Mechanistic Drifting Forecast Model for A Small Semi-Submersible Drifter Under Tide–Wind–Wave Conditions

Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide–wind–wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5–6; while wind drag contributes mostly at wind scale 2–4.

Mechanistic Model for Predicting NO_3-N Uptake by Plants and Its Verification

Some mechanistic models have been proposed to predict the No3- concentrations in the soil solution at root surface and the NO3-N uptake by plants, but all these relatively effective non-steady state models have not yet been verified by any soil culture experiment. In the present study, a mathematical model based on the nutrient transport to the roots, root length and root uptake kinetics as well as taking account of the inter-root competition was used for calculation, and soil culture experiments with rice, wheat and rape plants grown on alkali, neutral and acid soils in rhizoboxes with nylon screen as a isolator were carried out to evaluate the prediction ability of the model through comparing the measured NO3-concentrations at root surface and N uptake with the calculated values. Whether the inter-root competition for nutrients was accounted for in the model was of less importance to the calculated N uptake but could induce significant changes in the relative concentrations of NO3- at root surface. For the three soils and crops, the measured NO3-N uptake agreed well with the calculated one, and the calculated relative concentrations at root surface were approximate to the measured values. But an appropriate rectification for some conditions is necessary when the plant uptake parameter obtained in solution culture experiment is applied to soil culture. In contrast with the present non-steady state model, the predicted relative concentrations, which show an accumulation, by the Phillips' steady-state model were distinct from the measured values which show a depletion, indicating that the present model has a better prediction ability than the steady-state model.

VIBRATION IN HIGH-SPEED MILLING OF THIN-WALLED COMPONENTS

To investigate the vibration principle in machining thin-walled components, a dynamic model for end milling of flexible structures is built based on considering the variations in the dynamic chip thickness and the differences between up-milling and down-milling. Two milling experiments verify the model. Experimental results show that the model can predict the milling force and displacements simultaneously in the dynamic milling process.

Online temperature estimation of Shell coal gasification process based on extended Kalman filter

Obtaining the temperature inside the gasifier of a Shell coal gasification process(SCGP)in real-time is very important for safe process operation.However,this temperature cannot be measured directly due to the harsh operating condition.Estimating this temperature using the extended Kalman filter(EKF)based on a simplified mechanistic model is proposed in this paper.The gasifier is partitioned into three zones.The quench pipe and the transfer duct are seen as two additional zones.A simplified mechanistic model is developed in each zone and formulated as a state-space representation.The temperature in each zone is estimated by the EKF in real-time.The proposed method is applied to an industrial SCGP and the effectiveness of the estimated temperatures is verified by a process variable both qualitatively and quan-titatively.The prediction capability of the simplified mechanistic model is validated.The effectiveness of the proposed method is further verified by comparing it to a Kalman filter-based single-zone temperature estimation method.

On-line Estimation in Fed-batch Fermentation Process Using State Space Model and Unscented Kalman Filter

On-line estimation of unmeasurable biological variables is important in fermentation processes,directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product.In this study,a novel strategy for state estimation of fed-batch fermentation process is proposed.By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model,a state space model is developed.An improved algorithm,swarm energy conservation particle swarm optimization(SECPSO) ,is presented for the parameter identification in the mechanistic model,and the support vector machines(SVM) method is adopted to establish the nonlinear measurement model.The unscented Kalman filter(UKF) is designed for the state space model to reduce the disturbances of the noises in the fermentation process.The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.

Removal of PCDDs/Fs from municipal solid waste incineration by entrained-flow adsorption technology

Entrained flow adsorption using activated carbon as the adsorbent is widely adopted for PCDDs/Fs-abatement in municipal solid waste incineration (MSWI) process. The effects of operating parameters including flue gas temperature, feeding rate of activated carbon, polychlorinated dibenzodioxins and polychlorinated dibenzofurans (PCDDs/Fs) concentration at the inlet of the air pollution control device (APCD), filter materials, pressure drop on PCDDs/Fs removal efficiency are reviewed and commented upon in this paper. Evaluation on the various mechanistic models for entrained flow adsorption is carried out based on the computational simulation in terms of the actual operating condition and theoretical analysis. Finally, an advancement of en- trained flow adsorption in combination of dual bag filter is introduced.

