Key Characteristic Parameters Research Articles

Impacts of micro/nano plastics on the ecotoxicological effects of antibiotics in agricultural soil: A comprehensive study based on meta-analysis and machine learning prediction

Micro/nano plastics (M/NPs) and antibiotics, as widely coexisting pollutants in environment, pose serious threats to soil ecosystem. The purpose of this study was to systematically evaluate the ecological effects of the co-exposure of M/NPs and antibiotics on soil organisms through the meta-analysis and machine learning prediction. Totally, 1002 data set from 38 articles were studied. The co-exposure of M/NPs significantly promoted the abundance (62.68 %) and migration level (55.22 %) of antibiotic contamination in soil, and caused serious biotoxicity to plants (−12.31 %), animals (−12.03 %), and microorganisms (35.07 %). Using 10 variables, such as risk response categories, basic physicochemical properties, exposure objects, and exposure time of M/NPs, as data sources, Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) models were developed to predict the impacts of M/NPs on the ecotoxicological effects of antibiotics in agricultural soil. The effective R2 values (0.58 and 0.60, respectively) indicated that both models can be used to predict the future ecological risk of M/NPs and antibiotics coexistence in soil. Particle size (13.54 %), concentration (5.02 %), and type (11.18 %) of M/NPs were the key characteristic parameters that affected the prediction results. The findings of this study indicate that the co-exposure of M/NPs and antibiotics in soil not only exacerbates antibiotic contamination levels but also causes severe toxic effects to soil organism. Furthermore, this study provides an effective approach for ecological risk assessment of the coexistence of M/NPs and antibiotics in environment.

A Working Conditions Warning Method for Sucker Rod Wells Based on Temporal Sequence Prediction

The warning of the potential faults occurring in the future in a sucker rod well can help technicians adjust production strategies in time. It is of great significance for safety during well production. In this paper, the key characteristic parameters of dynamometer cards were predicted by a temporal neural network to implement the warning of different working conditions which might result in failures. First, a one-dimensional damped-wave equation was used to eliminate the dynamic loads’ effect of surface dynamometer cards by converting them into down-hole dynamometer cards. Based on the down-hole dynamometer cards, the characteristic parameters were extracted, including the load change, the position of the valve opening and closing point, the dynamometer card area, and so on. The mapping relationship between the characteristic parameters and working conditions (classification model) was obtained by the Xgboost algorithm. Meanwhile, the noise in these parameters was reduced by wavelet transformation, and the rationality of the results was verified. Second, the Encoder–Decoder and multi-head attention structures were used to set up the time series prediction model. Then, the characteristic parameters were predicted in a sequence-to-sequence way by using historical characteristic parameters, date, and pumping parameters as input. At last, by inputting the predicted results into the classification model, a working conditions warning method was created. The results showed that noise reduction improved the prediction accuracy significantly. The prediction relative error of most characteristic parameters was less than 15% after noise reduction. In most working conditions, their F1 values were more than 85%. Most Recall values could be restored to over 90% of those calculated by real parameters, indicating few false negative cases. In general, the warning method proposed in this paper can predict faulty working conditions that may occur in the future in a timely manner.

Characteristics of concurrent flame spread over convex charring materials

In real forest fire scenarios, there are a large number of convex and concave terrains at the junction of flat land and slopes. In this paper, a typical charring material, kraft paper, is selected as the experimental sample, and the variation law of key characteristic parameters of flame spread over convex solid surface are investigated. The experimental results show that all the flame spread processes have obvious deceleration behaviors, and the influence mechanism of flame tangent angle on the flame spread deceleration of convex surfaces is revealed. The characteristic length of the pyrolysis zone shows a tendency of increasing and then decreasing with time. The variation curves of radiant heat flux present two peaks. Combining with the characteristic length, the reason for the occurrence of two peaks is explained. The exponential relationship between the instantaneous mass loss rate and the characteristic length of the pyrolysis zone is obtained. Based on the range of power exponents, the heat transfer controlling mechanism in the pyrolysis zone is classified into three categories for all the working conditions. Moreover, a piecewise power-law relationship between the dimensionless maximum flame height and the dimensionless heat release rate is established. The results of this study will fill the research gap of flame spread of solid materials with curved surfaces, and will be of great academic value for the improvement and development of theories on building and forest fire safety.

