Performance In Stability Research Articles

Intelligent Digital Twin Modelling for Hybrid PV-SOFC Power Generation System

Hydrogen (H2) energy is an ideal non-polluting renewable energy and can achieve long-term energy storage, which can effectively regulate the intermittence and seasonal fluctuation of solar energy. Solid oxide fuel cells (SOFC) can generate electricity from H2 with only outputs of water, waste heat, and almost no pollution. To solve the power generation instability and discontinuity of solar photovoltaic (PV) systems, a hybrid PV-SOFC power generation system has become one feasible solution. The “digital twin”, which integrates physical systems and information technology, offers a new view to deal with the current problems encountered during smart energy development. In particular, an accurate and reliable system model is the basis for achieving this vision. As core components, the reliable modelling of the PV cells and fuel cells (FCs) is crucial to the whole hybrid PV-SOFC power generation system’s optimal and reliable operation, which is based on the reliable identification of unknown model parameters. Hence, in this study, an artificial rabbits optimization (ARO)-based parameter identification strategy was proposed for the accurate modelling of PV cells and SOFCs, which was then validated on the PV double diode model (DDM) and SOFC electrochemical model under various operation scenarios. The simulation results demonstrated that ARO shows a more desirable performance in optimization accuracy and stability compared to other algorithms. For instance, the root mean square error (RMSE) obtained by ARO are 1.81% and 13.11% smaller than that obtained by ABC and WOA algorithms under the DDM of a PV cell. Meanwhile, for SOFC electrochemical model parameter identification under the 5 kW cell stack dataset, the RMSE obtained by ARO was only 2.72% and 4.88% to that of PSO for the (1 atm, 1173 K) and (3 atm, 1273 K) conditions, respectively. By establishing a digital twin model for PV cells and SOFCs, intelligent operation and management of both can be further achieved.

Oriented immobilization of Thermomyces lanuginosus lipase by SBPs on silica-containing carriers for efficient bioproduction of biodiesel from Jatropha curcas oil

Biodiesel is recognized as green and renewable energy with low carbon intensity and less environmental impacts. The preparation of an economical immobilized lipase becomes much urgent challenge in the enzymatic production of biodiesel. In this study, recombinant Thermomyces lanuginosus lipase (TLL) was immobilized on the surface of silica-containing materials mediated by solid-binding peptides (SBPs). The obtained immobilized biocatalysts were effective in converting the oil from Jatropha curcas into biodiesel. The appropriate attachment mode of SBPs and TLL was investigated firstly, and the results showed that the activity of TLL was not negatively affected only when SBPs were fused at the C-terminus of TLL. Secondly, the immobilization efficiency of different SBPs (CecA, EctP1, VKT) on different silica-containing materials (SiO2, zeolite ZSM-5, SAR-100, MCM-41 and Na-Y) was systematically studied and it was concluded that the best immobilization efficiency was achieved for TLL-EctP1 immobilized on zeolite ZSM-5 (TLL- EctP1 @ZSM-5). TLL-EctP1 @ZSM-5 was found great performance in stability of pH, temperature, elution and storage. Eventually, biodiesel yield of 93 wt% was achieved using TLL-EctP1 @ZSM-5 as a biocatalyst to catalyze J. curcas oil for 48 h, and 81% of its initial yield was retained after 7 cycles of reuse. These results highlight the application of solid-binding peptides in enzyme immobilization for biocatalysts used in preparation of biodiesel or other chemicals.

Progressive Damage Analysis for Spherical Electrode Particles with Different Protective Structures for a Lithium-Ion Battery.

Charge-discharge in a lithium-ion battery may produce electrochemical adverse reactions in electrodes as well as electrolytes and induce local inhomogeneous deformation and even mechanical fracture. An electrode may be a solid core-shell structure, hollow core-shell structure, or multilayer structure and should maintain good performance in lithium-ion transport and structural stability in charge-discharge cycles. However, the balance between lithium-ion transport and fracture prevention in charge-discharge cycles is still an open issue. This study proposes a novel binding protective structure for lithium-ion battery and compares its performance during charge-discharge cycles with unprotective structure, core-shell structure and hollow structure. First, both solid and hollow core-shell structures are reviewed, and their analytical solutions of radial and hoop stresses are derived. Then, a novel binding protective structure is proposed to well-balance lithium-ionic permeability and structural stability. Third, the pros and cons of the performance at the outer structure are investigated. Both analytical and numerical results show that the binding protective structure serves with great fracture-proof effectiveness and high lithium-ion diffusion rate. It has better ion permeability than solid core-shell structure but worse structural stability than shell structure. A stress surge is observed at the binding interface with an order of magnitude usually higher than that of the core-shell structure. The radial tensile stress at interface may more easily induce interfacial debonding than superficial fracture.

