Operational Parameters Research Articles

Utilizing olive leaves biomass as an efficient adsorbent for ciprofloxacin removal: characterization, isotherm, kinetic, and thermodynamic analysis.

Olive leaves were utilized to produce activated biomass for the removal of ciprofloxacin (CIP) from water. The raw biomass (ROLB) was activated with sodium hydroxide, phosphoric acid, and Dead Sea water to create co-precipitated adsorbent (COLB) with improved adsorption performance. The characteristics of the ROLB and COLB were examined using SEM images, BET surface area analyzer, and ATR-FTIR spectroscopy. COLB has a BET surface area of 7.763 m2/g, markedly higher than ROLB's 2.8 m2/g, indicating a substantial increase in adsorption sites. Through investigations on operational parameters, the optimal adsorption efficiency was achieved by COLB is 77.9% within 60min, obtained at pH 6, and CIP concentration of 2mg/mL. Isotherm studies indicated that both Langmuir and Freundlich models fit the adsorption data well for CIP onto ROLB and COLB, with R2 values exceeding 0.95, suggesting effective monolayer and heterogeneous surface adsorption. The Langmuir model revealed maximum adsorption capacities of 636mg/g for ROLB and 1243mg/g for COLB, highlighting COLB's superior adsorption capability attributed to its enhanced surface characteristics post-modification. Kinetic data fitting the pseudo-second-order model with R2 of 0.99 for ROLB and 1 for COLB, along with a higher calculated qe for COLB, suggest its modified surface provides more effective binding sites for CIP, enhancing adsorption capacity. Thermodynamic analysis revealed that the adsorption process is spontaneous (∆Go < 0), and exothermic (∆Ho < 0), and exhibits a decrease in randomness (∆So < 0) as the process progresses. The ΔH° value of 10.6kJ/mol for ROLB signifies physisorption, whereas 35.97kJ/mol for COLB implies that CIP adsorption on COLB occurs through a mixed physicochemical process.

Production of carbon-11 for PET preclinical imaging using a high-repetition rate laser-driven proton source

Most advanced medical imaging techniques, such as positron-emission tomography (PET), require tracers that are produced in conventional particle accelerators. This paper focuses on the evaluation of a potential alternative technology based on laser-driven ion acceleration for the production of radioisotopes for PET imaging. We report for the first time the use of a high-repetition rate, ultra-intense laser system for the production of carbon-11 in multi-shot operation. Proton bunches with energies up to 10–14 MeV were systematically accelerated in long series at pulse rates between 0.1 and 1 Hz using a PW-class laser. These protons were used to activate a boron target via the 11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{11}$$\\end{document}B(p,n)11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{11}$$\\end{document}C nuclear reaction. A peak activity of 234 kBq was obtained in multi-shot operation with laser pulses with an energy of 25 J. Significant carbon-11 production was also achieved for lower pulse energies. The experimental carbon-11 activities measured in this work are comparable to the levels required for preclinical PET, which would be feasible by operating at the repetition rate of current state-of-the-art technology (10 Hz). The scalability of next-generation laser-driven accelerators in terms of this parameter for sustained operation over time could increase these overall levels into the clinical PET range.

Modeling Specific Capacitance of Carbon Nanotube-Based Supercapacitor Electrodes by Machine Learning Algorithms

Abstract Carbon nanotubes (CNTs) have emerged as promising materials for supercapacitors (SCs) due to their unique properties and exceptional electrical conductivity. These cylindrical structures composed of carbon atoms offer several advantages for SC electrode applications. The electrochemical performance of CNT-based electrodes is strongly influenced by factors such as surface area, pore structure, and ID/IG ratio. However, the lack of a credible physical model capable of accurately predicting the performance of SCs based on these physicochemical properties of CNTs poses a challenge. In this study, we propose the utilization of a data-driven approach employing various models including a gradient boosting regression (GBR), Bayesian regression (BR), ridge regression (RR), and stochastic gradient descent (SGD) model to predict the performance of SCs with CNT electrodes based on the microstructural properties of the electrode material and electrochemical operational parameters. The developed GBR model demonstrates its feasibility by achieving a low root mean square error (RMSE) value of approximately 36.31 for the prediction of specific capacitance for test split. Additionally, a sensitivity analysis was conducted to investigate the influence of independent input parameters on a single output parameter, specifically the specific capacitance. This analysis provides insights into the relative importance and impact of various input parameters on the specific capacitance of CNT-based SCs.&amp;#xD;