Identification method for inter-turn faults in transformers based on digital twin concept

A transformer inter-turn fault identification method is proposed based on the digital twin concept to tackle the challenges of high operational complexity and low accuracy associated with traditional transformer fault identification methods. Initially, the Bald Eagle Search algorithm is employed to optimize the critical parameters of the Extreme Learning Machine (ELM), determining the optimal input layer weight and hidden layer threshold of the Extreme Learning Machine. Subsequently, leveraging the digital twin concept, a digital replica of the physical transformer is established, enabling multi-physical field coupling simulation encompassing electrical, thermal, and acoustic domains to elucidate the variation patterns of various physical parameters across different operational scenarios and fault scenarios. Furthermore, key physical characteristic parameters such as sound pressure and winding hot spot temperature are carefully selected to drive a fault identification model tailored to inter-turn faults within the framework of the digital twin concept. Through a detailed investigation using 630 kV A/10 kV transformers as a case study, the results exhibit an impressive fault identification accuracy of 95.24% for the proposed method. Comparative analysis reveals notable enhancements in fault identification accuracy of 12.22%, 7.85%, and 3.73% for ELM, Support Vector Machine and Tuna Swarm Optimization—ELM models, respectively. These findings underscore the effectiveness and practicality of the transformer inter-turn fault identification method based on the digital twin concept, offering valuable insights for the real-time monitoring and diagnosis of inter-turn faults in transformers.

Leaf‐density estimation for fruit‐tree canopy based on wind‐excited audio

AbstractIt is important to obtain real‐time leaf density of fruit‐tree canopies for the precision spray control of plant‐protection robots. However, conventional detection techniques for the characteristics of fruit‐tree canopies cannot acquire the canopy internal information, which may provide an unsatisfactory accuracy of detection of leaf densities. This paper proposes a method for estimating canopy leaf density of fruit trees based on wind‐excited audio. A wind‐exciting implement was used to force fruit‐tree canopy leaves vibrating to produce audio. Then, some correlation analysis methods were used to extract key characteristic parameters of wind‐excited audio that were significantly correlated with leaf density. Finally, based on the data set of wind‐excited audio, a few machine‐learning methods were used to develop leaf‐density estimation models. Test results showed that: (1) there were five key feature parameters of wind‐excited audio that were significantly correlated with leaf density: the short‐time energy, spectral centroid, the frequency average energy, the peak frequency, and the standard deviation of frequency. (2) the estimation model of leaf density developed based on backpropagation neural network for fruit‐tree canopy showed the optimal estimation results, which can achieve the estimation of leaf density of fruit‐tree canopies accurately. The overall correlation coefficient (R) of the estimation model was more than 0.84, the root‐mean‐square error was less than 0.73 m2 m−3, and the mean absolute error was less than 0.53 m2 m−3. This study is expected to provide a technical solution for the leaf‐density detection of fruit‐tree canopies of plant‐protection robots.

Optimization of Boiler Low Nitrogen Conversion Strategy and Intelligent Phase Change Control and Thermal Management: From the Perspective of Compound Neural Network Prediction and Optical Image Analysis

ABSTRACT The optimization of boiler low nitrogen conversion technology, intelligent phase change control and thermal management are studied from the perspective of compound neural network prediction and optical image analysis. Firstly, the application concept of optical imaging technology based on compound neural network prediction technology is described in detail. Then, a prediction model of boiler NOx emission based on compound neural network is established, and the key characteristic parameters extracted are trained by using Convolutional Neural Network (CNN) technology and Long Short-Term Memory (LSTM) network as input data. Finally, based on the prediction model, a practical transformation strategy of low nitrogen boiler is created, and the model is comprehensively evaluated. This strategy aims to control nitrogen oxides under the minimum emission concentration limit. The results show that the average failure rate of other traditional models is 40% and the lowest failure rate is 20%, while the highest and lowest failure rates of the model developed in this paper are about 11% and 6% respectively. Compared with the established technical methods, the research presented has a significant improvement in performance. In addition to promoting the development of environmental governance, this paper also provides support for the optimization of energy management technology.

