 Al-Li alloy with different pre-strains (0%, 4%, and 8%) was investigated. As the pulse current increases within the first 1h creep aging process of the various pre-strain samples, the 1h creep strain significantly increases and the subsequent steady-state creep rate slightly reduce. Moreover, the larger the pre-strain, the higher the total creep strain. Pulse current promotes the movement and recovery of initial dislocations, and promotes the fine formation of T1 phase and suppresses the nucleation of θ' and phase, thereby improving mechanical properties. Compared with the conventional creep ageing process, the pulse current acting on pre-strained 2195 Al-Li alloy significantly increased the peak aged strengthen and shorten the peak aging time. Meanwhile, the peak time platform period has also been significantly extended. It is evident that the application of pulse current assisted creep ageing forming process to pre-strained 2195 Al-Li alloy plates can significantly improve formability and mechanical properties, enlarges the forming process window and realize collaborative manufacturing of Al-Li alloy component shape and performance.

Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration.

The deep unfolding approach has attracted significant attention in computer vision tasks, which well connects conventional image processing modeling manners with more recent deep learning techniques. Specifically, by establishing a direct correspondence between algorithm operators at each implementation step and network modules within each layer, one can rationally construct an almost "white box" network architecture with high interpretability. In this architecture, only the predefined component of the proximal operator, known as a proximal network, needs manual configuration, enabling the network to automatically extract intrinsic image priors in a data-driven manner. In current deep unfolding methods, such a proximal network is generally designed as a CNN architecture, whose necessity has been proven by a recent theory. That is, CNN structure substantially delivers the translational symmetry image prior, which is the most universally possessed structural prior across various types of images. However, standard CNN-based proximal networks have essential limitations in capturing the rotation symmetry prior, another universal structural prior underlying general images. This leaves a large room for further performance improvement in deep unfolding approaches. To address this issue, this study makes efforts to suggest a high-accuracy rotation equivariant proximal network that effectively embeds rotation symmetry priors into the deep unfolding framework. Especially, we deduce, for the first time, the theoretical equivariant error for such a designed proximal network with arbitrary layers under arbitrary rotation degrees. This analysis should be the most refined theoretical conclusion for such error evaluation to date and is also indispensable for supporting the rationale behind such networks with intrinsic interpretability requirements. Through experimental validation on different vision tasks, including blind image super-resolution, medical image reconstruction, and image de-raining, the proposed method is validated to be capable of directly replacing the proximal network in current deep unfolding architecture and readily enhancing their state-of-the-art performance. This indicates its potential usability in general vision tasks.

Unexpected increase in microalgal removal of doxylamine induced by bicarbonate addition: synergistic chem-/bio-degradation mechanisms

Microalgae-based approaches serves as promising methods for remediation of pharmaceutical contaminants (PCs) compare to conventional wastewater treatment processes. However, how to decrease hydraulic retention times of the microalgal system currently has been one of the main bottlenecks. This study constructed an unexpected synergistic extra-chem-/intra-bio-degradation system by addition of 5.95 mM bicarbonate in the microalgal system, which achieved complete removal (100%) of a representative PC, doxylamine (DOX) in 96 h compare to that 192 h in the control. Removal capacities and mass balance analyses demonstrated that biodegradation rate per unit microalgal density was significantly increased by 207%. Further analyses using transcriptomic, enzymatic inhibitory test, and high-resolution mass spectrometry revealed that after addition of bicarbonate for metabolism of DOX, a hydrolase (CYP97C1) and a primary amine oxidase (TynA) can catalyze DOX into doxylamine N-oxide and an intermediate (C15H17NO2) with a m/z of 244.1335. Meanwhile, bicarbonate reacted with microalgae-excreted hydrogen peroxide to form more oxidative radicals including superoxide and hydroxyl radicals extracellularly, which promised the extracellular degradation of DOX according to the oxidative radical inhibiting tests. Further investigation showed addition of bicarbonate in microalgal system improved the removal rate of 17 PCs by up to 500.8%. Therefore, this study not only developed an approach to enhance treatment efficiencies of diverse PCs by microalgae within a shorter time, but also carried unique mechanistic insights into the underlying principles.

