He Xin

He Xin  

Personal Particulars
Research Fellow
NUS Environmental Research Institute,

National University of Singapore,

1 CREATE Way, #15-02 CREATE Tower,

Singapore, 138602

Office: E2S2 15-02-22
Phone: (65) 97167443
Email: erihex@nus.edu.sg



Ph.D., Chemical Engineering, West Virginia University, United States, 2014-2019.

M.Eng., Thermal Engineering, Tsinghua University, China, 2011-2014.

B.Eng., Thermal Engineering, Tsinghua University, China, 2007-2011.


Research Interests

  • Process modeling and simulation with the integration of engineering science, including artificial intelligence, machine learning, model predictive control, and process intensification
  • Process performance assessment, including energy assessment, life-cycle assessment, and techno-economic assessment
  • Process multi-objective optimization
  • Surrogate-based global optimization method for computationally expensive problems


Research highlights

  • Modeling and simulation of biomass fixed-bed gasification


Two models are developed to analyze the biomass fixed-bed gasification process: the 1D four-stage benchmark model and the 2D (1D + 1D) model.

In the 1D benchmark model, the biomass fed into the gasifier passes through four stages: drying stage, pyrolysis stage, oxidation stage, and reduction stage. In the 2D model, a 1D model is developed to describe the entire fixed-bed gasifier and in each discretized gasifier cell a 1D representative particle is employed based on a hybrid peripheral fragmentation and shrinking-core model.

The syngas generation, biochar productions, and PM emission transients can be quantified in the developed 2D model. To further improve the accuracy of syngas transient predictions, the kinetic model is calibrated by introducing pre-exponential correction in both gas phase homogeneous reactions and solid-gas surface heterogeneous reactions. Two surrogate-based optimization methods are employed for the computationally expensive tunning process: DYCORS (Dynamic Coordinate search using Response Surface model) and GOMORS (Gap Optimized Multi-objective Optimization using Response Surfaces)

  • Integration of discrete wavelet transform method for the gasification PM emission model

The data-driven PM emission model developed for gasification solid conversion, which is a radial basis function-based response surface model and employs experimental data pretreated by discrete wavelet transforms.

  • Artificial neural network for catalytic biomass pyrolysis model




  • Xin He, Chi-Hwa Wang, and Christine Annette Shoemaker. Multi-objective Optimization of an Integrated Biomass Waste Fixed-bed Gasification System for Power and Biochar Co-production. Computers and Chemical Engineering (2021):107457.
  • Xin He, Qiang Hu, Christine Annette Shoemaker, Haiping Yang, and Chi-Hwa Wang. Dynamic Modeling with Experimental Calibration for the Syngas Production from Biomass Fixed-bed Gasification. AIChE Journal (2021): e17366.
  • Qiang Hu, Xin He, Zhiyi Yao, Yanjun Dai, Chi-Hwa Wang. Gaseous production kinetics and solid structure analysis during thermochemical conversion of biomass pellet. Journal of Energy institute 98 (2021): 53-62.
  • Fanghua Li, Xin He, Arora Srishti, Shuang Song, Hugh Tiang Wah Tan, Daniel J. Sweeney, Subhadip Ghosh, and Chi-Hwa Wang. “Water hyacinth for energy and environmental applications: A review.” Bioresource Technology (2021): 124809.
  • Xin He and Fernando V. Lima. A modified SQP-based model predictive control algorithm: application to supercritical coal-fired power plant cycling. Industrial & Engineering Chemistry Research 59, no. 35 (2020): 15671-15681.
  • Fanghua Li, Xin He, Christine Annette Shoemaker, and Chi-Hwa Wang. “Experimental and numerical study of biomass catalytic pyrolysis using Ni2P-loaded zeolite: Product distribution, characterization and overall benefit.” Energy Conversion and Management 208 (2020): 112581.
  • Zhiyi Yao, Xin He, Qiang Hu, Wei Cheng, Haiping Yang, and Chi-Hwa Wang. “A Hybrid Peripheral Fragmentation and Shrinking-core Model for Fixed-bed Biomass Gasification.” Chemical Engineering Journal (2020): 124940.
  • Xin He and Fernando V. Lima. Development and implementation of advanced control strategies for power plant cycling with carbon capture. Computers & Chemical Engineering 121 (2019): 497-509.
  • Xin He, Yifan Wang, Debangsu Bhattacharyya, Fernando V. Lima and Richard Turton. Dynamic modeling and advanced control of post-combustion CO2 capture plants. Chemical Engineering Research and Design, 131 (2018):430-439.
  • Fernando V. Lima, Xin He, Rishi Amrit and Prodromos Daoutidis. Advanced control strategies for IGCC plants with membrane reactors for CO2 capture. In Process systems and materials for CO2 capture: modelling, design, control and integration, A.I. Papadopoulos and P. Seferlis (eds.), Wiley, 2017.
  • Zhaofeng Xu, Xin He, Yali Xue, and Zheng Li. Dynamic simulation of post-combustion capture system. Energy Procedia 37 (2013): 2164-2171.

Conference Presentations (selected)

  • Xin He, Dingding Yao and Chi-Hwa Wang. Carbon Footprint of Plastic and Sludge Waste Streams in Singapore. 8th International Conference on Sustainable Solid Waste Management, Thessaloniki, Greece, June 2021.
  • Xin He, Hailin Tian, Yen-Wah Tong and Chi-Hwa Wang. Life-cycle greenhouse gas emission analysis for integrated sewage sludge and food waste management strategy. In proceeding of the 12th International Conference on Applied Energy (ICAE 2020), December 2020.
  • Xin He, Zhiyi Yao and Chi-Hwa Wang. Optimal Design and Operation of Biomass Waste Gasification for Energy and Biochar Production. AIChE Annual Meeting, Orlando, FL, November 2019
  • Xin He and Fernando V. Lima. A modified SQP method for MPC of a supercritical pulverized coal-fired power plant during cycling. AIChE Annual Meeting, Pittsburgh, PA, November 2018.
  • Xin He and Fernando V. Lima. Design and implementation of model predictive control strategies for IGCC power plant cycling with carbon capture. AIChE Annual Meeting, Minneapolis, MN, November 2017.
  • Xin He and Fernando V. Lima. Design and implementation of model predictive control strategies for IGCC power plant cycling with carbon capture. AIChE Annual Meeting, San Francisco, CA, November 2016.
  • Xin He, Rishi Amrit, Richard Turton and Fernando V. Lima. Model predictive control of integrated gasification combined cycle power plants with membrane reactors for carbon capture. AIChE Annual Meeting, Salt Lake City, Utah, November 2015.