Effect of initial pore water content and salinity on the resistivity of methane hydrate-bearing fine sediments

Methane hydrates are widely distributed in the soft silty-clayey soils of continental margin sediments. The resistivity method is generally used to detect their distribution state and monitor their kinetic reaction process. However, there are some major details to be elaborated on as to the occurrence state and the evolution of key characteristic parameters. Based on the kinetic reaction experiment under different lithological conditions (initial water content and pore water salinity), this research records the changing resistance of natural sand porous medium system during the cooling formation of hydrates by in-situ measurement. The Archie formula is further used to study the influencing factors of the resistance of hydrate-bearing sediments and how the resistance changes during hydrate formation. The results demonstrate that these two lithological parameters can significantly affect the resistivity of the hydrate-bearing natural sand porous media system. It is found that within a certain range, NaCl in pore water will inhibit the formation reaction of hydrates, while an increase of initial water content will enhance the hydrate production amount in the system. With the hydrate formation in the pores, the porosity and skeleton composition of the natural sand porous medium change as well, coupled with the salt-removing effect, which further changes the system resistivity. During hydrate formation, the system resistivity changes regularly in the cooling stage, hydrate nucleation stage, and hydrate formation stage. Our study systematically considers the influence of these geological parameters on resistivity characterization at different reaction stages during the formation of natural hydrates. Thus, it can serve as a technical reference for hydrate reservoir logging and other related issues.

Experimental study and model optimization of thermodynamic performance of a single screw water vapor compressor

Single screw water vapor compressor has greater advantages under conditions of low suction temperature and high compression ratio, however, the current study has the following limitations: (1) the volumetric and isentropic efficiency were substituted as a fixed value into the models of compressor, (2) The suction temperature is concentrated in the medium-high region, (3) The compression ratio is concentrated in the medium-low range, which failed to accurately describe the water vapor compression process. Therefore, the relationship between the volumetric efficiency, isentropic efficiency and key characteristic parameters of compressor is studied experimentally, the mathematical models of the volume flow and compression power are established and verified by experiments, the changes of the mass flow, compression power, COP and SMER with suction temperature, discharge pressure and compression ratio are analyzed, the operation process parameters of the single-screw water vapor compressor are optimized.

Research on the application of deep learning-based machine vision in automated inspection

Abstract The continuous updating of deep learning algorithms and theories has laid a solid foundation for the development of the machine vision field. Automated detection technology has become a hot topic of research in the field of machine vision in recent years. In this paper, we first introduce the traditional one-dimensional and two-dimensional image segmentation algorithm and then optimize the image segmentation algorithm by combining the conventional pigeon flocking algorithm and chaotic search algorithm so as to obtain a more accurate detection target image. Then, on the basis of a deep learning network, an attention mechanism is introduced to construct a Swin-Transformer image detection model to realize automatic detection of machine vision. Finally, the performance of the model is tested, and it is applied to watermelon seedling quality detection to explore its application value. The results show that in the performance test experiment of the image segmentation algorithm of this paper, the three indexes of F1, IoU, and accuracy of the image segmentation algorithm designed in this paper on the ISIC-2020 dataset are 93.90%, 93.74%, and 98.37%, which are ranked the first among the algorithms participating in the experiment. The precision, recall, and mAP values of the image detection model designed in this paper are 92.87%, 77.13%, and 83.21% on the experimental data test set, which are higher than those of other models participating in the experiment. The image detection model designed in this paper was practically applied to watermelon seedling quality detection. The accuracy of the model in detecting the presence or absence of diseased spots and cotyledon area, two key characteristic parameters of seedling quality, reached 100%. The model showed high reliability in practical application. The image segmentation algorithm and image detection model developed in this paper are highly useful in automated detection.

