Dissolution is inherent to fluid-mineral systems. Yet its impact on minerals reacting with electrolytes is overlooked. Here, a novel nonmonotonic behavior for the surface interactions of carbonates (calcite and Mg-calcite) with organic acids is reported. Applying a bioinspired approach, Mg-calcite sensors via amorphous precursors, avoiding any preconditioning with functional groups are synthesized. A quartz crystal microbalance is used to study the mass changes of the mineral on contact with organic acids under varying ionic conditions, temperatures, and flow velocities. Supported by confocal Raman microscopy and potentiometric titrations, nonmonotonous mass developments are found as a function of Ca2+ concentration and flowrate, and attributed to three coupled chemical reactions: i) carbonate dissolution via Ca2+ ion complexation with organic molecules, and the formation of organo-calcium compounds as ii) a surface phase at the mineral–water interface, and iii) particles in the bulk fluid. These processes depend on local ion contents and the precipitation onset (i.e., saturation index) of organo-calcium salts, both of which substantially differ in the bulk fluid and in the fluid boundary layer at mineral interfaces. This continuum between dissolution and precipitation provides a conceptual framework to address reactions at mineral interfacial across disciplines including biomineralization, ocean acidification and reservoir geochemistry. 相似文献
To manufacture parts with nano- or micro-scale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy. 相似文献
Metallurgical and Materials Transactions B - An experimental investigation of the reduction of magnetite concentrate particles was conducted in a laboratory-scale flash reactor representing a novel... 相似文献
Engineering with Computers - In this paper, multi-stage continuous belt (MSCB) dryer was used for carrot slices drying. Experiments were performed at three air speeds (1, 1.5, and 2 m/s)... 相似文献
Studies have shown that the major cause of the bridge failures is the local scour around the pier foundations or their abutments. The local scour around the bridge pier is occurred by changing the flow pattern and creating secondary vortices in the front and rear of the bridge piers. Until now, many researchers have proposed empirical equations to estimate the bridge pier scour based on laboratory and field datasets. However, scale impact, laboratory simplification, natural complexity of rivers and the personal judgement are among the main causes of inaccuracy in the empirical equations. Therefore, due to the deficiencies and disadvantages of existing equations and the complex nature of the local scour phenomenon, in this study, the adaptive network-based fuzzy inference system (ANFIS) and teaching–learning-based optimization (TLBO) method were combined and used. The parameters of the ANFIS were optimized by using TLBO optimization method. To develop the model and validate its performance, two datasets were used including laboratory dataset that consisted of experimental results from the current study and previous ones and the field dataset. In total, 27 scaled experiments of different types of pier groups with different cross sections and side slopes were carried out. To evaluate the model ability in prediction of scour depth, results were compared to the standard ANFIS and empirical equations using evaluation functions including Hec-18, Froehlich and Laursen and Toch equations. The results showed that adding TLBO to the standard ANFIS was efficient and can increase the model capability and reliability. Proposed model achieved better results than Laursen and Toch equation which had the best results among empirical relationships. For instance, proposed model in comparison with the Laursen and Toch equation, based on the RMSE function, yielded 50.4% and 71.8% better results in laboratory and field datasets, respectively.
The Journal of Supercomputing - Large-scale computing platforms become essential in nowadays business and scientific activities. The electrical energy consumed by such platforms increases... 相似文献