Characterization and optimization of ultrasound assisted oxidative desulfurization of a model fuel using a novel magnetic deep eutectic solvent

ABSTRACT In various studies, researchers have utilised different solvents to extract sulfur compounds from the fuels. This paper introduces a novel magnetic deep eutectic solvent employed in the ultrasound-assisted oxidative desulfurisation of dibenzothiophene within a model fuel. The solvent, synthesised at 85°C by combining choline chloride, p-toluene sulfonic acid, and FeCl₃, underwent characterisation through Fourier transform-infrared, Vibrating-sample magnetometer, and Raman spectrometry. Exploration of the key parameters, including oxidant to model fuel volumetric fraction percentage (0.2–0.3), Iron (III) chloride to choline chloride mole fraction (0.1–0.3), and Magnetic deep eutectic solvent to model fuel volumetric fraction (0.2–0.3), was conducted to assess their impact on dibenzothiophene conversion rates. Employing response surface methodology based on central composite experimental design, optimal operational parameters for the ultrasound-assisted oxidative desulfurisation process were identified, resulting in a sulfur removal rate of 98.7%. The findings highlight the solvent sustained efficiency even at higher dibenzothiophene concentrations. Additionally, the solvent paramagnetic properties facilitated an expedited separation from the fuel, leading to the significantly reduced process times.

Using Quality Function Deployment to Assess the Efficiency of Mini-Channel Heat Exchangers

This article addresses the design of a compact heat exchanger for the cooling of electronic systems. The Quality Function Deployment (QFD) method is used to identify crucial product features to improve device performance and key customer requirements. The QFD simplifies management processes, allowing modifications to device components, such as design parameters (dimensions and materials) and operating conditions (flow type and preferred temperature range). The study was applied to analyse the fundamental features of a compact heat exchanger, assessing their impact on enhancing heat transfer intensity during fluid flow through mini-channels. The thermal efficiency of the compact heat exchanger was tested experimentally. The results allow to verify the results obtained from the numerical simulations due to Simcenter STAR-CCM+. Consequently, the experimental part was reduced in favour of numerical simulations conducted using this commercial CFD software version 2020.2.1 Build 15.04.01. The numerical simulations performed with the aid of CFD showed increases in the heat transfer coefficient of up to 180% compared to the case treated as a reference. The application of the QFD matrix significantly reduces the time required to develop suitable design and material solutions and determine the operating parameters for the cooling of miniature electronic devices.

Advanced analysis of operating parameters utilizing big data to improve building cooling equipment energy efficiency standards

The rapid increase in building energy consumption has highlighted the critical contribution of building cooling/heating equipment, including room air conditioners and variable refrigerant flow (VRF) systems. However, existing energy efficiency standards for such equipment often specify testing conditions that differ from real-world usage scenarios and are unsupported by in-depth investigations, resulting in evaluations of equipment performance that do not align with actual performance. Therefore, the increasing availability of large-scale real-time monitoring datasets was leveraged in this study to develop an innovative technical approach to derive a more accurate description of VRF operation-related parameters. This approach was applied to inform suggestions for the enhancement of energy efficiency standards for VRF devices by collecting and preprocessing data, extracting operating parameters, conducting data analysis and comparisons, and discussing potential applications. The results of this study indicate that the type of building significantly influences the actual performance of a VRF and imply that large-scale monitoring data can provide a foundation for the revision of building equipment energy efficiency standards as well as future investigations of building equipment energy policies.