Biochar-promoted methane production and mitigation of acidification during thermophilic anaerobic co-digestion of food waste with crude glycerol: comparison with re-inoculation

Food waste and crude glycerol were anaerobically co-digested for 100 days at 52 ± 1 °C with an organic loading rate of 1.0 g L−1 d−1. This long-term thermophilic anaerobic digestion (AD) system encountered severe inhibition from volatile fatty acids (VFAs). The study investigated the impacts of re-inoculation (RI) and biochar addition (BA) on this AD process, and monitored the variation of pH, VFAs, total alkalinity and total ammonia nitrogen during treatment. RI treatment was effective in the short term by recovering reactivity after inhibited sludge was mixed 1:1 with active inoculant. In the long term, RI could not reverse process imbalance and finally failed on day 56. Superior performance in methane production and process stability was observed in BA reactors when compared with control and RI reactors. Overall, the biochar contributed to alkalinity and facilitated the activation of methanogenesis and stimulated the conversion of VFAs.

Sub-kHz-linewidth continuous-wave single-frequency ring-cavity fiber laser based on high-gain Er: YAG crystal-derived silica fiber.

Throughout the development of single frequency fiber lasers (SFFLs), gain fiber is one of the most important components, which can greatly affect the quality of SFFLs. Here, we fabricated an Er: YAG crystal-derived silica fiber (EYDSF) using a CO2 laser-heating drawing technique, with a high gain coefficient of 1.74 dB/cm. Employing the EYDSF of only 10 cm as a gain medium, we constructed a continuous-wave ring-cavity SFFL with an all-fiber system. An ultra-narrow linewidth <660 Hz was achieved harnessing a homemade low-concentration Er-doped silica fiber as a saturable absorber. Importantly, the SFFL output power was up to 32.7 mW at 1560 nm. What's more, no multi longitudinal mode or mode hopping were found in 2 hours, and the fluctuation of power was <0.63% in 8 hours. Furthermore, the relative intensity noise was lower to -145 dB/Hz at frequencies over 1 MHz. The results indicate that the ring-cavity SFFL has desirable performance in output power, linewidth, stability and noise, which serves a prospective candidate applied to coherent optical communications, high-precision sensors, laser radars and other advanced fields.

A Discrete Soft Actor-Critic Decision-Making Strategy With Sample Filter for Freeway Autonomous Driving

Autonomous driving is a promising technology to reduce traffic accidents and improve driving efficiency. Although significant progress has been achieved, existing decision-making systems of autonomous vehicle still cannot meet the safety and driving efficiency requirements in highly dynamic environments. In this work, we design a discrete decision-making strategy based on the discrete soft actor-critic with sample filter algorithm (DSAC-SF) to improve driving efficiency and safety on freeways with dynamics traffic. Specifically, we first propose a sample filter method for discrete soft actor-critic, which improves the sample efficiency and stability of the algorithm via enhancing the utilization of effective samples. Subsequently, we construct the discrete decision-making strategy for autonomous driving based on the DSAC-SF algorithm, and further design the area observation method and the multi-objective reward function to improve the driving safety and efficiency. Finally, we carry out comparison and ablation experiments on the the scalable multi-agent reinforcement learning training school (SMARTS) simulation environment. Experimental results indicate that our strategy obtains a high success rate and a fast vehicle speed in the decision-making tasks on freeways. Moreover, our DSAC-SF algorithm also achieves improved performance in training efficiency and stability compared to the commonly used discrete reinforcement learning algorithm.