Evaluation of Potential Productivity of Woody Energy Crops on Marginal Land in China

Energy crops are a basic material in the bioenergy industry, and they can also mitigate carbon emissions and have environ- mental benefits when planted on marginal lands. The aim of this study was to evaluate the potential productivity of energy crops on marginal lands in China. A mechanistic model, combined with energy crop and land use characteristics, and meteorological and soil parameters, was used to simulate the potential productivity of energy crops. There were three main results. 1） The total marginal land in China was determined to be 104.78 × 106 ha. The 400-mm precipitation boundary line, which is the dividing line between the semi-humid and semi-arid zones in China, also divided the marginal land into shrub land and sparse forest land in the southeast and bare land, bare rock land, and saline alkali land in the northeast. 2） The total area of the marginal land suitable for planting energy crops was determined to be 55.82 × 106 ha, with Xanthoceras sorbifolia and Cerasus humilis mainly grown in the northern China, Jatropha curcas and Comus wilsoniana mainly grown in the southwest and southeast, and Pistacia chinensis mainly grown in the central area, while also having a northeast-southwest zonal distribution. 3） Taking the highest yield in overlapping areas, the potential productivity of target energy crops was determined to be 32.63 × 106 t/yr. Without considering the overlapping areas, the potential productivity was 6.81× 106 t/yr from X. sorbifolia, 8.86× 106t/yr from C. humilis, 7.18 ×106t/yr from J. curcas, 9.55 × 106t/yr from P. chinensis, and 7.78 ×106 t/yr from C. wilsoniana.

Bridge Plug Drillouts Cleaning Practices—An Overview

Horizontal fracture-simulated completions remain the most reliable method of producing hydrocarbons from shale formations. The vast majority of unconventional wells are completed using the “Plug and Perf” method. This method involves using either a coiled tubing (CT) with a positive displacement motor or a jointed pipe to mill out composite plugs after fracturing operations are completed. An estimated average of 120,000 composite plugs is installed in the US alone each year. Bridge plug drillouts from milling operations tend to accumulate in horizontal wells and can cause stuck pipe incidents and loss of well control. Efficient removal of composite plugs’ debris is crucial in achieving operational efficacies and full production potential. This paper provides an overview of the various bridge plug drillouts cleaning practices adopted in horizontal wells. It discusses several case histories, showcasing how operators solved cleanout challenges. Developed mechanistic models to better understand hole cleaning are also reviewed. As more unconventional wells are being set at more extensive depths, an economical and optimized coiled tubing process becomes increasingly important. This paper focuses on delivering a more conclusive set of recommendations to increase efficiency and improve current composite plug coiled tubing cleaning-milling practices, increase operational efficiency and reduce cost.

Mechanistic identification of cutting force coefficients in bull-nose milling process

An improved method to determine cutting force coefficients for bull-nose cutters is proposed based on the semi-mechanistic cutting force model. Due to variations of cutting speed along the tool axis in bull-nose milling, they affect coefficients significantly and may bring remarkable discrepancies in the prediction of cutting forces. Firstly, the bull-nose cutter is regarded as a finite number of axial discs piled up along the tool axis, and the rigid cutting force model is exerted. Then through discretization along cutting edges, the cutting force related to each element is recalculated, which equals to differential force value between the current and previous elements. In addition, coefficient identification adopts the cubic polynomial fitting method with the slice elevation as its horizontal axis. By calculating relations of cutting speed and cutting depth, the influences of speed variations on cutting force can be derived. Thereby, several tests are conducted to calibrate the coefficients using the improved method, which are applied to later force predictions. Eventually, experimental evaluations are discussed to verify the effectiveness. Compared to the conventional method, the results are more accurate and show satisfactory consistency with the simulations. For further applications, the method is instructive to predict the cutting forces in bull-nose milling with lead or tilt angles and can be extended to the selection of cutting parameters.

CO_2 permeability of fractured coal subject to confining pressures and elevated temperature: Experiments and modeling

The CO_2 permeability of fractured coal is of great significance to both coalbed gas extraction and CO_2 storage in coal seams, but the effects of high confining pressure, high injection pressure and elevated temperature on the CO_2 permeability of fractured coal with different fracture extents have not been investigated thoroughly. In this paper, the CO_2 permeability of fractured coals sampled from a Pingdingshan coal mine in China and artificially fractured to a certain extent is investigated through undrained triaxial tests. The CO_2 permeability is measured under the confining pressure with a range of 10–25 MPa, injection pressure with a range of 6–12 MPa and elevated temperature with a range of 25–70°C. A mechanistic model is then proposed to characterize the CO_2 permeability of the fractured coals. The effects of thermal expansion, temperature-induced reduction of adsorption capacity, and thermal micro-cracking on the CO_2 permeability are explored. The test results show that the CO_2 permeability of naturally fractured coal saliently increases with increasing injection pressure. The increase of confining pressure reduces the permeability of both naturally fractured coal and secondarily fractured coal. It is also observed that initial fracturing by external loads can enhance the permeability, but further fracturing reduces the permeability. The CO_2 permeability decreases with the elevation of temperature if the temperature is lower than 44°C, but the permeability increases with temperature once the temperature is beyond 44°C. The mechanistic model well describes these compaction mechanisms induced by confining pressure, injection pressure and the complex effects induced by elevated temperature.

