Fluidized bed membrane reactor for steam reforming of higher hydrocarbons

With growing demand for hydrogen in the industrial and energy sectors, research on novel hydrogen production processes is gaining importance. Fluctuations in price and availability of different hydrocarbons emphasize the need to diversify feedstock options beyond natural gas, the major source for hydrogen. Traditional steam reformers for making hydrogen from hydrocarbons suffer from low catalyst effectiveness factors, poor heat transfer and limited hydrogen yield due to thermodynamic equilibrium constraints. A fluidized bed membrane reactor (FBMR) was designed, fabricated, installed with close attention to safety and operated with methane, propane and heptane as feedstocks at average bed temperatures up to 550°C and pressures up to 800 kPa. When operated without membranes, near-equilibrium conditions were achieved inside the reactor with fluidized catalyst due to the fast reforming reactions. Installation hydrogen permselective Pd₇₇Ag₂₃ membrane panels inside the reactor to extract pure hydrogen shifted the reaction towards complete conversion of the hydrocarbons, including methane, the key intermediate when propane and heptane were the feed hydrocarbons. Reforming of higher hydrocarbons was found to be limited by the reversibility of the steam reforming of this methane. To assess the performance due to hydrogen in situ withdrawal, experiments were conducted with one and six membrane panels along the reactor. The results demonstrated that the FBMR could produce pure hydrogen from higher hydrocarbon feedstocks at moderate operating temperatures of 475-550°C. A two-phase fluidized bed reactor model was developed, with gas assumed to be in plug flow in both the bubble and dense phases. Diffusional mass transfer, as well as bulk convective flow between the phases, was incorporated to account for concentrations changing due to reactions predominantly in the dense phase, and due to increased molar flow due to reaction. Membranes withdraw hydrogen from both the dense and bubble phases. These studies show that an FBMR can provide compact reactor system with favourable hydrogen yield, and high purity. The model predicted feedstock flexibility capabilities achieved by the experiments, with the higher hydrocarbon feedstock rapidly producing methane and the non-permeate mixture approaching chemical equilibrium.Applied Science, Faculty ofChemical and Biological Engineering, Department ofGraduat

NUMERICAL SIMULATION OF MIXING IN A JET AGITATED LARGE HORIZONTAL CYLINDRICAL TANK

Mixing in a fluid jet agitated horizontal cylindrical tank has been simulated using computational fluid dynamics. A known volume of hot fluid is allowed to mix with the main fluid in the large tank which is set at a lower temperature. The fluid jet is provided using a simple pump around. Temperature measurements at various monitoring points inside the tank are used to quantify mixing. Results show that blending time is largely dependent on the flow patterns generated inside the tank. These flow patterns are a function of the tank geometry, the location and the angle at which the jet is injected. The role played by the length of the jet in determining the blending time is not as major as was thought by earlier workers. Significant reduction in blending times is achieved by changing the location and/or the angle of the incoming jet in a way that results in a better flow circulation

Full Length Research Paper Arsenic Contamination: Food Toxicity and Local Perception

Abstract. This study was performed based on local perception on arsenic toxicity with respect to socioeconomic status at the Achintanagar village in the southwestern part of Bangladesh. The questionnaire was distributed randomly at the local scale in the study area. It was found that the respondents are highly contaminated with arsenic and most of them have suffered from economic crisis and ignorance. Arsenic contamination and food poisoning concept has direct bearing on educational level which is emphasized on socioeconomic status. Correlation coefficient matrix had revealed that the whole aspects of socioeconomic component negatively correlated with arsenic toxicity for group one and two. Female participant were observed to be more unconscious than male respondents on food contamination through arsenic accumulation. Results showed that, most of the respondents continued to eat and practiced the sale of contaminated food in the community. It was observed that this practice is highly contagious for human and environmental health