Digital Simulation and Analysis of Assembly-Deviation Prediction Based on Measurement Data

For various power equipment items such as aircraft engines and gas turbines with numerous components demanding requirements on processing accuracy and high structural complexity, processing errors tend to cause such problems as assembly rework, forced assembly and prolonged research and development cycle. In this paper, the core machine of micro gas turbine is taken as the research object to construct a simulation model of assembly-deviation prediction based on the design tolerance and actual measurement data. Then, an analysis is conducted on the assemblability of the design model and the key factors causing the deviation of the assembly. After conducting deviation calculations and simulation analysis, it was determined that the current mounting position is deemed to be suboptimal. In light of this finding, an optimized solution is proposed, which involves advancing the mounting phase by 0.47 mm. This adjustment effectively resolves the interference issue resulting from the design error. Moreover, the geometry, shape and three-dimensional contour of the key components are measured with high accuracy and precision to identify the key characteristic parameters affecting the outcome of the assembly. With an assembly-deviation-prediction and -analysis model established on the basis of actual measurement data, the results of the assembly-deviation analysis are compared with the outcome of the assembly, showing high consistency. The assembly-deviation-prediction method proposed in this paper on the basis of design tolerance and actual measurement data is applicable to the manufacture of aviation and combustion engines.

Study on a semi-resolved CFD-DEM method for rod-like particles in a gas-solid fluidized bed

Based on a semi-resolved CFD-DEM coupling method, this study proposed a method that uses the minimum distance between the fluid grid and the particle boundary as a reference value to determine the degree of influence of the target fluid grid on the particle's drag force. A fluidized bed of rod-like particles was chosen as a typical case to investigate the effect of different fluid grid scales on various fluidized bed characteristic parameters. The calculation performance of the semi-resolved and unresolved CFD-DEM coupling algorithm on key fluidized bed characteristic parameters such as average pressure drop, particle frequency distribution with bed height, and particle orientation distribution were compared. It was found that the semi-resolved CFD-DEM coupling algorithm gradually obtained results with higher consistency with decreasing fluid grid scale for key parameters such as particle frequency distribution with bed height, particle orientation distribution, and time-history mixing index, exhibiting a phenomenon similar to grid independence in fluid simulation. By comparing with experimental results, it was verified that the semi-resolved CFD-DEM coupling algorithm can be applied to simulate multi-granular gas-solid systems with fluid grid scales equivalent to particle scales. This algorithm solves the limitation of fluid grid scale in the unresolved CFD-DEM coupling framework and improves the grid adaptability of the CFD-DEM coupling simulation algorithm.

Operating State Evaluation Method of UHV Oil Immersed Shunt Reactors

The oil-immersed shunt reactor is one of the most important reactive power compensation equipment in the ultra-high voltage (UHV) long-distance transmission and transformation system, which can solve the problem of large power and long-distance transmission that makes the capacitance effect of transmission lines obvious. At present, the commonly used state evaluation methods for UHV oil-immersed shunt reactors are mostly based on a certain factor or several factors to make judgments, without a comprehensive consideration of the overall operating condition information of the shunt reactors. There are various uncertainties in the process of fault diagnosis and state evaluation of the shunt reactors, and the accuracy and timeliness of the diagnosis results are poor. To solve the above problems, this paper combines the key characteristic parameters of oil immersed shunt reactor, such as vibration, chromatography, partial discharge, and oil temperature, and establishes a comprehensive evaluation method for the operating state of oil immersed shunt reactor based on Gaussian Cloud Model (GCM), and verifies its effectiveness through test data, whose R2 over 0.98. The test data show that this method can effectively judge the operation state of the UHV oil-immersed shunt reactor, which accuracy rate of state diagnosis is over 91%. The results of this paper can provide technical support for the application of UHV oil-immersed shunt reactor state assessment and diagnosis, and further improve the level of equipment operation and maintenance.