Development of an electrocoagulation method using alternating pulse current to treat wastewater generated by tanneries

ABSTRACT Direct current electrocoagulation has various drawbacks, including electrode passivation, heat creation from energy consumption, and high sludge formation. The restrictions limit its usage in tannery wastewater treatment. Therefore, alternating pulse current EC (APC-EC) was developed to solve these obstacles. The study was empirically examined, considering high-frequency (3,000–11,000 Hz), stirring speed (400–1,200 rpm), and reaction time (15–30 min). This research examined how factors affect chemical oxygen demand (COD) removal efficiency, turbidity from trivalent chromium (Cr+3), and energy consumption for perforated aluminum electrodes. The central composite design in the surface response design approach has improved various operational parameters in the APC-EC process for tannery wastewater treatment. The employment of mathematical and statistical methods resulted in optimal removal of COD, Cr+3, and turbidity while reducing energy usage. Using mathematical and statistical methods, we achieved maximum COD, Cr+3 ion, and turbidity reduction while reducing energy use. The investigation found that COD (70.3%), Cr+3 (89.56%), and turbidity (96%) were the most rapidly removed components at 11,000 Hz, 576 rpm, and 30 min. Surface response data explain high-frequency APC-EC dynamics.

Enhanced H2S decomposition using membrane reactor

H2S is a toxic gas released during many industrial processes, especially oil and gas refining. Currently, H2S is converted to sulfur using Claus process, but it leads to loss of another valuable product, i.e., hydrogen, in the form of water. Thermal decomposition of H2S in a membrane reactor is a promising route for recovering both hydrogen as well as sulfur in an efficient manner. In the present work, the authors report H2S decomposition studies with packed-bed membrane reactor (PBMR) using in-house developed clay-alumina ceramic membrane. In order to have a comparative evaluation, these studies were conducted in packed bed reactor (PBR) as well as in single-tube PBMR at different operation parameters. In PBMR, conversion of ∼90% was obtained at 1573 K and 0.5 bar, whereas a conversion of ∼50% was obtained in PBR. CFD simulations for H2S decomposition using ceramic PBMR were carried out and validated using experimental results. 3D simulations for parametric optimisation of multi-tube PBMR were also carried out by extrapolation of validated model. This is for the first time, CFD studies on H2S decomposition and its validation using ceramic PBMR are being reported in this work.

Abstract IA020: AI-powered tool for rapid &amp; reliable bladder cancer screening and surveillance: multicenter validation efforts

