Recent advances in dynamic modeling and control studies of biomass gasification for production of hydrogen rich syngas

• Main Authors: Hussain, M., Ali, O., Raza, N., Zabiri, H., Ahmed, A., Ali, I.
• Format: Article
• Published: Royal Society of Chemistry 2023
• Online Access: http://scholars.utp.edu.my/id/eprint/37418/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170554031&doi=10.1039%2fd3ra01219k&partnerID=40&md5=9f8845a40a4a9f52033f713298c08f1c

The conversion of biomass through thermochemical processes has emerged as a promising approach to meet the demand for alternative renewable fuels. However, these processes are complex, labor-intensive, and time-consuming. To optimize the performance and productivity of these processes, modeling strategies have been developed, with steady-state modeling being the most commonly used approach. However, for precision in biomass gasification, dynamic modeling and control are necessary. Despite efforts to improve modeling accuracy, deviations between experimental and modeling results remain significant due to the steady-state condition assumption. This paper emphasizes the importance of using Aspen Plus® to conduct dynamics and control studies of biomass gasification processes using different feedstocks. As Aspen Plus® is comprising of its Aspen Dynamics environment which provides a valuable tool that can capture the complex interactions between factors that influence gasification performance. It has been widely used in various sectors to simulate chemical processes. This review examines the steady-state and dynamic modeling and control investigations of the gasification process using Aspen Plus®. The software enables the development of dynamic and steady-state models for the gasification process and facilitates the optimization of process parameters by simulating various scenarios. Furthermore, this paper highlights the importance of different control strategies employed in biomass gasification, utilizing various models and software, including the limited review available on model predictive controller, a multivariable MIMO controller. © 2023 The Royal Society of Chemistry.