☆ 4.8 Review

Machine learning for hydrothermal treatment of biomass: A review

BIORESOURCE TECHNOLOGY (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Energy & Fuels

Applications of machine learning in thermochemical conversion of biomass-A review

Muzammil Khan et al.

Summary: Thermochemical conversion of biomass has been recognized as a promising technique for producing renewable fuels. Machine learning has gained significant interest in optimizing and controlling these processes. This study provides a comprehensive review of state-of-the-art machine learning applications in various thermochemical conversion processes and highlights the advantages of hybrid models over traditional models.

FUEL (2023)

添加到收藏夹

Article Agricultural Engineering

Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar

Hailong Li et al.

Summary: Machine learning methods can effectively predict and optimize the properties of biochar, including yield, specific surface area (SSA), and total pore volume (TPV). Random forest and gradient boosting regression models were used to predict biochar properties, with the most important features being pyrolysis temperature, biomass ash, and volatile matter. Optimum schemes to obtain high SSA and TPV were experimentally verified, demonstrating the great potential of machine learning for biochar engineering.

BIORESOURCE TECHNOLOGY (2023)

添加到收藏夹

Article Agricultural Engineering

Integrated biorefinery processes for conversion of lignocellulosic biomass to value added materials: Paving a path towards circular economy

G. Velvizhi et al.

Summary: Lignocellulosic biomass serves as an effective and sustainable alternative for producing biofuels and bio-based products, but its degradation poses a significant challenge. Integrating multiple unit processes is essential to produce a wider range of bio-based products, which can enhance yield, reduce reaction time, and lower costs. Process integration can lead to various outcomes that guide towards the development of circular economy.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Review Agricultural Engineering

Recent advances of thermochemical conversion processes for biorefinery

Myung Won Seo et al.

Summary: The study shows that replacing biological processes with thermochemical conversion processes in biorefineries is feasible, easy, and effective. Challenges in thermochemical conversion processes were also identified, and the potential of artificial intelligence and machine learning for bio-oil and syngas production processes was highlighted.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Article Agricultural Engineering

Yield prediction of Thermal-dissolution based carbon enrichment treatment on biomass wastes through coupled model of artificial neural network and AdaBoost

Zhenzhong Hu et al.

Summary: The study demonstrated the efficiency of thermal-dissolution based carbon enrichment method in converting biomass wastes into high-quality carbon materials, and established the correlation between product yield and experimental parameters. The model combining Adaboost with artificial neural network algorithm showed outstanding predicting performance, especially with a coefficient of determination of 0.97 for predicting residue yield, and revealed the coupling effect of temperature and time. This research not only validates the correlation between selected experimental parameters and product yields, but also provides a quick and reliable method for material selection and condition optimization.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Article Agricultural Engineering

Machine learning based analysis of reaction phenomena in catalytic lignin depolymerization

Abraham Castro Garcia et al.

Summary: A predictive model was developed using the Random Forest regression method to study the impact of catalyst surface properties and lignin molecular weight on bio-oil yield, char yield, and reaction time in lignin solvolysis. The models showed high coefficients of determination and explained the feature importance for each case, with average pore diameter contributing 3% to reaction time.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Article Agricultural Engineering

Catalytic co-hydrothermal carbonization of food waste digestate and yard waste for energy application and nutrient recovery

Mingjing He et al.

Summary: Hydrothermal carbonization (HTC) is a promising alternative for valorizing food waste digestate (FWD) and avoiding disposal issues. Co-HTC of FWD with wet lignocellulosic biomass, such as wet yard waste (YW), and 0.5 M HCl shows superior attributes in terms of energy recovery and nutrient recovery from process water. However, attention should be paid to the carbon loss issue during the process.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Article Agricultural Engineering

Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes

Tossapon Katongtung et al.

Summary: Machine learning techniques were used to predict biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes. An extreme gradient boosting model showed the best prediction accuracy with temperature being the most influential feature. Feedstock characteristics contributed more than 55% to the model, and individual effects and interactions of the most important features were also examined for better understanding of the system.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Article Energy & Fuels

Biofuels production from pine needles via pyrolysis: Process parameters modeling and optimization through combined RSM and ANN based approach

Shubhi Gupta et al.

Summary: The study aimed to assess the pyrolysis potential of pine needles and determined that a combined approach of response surface methodology and artificial neural network techniques showed better capability in modeling the process. The ANN model demonstrated higher R-2 values and lower MSE values, indicating its superiority in predicting process yield compared to RSM modeling. Temperature was identified as the most predominant variable influencing product yield, with optimized conditions predicting maximum bio-oil production.