Evaluation of aging and degradation for silicone rubber composite insulator based on machine learning

Silicone rubbers (SIRs), as the main material of composite insulator sheds, have aging phenomenon for the long-term operation in the outdoor, which has an important impact on the performance degradation of composite insulator. Periodic examine and replacement of severely aged composite insulator is of great significance for the safe operation of power system. However, there is no clear standard to estimate the aging degree. In order to evaluate the degree of aging and degradation more accurately, the experimental test and analysis of the SIR sheds were carried out from three aspects: physical characteristics, chemical composition and electrical properties. The physical characteristic results show that the surface of the aging SIR will appear obvious holes and cracks, as well as hardening. Chemical composition results show that the internal composition of the continuous decomposition, while a large number of heavy metal elements accumulate in the pollution. The change of physical characteristics and chemical composition of SIR sheds may lead to the deterioration of electrical properties. According to the above results, the influence rule of measurement parameters and aging degree was obtained, and the key characteristic parameters of aging were summarized and extracted. Based on the characteristic parameters, a decision tree aging evaluation model is established for state evaluation. The accuracy of machine learning is 100% and 93.2% respectively. For the on-site application, early warning of moderately aged insulators and replacement of severely aged insulators are proposed in the on-site maintenance of SIR composite insulators.

A framework to identify critical dynamics of water quality for diagnosing river basin ecosystem resilience and management

This study proposed a novel framework to identify critical water quality dynamics as early warning signals for diagnosing changes in the resilience of river basin ecosystems. We established empirical linkages between the theoretical background of three resilience capacities (robustness, adaptability and transformability) and water quality dynamics. Then, the processes of resilience degradation and their risk transfer or accumulation have been identified based on the shifts among different states. The methods of time-domain analysis and frequency-domain analysis were integrated into this framework, aiming to identify gradual and transient responses of water quality and its periodic fluctuation characteristics at multiple temporal scales. The time-domain analysis methods obtained the trend, cumulative periodic fluctuation of water quality by extracting the key characteristic parameters from the time-series data. The wavelet transform methods were introduced into the frequency-domain analysis to reveal the water quality fluctuation patterns at specific temporal scales. We tested the proposed framework in a typical agriculture-intensive watershed in eastern China. The results showed that this framework can be effectively used to identify three resilience states of river basin ecosystems. The degraded resilience regions were mainly distributed in the downstream area, which was influenced by their specific land use/cover and different agricultural soil health conditions. Particularly, urban sewage discharge was the main cause of periodic fluctuation in water quality time series at multiple high-frequency scales. The theoretical background of resilience capacities was elaborated in non-equilibrium dynamics before resilience degradation. Thus, this novel framework could reveal the pollution processes and driving mechanisms in different river reaches, and it also can provide adaptive management suggestions according to resilience dynamics traits.

Effects of circumferentially non-uniform clearance on the spanwise flow characteristics in a transonic compressor rotor

The circumferentially non-uniform clearance has significant effect on the overall performance and operation safety of high loading compressor rotors. However, both the variation rules and mechanism of flow characteristic in the circumferential, radial and streamwise directions caused by the asymmetric clearance are not fully clarified, which restricts the performance degradation assessment and rubust design of high loading compressors. Therefore, different types of asymmetric tip clearance were designed in this paper to reveal the circumferential fluctuation laws and induced mechanism of key characteristic parameters in full-channel domains of compressor rotor with circumferentially non-uniform clearance. It is shown that the circumferentially non-uniform clearance has less effect on the total pressure ratio and efficiency of compressor rotor, while it causes an obvious deterioration of the stall margin. Furthermore, the circumferentially non-uniform clearance causes the circumferential fluctuations of performance parameters, and the fluctuation amplitude has been affected by the eccentricity ratio and operating condition of rotor directly. In addition, the leakage flow originated from a single channel may pass through about three adjacent channels in rotor with uniform clearance, while it covers almost seven adjacent channels in the rotor with circumferentially non-uniform clearance. In consequence, it is precisely the difference of strength variation of leakage flow in tip region caused by the circumferential distributions of tip clearance that produces an alternated variation of low-velocity zone in rotor prototype and a concentrated distribution of low-velocity zone in the range of 270° to 360° of rotor with non-uniform clearance. Due to the serious blockage appearing in this range, the rotating stall of transonic rotor with circumferentially non-uniform clearance has occurred in advance, even with a maximum drop of 35.94% in stall margin.

