دورية أكاديمية
Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory.
| العنوان: | Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory. |
|---|---|
| المؤلفون: | Al-Qaness MAA; College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China. alqaness@zjnu.edu.cn., Ewees AA; Department of e-Systems, University of Bisha, Bisha, 61922, Kingdom of Saudi Arabia.; Department of Computer, Damietta University, Damietta, Egypt., Thanh HV; Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam.; Faculty of Mechanical - Electrical and Computer Engineering, Van Lang University, Ho Chi Minh City, Vietnam., AlRassas AM; School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, China., Dahou A; LDDI Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, 01000, Adrar, Algeria., Elaziz MA; Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt.; Faculty of Computer Science & Engineering, Galala University, Suze, 435611, Egypt.; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates.; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon. |
| المصدر: | Environmental science and pollution research international [Environ Sci Pollut Res Int] 2023 Mar; Vol. 30 (12), pp. 33780-33794. Date of Electronic Publication: 2022 Dec 10. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: <2013->: Berlin : Springer Original Publication: Landsberg, Germany : Ecomed |
| مواضيع طبية MeSH: | Air Pollution*/prevention & control , Groundwater*, Humans ; Carbon Dioxide/analysis ; Memory, Short-Term ; Fossil Fuels |
| مستخلص: | A sustainable environment by decreasing fossil fuel utilization and anthropogenic greenhouse gases is a globally main goal due to climate change and serious air pollution. Carbon dioxide (CO2) is a heat-trapping (greenhouse) that is released into the earth's atmosphere from natural processes, such as volcanic respiration and eruptions, as well as human activities, such as burning fossil fuels and deforestation. Due to this fact, underground carbon storage (UCS) is a promising technology to cut carbon emissions. However, there are some barriers to prevent UCS from applying globally. One of them is evaluating the feasibility of storage projects. Thus, the prediction accuracy of CO2 storage efficiencies may promote the attention of the community for UCS. In this study, we utilize the recent advances of swarm intelligence to develop a hybrid algorithm called AOSMA, employed to train the long short-term memory (LSTM). The developed swarm intelligence method (AOSMA) is an enhanced Aquila optimizer (AO) using the search mechanism of the slime mould algorithm (SMA). It is used to boost the prediction capability of the LSTM by optimizing its parameters. We considered two CO2 trapping indices, called residual trapping index (RTI) and solubility trapping index (STI). The evaluation experiments have shown that the AOSMA achieved significant results compared to the original AO and SMA and several swarm intelligence and optimization algorithms. The developed smart tools could use as a game changer to provide fast and accurate storage efficiency for projects that have similar parameters falling within the range of the database. (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.) |
| References: | Abbaszadeh M, Shariatipour SM (2020) Enhancing CO2 solubility in the aquifer with the use of a downhole cooler tools. Int J Greenhouse Gas Control 97:103039. Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250. Ahmmed B, Karra S, Vesselinov VV, Mudunuru MK (2021) Machine learning to discover mineral trapping signatures due to CO2 injection. Int J Greenhouse Gas Control 109:103382. Ajayi T, Awolayo A, Gomes JS, Parra H, Hu J (2019) Large scale modeling and assessment of the feasibility of CO2 storage onshore abu dhabi. Energy 185:653–670. Al-Khdheeawi EA, Vialle S, Barifcani A, Sarmadivaleh M, Iglauer S (2018a) Effect of wettability heterogeneity and reservoir temperature on CO2 storage efficiency in deep saline aquifers. Int J Greenhouse Gas Control 68:216–229. Al-Khdheeawi EA, Vialle S, Barifcani A, Sarmadivaleh M, Iglauer S (2018b) Enhancement of CO2 trapping efficiency in heterogeneous reservoirs by water-alternating gas injection. Greenhouse Gases: Science and Technology 8:920–931. Al-Khdheeawi EA, Vialle S, Barifcani A, Sarmadivaleh M, Iglauer S (2018c) Impact of injection scenario on CO2 leakage and CO2 trapping capacity in homogeneous reservoirs. In: Offshore Technology Conference Asia. OnePetro. Al-Mudhafar WJ (2020) Integrating machine learning and data analytics for geostatistical characterization of clastic reservoirs. J Pet Sci Eng 195:107837. Al-qaness MA, Abd Elaziz M, Ewees AA, Cui X (2019) A modified adaptive neuro-fuzzy inference system using multi-verse optimizer algorithm for oil consumption forecasting. Electronics 8:1071. Al-Qaness MA, Fan H, Ewees AA, Yousri D, Abd Elaziz M (2021a) Improved ANFIS model for forecasting Wuhan City air quality and analysis COVID-19 lockdown impacts on air quality. Environ Res 194:110607. Al-Qaness MA, Saba AI, Elsheikh AH, Abd Elaziz M, Ibrahim RA, Lu S, Hemedan AA, Shanmugan S, Ewees AA (2021b) Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil. Process Safety and Environ Protection 149:399–409. Alcalde J, Flude S, Wilkinson M, Johnson G, Edlmann K, Bond C, Scott V, Gilfillan S, Ogaya Xènia, Haszeldine S (2018) Quantifying geological CO2 storage security to deliver on climate mitigation. In: 14th Greenhouse Gas Control Technologies Conference Melbourne, pp 21–26. Ali A (2021) Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns. Energy 229:120648. Ali M, Jiang R, Ma H, Pan H, Abbas K, Ashraf U, Ullah J (2021) Machine learning-a novel approach of well logs similarity based on synchronization measures to predict shear sonic logs. J Pet Sci Eng 203:108602. Alinaghian M, Goli A (2017) Location, allocation and routing of temporary health centers in rural areas in crisis, solved by improved harmony search algorithm. Int J Comput Intell Syst 10:894–913. AlRassas AM, Al-qaness MA, Ewees AA, Ren S, Abd Elaziz M, Damaševičius R., Krilavičius T. (2021a) Optimized ANFIS model using Aquila optimizer for oil production forecasting. Processes 9:1194. AlRassas AM, Al-Qaness MA, Ewees AA, Ren S, Sun R, Pan L, Abd Elaziz M (2021b) Advance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm. Journal of Petroleum Exploration and Production Technology, 1–13. AlRassas AM, Vo Thanh H, Ren S, Sun R, Le Nguyen Hai N, Lee Kang-Kun (2022) Integrated static modeling and dynamic simulation framework for CO2 storage capacity in Upper Qishn Clastics, S1A reservoir, Yemen. Geomechanics and Geophysics for Geo-Energy and Geo-Resources 8:1–23. Amooie MA, Hemmati-Sarapardeh A, Karan K, Husein MM, Soltanian Mohamad Reza, Dabir B (2019) Data-driven modeling of interfacial tension in impure CO2-brine systems with implications for geological carbon storage. Int J Greenhouse Gas Control 90:102811. Anchliya A, Ehlig-Economides C, Jafarpour B (2012) Aquifer management to accelerate CO2 dissolution and trapping. SPE J 17:805–816. Arif M, Barifcani A, Lebedev M, Iglauer S (2016) Structural trapping capacity of oil-wet caprock as a function of pressure, temperature and salinity. Int J Greenhouse Gas Control 50:112–120. Ashraf U, Zhang H, Anees A, Mangi HN, Ali M, Zhang X, Imraz M, Abbasi SS, Abbas A, Ullah Z, et al. (2021) A core logging, machine learning and geostatistical modeling interactive approach for subsurface imaging of lenticular geobodies in a clastic depositional system, SE Pakistan. Nat Resour Res 30:2807–2830. Ashraf U, Zhang H, Anees A, Nasir Mangi H, Ali M, Ullah Z, Zhang X (2020) Application of unconventional