Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning

Palansooriya, Kumuduni Niroshika; Li, Jie; Dissanayake, Pavani Dulanja; Suvarna, Manu; Li, Lanyu; Yuan, Xiangzhou; Sarkar, Binoy; Tsang, Daniel C.W.; Rinklebe, Jörg; Wang, Xiaonan; Ok, Yong Sik

doi:10.1021/acs.est.1c08302

Cited by 167 publications

(68 citation statements)

References 77 publications

(161 reference statements)

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“…The atomic ratio (O + N)/C of biochar and activated carbon, representing the quantity of O-/N-containing functional groups, also ranked after SSA to predict the adsorption of pharmaceuticals and personal care products (Zhu et al 2022). In a recent ML study, the N content of biochar (N-char) even ranked the first among the 20 variables that affect immobilization of heavy metal by biochar in soil, while the effect of SSA was negligible (ranked 17) (Palansooriya et al 2022). The presence of N-containing functional groups on the biochar surface provides active sites for heavy metals immobilization through strong covalent bonding, H bonding, chelation, and electrostatic attraction (Cao et al 2021;Palansooriya et al 2022), and the general sites of biochar (represented by SSA) should only have a low affinity to heavy metals.…”

Section: Graphical Abstractmentioning

confidence: 99%

Machine learning predicting and engineering the yield, N content, and specific surface area of biochar derived from pyrolysis of biomass

Leng

Yang

Lei

et al. 2022

Biochar

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Biochar produced from pyrolysis of biomass has been developed as a platform carbonaceous material that can be used in various applications. The specific surface area (SSA) and functionalities such as N-containing functional groups of biochar are the most significant properties determining the application performance of biochar as a carbon material in various areas, such as removal of pollutants, adsorption of CO2 and H2, catalysis, and energy storage. Producing biochar with preferable SSA and N functional groups is among the frontiers to engineer biochar materials. This study attempted to build machine learning models to predict and optimize specific surface area of biochar (SSA-char), N content of biochar (N-char), and yield of biochar (Yield-char) individually or simultaneously, by using elemental, proximate, and biochemical compositions of biomass and pyrolysis conditions as input variables. The predictions of Yield-char, N-char, and SSA-char were compared by using random forest (RF) and gradient boosting regression (GBR) models. GBR outperformed RF for most predictions. When input parameters included elemental and proximate compositions as well as pyrolysis conditions, the test R2 values for the single-target and multi-target GBR models were 0.90–0.95 except for the two-target prediction of Yield-char and SSA-char which had a test R2 of 0.84 and the three-target prediction model which had a test R2 of 0.81. As indicated by the Pearson correlation coefficient between variables and the feature importance of these GBR models, the top influencing factors toward predicting three targets were specified as follows: pyrolysis temperature, residence time, and fixed carbon for Yield-char; N and ash for N-char; ash and pyrolysis temperature for SSA-char. The effects of these parameters on three targets were different, but the trade-offs of these three were balanced during multi-target ML prediction and optimization. The optimum solutions were then experimentally verified, which opens a new way for designing smart biochar with target properties and oriented application potential. Graphical Abstract

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Section: Graphical Abstractmentioning

confidence: 99%

Machine learning predicting and engineering the yield, N content, and specific surface area of biochar derived from pyrolysis of biomass

Leng

Yang

Lei

et al. 2022

Biochar

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“…It learns from previous data to perform and optimise operations. Attempts have been made to adapt machine learning in biochar [16,17].…”

Section: Introductionmentioning

confidence: 99%

Biochar and Application of Machine Learning: A Review

Ukoba¹,

Jen²

2023

Biochar - Productive Technologies, Properties and Applications

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This study discusses biochar and machine learning application. Concept of biochar, machine learning and different machine learning algorithms used for predicting adsorption onto biochar were examined. Pyrolysis is used to produce biochar from organic materials. Agricultural wastes are burnt in regulated conditions to produce charcoal-like biochar using pyrolysis. Biochar plays a major role in removing heavy metals. Biochar is eco-friendly, inexpensive and effective. Increasing interest in biochar is due to stable carbon skeleton because of ease of sourcing the precursor feedstock and peculiar physicochemical. However, artificial intelligence is a process of training computers to mimic and perform duties human. Artificial intelligence aims to enable computers to solve human challenges and task like humans. A branch of artificial intelligence that teaches machine to perform and predict task using previous data is known as machine learning. It uses parameters called algorithms that convert previous data (input) to forecast new solution. Algorithms that have been used in biochar applications are examined. It was discovered that neural networks, eXtreme Gradient Boosting algorithm and random forest for constructing and evaluating the predictive models of adsorption onto biochar have all been used for biochar application. Machine learning prevents waste, reduces time and reduces cost. It also permits an interdisciplinary means of removing heavy metals.

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“…Therefore, other materials with high durability to organic decomposition are needed. Current literature suggests that biochar will be the best candidate for this purpose [38,44,45,[47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62]. As reported by [45,57], biochar is produced through pyrolysis or charring, causing their structure and composition to be more stable and durable in soil system.…”

Section: Introductionmentioning

confidence: 99%

The Potential Roles of Biochar in Restoring Heavy-Metal-Polluted Tropical Soils and Plant Growth

Salam¹

2023

Biochar - Productive Technologies, Properties and Applications

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Biochar shows interesting and environmentally useful properties, among which is its relatively high cation exchange capacity (CEC). High CEC may lower the easily plant-available heavy metals in soils due to the increase in the soil adsorption capacity resulted from biochar application. Quite a lot of current researches reveal that the extracted heavy metals in tropical soils particularly Cu and Zn were significantly lowered in the presence of biochar at 5−10 Mg ha−1. Heavy metal–contaminated tropical soils planted with corn plants (Zea mays L.) show significant decreases in Cu and Zn concentrations at moderate- and high-level addition of heavy metal–containing waste. The growth and dry masses of roots and shoot of corn plant improved immediately as a result of biochar amendment. Planting heavy metal–polluted soils treated with biochar with thorny amaranth (Amaranthus spinosus) also demonstrated a similar phenomenon.

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Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning

Cited by 167 publications

References 77 publications

Machine learning predicting and engineering the yield, N content, and specific surface area of biochar derived from pyrolysis of biomass

Machine learning predicting and engineering the yield, N content, and specific surface area of biochar derived from pyrolysis of biomass

Biochar and Application of Machine Learning: A Review

The Potential Roles of Biochar in Restoring Heavy-Metal-Polluted Tropical Soils and Plant Growth

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