Low‐Fluence Neutron Irradiation Effects on AlGaN/GaN HEMTs

AbstractAlGaN/GaN‐based high electron mobility transistors (HEMTs) are ideal candidates for power electronics in consumer, industrial, and space applications. Attributed to their excellent performance in electrical output and chemical stability, AlGaN/GaN HEMTs enable tolerant neutron irradiation in harsh environments. Different from high‐fluence irradiation‐induced device failure, the investigation of low‐fluence neutron irradiation is of great significance to understand the early damage mechanism of HEMTs. Here, the modulated electrical properties are presented of AlGaN/GaN HEMTs under low‐fluence neutron irradiations of 4.5 × 1013 and 6.0 × 1013 cm−2. After irradiation, the electrical characteristics of the samples are carefully measured, the output performance of different irradiated devices shows a similar change trend, i.e., almost no changes or only decreased slightly (≤14%) near the knee voltage. For leakage current, the samples irradiated with different fluences show different characteristics, including a slight decrease with the fluence of 4.5 × 1013 cm−2, and a slight increase with the fluence of 6.0 × 1013 cm−2. To further investigate the internal mechanisms, the performance changes are also simulated of the device by using Crosslight software. According to the measurements, it is concluded that the low‐fluence neutron irradiation will initially affect the 2DEG mobility and the surface states of the HEMTs.

Facile synthesis of M-CuFe2O4 (M= Al2O3, CeO2, La2O3, ZrO2, Mn2O3) catalysts using a one-pot method and their applications in high-temperature water gas shift reaction

Water gas shift reaction is an essential process of hydrogen production and carbon monoxide removal from syngas. In this study, the promotional effect of ZrO2, CeO2, La2O3, Al2O3, and Mn2O3 was investigated on the CO conversion and thermal stability of the copper ferrite in high-temperature water gas shift reaction (HTSR) and hydrogen purification. The powders were synthesized by a simple solid-state route and characterized by XRD, H2-TPR, SEM, FT-IR, TG-DTA, and BET analyses. Promoters (ZrO2, CeO2, La2O3, Al2O3, and Mn2O3) could affect the WGSR performance in activity and stability. In the M-CuFe2O4 catalyst, alumina acts as a texture promoter and aids in the fine dispersion of copper ferrite. The results indicated that the surface area of the Al2O3–CuFe2O4 (210 m2/g) catalyst was higher than the other samples. This catalyst presented higher CO conversion in HTSR and had higher stability at 1000 min on stream. It was found that the incorporation of different contents of alumina had a significant influence on the textural and catalytic properties of the CuFe2O4-based catalysts. The 30%Al2O3–70%CuFe2O4 catalyst exhibited the highest CO conversion of 65% at 350 °C, uniform pore size distribution, and intense interaction between copper ferrite and alumina, causing the effective stabilization of the active phase in the catalyst structure. The findings of this study represent that the solid-state method, due to its simplicity and creation of a mesoporous structure, can also be applied for the preparation of many heterogeneous metal oxide catalysts.

Unraveling Factors Affecting Performance of Quinone‐based Polymer Cathode in Aqueous Zinc‐Ion Battery

AbstractOrganic cathode materials for aqueous rechargeable Zinc‐ion batteries (ZIBs) include a large variety of aromatic molecules with redox‐activity, and such polymer molecules vary largely in terms of specific capacity, discharge voltage plateau, and cycling stability. Here, three different quinone polymers are prepared by electropolymerizing dihydroxynaphthalene (DHN) isomers of 1,5‐DHN, 1,6‐DHN, and 1,7‐DHN, respectively, and their energy storage performances in aqueous ZIBs are compared. Among the three cathode materials, the poly(1,6‐DHN) cathode shows the best performance in specific capacity and cycling stability. The Zn||poly(1,6‐DHN) cell shows a high areal capacity of 1.4 mAh cm−2 and a high capacity retention of 90 % after 5000 cycles. By combing experiment with computation, the HOMO level of the polymer molecule is found to play a key role behind the specific capacity. Using the poly(1,7‐DHN) cathode that suffers from faster capacity decay in the cycling process, our investigation suggests that residual ions stuck between molecular chains in the insertion/deinsertion process account for the capacity loss. This study provides a further understanding of organic cathode materials for aqueous ZIBs.

Computational comparison of anterior lumbar interbody fusion and oblique lumbar interbody fusion with various supplementary fixation systems: a finite element analysis