seismic attributes and unsupervised machine learning for the identification of fault and fracture network. Appl Sci 10:3864. Bachu S, Melnik A, Bistran R (2014) Approach to evaluating the CO2 storage capacity in Devonian deep saline aquifers for emissions from oil sands operations in the Athabasca area, Canada. Energy Procedia 63:5093–5102. Bakhshian S (2021) Dynamics of dissolution trapping in geological carbon storage. Int J Greenhouse Gas Control 112:103520. Bukhari AH, Raja MAZ, Sulaiman M, Islam S, Shoaib M, Kumam P (2020) Fractional neuro-sequential ARFIMA-LSTM for financial market forecasting. IEEE Access 8:71326–71338. Choi H, Cho K, Bengio Y (2018) Fine-grained attention mechanism for neural machine translation. Neurocomputing 284:171–176. Dai Z, Zhang Y, Bielicki J, Amooie MA, Zhang M, Yang C, Zou Y, Ampomah W, Xiao T, Jia W, et al. (2018) Heterogeneity-assisted carbon dioxide storage in marine sediments. Appl Energy 225:876–883. d’Amore F, Romano MC, Bezzo F (2021) Carbon capture and storage from energy and industrial emission sources: a Europe-wide supply chain optimisation. J Clean Prod 290:125202. De Silva PNK, Ranjith PG (2012) A study of methodologies for CO2 storage capacity estimation of saline aquifers. Fuel 93:13–27. Ewees AA, Abualigah L, Yousri D, Algamal Zakariya Yahya, Al-qaness Mohammed AA, Ibrahim Rehab Ali, Abd Elaziz M (2021) Improved slime mould algorithm based on firefly algorithm for feature selection: a case study on QSAR model. Engineering with Computers, 1–15. Fatani A, Dahou A, Al-Qaness MA, Lu S, Abd Elaziz M (2022) Advanced feature extraction and selection approach using deep learning and Aquila optimizer for IoT intrusion detection system. Sensors 22:140. Foroozesh J, Dier MA, Rezk MG (2018) A simulation study on CO2 sequestration in saline aquifers: trapping mechanisms and risk of CO2 leakage. In: MATEC Web of Conferences (p. 03004). EDP Sciences volume 225. Gershenzon NI, Ritzi Jr RW, Dominic DF, Soltanian M, Mehnert E, Okwen RT (2015) Influence of small-scale fluvial architecture on CO2 trapping processes in deep brine reservoirs. Water Resour Res 51:8240–8256. Ghassemzadeh S, Perdomo MG, Haghighi M, Abbasnejad E (2021) A data-driven reservoir simulation for natural gas reservoirs. Neural Computing and Applications, 1–22. Goli A, Keshavarz T (2021) Just-in-time scheduling in identical parallel machine sequence-dependent group scheduling problem. Journal of Industrial and Management Optimization. Goli A, Khademi-Zare H, Tavakkoli-Moghaddam R, Sadeghieh A, Sasanian M, Malekalipour Kordestanizadeh R (2021) An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: a case study. Netw Comput Neural Syst 32(1):1–35. Goli A, Malmir B (2020) A covering tour approach for disaster relief locating and routing with fuzzy demand. Int J Intell Transp Syst Res 18:140–152. Goli A, Mohammadi H (2022) Developing a sustainable operational management system using hybrid Shapley value and Multimoora method: case study petrochemical supply chain. Environ Dev Sustain 24:10540–10569. Goli A, Zare Hasan Khademi, Tavakkoli-Moghaddam R, Sadeghieh A (2019) Hybrid artificial intelligence and robust optimization for a multi-objective product portfolio problem case study: the dairy products industry. Computers & Industrial Engineering 137:106090. Hesse M, Tchelepi HA, Orr FM (2006) Scaling analysis of the migration of CO2 in saline aquifers. In: SPE Annual Technical Conference and Exhibition. OnePetro. Hsieh B-Z, Nghiem L, Shen C-H, Lin Z-S (2013) Effects of complex sandstone–shale sequences of a storage formation on the risk of CO2 leakage: case study from Taiwan. Int J Greenhouse Gas Control 17:376–387. Hwang J, Baek S, Lee H, Jung W, Sung W (2016) Evaluation of CO2 storage capacity and injectivity using a relief well in a saline aquifer in Pohang basin, offshore