Thermal runaway propagation behavior and energy flow distribution analysis of 280 Ah LiFePO4 battery

Thermal runaway propagation (TRP) of lithium iron phosphate batteries (LFP) has become a key technical problem due to its risk of causing large-scale fire accidents. This work systematically investigates the TRP behavior of 280 Ah LFP batteries with different SOCs through experiments. Three different SOCs including 40 %, 80 %, and 100 % are chosen. In addition to key TRP characteristic parameters such as temperature, TRP time and speed are analyzed, more importantly, the energy flow distribution during the TRP of large-size LFP module is also revealed. The results indicate that among the three groups of modules, TRP occurs only in the module with 100 % SOC, which is attributed to the higher internal energy (666.11 kJ) and heat transfer power (264.07 W). For the module with 100 % SOC, the TRP time interval fluctuates from 667 s to 1305 s, and the TRP speed is in the range of 0.05–0.12 mm/s. Furthermore, the energy flow distribution indicates that more than 75 % of the energy is used to heat battery itself, and approximately 20 % is carried out by ejecta. Less than 10 % can trigger neighboring batteries into thermal runaway. This work may provide important guidance for the process safety design of energy storage power stations.

Relationship between the Dynamic Characteristics of Tomato Plant Height and Leaf Area Index with Yield, under Aerated Drip Irrigation and Nitrogen Application in Greenhouses

The current study was undertaken to investigate the dynamic characteristics of the tomato crop, such as its plant height and leaf area index (LAI), based on the effective cumulative temperature. This was assessed under aerated drip irrigation (ADI) conditions and the application of a specific nitrogen (N) dose, and their relationship with the yield of the crop was formulated. The study was conducted in a greenhouse located in Zhengzhou, Henan province, China. The assessment conditions were the two irrigation methods, ADI and conventional drip irrigation (CK), and the three N application rates, i.e., 0, 140, and 210 kg ha−1. The logistic and Richards models were used to fit dynamic equations for plant height and LAI under the different treatments to quantify the characteristic parameters and understand their relationship with yield. The results revealed that the growth of the tomato plant fitted well with the logistic and Richards model at R2 > 0.98 (p < 0.01), regardless of the treatments. ADI and N application were found to significantly increase the maximum growth rate and average growth rate over the rapid growth period based on the tomato plant height and LAI. They were also noted to reduce the effective cumulative temperature at which plant height entered the rapid growth period (p < 0.05), thereby increasing the time spent in the nutritional growth phase. This is an essential precursor for the better development of subsequent reproductive organs. Tomato yields also confirm it: the highest yield of 85.87 t ha−1 was obtained with 210 kg N ha−1 for the ADI treatment, with an increase of 13.8%, 12.2%, and 39.6% compared to the CK–210 kg N ha−1, ADI–140 kg N ha−1, and ADI–0 kg N ha−1 treatments, respectively (p < 0.05). Grey correlation analysis showed that the characteristic parameters closely related to yield were all from the ADI and N application treatments. Furthermore, it was observed that the effective cumulative temperature and the maximum growth rate of the LAI at which the LAI entered the slow growth phase were the key growth characteristic parameters affecting tomato yield. This study provides a scientific basis for regulating the growth dynamics and yield of vegetables in greenhouse facilities under ADI and N application.