Background and objectiveAnterior lumbar interbody fusion (ALIF) and oblique lumbar interbody fusion (OLIF) have shown a great surgical potential, while it has always been controversial which surgical approach and which type of fixation system should be selected. This study investigated the biomechanical response of ALIF and OLIF with various supplementary fixation systems using the finite element method.Materials and methodsLumbar L4–L5 ALIF and OLIF models stabilized by different supplementary fixation systems (stand-alone cage, integrated stand-alone cage, anterior plate, and bilateral pedicle screw) were developed to assess the segmental range of motion (ROM), endplate stress (EPS), and screw-bone interface stress (SBIS).Experimental resultsALIF showed lower ROM and EPS than OLIF in all motion planes and less SBIS in the most of motion planes compared with OLIF when the anterior plate or pedicle screw was used. ALIF induced higher ROM, while lower EPS and SBIS than OLIF in the majority of motion planes when integrated stand-alone cage was utilized. Using a stand-alone cage in ALIF and OLIF led to cage migration. Integrated stand-alone cage prevented the cage migration, whereas caused significantly larger ROM, EPS, and SBIS than other fixation systems except for the rotation plane. In the most of motion planes, the pedicle screw had the lowest ROM, EPS, and SBIS. The anterior plate induced a slightly larger ROM, EPS, and SBIS than the pedicle screw, while the differences were not significant.ConclusionALIF exhibited a better performance in postoperative segmental stability, endplate stress, and screw-bone interface stress than OLIF when the anterior plate or the pedicle screw was used. The pedicle screw could provide the greatest postoperative segmental stability, less cage subsidence incidence, and lower risk of fixation system loosening in ALIF and OLIF. The anterior plate could also contribute to the stability required and fewer complications, while not as effectively as the pedicle screw. Extreme caution should be regarded when the stand-alone cage is used due to the risk of cage migration. The integrated stand-alone cage may be an alternative method; however, further optimization is needed to reduce complications and improve postoperative segmental stability.

IC Pattern Based Power Factor Maximization Model for Improved Power Stabilization

The voltage fluctuation in electric circuits has been identified as key issue in different electric systems. As the usage of electricity growing in rapid way, there exist higher fluctuations in power flow. To maintain the flow or stability of power in any electric circuit, there are many circuit models are discussed in literature. However, they suffer to maintain the output voltage and not capable of maintaining power stability. To improve the performance in power stabilization, an efficient IC pattern based power factor maximization model (ICPFMM) in this article. The model is focused on improving the power stability with the use of IC (Inductor and Conductor) towards identifying most efficient circuit for the current duty cycle according to the input voltage, voltage in capacitor and output voltage required. The model with boost converter diverts the incoming voltage through number of conductors and inductors. By triggering specific inductor, a specific capacitor gets charged and a particular circuit gets on. The model maintains number of IC (Inductor and Conductor) patterns through which the power flow occurs. According to that, the pattern available, the mofset controls the level of power to be regulated through any circuit. From the pattern, the model computes the Circuits Switching Loss and Circuits Conduction Loss for various circuits. According to the input voltage, the model estimates Circuit Power Stabilization Support (CPSS) according to the voltage available in any capacitor and input voltage. Using the value of CPSS, the model trigger optimal number of circuits to maintain voltage stability. In this approach, more than one circuit has been triggered to maintain output voltage and to get charged. The proposed model not only maintains power stability but also reduces the wastage in voltage which is not utilized. The proposed model improves the performance in voltage stability with less switching loss.

A Multimodal Network Security Framework for Healthcare Based on Deep Learning.

As the network is closely related to people's daily life, network security has become an important factor affecting the physical and mental health of human beings. Network flow classification is the foundation of network security. It is the basis for providing various network services such as network security maintenance, network monitoring, and network quality of service (QoS). Therefore, this field has always been a hot spot of academic and industrial research. Existing studies have shown that through appropriate data preprocessing techniques, machine learning methods can be used to classify network flows, most of which, however, are based on manually and expert-originated feature sets; it is a time-consuming and laborious work. Moreover, only features extracted by a single model can be used in classification tasks, which can easily make the model inefficient and prone to overfitting. In order to solve the abovementioned problems, this study proposes a multimodal automatic analysis framework based on spatial and sequential features. The framework is completely based on the deep learning method and realizes automatic extraction of two types of features, which is very suitable for processing large-flow information; this improves the efficiency of network flow classification. There are two types of frameworks based on pretraining and joint-training, respectively, with analyzing the advantages and disadvantages of them in practice. In terms of evaluation, compared with the previous methods, the experimental results show that the framework has good performance in both accuracy and stability.

Employing non-canonical amino acids towards the immobilization of a hyperthermophilic enzyme to increase protein stability.

A carboxylesterase derived from Sulfolobus solfataricus P1 was immobilized onto an epoxy-activated Sepharose resin via non-canonical amino acids. The immobilized enzyme exhibited heightened performance in organic solvents, recyclability, and stability at room temperature for over two years. The incorporation of a non-canonical amino acid afforded a high degree of control over the bioorthogonal immobilization reaction. These results indicate that the specificity conferred by genetic code expansion produces advantages in protein immobilization and broadens the utility of such proteins to non-biological settings.

