Working Smarter: Leveraging Machine Learning to Optimize CO₂ Adsorption

▲ main authors : (from left) Xiangzhou Yuan (Research Professor Korea Univ), Manu Suvarna (Research Associate, NUS), Xiaonan Wang (Assistant Professor, NUS), Yong Sik Ok (Full Professor, Korea U)

Scientists employ artificial intelligence to guide the design of biomass waste-derived novel materials for CO₂ capture

Biomass waste can be used to produce porous carbons capable of sequestering CO₂ gas emitted from large point sources (e.g., power plants, cement industries). However, there are no general guidelines on how such high-quality porous carbons should be synthesized or their optimal operational conditions. In a recent study, scientists employed machine learning-based method to determine which core factors should be prioritized in biomass waste-derived porous carbons to achieve the best CO₂ adsorption performance, paving the way to a circular economy.

If we are to mitigate climate change, we must find cost-effective and sustainable ways to reduce industrial carbon dioxide (CO₂) emissions. Unfortunately, most well-established methods for carbon capture and storage (CCS) in industrial post-combustion sources bear significant downsides, such as a high cost, environmental toxicity, or durability issues. Against this backdrop, many researchers have focused on what may be our best bet for next-generation CCS systems: CO₂ adsorption using solid porous carbon materials.

One notorious advantage of using porous carbons for CO₂ sequestration is that they can be produced from biomass waste, such as agricultural waste, food waste, animal waste, and forest debris. This makes biomass waste-derived porous carbons (BWDPCs) attractive not only due to their low cost, but also because they provide an alternative way to put biomass waste to good use. Although BWDPCs could definitely bring us closer to a circular economy, this field of study is relatively young, and no clear guidelines or consensus exist between scientists as to how BWDPCs should be synthesized or what material properties and compositions they should strive for.

Could artificial intelligence (AI) help us out in this conundrum? In a recent study published in Environmental Science and Technology, a collaborative research team from Korea University and the National University of Singapore employed a machine learning-based approach that may guide the development of future porous carbon synthesis strategies. The scientists noted that there are three core factors influencing the CO2 adsorption properties in BWDPCs: the elemental composition of the porous solid, its textural properties, and the adsorption parameters at which it operates, such as temperature and pressure. However, how these core factors should be prioritized when developing BWDPCs has remained unclear, until now.

To help settle this matter, the team first conducted a literature review and selected 76 publications describing both the synthesis and performance of various BWDPCs. After curation, these papers provided over 500 datapoints that were used to train and test three tree-based models. “The main purpose of our work was to elucidate how machine learning tools can be leveraged for predictive analytics and used to draw valuable insights into the process of CO₂ adsorption using BWDPCs,” explains Professor Yong Sik Ok from Korea University, who led the study.

The input features of the models were the three core factors, whereas the output was the level of CO₂ adsorption. Although the models themselves become essentially ‘black boxes’ after the training process, they can be used to make accurate predictions on the performance of BWDPCs based solely on the core factors considered. Most importantly, through feature analyses, the research team determined the relative importance of each of the input features for making accurate predictions. In other words, they established which of the core factors is the most important to achieve high CO2 adsorption. The results indicate that the adsorption parameters contributed much more than the other two core factors for the models to make correct predictions, underlining the importance of optimizing operational conditions first. The textural properties of the BWDPCs, such as their pore size and surface area, came in second place, and their elemental composition came last.

Worth noting, the predictions of the models and the results of the feature importance analyses were backed by existing literature and our current understanding of the mechanisms behind the CO₂ capture process. This cemented the real-world applicability of this data-driven strategy not only for BWDPCs, but for other types of materials, as Prof. Ok explains, “Our modeling approach is cross-deployable and can be used to investigate other types of porous carbons for CO₂ adsorption, such as zeolites and metal−organic frameworks, and not just those derived from biomass waste.”

The team now plans to devise a synthesis strategy for BWDPCs by focusing on optimizing the two most important core factors. Moreover, they will keep adding experimental data points to the database used in this study and make it open source so that the research community may also benefit from it.

Let us hope that all these efforts lead us to truly sustainable societies that can stop climate change and achieve the UN Sustainable Development Goals, such as Goal 13: Climate Action.


Reference
 
Authors: Xiangzhou Yuan (1,2), Manu Suvarna (3), Sean Low (3), Pavani Dulanja Dissanayake (1), Ki Bong Lee (4), Jie Li3, Xiaonan Wang (3), and Yong Sik Ok (1)
 
Title of original paper: Applied Machine Learning for Prediction of CO2 Adsorption on Biomass Waste-Derived Porous Carbons
 
Journal: Environmental Science and Technology
                                                                 
DOI: https://doi.org/10.1021/acs.est.1c01849
 
Affiliations:
1.Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University
2.R&D Centre, Sun Brand Industrial Inc.
3.Department of Chemical and Biomolecular Engineering, National University of Singapore
4.Department of Chemical & Biological Engineering, Korea University



About Professor Yong Sik Ok
Yong Sik Ok is a Full Professor and global research director at Korea University in Seoul, Korea. His academic background covers waste management, the bioavailability of emerging contaminants, and bioenergy and value-added products (such as biochar). Further, Professor Ok has experience in fundamental soil science and the remediation of various contaminants in soils and sediments. In collaboration with graduate students and colleagues, Professor Ok has published over 600 research papers, 88 of which have been ranked as Web of Science ESI top papers since 2009. In 2019, he became the first Korean to be selected as an HCR in the field of Environment and Ecology.

He currently serves as Director of the Sustainable Waste Management Program for the Association of Pacific Rim Universities (APRU) and Co-President of the International ESG Association. Moreover, he has served as chairman of numerous major conferences such as Engineering Sustainable Development 2019, organized by the APRU and the Institute for Sustainability of the American Institute of Chemical Engineers (AIChE). Prof. Ok will host the first Nature conference among South Korean universities in Seoul in 2021 on waste management and valorization for a sustainable future