Crystallization Solvent Design Based on a New Quantitative Prediction Model of Crystal Morphology

AIChE Journal ◽  
2021 ◽  
Author(s):  
Shiyang Chai ◽  
Enhui Li ◽  
Lei Zhang ◽  
Jian Du ◽  
Qingwei Meng
Keyword(s):  
Prediction Model ◽  
Crystal Morphology ◽  
Quantitative Prediction ◽  
Solvent Design

Ray Acoustics Approach to Quantitative Prediction of Highway Traffic Noise

2010 ◽  
Author(s):  
Liming Dai ◽  
Huay Seen Lee
Keyword(s):  
Prediction Model ◽  
Traffic Noise ◽  
Environmental Noise ◽  
Quantitative Prediction ◽  
Highway Traffic ◽  
Noise Levels ◽  
Modeling Approach ◽  
Highway Noise

A Highway Prediction Model (HPM) using the ray acoustics modeling approach is developed in this research. The HPM model can be used to quantitatively predict the environmental noise levels on highways of different pavements. Comparison between the measured noise levels using the SPB method and predicted noise levels with the model developed shows that the prediction model established is reliable for estimating highway noise in Canada.

Regional Short-Term Earthquake Prediction Model Based on BP Neural Network

2012 ◽  
Vol 166-169 ◽  
pp. 2309-2314
Author(s):  
Wu Sheng Hu ◽  
Hao Wang ◽  
Hong Lin Nie
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Prediction Model ◽  
Earthquake Prediction ◽  
Linear Regression Analysis ◽  
Earthquake Magnitude ◽  
Quantitative Prediction ◽  
Earthquake Parameters ◽  
Advantages And Disadvantages ◽  
Model Based

A method which gives the quantitative prediction for earthquake magnitude is proposed in this paper. By this method, after calculating the earthquake parameters and the astronomical time-varying parameters, an earthquake prediction model can be established to gives the quantitative prediction for earthquake magnitude in the future prediction period. In this research, the research object was the experimental areas, the prediction period was 6 months, and Linear Regression analysis and conventional BP (Back Propagation) Neural Network were used respectively in prediction. Through backtracking test, the RMSEs(root mean square error) of earthquake magnitude prediction are ±0.78 ML and ±0.61 ML. Then after summarizing the advantages and disadvantages of the two methods, an integrated model based on linear regression and neural network was proposed. Through backtracking test, the RMSE of earthquake magnitude prediction reaches ± 0.41 ML, results improving significantly.

Inclusion of frequency dependences into prediction model of conducted electromagnetic emissions for a VFD motor system

2021 ◽  
pp. 1-13
Author(s):  
Youpeng Huangfu ◽  
Shuhong Wang ◽  
Shuli Yin
Keyword(s):  
Prediction Model ◽  
Motor System ◽  
Synthesis Method ◽  
Measurement Data ◽  
Quantitative Prediction ◽  
Power Cable ◽  
Frequency Dependent ◽  
Variable Frequency Drive ◽  
Macro Modeling ◽  
Electromagnetic Emissions

This paper presents a quantitative prediction model of conducted electromagnetic emissions (EME) for a variable frequency drive (VFD) motor system based on the macro-modeling approach. This model relies on the effective representations of the frequency dependent characteristics of the parasitic behaviors between converter arms and heatsink, of shielded power cable, and of motor windings. The frequency dependent performances between converter arms and heatsink, and of the motor windings are obtained by processing measurement data. Then the vector fitting method (VFM) with passivity enforcement and circuit synthesis method are adopted to obtain the corresponding equivalent circuits. The equivalent circuit implementation of shielded power cable is achieved by applying a node-to-node admittance functions (NAFs) model, which considers the propagation phenomenon and frequency dependent losses accurately. The conducted EME model is compatible with commonly used transient circuit solvers since it only includes constant circuit elements. The conducted emissions of the VFD system are then evaluated by analyzing the common mode (CM) and differential mode (DM) voltages and currents.

scholarly journals Distribution Map of Frost Resistance for Cement-Based Materials Based on Pore Structure Change