Implantation of Coated Superconducting Materials in the Synchronous Machine for Superconducting Energy Storage

Numerical tools appear to be essential for modeling and designing devices based on superconducting materials. In this article different simulation results are presented, using a computer code based on the finite element method adopted for the resolution of the electromagnetic equations, in the case of an axisymmetric two-dimensional problem, with this code we study the variations of the different electromagnetic quantities. The second generation superconductor has been modeled as an interesting diamagnetic material as inductive pulse sources. The performance in magnetic field resistance, energy storage and thermal stability of the ribbon, known as YBCO, makes it possible to broaden its field of application. Two categories of machines have been proposed and analyzed, the first is classic and the second uses a superconducting ribbon. In addition, a comparative study between the two proposed models is carried out and the results are analyzed and discussed.

The one-step direct synthesis and structure of Au12Ag27Cu5 nanocluster

Ternary Au 12 Ag 27 Cu 5 alloy nanoclusters were rapidly synthesized of by one-step method. The molecular structure has exceptionally high symmetry, and it contains of a two-shell Au 12 @Ag 20 Keplerate core protected by five AgCu(SR) 5 units and one Ag 2 (SR) 5 unit. • Noble metal nanoclusters protected by various ligands have precise atomic arrangement and quantum size effects, which have been widely used in different research fields, such as electrochemistry, sensors, biology and catalysis. • With respect to alloy nanoclusters, the intermetal synergistic effect alters the electronic structure and physicochemical properties, resulting in enhanced performance in luminescence, catalysis, and stability, in comparison with single metal nanoclusters. • The single and binary alloy nanoclusters were widely synthesized. • There are only a few reports on the synthesis and structure of ternary nanoclusters. • Therefore, the synthesis of ternary alloy nanoclusters is lack. • Herein, we employed the one-step rapid synthesis to obtain Au 12 Ag 27 Cu 5 ternary nanocluster. • And the crystal structure was determined by X-ray crystallography. • Our research provides a simple one-step method to construct ternary alloy metal nanoclusters Metal nanoclusters have precise atomically structures, quantum size effect and molecule-like properties, intriguing great potential for applications in various fields. Controlling synthesis of nanocluster is very important in nanoscience. Here, we presented a new strategy for one-step simple synthesis of ternary alloy Au 12 Ag 27 Cu 5 nanoclusters. The atomically precise crystal structure was determined by X-ray crystallography. The optical property of Au 12 Ag 27 Cu 5 nanocluster shows the absorption peaks at 363, 478, 528 and 619 nm. This research provides a one-pot method to construct ternary alloy metal nanoclusters.

English

Fifteen improved and two local genotypes of Vigna unguiculata were evaluated for their performance in grain yield, yield components and yield stability from 2016 to 2018. Experiments were laid out in a Randomised Complete Block Design, with three replications. Data were collected on grain yield and yield components and subjected to analysis of variance (ANOVA). Stability analysis for grain yield was conducted using Francis and Kannenberg&rsquo;s genotype-grouping technique. Results of combined ANOVA revealed highly significant (p&lt;0.001) differences among genotypes, and across years for studied traits, except for days to physiological maturity, with non-significant yearly variations. Genotype by year interactions were non-significant except for number of seeds per pod, days to 50% flowering and hundred seed weight. Genotype, IT08K-126-19, gave the highest mean grain yield (3611 kgha-1), while &ldquo;Akidi elu&rdquo; gave the least (1695 kgha-1). Genotypes, IT08K-126-19, IT07K-210-1-1 and IT09K-456 gave higher and more stable grain yields. Meanwhile, IT08K-180-11, IT10K-837-1 and &ldquo;Akidi elu&rdquo;, gave lower and unstable yields. The results revealed that sufficient variability exists among genotypes, which can be exploited in breeding programmes. Genotypes with high and stable yields can be released to farmers to boost productivity. Key words: Vigna unguiculata, genotypes, yield, yield components, yield stability. &nbsp;

Rapid determination of thiram on apple using a flexible bacterial cellulose-based SERS substrate

A flexible nanocomposite composed of bacterial cellulose (BC) and gold nanoparticles (AuNPs) was developed as a SERS substrate to determine thiram on apple surface by two collection methods namely “paste-and-peel” and “wiping”. Enhancement factor of this SERS substrate for sensing thiram residues was determined to be 2.8 × 105. Compared to the benchtop Raman spectrometer, portable Raman spectroscopic device showed a lower sensitivity towards thiram residues with limit of detection at 0.98 ppm, satisfying maximum residue level of thiram on apple required by both Europe and America. A good linear correlation of SERS peak intensity at 1368 cm−1 and different concentrations of thiram (1–50 ppm) revealed a coefficient up to 0.99. This flexible BC-based SERS substrate has a great analytical performance in sensitivity, reproducibility and stability, and is suitable for rapid detection (<8 min) and quantitative analysis of pesticides on food surface.

