Tianjin University, China
Muyang Li is a dedicated researcher at Tianjin University, specializing in the integration of chemical engineering and data science. Currently pursuing his Master’s degree, he has already made significant contributions to the fields of crystallization process optimization, material property prediction, and AI-driven image analysis.
Profile:
🎓 Education:
- M.S. in Chemical Engineering and Technology (2022–Present), Tianjin University
- B.S. in Chemical Engineering and Technology (2018–2022), Tianjin University
🔬 Research Focus:
Muyang Li’s research bridges chemical engineering and computer vision, with notable contributions in:
- Crystallization process optimization using AI and image segmentation.
- Developing novel methodologies for virtual dataset synthesis and material property prediction.
- Implementing deep learning techniques (e.g., CNNs, Transformers, YOLOv8) for enhanced industrial applications.
🏆 Achievements:
- Authored 4 impactful publications in leading journals such as Powder Technology and Chemical Engineering Journal (2024).
- Recipient of prestigious awards, including the Samsung Scholarship (2020) and First-Class Scholarship for Master Students (2022).
- Recognized as an Excellent Graduate of Tianjin University (2022).
🧪 Key Research Contributions:
- Developed frameworks for optimizing crystallization processes via image and data enhancement strategies.
- Pioneered methods for synthesizing virtual datasets using advanced neural networks like CoCosNet.
- Advanced deep-learning applications for material properties prediction and dynamic emulsion analysis.
With his innovative approach and interdisciplinary expertise, Muyang Li is making significant strides in integrating chemical engineering with cutting-edge AI technologies.
Publication Top Notes:
1. Enhanced Powder Characteristics of Succinic Acid through Crystallization Techniques for Food Industry Application
- Authors: Hutagaol, T.J., Liu, J., Li, M., Gao, Z., Gong, J.
- Journal: Journal of Food Engineering
- Year: 2025, Volume: 388, Article: 112376
- Focus: Improved powder properties of succinic acid via advanced crystallization techniques tailored for food industry applications.
- Citations: 0
2. Modeling and Validation of Multi-Objective Optimization for Mixed Xylene Hybrid Distillation/Crystallization Process
- Authors: Chen, W., Yao, T., Liu, J., Gao, Z., Gong, J.
- Journal: Separation and Purification Technology
- Year: 2025, Volume: 354, Article: 128778
- Focus: Multi-objective optimization model validation for hybrid distillation/crystallization in mixed xylene processing.
- Citations: 0
3. A Deep Learning-Powered Intelligent Microdroplet Analysis Workflow for In-Situ Monitoring and Evaluation of a Dynamic Emulsion
- Authors: Liu, J., Li, M., Cai, J., Gao, Z., Gong, J.
- Journal: Chemical Engineering Journal
- Year: 2024, Volume: 499, Article: 155927
- Focus: Advanced deep-learning workflows for real-time dynamic emulsion monitoring.
- Citations: 0
4. Predicting Crystalline Material Properties with AI: Bridging Molecular to Particle Scales
- Authors: Chen, W., Li, M., Yao, T., Gao, Z., Gong, J.
- Journal: Industrial and Engineering Chemistry Research
- Year: 2024, Volume: 63(43), pp. 18241–18262
- Type: Review
- Focus: Utilizing AI for predicting crystalline material properties from molecular to particle scales.
- Citations: 0
5. Experiment of Simulation Study on Gas-Solid Fluidization in Martian Environments
- Authors: Ma, Y., Li, M., Ma, Z., Zhang, L., Liu, M.
- Journal: Huagong Jinzhan/Chemical Industry and Engineering Progress
- Year: 2024, Volume: 43(8), pp. 4203–4209
- Focus: Simulation studies of gas-solid fluidization under Martian environmental conditions.
- Citations: 0
6. Deep-Learning Based In-Situ Micrograph Analysis of High-Density Crystallization Slurry Using Image and Data Enhancement Strategy
- Authors: Li, M., Liu, J., Yao, T., Gao, Z., Gong, J.
- Journal: Powder Technology
- Year: 2024, Volume: 437, Article: 119582
- Focus: Application of deep-learning techniques for analyzing high-density crystallization slurry micrographs.
- Citations: 2