South Korea. Geosci J 20:239–245. Iglauer S (2018) Optimum storage depths for structural CO2 trapping. Int J Greenhouse Gas Control 77:82–87. Jahediesfanjani H, Anderson ST, Warwick PD (2019) Improving pressure-limited CO2 storage capacity in saline formations by means of brine extraction. Int J Greenhouse Gas Control 88:299–310. Jazayeri Noushabadi M, Brisset A, Thibeau S (2018) Investigation of CO2 storage security increase by brine alternative CO2 injection WAG_CCS. In: Abu Dhabi International Petroleum Exhibition & Conference. OnePetro. Jin M, Pickup G, Mackay E, Todd A, Sohrabi M, Monaghan A, Naylor M (2012) Static and dynamic estimates of CO2-storage capacity in two saline formations in the uk. SPE J 17:1108–1118. Jing J, Yang Y, Tang Z (2021) Assessing the influence of injection temperature on CO2 storage efficiency and capacity in the sloping formation with fault. Energy 215:119097. Joodaki S, Yang Z, Bensabat J, Niemi A (2020) Model analysis of CO2 residual trapping from single-well push pull test based on hydraulic withdrawal tests–Heletz, residual trapping experiment i. Int J Greenhouse Gas Control 97:103058. Jun C, Kim M, Shin H (2019) Optimization of well placement and operating conditions for various well patterns in CO2 sequestration in the Pohang basin, Korea. Int J Greenhouse Gas Control 90:102810. Kano Y, Ishido T, Sorai M (2018) Numerical study on the effects of contact angle change on capillary trapping. In: 14th Greenhouse Gas Control Technologies Conference Melbourne, pp 21–26. Kassa A, Gasda SE, Landa-Marbán D, Sandve TH, Kumar K (2022) Field-scale impacts of long-term wettability alteration in geological CO2 storage. Int J Greenhouse Gas Control 114:103556. Khudaida KJ, Das DB (2020) A numerical analysis of the effects of supercritical CO2 injection on CO2 storage capacities of geological formations. Clean Technol 2:333–364. Kim J, Song Y, Shinn Y, Kwon Y, Jung W, Sung W (2019) A study of CO2 storage integrity with rate allocation in multi-layered aquifer. Geosci J, 23. Kim Y, Jang H, Kim J, Lee J (2017) Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network. Appl Energy 185:916–928. Komeili Birjandi A, Fahim Alavi M, Salem M, Assad MEH, Prabaharan N (2022) Modeling carbon dioxide emission of countries in southeast of Asia by applying artificial neural network. Int J Low-Carbon Technol 17:321–326. Kumar A, Noh M, Pope GA, Sepehrnoori K, Bryant S, Lake LW (2004) Reservoir simulation of CO2 storage in deep saline aquifers. In: SPE/DOE Symposium on Improved Oil Recovery. OnePetro. Kumarasinghe HN, Moneravilla D, Muwanwella I, Ekanayake J (2019) An intelligent predicting approach based long short-term memory model using numerical and textual data: the case of Colombo stock exchange. Kumari P, Toshniwal D (2021) Deep learning models for solar irradiance forecasting: a comprehensive review. J Clean Prod 318:128566. Lee J, Park Y, Sung W, Lee Y (2010) A simulation of a trap mechanism for the sequestration of CO2 into Gorae V aquifer, Korea. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 32:796–808. Lee KJ (2020) Data-driven models to predict hydrocarbon production from unconventional reservoirs by thermal recovery. J Energy Res Technol 142:123301. Lee Y, Kim K, Sung W, Yoo I (2010) Analysis of the leakage possibility of injected CO2 in a saline aquifer. Energy & Fuels 24:3292–3298. Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323. Li X, Yi X, Liu Z, Liu H, Chen T, Niu G, Yan B, Chen C, Huang M, Ying G (2021) Application of novel hybrid deep leaning model for cleaner production in a paper industrial wastewater treatment system. J Cleaner Production 294:126343. Li Y-H, Shen C-H, Wu C-Y, Hsieh B-Z (2020) Numerical study of CO2 geological storage in saline aquifers without the risk of leakage. Energies 13:5259. Lin B, Tan Z (2021) How much impact will low oil price and carbon trading mechanism have on the value of carbon capture utilization and storage (CCUS) project? Analysis based on real option method. J Clean Prod 298:126768. Liner CL, Geng P, Zeng J, King H, Li J (2011) A CO2 sequestration simulation case study at the Dickman Field, Ness Co., Kansas. In: SPE Annual Technical Conference and Exhibition. OnePetro. Liu D, Li Y, Agarwal R (2020) Evaluation of CO2 storage in a shale gas reservoir compared to a deep saline aquifer in the Ordos basin of China. Energies 13:3397. Liu L, Chen J, Fieguth P, Zhao G, Chellappa R, Pietikäinen M. (2019) From bow to CNN: two decades of texture representation for texture classification. Int J Comput Vision 127:74–109. Lyu X, Voskov D, Rossen WR (2021) Numerical investigations of foam-assisted CO2 storage in saline aquifers. Int J Greenh Gas Control 108:103314. Mahmoudan A, Esmaeilion F, Hoseinzadeh S, Soltani M, Ahmadi P, Rosen M (2022) A geothermal and solar-based multigeneration system integrated with a TEG unit: development, 3E analyses, and multi-objective optimization. Appl Energy 308:118399. Martinez-Landa L, Carrera J, Niemi A, Bensabat J (2021) A methodology for the interpretation of aquifer tests: application to CO2 residual trapping experiments at the Heletz site. Int J Greenh Gas Control 112:103366. Menad NA, Hemmati-Sarapardeh A, Varamesh A, Shamshirband S (2019) Predicting solubility of CO2 in brine by advanced machine learning systems: application to carbon capture and sequestration. J CO2 Utilization 33:83–95. Metz B, Davidson O, De Coninck HC, Loos M, Meyer L (2005) IPCC special report on carbon dioxide capture and storage. Cambridge: Cambridge University Press, Cambridge. Mohajeri M, Shariatipour S (2019) Evaluation of enhancing CO2 sequestration by post-brine injection under different scenarios using the E300 compositional simulator. Petroleum Res 4:314–333. Nghiem L, Shrivastava V, Kohse B, Hassam M, Yang C (2010) Simulation and optimization of trapping processes for CO2 storage in saline aquifers. J Canadian Petroleum Technol 49:15–22. Niu G, Yi X, Chen C, Li X, Han D, Yan B, Huang M, Ying G (2020) A novel effluent quality predicting model based on genetic-deep belief network algorithm for cleaner production in a full-scale paper-making wastewater treatment. J Cleaner Product 265:121787. Nocito F, Dibenedetto A (2020) Atmospheric CO2 mitigation technologies: carbon capture utilization and storage. Curr Opin Green and Sustain Chem 21:34–43. Peng D, Zhang D, Liu C, Lu J (2020) BG-SAC: entity relationship classification model based on self-attention supported capsule networks. Appl Soft Comput 91:106186. Petersen NC, Rodrigues F, Pereira FC (2019) Multi-output bus travel time prediction with convolutional LSTM neural network. Expert Syst Appl 120:426–435. Pham VTH, Riis F, Gjeldvik I, Halland E, Tappel I, Aagaard P (2013) Assessment of CO2 injection into the south Utsira-Skade aquifer, the North Sea, Norway. Energy 55:529–540. Rezaei MH, Sadeghzadeh M, Alhuyi Nazari M, Ahmadi MH, Astaraei FR (2018) Applying GMDH artificial neural network in modeling CO2 emissions in four Nordic countries. Int J Low-Carbon Technol 13:266–271. Rosenzweig R (2016) Modeling CO2 migration and trapping in the Jurassic saline aquifers of the Negev first year report. Sifuentes WF, Giddins MA, Blunt MaJ (2009) Modeling CO2 storage in aquifers: assessing the key contributors to uncertainty. In: SPE Offshore Europe Oil and Gas Conference and Exhibition. OnePetro. Song Y, Sung W, Jang Y, Jung W (2020) Application of an artificial neural network in predicting the effectiveness of trapping mechanisms on CO2 sequestration in saline aquifers. Int J Greenh Gas Control 98:103042. Sun S, Kazemi-Razi SM, Kaigutha LG, Marzband M, Nafisi H, Al-Sumaiti AS (2022) Day-ahead