Abstract Bladder cancer is the most recurrent form of cancer, requiring frequent screening and surveillance, which contributes to its status as the costliest per capita. While urine cytology serves as a crucial adjunct to invasive cystoscopy, it is high volume, labor-intensive, and relatively inconsistent. Despite the implementation of The Paris System, which introduced updated quantitative guidelines, implementation of digital tools for urine cytology still trails the technological advancements seen in semi-automated Pap smear screenings. This lag can be attributed to the diverse cytomorphology of urine specimens and variation in specimen preparation and screening. Our team has developed AutoParis-X (APX), a state-of-the-art technology leveraging deep learning algorithms to extract objective (e.g., nucleus-to-cytoplasm ratio) and subjective (e.g., hyperchromasia) features from whole slide images (WSI). APX transforms these features into the Atypia Burden Score (ABS), offering a quantitative assessment of malignancy and recurrence risks on a scale from 0 to 1. Initially validated with 1300 samples (ThinPrep; Leica Aperio GT450) at Dartmouth Hitchcock, achieving an AUROC of 0.9, APX has evolved into APX-WEB, a user-friendly, web-based tool for cytopathologists. Notable challenges persist, including: 1) validating across nationally representative cohorts with varied specimen preparations and imaging, 2) addressing the intricacies of z-stacking for imaging non-monolayer preparations, and 3) integration of genomics to heighten the sensitivity of imaging assays. This study aimed to establish a performance baseline for APX's adaptability to these challenges without algorithmic adjustments. APX was applied across three distinct cohorts: 1) a preliminary national validation on 500 ThinPrep WSI in collaboration with Hologic; 2) assessment on non-monolayer specimen preparations (e.g., SurePath), using z-stacking, conducted with Johns Hopkins (n=100 WSI); and 3) a pilot study at Dartmouth (n=19) for integrating APX's imaging features with whole exomic sequencing (WES). Preliminary findings indicate: 1) Promising performance on a held-out Hologic national cohort (sensitivity=0.87, specificity=0.73; original APX study: sensitivity=0.88, specificity=0.83). 2) In SurePath z-stack WSI, the number of detected urothelial cells per cluster decreased with distance from the focus plane (rho =-0.13, p&amp;lt;0.001). 3) Of eight cases deemed negative or atypical by cytology, three exhibited TERT mutations, suggesting subclinical recurrence, in contrast to the consistent findings in all nine suspicious or positive cases. This highlights the potential of integrating genomics with image-based classifiers to enhance diagnostic precision. The findings underscore the need for future multicenter work aimed at refining machine learning models to achieve enhanced predictive performance across various operational parameters and settings, facilitating their implementation. Citation Format: Joshua Levy, Keluo Yao, Jonathan Marotti, Darcy Kerr, Edward Gutmann, Sam Harvey, Yoseph Sayegh, Chris VandenBussche, Michael Quick, Peter Costa, Lauren Wainman, Donald Green, Parth Shah, Xiaoying Liu, Louis Vaickus. AI-powered tool for rapid &amp; reliable bladder cancer screening and surveillance: multicenter validation efforts [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr IA020.

Modeling of Unburned Carbon in Pulverized Coal-Fired Supercritical Steam Generators

ABSTRACT In coal-fired boilers, one of the most important metrics for optimizing combustion is the proportion of unburned carbon (UC) in the ash. In most Indian power plants, it is still assessed offline through manual sample collection and laboratory analysis. The laborious processes of sample collection, preparation, analysis, and reporting require at least five to eight hours of work. The delayed results may not be useful to optimize combustion in the boiler furnace, as the boiler operating conditions may be completely different from the conditions when the samples were collected. Instead, the use of prediction algorithms may bring timely corrective action if the UC goes high. The present experimental study is taken up to obtain a correlation between UC and corresponding relevant boiler operational parameters and on the residence time of coal particles in the boiler furnace. Multiple regression analysis is performed using ANOVA tools. One hundred and ninety-four sets of coal, ash samples and boiler operating parameters are collected from 07 supercritical units. Coal proximate, coal fineness and ash UC analysis are carried out. The analyzed results and collected boiler operational data are statistically analyzed using Microsoft Excel’s ANOVA tool to understand the dependent behavior of UC on the boiler operational parameters and fired coal characteristics. The empirical equations are proposed for the prediction of UC in bottom and fly ash for supercritical boilers. Additionally, fresh 20 sets of coal, ash samples, and related operating data are gathered for the validation of the proposed empirical equations. For additionally collected samples, the predicted results are compared with the offline measured laboratory results. The maximum deviation between the predicted and laboratory results are within the range of ± 0.40% and ± 0.24% for bottom and fly ash samples, respectively.

Enhancing Surface Integrity and Quality through Roller Burnishing: A Comprehensive Review of Parameters Optimization, and Applications.

