Digitalization of biomass gasification plant in capturing and translating the technical and economic uncertainties – A review

Abstract

Drastic demand on the global renewable energy (RE) transition delineated in the 2030 Agenda for Sustainable Development and Circular Economy Action Plan have turned to a massive deployment and exploitation of biomass-based RE. Though, there are numbers of commercial RE plants available on the ground, progression of academic research with regards to the biomass technology is actively growing and evolving. This is due to the urge of technical and economic (TE) intervention to ensure sustainability, feasibility, and viability of large-scale biomass-energy technology such as for gasification plant. Digitalization via high-fidelity simulation and integrated optimization and machine learning is able to capture those TE uncertainties via Process System Engineering (PSE). PSE tools have evident to offer significant contribution to a body of knowledge especially on deciphering the technology/system, predicting, and capturing the TE uncertainties and solving the challenges face by the industries and investors before the technology can be commercialized. At present, no noticeable review articles have been conducted related to the deployment of PSE tools in the biomass technology research area. Thus, the objective of this study is to provide recent progress and highlight contribution and trend of PSE tools in capturing and translating the TE uncertainties subsequently to provide insight on the TE values of biomass-based RE technology. This comprehensive review encompasses of different types of computational tools such as Aspen Plus and Matlab. Concurrently, to evaluate how the tools plays a part towards experimental output and practical result targeting the TE key parameters for instance, net present value (NPV), payback period (PBP) and rate of return. Ultimately, we suggest that applying PSE techniques is critical for TE evaluation to access more compelling systems with maximum efficiency while improving a profound knowledge on TE sensitivity.