offering strategy in the market for concentrating solar power considering thermoelectric decoupling by a compressed air energy storage. Appl Energy 305 :117804. Sun X, Bi Y, Guo Y, Ghadiri M, Mohammadinia S (2021) CO2 geo-sequestration modeling study for contact angle estimation in ternary systems of brine, CO2, and mineral. J Cleaner Product 283:124662. Sung R-T, Li M-H, Dong J-J, Lin AT-S, Hsu S-K, Wang C-Y, Yang C-N (2014) Numerical assessment of CO2 geological sequestration in sloping and layered heterogeneous formations: a case study from Taiwan. Int J Greenh Gas Control 20:168–179. Thanh HV, Lee K-K (2022) Application of machine learning to predict CO2 trapping performance in deep saline aquifers. Energy 239:122457. Thanh HV, Sugai Y, Nguele R, Sasaki K (2019) Integrated workflow in 3D geological model construction for evaluation of CO2 storage capacity of a fractured basement reservoir in Cuu Long Basin, Vietnam. Int J Greenh Gas Control 90:102826. Thanh HV, Sugai Y, Nguele R, Sasaki K (2020) Robust optimization of CO2 sequestration through a water alternating gas process under geological uncertainties in Cuu Long Basin, Vietnam. J Natural Gas Sci Eng 76:103208. Thanh HV, Sugai Y, Sasaki K (2020) Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones. Scient Rep 10:1–16. Tian J, Yu L, Xue R, Zhuang S, Shan Y (2021) Global low-carbon energy transition in the post-COVID-19 era. Applied Energy, 118205. Tirkolaee EB, Goli A, Ghasemi P, Goodarzian F (2022) Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: pareto-based algorithms. J Cleaner Production 333:130056. Vichos E, Sifakis N, Tsoutsos T (2022) Challenges of integrating hydrogen energy storage systems into nearly zero-energy ports. Energy 241:122878. Wang X, Chen S, Su J (2020) Real network traffic collection and deep learning for mobile app identification. Wireless Communications and Mobile Computing. Wen G, Tang M, Benson SM (2021) Towards a predictor for CO2 plume migration using deep neural networks. Int J Greenh Gas Control 105:103223. Wu H, Jayne RS, Bodnar RJ, Pollyea RM (2021) Simulation of CO2 mineral trapping and permeability alteration in fractured basalt: implications for geologic carbon sequestration in Mafic reservoirs. Int J Greenh Gas Control 109:103383. Xiao T, McPherson B, Esser R, Jia W, Moodie N, Chu S, Lee Si-Yong (2019) Forecasting commercial-scale CO2 storage capacity in deep saline reservoirs: case study of Buzzard’s bench, Central Utah. Comput Geosci 126:41–51. You J, Ampomah W, Sun Q (2020) Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework. Appl Energy 279:115695. Zapata Y, Kristensen MR, Huerta N, Brown C, Kabir CS, Reza Z (2020) CO2 geological storage: critical insights on plume dynamics and storage efficiency during long-term injection and post-injection periods. J Natural Gas Sci Eng 83:103542. Zhang G, Lu P, Huang Y, Li G, Zhu C (2021) Investigation of mineral trapping processes based on coherent front propagation theory: a dawsonite-rich natural CO2 reservoir as an example. Int J Greenh Gas Control 110:103400. Zhang J, Feng Q, Zhang X, Shu C, Wang S, Wu K (2020) A supervised learning approach for accurate modeling of CO2–brine interfacial tension with application in identifying the optimum sequestration depth in saline aquifers. Energy & Fuels 34(6):7353–7362. |
| فهرسة مساهمة: | Keywords: Air pollution; Aquila optimizer; Carbon dioxide (CO2); LSTM; Slime mould algorithm; Sustainable environment |
| المشرفين على المادة: | 142M471B3J (Carbon Dioxide) 0 (Fossil Fuels) |
| تواريخ الأحداث: | Date Created: 20221210 Date Completed: 20230320 Latest Revision: 20230320 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1007/s11356-022-24326-5 |
| PMID: | 36495438 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1614-7499 |
|---|---|
| DOI: | 10.1007/s11356-022-24326-5 |