Unraveling the dose-response puzzle of L.monocytogenes:A mechanistic approach

Food-borne disease outbreaks caused by Listeria monocytogenes continue to impose heavy burdens on public health in North America and globally.To explore the threat L.monocytogenes presents to the elderly,pregnant woman and immuno-compromised individuals,many studies have focused on in-host infection mechanisms and risk evaluation in terms of dose-response outcomes.However,the connection of these two foci has received little attention,leaving risk prediction with an insufficient mechanistic basis.Consequently,there is a critical need to quantifiably link in-host infection pathways with the doseresponse paradigm.To better understand these relationships,we propose a new mathematical model to describe the gastro-intestinal pathway of L.monocytogenes within the host.The model dynamics are shown to be sensitive to inoculation doses and exhibit bistability phenomena.Applying the model to guinea pigs,we show how it provides useful tools to identify key parameters and to inform critical values of these parameters that are pivotal in risk evaluation.Our preliminary analysis shows that the effect of gastroenvironmental stress,the role of commensal microbiota and immune cells are critical for successful infection of L.monocytogenes and for dictating the shape of the doseresponse curves.

A General Mechanistic Model of Solid Oxide Fuel Cells

A comprehensive model considering all forms of polarization was developed. The model considers the intricate interdependency among the electrode microstructure, the transport phenomena, and the electrochemical processes. The active three-phase boundary surface was expressed as a function of electrode microstructure parameters （porosity, coordination number, contact angle, etc.）. The exchange current densities used in the simulation were obtained by fitting a general formulation to the polarization curves proposed as a function of cell temperature and oxygen partial pressure. A validation study shows good agreement with published experimental data. Distributions of overpotentials, gas component partial pressures, and electronic/ionic current densities have been calculated. The effects of a porous electrode structure and of vadous operation conditions on cell performance were also predicted. The mechanistic model proposed can be used to interpret experimental observations and optimize cell performance by incorporating reliable experimental data.

An adaptive neuro-fuzzy inference system white-box model for real-time multiphase flowing bottom-hole pressure prediction in wellbores

The majority of published empirical correlations and mechanistic models are unable to provide accurate flowing bottom-hole pressure(FBHP)predictions when real-time field well data are used.This is because the empirical correlations and the empirical closure correlations for the mechanistic models were developed with experimental datasets.In addition,most machine learning(ML)FBHP prediction models were constructed with real-time well data points and published without any visible mathematical equation.This makes it difficult for other readers to use these ML models since the datasets used in their development are not open-source.This study presents a white-box adaptive neuro-fuzzy inference system(ANFIS)model for real-time prediction of multiphase FBHP in wellbores.1001 real well data points and 1001 normalized well data points were used in constructing twenty-eight different Takagi eSugeno fuzzy inference systems(FIS)structures.The dataset was divided into two sets;80%for training and 20%for testing.Statistical performance analysis showed that a FIS with a 0.3 range of influence and trained with a normalized dataset achieved the best FBHP prediction performance.The optimal ANFIS black-box model was then translated into the ANFIS white-box model with the Gaussian input and the linear output membership functions and the extracted tuned premise and consequence parameter sets.Trend analysis revealed that the novel ANFIS model correctly simulates the anticipated effect of input parameters on FBHP.In addition,graphical and statistical error analyses revealed that the novel ANFIS model performed better than published mechanistic models,empirical correlations,and machine learning models.New training datasets covering wider input parameter ranges should be added to the original training dataset to improve the model's range of applicability and accuracy.