Bayesian self-calibration and imaging in very long baseline interferometry

Context. Self-calibration methods with the CLEAN algorithm have been widely employed in very long baseline interferometry (VLBI) data processing in order to correct antenna-based amplitude and phase corruptions present in the data. However, human interaction during the conventional CLEAN self-calibration process can impose a strong effective prior, which in turn may produce artifacts within the final image and hinder the reproducibility of final results. Aims. In this work, we aim to demonstrate a combined self-calibration and imaging method for VLBI data in a Bayesian inference framework. The method corrects for amplitude and phase gains for each antenna and polarization mode by inferring the temporal correlation of the gain solutions. Methods. We use Stokes I data of M87 taken with the Very Long Baseline Array (VLBA) at43 GHz, pre-calibrated using the rPICARD CASA-based pipeline. For antenna-based gain calibration and imaging, we use the Bayesian imaging software resolve. To estimate gain and image uncertainties, we use a variational inference method. Results. We obtain a high-resolution M87 Stokes I image at 43 GHz in conjunction with antenna-based gain solutions using our Bayesian self-calibration and imaging method. The core with counter-jet structure is better resolved, and extended jet emission is better described compared to the CLEAN reconstruction. Furthermore, uncertainty estimation of the image and antenna-based gains allows us to quantify the reliability of the result. Conclusions. Our Bayesian self-calibration and imaging method is able to reconstruct robust and reproducible Stokes I images and gain solutions with uncertainty estimation by taking into account the uncertainty information in the data.

Microstructures, mechanical properties and damping performance of wrought Mg–Cu–Ca alloys

Mg–Cu–Ca alloy sheets show good room temperature formability and excellent thermal conductivity; however, their strength is insufficient for load-bearing applications. In this study, Mg–Cu–Ca ternary alloys were subjected to a conventional extrusion process, and the microstructures and tensile properties were systematically evaluated. The addition of extremely small amounts of Cu and Ca to pure Mg (Mg–0.03Cu–0.05Ca, wt%) led to significant refinement of dynamically recrystallized (DRXed) grains while preserving a certain fraction of un-DRXed grains, which contributed to a substantial increase in tensile yield strength. Adjusting the extrusion parameters further increased the tensile yield strength to 329MPa, driven by the combined effects of further refined DRXed grains and an increased area fraction of un-DRXed grains. The damping performance of the Mg–0.03Cu–0.05Ca alloy sheet was superior to that of commercial AZ31(Mg–3Al–1Zn–0.3Mn in wt%) alloy sheet but lower than that of pure Mg, particularly at low frequencies. This revealed that the solute segregation at grain boundaries plays a crucial role in the damping performance of Mg alloys.

Deep brain stimulation versus vagus nerve stimulation for the motor function of poststroke hemiplegia: study protocol for a multicentre randomised controlled trial

IntroductionDeep brain stimulation (DBS) and vagus nerve stimulation (VNS) can improve motor function in patients with poststroke hemiplegia. No comparison study exists.Methods and analysisThis is a randomised, double-blind, controlled clinical...

Design and numerical investigation of the 3D reinforced re-entrant auxetic and hexagonal lattice structures for energy absorption properties

Lattice Structures (LS) are widely recognized for their light weight and excellent mechanical properties. In this study, strut reinforcement technique is applied to enhance the energy absorption properties of 3D re-entrant auxetic (Aux) and hexagonal (Hex) LS. Numerical investigation using finite element analysis (FEA) was carried out to understand the mechanical and energy absorption properties of the novel designs through quasi-static compression test. The uniaxial loading results of the reinforced designs were compared to the traditional 3D hexagonal and re-entrant auxetic LS. In order to numerically simulate the mechanical behaviour of 3D printed LS, mechanical properties of experimentally studied PA2200 matrix material (manufactured via additive manufacturing) was used. Study of the mechanical behaviour of the 3D printed LS parts is essential because additive manufacturing has the capacity to fabricate the complex LS compared to conventional manufacturing processes, and innovative LS design can provide high specific strength of parts. The stress–strain and energy absorption curve results indicate that the purposed new designs are excellent choice for energy absorption properties at large strain. The findings of this study contribute towards the novel design of 3D hexagonal and re-entrant auxetic LS for superior mechanical strength and specific energy absorption properties.

Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways

Stochastic techno-economic analysis (TEA) is pivotal in assessing the financial viability and risks inherent in biofuel production processes. In this method, the Monte Carlo approach entails the random sampling of input variables and multiple runs of the TEA model to create probability distributions of economic metrics. However, traditional Monte Carlo TEA, reliant on iterative calls to process simulation, is resource-intensive and time-consuming, hindering widespread adoption. To address these challenges, we present an accessible framework that harnesses machine learning methods to estimate techno-economic uncertainty in biofuel production pathways. Our approach streamlines the conventional simulation process by automating dataset generation and machine learning model training. These trained models enable rapid predictions of minimum fuel selling prices at any scale, accommodating randomized input variables based on their defined distributions. We illustrate the efficacy of our framework through examples from sustainable aviation fuel production pathways. Our research entails identifying the primary factors influencing uncertainties in minimum selling prices, exploring the synergistic effects of pathway inputs, and assessing how price variability is impacted by financial, technical, and supply chain factors. These examples underscore the framework's effectiveness in addressing breakeven price uncertainties in biofuel production across diverse input scenarios.