Kang Wang

Bio: Kang Wang is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: Combustion & NOx. The author has an hindex of 3, co-authored 4 publications receiving 86 citations.

Performance Analysis of Novel Flue Gas Self-Circulated Burner Based on Low-NOx Combustion

Abstract: A new type self-circulating structure for flue gas was designed, with a corresponding low-nitrogen oxides (NOx) burner developed. A three-dimensional finite element model was established, a...

Effects of Optimized Operating Parameters on Combustion Characteristics and NO x Emissions of a Burner based on Orthogonal Analysis

Abstract: To optimize the structure of the burner, improve the combustion performance, and reduce the emission of NOx, a self-circulating low NOx combustion technology was used to design a new type of flue gas self-circulating low NOx burner. Based on previous research on the numerical model of combustion and the composition of mixed gas on combustion and NOx emissions, the effect of various factors on the ejection coefficient of the flue gas self-circulating structure was analyzed using the orthogonal test method, and the burner operating parameters, such as preheating temperature and excess air coefficient, were deeply studied through the three-dimensional finite element numerical model in this paper. The results show that the diameter ratio of the nozzle and the length of the cylindrical section of the flue gas self-circulating structure have great influence on its ejection and mixing ability. The optimal ejection coefficient was 0.4829. Overall, the amount of NOx emissions greatly increased from 6.23×10−6 (volume fraction) at the preheating temperature 973 K to 3.5×10−3 at preheating temperature 1573 K. When the excess air coefficient decreased from 1.2 to 1, the maximum combustion temperature decreased from 2036.3 K to 1954.22 K, and the NOx emissions decreased from 352.29×10−6 to 159.73×10−6.

Nano Biosensors: Properties, applications and electrochemical techniques

Abstract: A sensor is a tool used to directly measure the test compound (analyte) in a sample. Ideally, such a device is capable of continuous and reversible response and should not damage the sample. Nanosensor refers to a system in which at least one of the nanostructures is used to detect gases, chemicals, biological agents, electric fields, light, heat, etc. in its construction. The use of nanomaterials significantly increases the sensitivity of the system. In biosensors, the part of the system used to attach to the analyte and specifically detect it is a biological element (such as a DNA strand, antibody, enzyme, whole cell). The “Nano Biosensors” series reviews various types of biosensors and biochips (including an array of biosensors), emphasizing the role of nanostructures, developed for medical and biological applications. Nano Biosensors Electrochemical sensors are sensors that use the biological element as a diagnostic component and the electrode as a transducer. The use of nanostructures in these systems is usually done to fill the gap between the converter and the bioreceptor, which is at the nanoscale. Given the nature of the biomaterial detection process, electrochemical biosensors are divided into catalytic and propulsion. Common electrochemical techniques common in sensors include potentiometric, chronometry, voltammetry, impedance measurement, and field effect transistor (FET). Simultaneous use of the advantages of nanostructures and electrochemical techniques has led to the emergence of sensors with high sensitivity and decomposition power. The use of nanostructures in these sensors is usually done to fill the gap between the converter and the bioreceptor, which is at the nanoscale. Various types of nanostructures including nanoparticles, nanotubes and nanowires, nanopores, self-adhesive monolayers and nanocomposites can be used to improve the performance and efficiency of sensors in their structure. Simultaneous use of the advantages of nanostructures and electrochemical techniques has led to the emergence of sensors with high sensitivity and decomposition power.

Modeling of CO2 capture ability of [Bmim][BF4] ionic liquid using connectionist smart paradigms

Abstract: The burning of fossil fuels produces large amounts of exhaust gases containing carbon dioxide (CO2). The emission of CO2 into the atmosphere is widely known as the leading cause of global warming and climate change. The separation processes are responsible for capturing the CO2 to reduce its undesirable effects on the environment. Since the conventional processes have their drawbacks, it is crucial to find a more environment-friendly process for CO2 capture. Recently, ionic liquids (ILs) have become an interesting candidate for CO2 capture. In this study, the solubility of CO2 in the 1-n-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) is estimated using six different artificial intelligence (AI) techniques, including four artificial neural networks (ANN), support vector machines (LS-SVM), adaptive neuro-fuzzy interface system (ANFIS). The cascade feed-forward neural network has been found as the best model for the considered matter. This model predicts overall experimental datasets with excellent accuracy of AARD = 6.88%, MSE = 8 × 1 0 − 4 , and R 2 = 0 . 98808 . The maximum mole fraction of CO2 in the ionic liquid (i.e., 0.8) can be obtained at the highest pressure and the lowest temperature.