CO_(2)corrosion prediction on 20#steel under the influence of corrosion product film

Based on corrosion thermodynamics and kinetics,considering the multi-field coupling effects of fluid flow,electrochemical reaction and mass transfer process,a new corrosion prediction mechanistic model was proposed by introducing the influence factor of corrosion product film on diffusion coefficient of ion mass transfer,which is based on the CO_(2) corrosion prediction model proposed by Nesic et al.The influence of temperature,flow rate and pH value on CO_(2) corrosion behavior on 20#steel was studied by orthogonal tests.Scanning electron microscopy(SEM)and energy spectrum analysis(EDS)was used to analyze the surface and cross section morphology of the corrosion product film,and the thickness of the corrosion product film was measured.The results show that the introduced influence factor can simplify the ion mass transfer calculation in the presence of corrosion product film,and the relative error between the predicted value of the modified model and the experimental results is satisfactorily controlled less than 10%.Compared with the prediction model without considering the influence of corrosion product film,the influence factor can effectively correct the high prediction value of the mechanistic model under the influence of corrosion product film,improve the accuracy and applicability of corrosion prediction,and provide important theoretical guidance for the design,manufacturing,operation and maintenance of oil and gas production pipelines and related facilities.

An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores

Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.

Action Mechanisms of Arbuscular Mycorrhizal Fungi in Phosphorus Uptake by Capsicum annuum L.

A pot experiment was conducted to investigate the action mechanisms phorus （P） uptake of Capsicum annuum L. in a sterilized fossil Oxisol of arbuscular mycorrhizal （AM） fungi in phos- Three P levels of 0, 10 and 200 mg kg-1 soil （P0, P10 and P200, respectively） without and with AM fungal inoculation were applied as Ca（H2PO4）2-H20. Shoot dry matter yields and shoot P uptake increased significantly （P 〉 0.05） by the inoculation of AM fungi at P0 and P10. Root length and P concentration in soil solution increased with the inoculation of AM fungi but the root：shoot ratio decreased or remained constant. Around 50% roots of inoculated plants were infected by AM and the external hyphae amounted to 20 m g^-1 soil at P10 and P200. The hyphae surface area of the infected root cylinder amounted to 11 and 2 cm^2 cm^-2 root at P0 and P10, respectively. The increased P uptake of inoculated plants was mainly because of an up to 5 times higher P influx of the infected root. Model calculations showed that the root alone could not have achieved the measured P influx in both infected and non-infected roots. But the P influx for hyphae calculated by the model was even much higher than the measured one. The P uptake capacity of hyphae introduced in the model was too high. Model calculations further showed that the depletion zone around roots or hyphae was very narrow. In the case of the root only 7% of the soil volume would contribute P to the plant, while in the case of hyphae it would be 100%. The results together with the model calculations showed that the increased P uptake of AM inoculated plants could be explained partly by the increased P concentration in the soil solution and by the increased P absorbing surface area coming from the external hyphae.

Effects of seed morphology and orientation on secondary seed dispersal by wind

Understanding how diaspore(hereafter‘seed’)morphology and orientation affect secondary seed dispersal by wind is important to link seed dispersal and post-dispersal processes,such as seed lodging,predation and germination.This study aims to describe the effects of seed morphology and orientation on secondary seed dispersal by wind via mechanistic modelling.We extend the mechanistic model of Schurr et al.(2005)in order to describe how secondary seed dispersal by wind is affected by wind conditions,ground surface,seed morphology and orientation.The model simulates the initial landing orientations,dispersal distances and stopping orientations of individual seeds.To parameterize the model,we measured orientation-specific vertical seed projection and seed lift-off velocity(the wind speed at which a seed starts moving on the ground)of the asymmetric seeds of heterocarpous Zygophyllum xanthoxylon,and determined orientation-specific model parameters that depend on properties of seeds and/or the environment.To validate the model,we conducted wind channel experiments in which we released seeds of Z.xanthoxylon onto a sand-coated tar paper,and recorded the initial landing orientations,dispersal distances and stopping orientations of the seeds.The extended model could precisely predict secondary dispersal distance,and explain up to 99%of variation in the observed proportions of seeds which stopped in various orientations.The model predicts that secondary dispersal distance increases with wind speed and decreases with aerodynamic roughness length,and that there might be a positive correlation between dispersal distance and germination success.

A Model for Flooding Prediction in Circular Tubes

Flooding phenomenon limits the stability and the flow of a liquid film falling along the walls of a channel in which a gas is flowing upwards. As known, the entrainment effect can completely prevent the liquid to fall from its natural flow. The present work proposes a new mechanistic model for the prediction of the onset of flooding in vertical and inclined pipes in the presence of obstructions, as well as taking into account the viscosity effect. The good performance of the model in the different geometrical conditions and for variable viscosities of the liquid component assesses the validity of the hypothesis that the instability of a wavelike disturbance limits the countercurrent flow in a channel.