Machine learning prediction of bio-oil characteristics quantitatively relating to biomass compositions and pyrolysis conditions

Zhang, Tonghuan; Cao, Danyang; Feng, Xin; Zhu, Jiahua; Lü, Xiaohua; Mu, Liwen; Qian, Hongliang

doi:10.1016/j.fuel.2021.122812

Cited by 59 publications

(11 citation statements)

References 58 publications

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“…In separate studies, pyrolysis models using secondary data with similar input variables were developed applying RFR and SVR for agricultural and forestry residue. − In a comparative study, RFR performed better than SVR for wet feedstocks (animal waste, organic municipal solid waste and sewage sludge) where secondary data was used to develop both models . Another study contrasted performances among different ML methods developed using secondary data of operational parameters and properties of the dry feedstocks and found that RFR provided better predictions than SVR, ANN, and XGBoost .…”

Section: Applications Of Data Science In Rrcc From Organic Waste Streamsmentioning

confidence: 99%

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Zaki,

Rowles,

Adjeroh

et al. 2023

ACS EST Eng.

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Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002−2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to costeffectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

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Section: Applications Of Data Science In Rrcc From Organic Waste Streamsmentioning

confidence: 99%

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Zaki,

Rowles,

Adjeroh

et al. 2023

ACS EST Eng.

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“…More and more artificial intelligence (AI) methods are being introduced into traditional research, and some remarkable achievements have been achieved. − Among the rest, machine learning methods are the core of AI, which mainly uses the selected model to learn the input data, extracts valuable features and information from complex data sets, and summarizes reasonable change trends for data prediction. It is a method that can readjust the parameters or structures in the model after comparing the bias between the actual and predicted values to increase the accuracy and dependability of the prediction .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction of the Yield and BET Area of Activated Carbon Quantitatively Relating to Biomass Compositions and Operating Conditions

Wang

Jiang

et al. 2023

Ind. Eng. Chem. Res.

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Although activated carbon’s yield (quantity index) and BET area (quality index) are crucial to its application, the two indexes must be accurately predicted. Herein, biomass compositions (ultimate analysis, proximate analysis, and chemical analysis), operating conditions (mass ratio, carbonization time, carbonization temperature, activation time, and activation temperature) under physical activation (CO2 and steam), and chemical activation (H3PO4, KOH, and ZnCl2) conditions as input parameters were used to predict the two indexes of activated carbon simultaneously through the random forest (RF) method for the first time. In total, the samples (>1500 data) identified from experiments in the literature were used to train, validate, and test the RF models. The results show that the model built on ultimate analysis is more suitable for predicting the BET area and yield of activated carbon prepared by both physical and chemical activation. Therein, the R 2 values of activated carbon’s yield and BET area under the H3PO4 activation condition were the highest, which were 0.98 and 0.97, respectively. In addition, the influence of various factors and interactions on the target variables was analyzed. The results show that the hydrogen content has a large impact on the yield under physical activation conditions, and the mass ratio has the most contribution to the BET area under chemical activation conditions. This study affords achievable hints to the quantitative prediction of porous materials affected by multiple compositions of raw materials and different operating conditions.

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“…To avoid this drawback, different solid acid catalysts have been developed, such as zeolites, sulfonic resins, heteropolyacids, and carbonaceous materials [18][19][20][21][22]. Bio-oil is produced by the flash pyrolysis of lignocellulosic biomass [23,24].…”

Section: Introductionmentioning

confidence: 99%

Acetalization of β-citronellal over a renewable carbon catalyst obtained from bio-oil sulfonation: a green route to obtain valuable feedstocks

Silva

Lopes

Ballotin

et al. 2023

Preprint

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In this work, an efficient renewable carbon catalyst obtained by sulfonation of bio-oil was used for the first time to produce terpene acetals by condensation of β-citronellal with alkyl alcohols (methyl, ethyl, propyl, butyl, isopropyl) at room temperature. The catalyst was obtained by a simple bio-oil reaction with sulfuric acid at 393 K for different times (15, 30, 60, 90, 120, and 240 min). Potentiometric titrations and FT-IR analyses showed the formation of very strong acid sites, likely -SO3H, with surface concentrations reaching 0.35 and 0.50 mmol/ gcatal, for the catalyst obtained with reactions 120 and 240 min. These catalysts were used in the β-citronellal condensation with alkyl alcohols to acetals with conversion and selectivity higher than 0 %, at room temperature using 3.96 wt.% catalyst, and 2 h reaction. The effects of temperature, time, different alcohols, and catalyst load were evaluated. The catalyst can be recovered and reused four times without significant activity loss.

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Machine learning prediction of bio-oil characteristics quantitatively relating to biomass compositions and pyrolysis conditions

Cited by 59 publications

References 58 publications

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Machine Learning Prediction of the Yield and BET Area of Activated Carbon Quantitatively Relating to Biomass Compositions and Operating Conditions

Acetalization of β-citronellal over a renewable carbon catalyst obtained from bio-oil sulfonation: a green route to obtain valuable feedstocks

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