Carbon capture technologies have been recognized as a potential alternative to alleviate global warming. Carbon capture and storage (CCS) is preferred over carbon conversion and utilization (CCU) due to its lower operating costs and higher CO2 reduction capability. Nevertheless, CO2 utilization has the potential to be more economical if value‐added products are produced. This highlights the importance of assessing CO2 utilization routes and alternatives in carbon management. This review paper aims to evaluate the carbon utilization potential of major CO2‐capturing absorbents including amine, hydroxide, ionic liquid, amino acids and carbonate absorbents. All absorbents show potential application for CO2 utilization except for ionic liquids (ILs) due to their unclear CO2 capture mechanisms. Absorbents that require a desorption process for CO2 utilization include MEA, MDEA, K2CO3 and Na2CO3 due to their high absorption capacity. Industries have utilized the desorbed CO2 as chemical feedstocks, enhanced oil recovery (EOR) and mineral carbonation. For hydroxide absorbents and CaCO3, desorption of CO2 is unnecessary as the absorbed CO2 can be directly utilized to produce construction materials. Apart from that, the incorporation of advanced technologies and business models introduced by the fourth industrial revolution are plausible considerations to accelerate the development of carbon capture technologies. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

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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.

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