A novel physical device to realize rate-independent linear damping for performance enhancement of seismically isolated structures

It is common concerned that seismically isolated structures may suffer excessive responses when subjected to extreme ground motions containing dominated long-period components. Supplementing damping via traditional damping devices is indeed beneficial in reducing the isolator displacement but perhaps at the expense of floor response accelerations. In the previous study, a conceptual rate-independent linear damping model (known as MNS model), which consists of linear Maxwell and negative stiffness elements in parallel, was found to be capable of simultaneously controlling response displacements and floor accelerations of low-frequency structures under strong ground motions. In the present study, a novel damping device, referred to as Maxwell-negative-stiffness damper (MNSD), is proposed to physically realize the conceptual rate-independent MNS model, and an efficient method of designing the properties of the MNSD with its nonlinearity embedded is presented. The performance of the proposed MNSD incorporated into a seismically isolated building structure is systematically studied by using both short- and long-period ground motions, and parametric studies are conducted to investigate the influences of key characteristic parameters on the control capability of the MNSD. Numerical examples illustrate the advantages of the proposed MNSD over the commonly used fluid viscous and hysteretic dampers, as well as the conventional negative stiffness dampers, in improving the performance of seismically isolated building structures under both sets of short- and long-period ground motions.

Adaptability of biogas slurry–water ratio and emitter types in biogas slurry drip irrigation system

Biogas slurry drip irrigation (BSDI) can not only save water, but also reduce the use of chemical fertilizers. Determining the appropriate biogas slurry–water ratio (BSWR) and emitter types can ensure long-term stable operation of systems. Therefore, this study proposes an obtaining the BSWR suitable for crop irrigation conductivity method based on conductivity biogas slurry and clean water. The three conductivity levels（1.3, 2.3 and 3.3 mS/cm）are set based on their suitability for crop growth. The corresponding BSWR is determined using the proposed method, i.e.,1:20, 1:8, and 1:4. The clogging dynamic process, clogging location, and characteristic parameters are analyzed using a hydraulic test, an industrial camera and the ordered regression method for three common emitters under three BSWR. The results show that the proposed method relative error is approximately 10%, which is considered feasible. Over time, the discharge and uniformity of emitters decrease, and subsequently remain constant. As the slurry concentration increases, the emitters clog more rapidly, and internal patch emitters (IPEs) show the lowest adaptability to BSDI systems. The pressure compensation (PCEs) and single-wing labyrinth emitters (SWLEs) is better than IPEs on anti-clogging performance. Clogging mainly occurs at the inlet grid, and the SWLEs are clogged at the internal flow channel and outlet. Moreover, the inlet grid and pressure compensation are key characteristic parameters affecting clogging. The adaptability of emitters can be improved by changing the inlet grid layout, increasing the cross-sectional area of the flow channel, and/or reducing the pathway length. Based on irrigation uniformity and economic cost, large discharge SWLEs and PCEs are recommended for one-time field crops and multi-year cash crops respectively. Furthermore, the BSWR should be at least 1:4, and ratios of 1:8–1:20 are most conducive to stable operation. This study serves as a guideline for future development of BSDI systems.

Current-Sensorless Control Strategy for the MPPT of a PV Cell: An Energy-Based Approach

A novel energy-based modelling and control strategy is developed and implemented to solve the maximum power point tracking problem when a photovoltaic cell array is connected to consumption loads. A mathematical model that contains key characteristic parameters of an energy converter stage connected to a photovoltaic cell array is proposed and recast using the port-Hamiltonian framework. The system consists of input-output power port pairs and storage and dissipating elements. Then, a current-sensorless control loop for a maximum power point tracking is designed, acting over the energy converter stage and following an interconnection and damping assignment passivity-based strategy. The performance of the proposed strategy is compared to a (classical) sliding mode control law. Our energy-based strategy is implemented in a hardware platform with a sampling rate of 122 Hz, resulting in lower dynamic power consumption compared to other maximum power point tracking control strategies. Numerical simulations and experimental results validate the performance of the proposed energy-based modelling and the novel control law approach.