白云石制备似立方体状方解石型碳酸钙晶体及其机理研究

以轻烧白云石粉、氯化铵和二氧化碳为原料,在未使用晶型控制剂的情况下,通过蒸氨-沉钙过程制备出了似立方体状碳酸钙。研究了反应温度、溶液中钙离子浓度、通气速率、搅拌速度以及陈化时间对碳酸钙中方解石相含量以及晶体形貌的影响,并探索了沉钙反应的晶型控制机理。结果表明,在反应温度40 ℃、钙离子浓度0.05 mol/L、通碳速率100 mL/min、搅拌速度400 r/min和陈化时间2 h的条件下,制备出形貌规整、粒径分布均匀的似立方体状碳酸钙,平均粒径为5~10 μm。该研究为提升白云石的使用价值、生产高附加价值的碳酸钙产品,以及提高白云石资源的利用率提供理论基础。     

自掺杂SnO2微球的水热合成及其可见光催化性能

本研究采用水热法,以柠檬酸为螯合剂,通过控制n(Sn⁴⁺)/n(Sn²⁺)的数值,合成了由具有丰富氧空位的SnO₂纳米晶体组装成的微球。通过X射线衍射(XRD)、扫描电子显微镜(SEM)、透射电子显微镜(TEM)、傅里叶红外光谱(FT-IR)、X射线光电子能谱(XPS)及UV-Vis漫反射光谱对SnO₂纳米微球进行表征分析,结果表明:在酸性水热条件和柠檬酸的螯合作用下,二氧化锡纳米晶体聚集形成微球;在Sn⁴⁺/Sn²⁺摩尔比例为3:7时,其微球尺寸最小,整体分散性较好;同时适量二价锡离子的掺杂使得该样品氧空位浓度达到最佳,氧空位的存在将使得样品光吸收范围拓展至可见光,因而该样品显示出较强的可见光催化效率,在8 min内完全降解甲基橙。     

基于流量测点的热网变动阻力系数优化辨识

随着供热计量系统的普及，系统可以根据负荷的变化进行调整，管网的阻力系数随即发生变化。对变动阻力系数进行优化辨识是了解供热管网实时运行状况的有效手段。提出一种基于流量监测数据的供热管网变动阻力系数优化辨识方法，并利用遗传算法进行求解。对洛阳市某小区供暖季管网实际运行数据进行验证，辨识结果的相对误差在5%以内。结果表明：该方法可以在只有流量观测数据时得到精度较高的供热管网变动阻力系数，为供热系统的运行调节提供指导。     

Full cross-diffusion limit in the stationary Shigesada-Kawasaki-Teramoto model

This paper studies the asymptotic behavior of coexistence steady-states of the Shigesada-Kawasaki-Teramoto model as both cross-diffusion coefficients tend to infinity at the same rate. In the case when either one of two cross-diffusion coefficients tends to infinity, Lou and Ni [18] derived a couple of limiting systems, which characterize the asymptotic behavior of coexistence steady-states. Recently, a formal observation by Kan-on [10] implied the existence of a limiting system including the nonstationary problem as both cross-diffusion coefficients tend to infinity at the same rate. This paper gives a rigorous proof of his observation as far as the stationary problem. As a key ingredient of the proof, we establish a uniform $L^{\infty }$ estimate for all steady-states. Thanks to this a priori estimate, we show that the asymptotic profile of coexistence steady-states can be characterized by a solution of the limiting system.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

A nonlinear model for marble sulphation including surface rugosity and mechanical damage

Here we propose and analyze a mathematical model that aims to describe the marble sulphation process occurring in a given material. The model accounts for rugosity as well as for damaging effects. This model is characterized by some technical difficulties that seem hard to overcome from a theoretical viewpoint. Therefore, we introduce some physically reasonable modifications in order to establish the existence of a suitable notion of solution on a given time interval. Numerical simulations are presented and discussed, also in view of further research.