Surface integrity is a critical aspect in ensuring the quality of components or products, particularly in industries such as automotive, aerospace, and machinery where superior surface finish and prolonged functionality of cylindrical parts are essential. Burnishing, a chip-less cold working finishing process, enhances the surface finish of machined workpieces through plastic deformation. This review paper delves into various aspects of the burnishing process and its impact on surface properties, drawing insights from multiple research projects. It explores topics including residual stresses, surface roughness, microhardness, cooling strategies, and modeling techniques. Key findings underscore the influence of burnishing parameters such as speed, feed rate, penetration depth, and number of passes on surface roughness and microhardness. Moreover, the process induces compressive residual strains on the workpiece surface, with their magnitude varying based on operational parameters. The choice of cooling methods, including kerosene, minimum quantity lubrication (MQL), cryogenic cooling, and hybrid cooling, significantly affects surface integrity. Various modeling approaches, such as Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Response Surface Methodology (RSM), aid in predicting and optimizing surface characteristics. Despite the extensive discussion on surface roughness, microhardness, residual stresses, cooling methods, and modeling, out-of-roundness in the burnishing process remains understudied and warrants further investigation. This review contributes valuable insights for enhancing surface qualities in manufacturing applications and deepening our understanding of the burnishing process.

A vibrating membrane ejector for direct ink writing of printed electronics

Direct ink writing (DIW) represents a technical branch of additive manufacturing technology, particularly suitable for prototyping or small-batch printing production of printed electronic components. However, the existing print heads required for near or sub-100 μm line width are quite sophisticated, limiting the accessibility and application of the DIW method. This paper reports the use of the vibrating membrane ejector (VME) as a new option for the print head of DIW. The structure of the VME-based print head was specially designed for this purpose. Finite element modeling and analysis of the VME’s vibration characteristics were performed to provide insights into the ejection conditions and behaviors. The factors influencing the size of printed structures were identified and analyzed through the printing of a metal–organic complex silver (Ag) ink. After optimizing several operational parameters to limit the spreading effects and suppress the satellite droplets, the DIW printed line width has reached about 100 μm. The effectiveness of the VME-based print head was further demonstrated through the DIW fabrication of interdigitated electrodes and microstrip transmission lines. This highlights the versatility of the VME-based print head as a practical tool for device prototyping and ink development in the field of printed electronics.

Modelling and prediction of aeration efficiency of the venturi aeration system using ANN-PSO and ANN-GA

The significance of this study involves the optimisation of the aeration efficiency (AE) of the venturi aerator using an artificial neural network (ANN) technique integrated with an optimisation algorithm, i.e., particle swarm optimisation (PSO) and genetic algorithm (GA). To optimise the effects of operational factors on aeration efficiency by utilising a venturi aeration system, aeration experiments were conducted in an experimental tank with dimensions of 90cm×55cm×45cm. The operating parameters of the venturi aerator include throat length (TL), effective outlet pipe (EOP), and flow rate (Q) to estimate the efficacy of the venturi aerator in terms of AE. A 3–6-1 ANN model was developed and integrated with the PSO and GA techniques to find out the best possible optimal operating variables of the venturi aerator. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) determined from the experimental and estimated data were used to assess and compare the performance of the ANN-PSO and ANN-GA modelling. It is shown that ANN-PSO provides a better result as compared to ANN-GA. The operational parameters, TL, EOP, and Q, were determined to have the most optimum values at 50 mm, 6 m, and 0.6 L/s, respectively. The optimised aeration efficiency of the venturi was found to be 0.105 kg O2/kWh at optimum operational circumstances. In fact, the neural network having an ideal design of (3-6-1) and a correlation coefficient value that is extremely close to unity has validated the results indicated above.

Hand Vibration Reduction Using Nonlinear Vibration Absorber for the Vibro-Impact Hammer Model

Abstract This work investigates the effectiveness of a passive vibration control device to minimize the amplitude of vibrations from a hand-held impact machine (HHIM) transmitted to a hand-arm system (HAS). For this purpose, we employ a simplified lumped parameter model of an HHIM and the hand-arm system consisting of lumped masses, springs, and viscous dampers. For a better understanding of the system dynamics, we use a vibro-impact model for a more accurate representation of HHIM–ground interactions. We then examine the effectiveness of a cubic nonlinear vibration absorber in minimizing these undesirable vibrations via a nonlinear bifurcation analysis of the system. A parametric study on the numerical bifurcation analysis of the system reveals the criticality of different design parameters of the proposed nonlinear vibration absorber on the system dynamics. Our findings demonstrate that an appropriate selection of absorber parameters could significantly change the amplitude and the periodicity of vibrations transmitted to the hand. Furthermore, we observe the existence of complex motions such as period-2, period-4, and chaotic attractors in the system for the given values of the absorber and operating parameters.

