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.

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.

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.

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.

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.

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.

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.

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.

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.