AUTHOR=Chai Slyvester Yew Wang , Phang Frederick Jit Fook , Yeo Lip Siang , Ngu Lock Hei , How Bing Shen TITLE=Future era of techno-economic analysis: Insights from review JOURNAL=Frontiers in Sustainability VOLUME=3 YEAR=2022 URL=https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2022.924047 DOI=10.3389/frsus.2022.924047 ISSN=2673-4524 ABSTRACT=

Techno-economic analysis (TEA) has been considered an important tool to evaluate the economic performance of industrial processes. Recently, the application of TEA has been observed to have exponential growth due to the increasing competition among businesses across various industries. Thus, this review presents a deliberate overview of TEA to inculcate the importance and relevance of TEA. To further support the aforementioned points, this review article starts with a bibliometric analysis to evaluate the applicability of TEA within the research community. Conventional TEA is widely known to be conducted via software modeling (i.e., Python, AMIS, MATLAB, Aspen HYSYS, Aspen Plus, HOMER Pro, FORTRAN, R, SysML and Microsoft Excel) without involving any correlation or optimization between the process and economic performance. Apart from that, due to the arrival of the industrial revolution (IR) 4.0, industrial processes are being revolutionized into smart industries. Thus, to retain the integrity of TEA, a similar evolution to smart industries is deemed necessary. Studies have begun to incorporate data-driven technologies (i.e., artificial intelligence (AI) and blockchain) into TEA to effectively optimize both processes and economic parameters simultaneously. With this, this review explores the integration of data-driven technologies in the TEA framework. From literature reviews, it was found that genetic algorithm (GA) is the most applied data-driven technology in TEA, while the applications of blockchain, machine learning (ML), and artificial neural network (ANN) in TEA are still considerably scarce. Not to mention other advanced technologies, such as cyber-physical systems (CPS), IoT, cloud computing, big data analytics, digital twin (DT), and metaverse are yet to be incorporated into the existing TEA. The inclusion of set-up costs for the aforementioned technologies is also crucial for accurate TEA representation of smart industries deployment. Overall, this review serves as a reference note for future process engineers and industry stakeholders who wish to perform relevant TEA, which is capable to cover the new state-of-art elements under the new modern era.