High-Dimensional Model Representation-Based Surrogate Model for Optimization and Prediction of Biomass Gasification Process

Author:

Ayub Yousaf1ORCID,Zhou Jianzhao1ORCID,Ren Jingzheng1ORCID,Shi Tao1ORCID,Shen Weifeng2ORCID,He Chang3ORCID

Affiliation:

1. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China

2. Department of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China

3. School of Materials Science and Engineering, Guangdong Engineering Centre for Petrochemical Energy Conservation, Sun Yat-sen University, Guangzhou 510275, China

Abstract

Biomass gasification process has been predicted and optimized based on process temperature, pressure, and gasifying agent ratios by integrating Aspen Plus simulation with the high-dimensional model representation (HDMR) method. Results show that temperature and biomass to air ratio (BMR) have significant effects on gasification process. HDMR models demonstrated high performance in predicting H2, net heat (NH), higher heating value (HHV), and lower heating value (LHV) with coefficients of determination 0.96, 0.97, 0.99, and 0.99, respectively. HDMR-based single-objective optimization has maximum outputs for H2, HHV, and LHV (0.369 of mole fractions, 340 kJ/mol, and 305 kJ/mol, respectively) but NH would be negative at these conditions, which indicates that process is not energy-efficient. The optimal solution was determined by the multiobjective which produced 0.24 mole fraction of H2, 158.17 kJ/mol of HHV, 142.48 kJ/mol of LHV, and 442.37 kJ/s NH at 765°C, 0.59 BMR, and 1 bar. Therefore, these parameters can provide an optimal solution for increasing gasification yield, keeping process energy-efficient.

Funder

Hong Kong Polytechnic University

Publisher

Hindawi Limited

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

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