Hydrogenation of CO2 to Olefins over Iron-Based Catalysts: A Review

The widespread use of fossil fuels has caused high CO2 concentrations in the atmosphere, which have had a great impact on climate and the environment. Methods for efficiently utilizing CO2 to produce high value-added chemicals have received increasing attention. Among the products of CO2 hydrogenation, olefins, an important petrochemical feedstock, are one of the essential target products. Therefore, CO2 hydrogenation to olefins has been extensively studied, especially for the development of high-performance catalysts. Iron-based catalysts, which are widely used in Fischer–Tropsch synthesis reactions, have also been considered attractive for use in the CO2 hydrogenation to olefins due to their excellent performance in catalytic activity and reaction stability. Most studies have focused on the modulation of morphology; reduction and adsorption properties by tuning the methods of catalyst syntheses; pretreatment conditions and the composition of catalysts, in order to improve hydrogenation activity and olefin yield. In this review, we briefly discuss a thermodynamic overview of the CO2 hydrogenation to olefins reaction, the optimization of catalyst modifications, and current insights into the reaction mechanism; moreover, we summarize current challenges and future trends in the CO2 hydrogenation to olefins.

A novel improved whale optimization algorithm for optimization problems with multi-strategy and hybrid algorithm

Whale optimization algorithm (WOA), as an advanced optimization algorithm with simple structure, has been favored by various fields. However, there are some disadvantages of WOA, such as slow convergence speed, low precision and falling into local optimal value easily. In this paper, a novel improved whale optimization algorithm (IWOA) with multi-strategy and hybrid algorithm is proposed to overcome above shortcomings. Firstly, IWOA initializes the population by chaotic mapping to avoid the initial population distribution of WOA deviating from the optimal value. Secondly, IWOA combines the pheromone of the black widow algorithm and the opposition-based learning strategy to modify the population, which improves the convergence speed and the global performance of WOA respectively. Finally, the adaptive coefficients and the new update modes replace the original update modes, which makes the structure of WOA simpler and more accurate. In addition, the convergence of IWOA is also proved in this paper. On the one hand, to demonstrate the effectiveness of IWOA, 23 benchmark functions are used to test various performance of the algorithm. On the other hand, in order to prove the superiority of IWOA, the experimental results are compared and analyzed with other optimization algorithms. Simulation results show that IWOA proposed in this paper owns excellent performance in convergence speed, stability, accuracy and global performance, compared with other algorithms.

Fast and Accurate Pose Estimation with Unknown Focal Length Using Line Correspondences.

Estimating camera pose is one of the key steps in computer vison, photogrammetry and SLAM (Simultaneous Localization and Mapping). It is mainly calculated based on the 2D-3D correspondences of features, including 2D-3D point and line correspondences. If a zoom lens is equipped, the focal length needs to be estimated simultaneously. In this paper, a new method of fast and accurate pose estimation with unknown focal length using two 2D-3D line correspondences and the camera position is proposed. Our core contribution is to convert the PnL (perspective-n-line) problem with 2D-3D line correspondences into an estimation problem with 3D-3D point correspondences. One 3D line and the camera position in the world frame can define a plane, the 2D line projection of the 3D line and the camera position in the camera frame can define another plane, and actually the two planes are the same plane, which is the key geometric characteristic in this paper's estimation of focal length and pose. We establish the transform between the normal vectors of the two planes with this characteristic, and this transform can be regarded as the camera projection of a 3D point. Then, the pose estimation using 2D-3D line correspondences is converted into pose estimation using 3D-3D point correspondences in intermediate frames, and, lastly, pose estimation can be finished quickly. In addition, using the property whereby the angle between two planes is invariant in both the camera frame and world frame, we can estimate the camera focal length quickly and accurately. Experimental results show that our proposed method has good performance in numerical stability, noise sensitivity and computational speed with synthetic data and real scenarios, and has strong robustness to camera position noise.