Advancements in adsorption based carbon dioxide capture technologies- A comprehensive review

Das, Arnob; Peu, Susmita Datta; Hossain, Md Sanowar; Nahid, Md Mahafujul Alam; Karim, Fazlur Rahman Bin; Chowdhury, Hribhu; Porag, Mahmudul Hasan; Argha, Debo Brata Paul; Saha, Sabhasachi; Islam, Abu Reza Md Towfiqul; Salah, Mostafa M.; Shaker, Ahmed

doi:10.1016/j.heliyon.2023.e22341

Cited by 16 publications

(4 citation statements)

References 243 publications

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“…Sustainable farming is improved by AI-powered precision agriculture, which gives farmers a bigger advantage by optimizing crop yield while minimizing resource usage [78][79][80]. On top of that, AI boosts the efficiency of carbon dioxide capture systems that rely on adsorption, which in turn helps fight climate change by lowering operational costs and improving capture procedures [81,82]. More precise forecasting of renewable output enables generators and energy traders to participate in wholesale and balancing markets with increased accuracy [83][84][85][86].…”

Section: Economic Optimization By Aimentioning

confidence: 99%

Artificial Intelligence’s Evolution and Impact: From the Birth of Intelligent Agents to Navigating Sustainable and Renewable Energy in the Digital Era

Khan

2023

Preprint

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AI applications are transforming the technology and digital sector, seamlessly integrating into the daily interactions of individuals with the digital world. Key entry points to the internet, such as search engines, are now complemented by recommendation systems that curate content on social networks, streaming platforms, and online marketplaces. Additionally, virtual assistants have become ubiquitous across various platforms. While these groundbreaking changes are gradually extending to several industries, there remains significant untapped potential for the application of artificial intelligence in diverse domains. The renewable energy sector, in particular, continually reinvents itself through research and innovation to meet the escalating demand. Positioned at the forefront of technological evolution, this industry is well-placed to effectively adopt and tailor the innovations of artificial intelligence to its specific needs. Presently, both emerging and established tools leverage elements of AI to enhance processes and introduce functionalities that were previously unattainable. If the current trajectory persists, AI-enhanced tools have the potential to assume a role as crucial to the future of engineering as computer-aided design (CAD) software is in contemporary times.

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Section: Economic Optimization By Aimentioning

confidence: 99%

Artificial Intelligence’s Evolution and Impact: From the Birth of Intelligent Agents to Navigating Sustainable and Renewable Energy in the Digital Era

Khan

2023

Preprint

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“…Thus, adsorbents are solids that, through Disciplinarum Scientia. Série: Naturais e Tecnológicas, Santa Maria, v. 24, n. 3, p. 11-20, 2023. 13 physical-chemical interactions can maintain the solute on the surface certain substances present in the solution separating them from other unwanted components (Das et al, 2023). Furthermore, the way in which the adsorbates are superficially fixed under the adsorbents determines the adsorption classification (Atkins; Paula, 2013).…”

Section: Adsorption Processmentioning

confidence: 99%

Alternative biosorbents from agro-industrial waste: a review for application in the removal of organic pollutants

Pedebos,

Rosa,

Druzian

et al. 2023

DS-CNT

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Environmental conservation issues are widespread today because of the scarcity of renewable resources. Faced with this, pollution of water resources becomes a cause for concern, as its treatment is not feasible. As in Brazil there is a significant production of agro-industrial products, its residues also become worrisome if not discarded correctly. As a result of this, the alternative biosorbents that derive from the use of the agro(industrial) residues and that aid in the process of treatment of effluents arise. To solve the problem of water pollution, a viable alternative is the adsorption with the use of alternative biosorbents for removal of undesirable components. In this context, the present work presents a study on the application in the removal of organic pollutants from effluents from alternative biosorbents from agro(industrial) waste.

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“…The nation experiences a severe water shortage throughout the year, particularly during the dry season, as a result of excessive groundwater extraction, unmanaged surface water contamination, the effects of disasters brought on by climate change, saline intrusion, etc. 6 , 47 – 49 . Additionally, the amount of agricultural land is constantly declining, and the increase in impermeable surfaces caused by vegetation has only made matters worse 50 – 52 .…”

Section: Introductionmentioning

confidence: 99%

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Sarkar,

Rudra,

Talukdar

et al. 2024

Sci Rep

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The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

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Advancements in adsorption based carbon dioxide capture technologies- A comprehensive review

Cited by 16 publications

References 243 publications

Artificial Intelligence’s Evolution and Impact: From the Birth of Intelligent Agents to Navigating Sustainable and Renewable Energy in the Digital Era

Artificial Intelligence’s Evolution and Impact: From the Birth of Intelligent Agents to Navigating Sustainable and Renewable Energy in the Digital Era

Alternative biosorbents from agro-industrial waste: a review for application in the removal of organic pollutants

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

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