Materials ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2509
Author(s):  
Nguyen Xuan Quy ◽  
Takumi Noguchi ◽  
Seunghyun Na ◽  
Jihoon Kim ◽  
Yukio Hama
Keyword(s):  
Prediction Model ◽  
Pore Structure ◽  
Frost Resistance ◽  
Frost Damage ◽  
Structure Change ◽  
Quantitative Prediction ◽  
Weather Data ◽  
Distribution Map ◽  
Cement Based Materials ◽  
Damage Risk

This paper presents a prediction method and mathematical model based on experimental results for the change in pore structure of cement-based materials due to environmental conditions. It focuses on frost damage risk to cement-based materials such as mortar. Mortar specimens are prepared using water, ordinary Portland cement, and sand and the pore structure is evaluated using mercury intrusion porosimetry. New formulas are proposed to describe the relationship between the pore structure change and the modified maturity and to predict the durability factor. A quantitative prediction model is established from a modified maturity function considering the influences of environmental factors like temperature and relative humidity. With this model, the frost resistance of cement-based materials can be predicted based on weather data. Using the prediction model and climate data, a new distribution map of frost damage risk is created. It is found that summer weather significantly affects frost resistance, owing to the change in pore structure of cement-based mortar. The model provides a valuable tool for predicting frost damage risk based on weather data and is significant for further research.

Porosity Parameters Quantitatively Predicted of Ultra-Low Permeability Reservoir in Hejiaji Area

2015 ◽  
Vol 1092-1093 ◽  
pp. 1440-1443
Author(s):  
Wei Huang ◽  
Xiao Li Zhang ◽  
Xi Zheng ◽  
Gao Run Zhong
Keyword(s):  
Prediction Model ◽  
Effective Porosity ◽  
Quantitative Prediction ◽  
Low Permeability ◽  
Core Analysis ◽  
Response Characteristics ◽  
The Core ◽  
Point Analysis ◽  
Set Up ◽  
Interpretation Model

Using the acoustictime logging data to establish reasonable and effective porosity parameter quantitative prediction model, can predict quality reservoir and favorable petroliferous province. Chang 6 formation in Hejiaji area is taken as an example to studing the reservoir physical properties and logging response characteristics, and the acoustictime logging and core analysis porosity are used to set up porosity logging interpretation model, then acculate porosity according to the model. The results show that after the core place treatment, the porosity quantitative prediction model established by acoustictime and layer point analysis porosity has high precision. To verify the porosity logging interpretation model of the calculated porosity and core analysis porosity, the application effect is good.

scholarly journals Crystallization Solvent Design Based on a New Quantitative Control Model of Crystal Morphology

2021 ◽  
Author(s):  
Shiyang Chai ◽  
Enhui Li ◽  
Lei Zhang ◽  
Jian Du ◽  
Qingwei Meng
Keyword(s):  
Crystal Morphology ◽  
Molecular Design ◽  
Quantitative Relationship ◽  
Active Pharmaceutical Ingredient ◽  
Control Model ◽  
Mixed Integer ◽  
Separation Unit ◽  
Factors Affecting ◽  
Solvent Design ◽  
Minlp Model

Solution crystallization is an important separation unit operation in active pharmaceutical ingredient (API) production. Solvent is one of the important factors affecting crystal morphology. How to select/design suitable crystallization solvents is still one of the most urgent problems in the crystallization field. In this paper, a framework for crystallization solvent design based on the developed quantitative control model of crystal morphology is proposed. First, molecular dynamics is used to predict the crystal morphology in solvents. Next, nine solvent descriptors are selected. Then, the quantitative relationship between crystal aspect ratio and solvent descriptors is developed. Subsequently, Computer-Aided Molecular Design (CAMD) method is integrated with the developed quantitative control model. The crystallization solvent design problem is expressed as a Mixed-Integer Non-Linear Programming (MINLP) model, which is solved by the decomposition algorithm. Finally, the crystallization solvent design framework is applied to two cases: benzoic acid and ibuprofen, and experimental verification is implemented.

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