Enhanced uranium removal from leach liquor using raw and activated sludge as sustainable adsorbents

ABSTRACT To eliminate uranium from sulphate leach solution, this study introduces an economical, environmentally friendly adsorbent derived from dewatered municipal sludge (RS) and activated (AS) using calcium oxide. Various analytical methods, including scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), Brunauer – Emmett – Teller (BET) analysis, and X-ray fluorescence spectroscopy (XRF), were employed in the characterisation of both materials. The efficacy of these adsorbents in removing uranium from sulphate leach fluid was systematically explored resulting in the achievement of a maximum adsorption capacity of 33.0 mg g−1 and 60.2 mg g−1 for RS and AS respectively. These adsorption capacity were detected using the following operational parameters: solution pH equals 2.0, 240 min as a contact time between the adsorbent and U(VI) solution, U(VI) initial concentration of 250 mg L−1, adsorbent dosage of 5.0 g L−1 and room temperature. The pseudo-second order kinetic and Langmuir isotherm models were determined to be the most suitable for the U(VI) adsorption process, showing a chemisorption, monolayer, and uniform process for the uranium adsorption by both materials. Using 1.0 M hydrochloric acid, over 96% of the U(VI) was successfully desorbed from the loaded sorbent, and AS sorbent held its stability for six sorption/desorption cycles in a row. Overall, this study offers a promising solution for sewage sludge disposal while addressing economic and environmental concerns.

Enhanced predictive optimization of methane dry reforming via ResponseSurface methodology and artificial neural network approaches: Insights using a novel nickel-strontium-zirconium-aluminum catalyst

This study investigates the molecular dynamics of methane dry reforming catalyzed by a novel nickel-strontium-zirconium-aluminum (5Ni+3Sr/10Zr+Al) catalyst, leveraging both Response Surface Methodology (RSM) and Radial Basis Function Neural Network (RBFNN) for predictive optimization. Focusing on the impact of operational parameters—hourly space velocity, reaction temperature, and CO2:CH4 mole ratio—on the conversion rates and formation of reaction components, we aim to predict optimal conditions and corresponding process variables. Through a comparison of a three-layer Feed Forward Neural Network, optimized at a 3:10:1 topology, with traditional RSM approaches, our findings highlight the superior predictive capabilities of ANN models. Notably, ANN demonstrated exceptional performance with Radj2and F_Ratio values significantly surpass those of RSM, alongside lower statistical error terms. This superiority is attributed to ANN's robust handling of nonlinear relationships between inputs and outputs, asserting its potential for enhancing predictive accuracy in chemical process optimization. At optimum predicted conditions like 1 CH4/CO2,750 °C reaction temperature, 12000 cm3g−1h−1 space velocity, NiSrZrAl outperformed with > 85 % CH4 and CO2 conversion with H2/CO ∼1 up to 20 h time on stream. Our research underscores the importance of integrating advanced modeling techniques for the efficient and accurate prediction of catalytic reactions, offering valuable insights for future applications in chemical engineering and catalysis.

Prediction of Cleaning Loss of Rice Wheat Combine Harvester Based on Dynamic Bayesian

The cleaning loss rate is a crucial performance metric for rice-wheat combine harvesters. Most studies on the relationship between the cleaning operation parameters of the combined harvester and the cleaning loss rate lack research on the dynamic correlation between harvest losses and operational parameters, specifically focusing on the dynamic correlation between harvest losses and multiple operational parameters such as the cleaning loss rate. In this paper, we formulate a dynamic deductive model for the scenario of harvesting loss regulation involving multiple operational parameters, delving into the dynamic associations between cleaning loss and the regulation of multiple operational parameters and constructing dynamic Bayesian network prediction model. Through experimental analysis of the dynamic Bayesian network prediction model for cleaning loss rates, the comparison of results among the other three algorithms demonstrated that it was efficient for our DBN to predict the cleaning loss rate.

