Future era of techno-economic analysis: Insights from review

被引:25
作者
Chai, Slyvester Yew Wang [1 ]
Phang, Frederick Jit Fook [1 ]
Yeo, Lip Siang [1 ]
Ngu, Lock Hei [1 ]
How, Bing Shen [1 ]
机构
[1] Swinburne Univ Technol Sarawak Campus, Fac Engn Comp & Sci, Kuching, Malaysia
来源
FRONTIERS IN SUSTAINABILITY | 2022年 / 3卷
关键词
techno-economic analysis; data-driven technology; industrial revolution 4.0; smart industry; future era; HYDROGEN-PRODUCTION; CARBON CAPTURE; BIG DATA; BLOCKCHAIN TECHNOLOGIES; GENETIC-ALGORITHM; ENERGY; OPTIMIZATION; PERFORMANCE; SYSTEM; FEASIBILITY;
D O I
10.3389/frsus.2022.924047
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:22
相关论文
共 186 条
[1]   Core components of blockchain [J].
Aggarwal, Shubhani ;
Kumar, Neeraj .
BLOCKCHAIN TECHNOLOGY FOR SECURE AND SMART APPLICATIONS ACROSS INDUSTRY VERTICALS, 2021, 121 :193-209
[2]   Portfolio Optimisation of Integrated Renewable Energy Cogeneration Systems [J].
Al-Obaidli, Houd ;
AlNouss, Ahmed ;
Bicer, Yusuf ;
Al-Ansari, Tareq .
30TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PTS A-C, 2020, 48 :1435-1440
[3]   Removal of inorganic pollutants using electrocoagulation technology: A review of emerging applications and mechanisms [J].
Al-Raad, Abbas A. ;
Hanafiah, Marlia M. .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2021, 300
[4]   Subscription-Based Data-Sharing Model Using Blockchain and Data as a Service [J].
Al-Zahrani, Fahad Ahmad .
IEEE ACCESS, 2020, 8 :115966-115981
[5]   Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems [J].
Alcacer, V. ;
Cruz-Machado, V. .
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH, 2019, 22 (03) :899-919
[6]   A comprehensive techno-economic analysis and multi-criteria optimization of a compressed air energy storage (CAES) hybridized with solar and desalination units [J].
Alirahmi, Seyed Mojtaba ;
Mousavi, Shadi Bashiri ;
Razmi, Amir Reza ;
Ahmadi, Pouria .
ENERGY CONVERSION AND MANAGEMENT, 2021, 236
[7]   Warranty and maintenance analysis of sensor embedded products using internet of things in industry 4.0 [J].
Alqahtani, Ammar Y. ;
Gupta, Surendra M. ;
Nakashima, Kenichi .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2019, 208 (483-499) :483-499
[8]  
[Anonymous], 1991, A Parallel Genetic Algorithm for Solving the School Timetabling Problem
[9]   Optimization and techno-economic analysis of photovoltaic-wind-battery based hybrid system [J].
Anoune, Kamal ;
Ghazi, Mohamed ;
Bouya, Mohsine ;
Laknizi, Azzeddine ;
Ghazouani, Mokhtar ;
Ben Abdellah, Abdellatif ;
Astito, Abdelali .
JOURNAL OF ENERGY STORAGE, 2020, 32
[10]   Techno-Economic Implications of Fed-Batch Enzymatic Hydrolysis [J].
Argo, Ellen ;
Keshwani, Deepak R. .
PROCESSES, 2019, 7 (11)