Mitigating flow induced vibration in heater radiant coil

International audienceVibration of process heater tubes in a fired heater can cause fretting-wear damage of the tubes at the locations of contact points with the supports. For the reboiler of a naphtha splitter in a naphtha hydrotreating unit, a scenario of fretting-wear damage was observed exactly at contact areas between the top return bends and the hanger supports, which likely indicated constant rubbing between them during vibration. A root-cause analysis of this tube vibration problem was carried out through a combined study of process simulation, Computational Fluid Dynamics (CFD) and vibration analysis. Results from CFD simulations revealed dual phase flow inducing pressure fluctuations inside the radiant tube. The predicted pressure fluctuations were further analyzed using Fast Fourier Transform (FFT) to identify the dominant frequency of pressure fluctuations. Some of the resulting dominant frequencies were within 20% band of the estimated natural frequency of the tube, which could lead to resonance mode. This predicted resonant vibration matched with the locations of severe grooving, as reported in the heater inspection report. A scenario of mitigating this resonance mode has also been presented through decreasing feed flow rates to the radiant tube coils and installing additional support at the mid-height of the radiant tube coils

Mitigating flow induced vibration in heater radiant coil

Vibration of process heater tubes in a fired heater can cause fretting-wear damage of the tubes at the locations of contact points with the supports. For the reboiler of a naphtha splitter in a naphtha hydrotreating unit, a scenario of fretting-wear damage was observed exactly at contact areas between the top return bends and the hanger supports, which likely indicated constant rubbing between them during vibration. A root-cause analysis of this tube vibration problem was carried out through a combined study of process simulation, Computational Fluid Dynamics (CFD) and vibration analysis. Results from CFD simulations revealed dual phase flow inducing pressure fluctuations inside the radiant tube. The predicted pressure fluctuations were further analyzed using Fast Fourier Transform (FFT) to identify the dominant frequency of pressure fluctuations. Some of the resulting dominant frequencies were within 20% band of the estimated natural frequency of the tube, which could lead to resonance mode. This predicted resonant vibration matched with the locations of severe grooving, as reported in the heater inspection report. A scenario of mitigating this resonance mode has also been presented through decreasing feed flow rates to the radiant tube coils and installing additional support at the mid-height of the radiant tube coils

Comparing Machine Learning Techniques for Detecting Chronic Kidney Disease in Early Stage

In medical care, side effect trial and error processes are utilized for the discovery of hidden reasons for ailments and the determination of conditions. In our exploration, we used a crossbreed strategy to refine our optimal model, improving the Pearson relationship for highlight choice purposes. The underlying stage included the choice of ideal models through a careful survey of the current writing. Hence, our proposed half-and-half model incorporated a blend of these models. The base classifiers utilized included XGBoost, Arbitrary Woods, Strategic Relapse, AdaBoost, and the Crossover model classifiers, while the Meta classifier was the Irregular Timberland classifier. The essential target of this examination was to evaluate the best AI grouping techniques and decide the best classifier concerning accuracy. This approach resolved the issue of overfitting and accomplished the most elevated level of exactness. The essential focal point of the assessment was precision, and we introduced a far-reaching examination of the significant writing in even configuration. To carry out our methodology, we used four top-performing AI models and fostered another model named "half and half," utilizing the UCI Persistent Kidney Disappointment dataset for prescient purposes. In our experiment, we found out that the AI model XGBoost classifier gains almost 94% accuracy, a random forest gains 93% accuracy, Logistic Regression about 90% accuracy, AdaBoost gains 91% accuracy, and our proposed new model named hybrid gains the highest 95% accuracy, and performance of Hybrid model is best on this equivalent dataset. Various noticeable AI models have been utilized to foresee the event of persistent kidney disappointment (CKF). These models incorporate Naïve Bayes, Random Forest, Decision Tree, Support Vector Machine, K-nearest neighbor, LDA (Linear Discriminant Analysis), GB (Gradient Boosting), and neural networks. In our examination, we explicitly used XGBoost, AdaBoost, Logistic Regression, Random Forest, and Hybrid models with the equivalent dataset of highlights to analyze their accuracy scores