A method determining critical operating parameters for landing aircraft based on runway pavement skid resistance

ABSTRACT Poor skid resistance of airport pavement is the critical cause of runway excursion accidents. While the current determination methods of skid resistance are limited to the independent operating parameters and the discontinuously monitoring the skid resistance, the empirically calculating critical operating parameters. In this study, an aircraft tire-fluid-airport pavement skid resistance model was developed using numerical simulation technology. The wide-body aircraft B777 was taken as an example, the available skid number (SN) was used to continuously track the skid resistance of pavement under various landing conditions. The combined effects of groove type, water film thickness and landing weight on the pavement skid resistance performance were evaluated when the pavement reached the maintenance threshold state. Results show that the skid resistance of the ungrooved pavement was subpar according to FAA. The skid resistance was positively connected with the grooved size and landing weight but negatively correlated with the water film thickness. Under certain operating conditions, the FAA maintenance threshold groove still meets the requirements for aircraft landing safety throughout the whole range of landing speeds. A real-time determination method of pavement skid resistance was proposed. It can guide pavement maintenance management departments to avoid runway excursion accidents.

Bioethanol from Arthrospira platensis biomass using a combined pretreatment

With the aim to reduce the reliance on gasoline by supporting efficient bioethanol production to meet the biofuel goal of 46 % in the transportation sector by 2050, the present study proposes a combined pretreatment of microwave-assisted acid hydrolysis for bioethanol production, which was applied to commercially-produced microalgal biomass of Arthrospira platensis. The Separated Hydrolysis and Fermentation (SHF) method was applied to prevent sugar loss and produce a higher bioethanol yield. After examining the effect of acid concentrations (0, 0.1, 0.2, and 0.3 M) and hydrolysis duration (30, 60, 90, 120, and 180 min) on the fermentable sugars, it was discovered that both operation parameters had a major impact on the synthesis of monosaccharides. With a 0.3 M H2SO4 concentration and a 90-minute hydrolysis time at 100 °C, the maximum concentration of fermentable sugars of 11.60 g/L was obtained. Regarding the fermentable sugar yield, the suggested combined pretreatment (78.4 %) outperforms the literature-reported single pretreatment of acid hydrolysis (55 %), under comparable operating settings. After 96 h of anaerobic fermentation with Saccharomyces cerevisiae, the hydrolysate of the fermentable sugars produced 0.06 g ethanol/g biomass, corresponding to 0.35 g ethanol/g total carbohydrates in Arthrospira platensis.

Clearance Nonlinear Control Method of Electro-Hydraulic Servo System Based on Hopfield Neural Network

The electro-hydraulic servo system has advantages such as high pressure, large flow, and high power, etc., which can also realize stepless regulation, so it is widely used in many engineering machineries. A linear model is sometimes only a simple approximation of an idealized model, but in an actual system, there may be nonlinear and transient variation characteristics in the systems. Coupling is reflected in the fact that the components or functional structures implemented by each system used for the design of hydraulic systems are not completely or independently related to each other, but affect each other. The nonlinear clearance between the actuator and the load reduces the control accuracy of the system and increases the impact, thus losing stable working conditions. In the paper, based on the nonlinear clearance problem of the electro-hydraulic servo system, a mathematical transfer model with clearance is established, and on this basis, a clearance compensation method based on the Hopfield neural network is proposed. In this way, clearance compensation can be realized by adjusting the parameters of neural network nodes, through simple and convenient operation. Finally, by setting different clearance values, the results of the step response and sine response curve before and after clearance compensation of the hydraulic system are compared, and the effectiveness of Hopfield neural network compensation clearance control is verified based on the comparison simulation results.