FUEL (2022)

添加到收藏夹

Article Automation & Control Systems

Selecting an appropriate supervised machine learning algorithm for predictive maintenance

Abdelfettah Ouadah et al.

Summary: This paper aims to improve the performance of predictive maintenance and achieve its goals by selecting the most suitable supervised machine learning algorithm. Three supervised machine learning algorithms, Random forest, Decision tree and KNN, were selected from a comparative study and tested on real-world and simulation scenarios. The results showed that Random forests and Decision trees had slightly different performance, with KNN being a better classification algorithm for extensive volumes of data and Random forest performing better in the case of small datasets.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2022)

添加到收藏夹

Review Engineering, Environmental

Elemental migration and transformation during hydrothermal liquefaction of biomass

Jianwen Lu et al.

Summary: This paper investigates the application of hydrothermal liquefaction (HTL) technology in various types of biomass, compares the biochemical composition and product distribution of biomass, and explores the transformation of organic and inorganic elements in biomass during the HTL process. The research findings contribute to the future industrialization of HTL and the upgrading of biocrude.

JOURNAL OF HAZARDOUS MATERIALS (2022)

添加到收藏夹

Article Agricultural Engineering

Effects of hydration parameters on chemical properties of biocrudes based on machine learning and experiments

Xinxing Zhou et al.

Summary: The study investigated the effects of temperature and biomass concentration on the chemical properties of biocrudes using machine learning. It was found that hydration temperature significantly influenced the elemental components, functional groups, molecular weight, and structures of biocrudes. The Support Vector Machine Linear Kernel method was identified as suitable for heat value prediction.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Article Thermodynamics

Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge

Oraleou Sangue Djandja et al.

Summary: This study proposes a data-driven model to predict the total phosphorous content in sewage sludge hydrochar. The results show that the total phosphorous content in the sludge, ash content, reaction temperature, reaction time, and pH of the feedwater have a positive effect on the total phosphorous content in the hydrochar, while the contribution of volatile matter in the sludge is mostly negative. There is no clear monotonic relationship between dry matter loading and the total phosphorous content in the hydrochar.

ENERGY (2022)

添加到收藏夹

Article Thermodynamics

A novel strategy to simultaneously enhance bio-oil yield and nutrient recovery in sequential hydrothermal liquefaction of high protein microalgae

Jie Chen et al.

Summary: This study aims to improve the bio-oil yield, energy recovery, and nutrient recovery of sequential hydrothermal liquefaction (SEQ-HTL) by integrating aqueous phase (AP) recirculation and fungal-microalgal cultivation. The results show that this novel strategy can enhance the economic feasibility of processing high-protein microalgae biomass using the SEQ-HTL technique and has promising industrial applications for microalgae.

ENERGY CONVERSION AND MANAGEMENT (2022)

添加到收藏夹

Article Energy & Fuels

Prediction and evaluation of fuel properties of hydrochar from waste solid biomass: Machine learning algorithm based on proposed PSO-NN model

Lin Mu et al.

Summary: Hydrothermal carbonization is an effective biomass pretreatment technology that converts high moisture biomass into carbon-rich hydrochar. This study used machine learning models to predict the fuel properties of hydrochar based on hydrothermal conditions and biomass characteristics. The results showed that the optimal PSO-NN model can accurately predict the properties of hydrochar, with a high R-2 value. The study also identified important factors such as carbon content, hydrothermal temperature, and nitrogen content that affect the properties of hydrochar.

FUEL (2022)

添加到收藏夹

Review Energy & Fuels

Hydrothermal liquefaction of biomass for bio-crude production: A review on feedstocks, chemical compositions, operating parameters, reaction kinetics, techno-economic study, and life cycle assessment

Ranjeet Kumar Mishra et al.

Summary: This review provides a comprehensive examination of the hydrothermal liquefaction (HTL) technology, focusing on feedstock processing, process parameters, product characteristics, techno-economic analysis, life cycle assessment, and potential commercialization opportunities.

FUEL (2022)

添加到收藏夹

Article Green & Sustainable Science & Technology

Machine learning methods for modelling the gasification and pyrolysis of biomass and waste

Simon Ascher et al.

Summary: The use of machine learning in modeling biomass and waste gasification/pyrolysis has grown rapidly over the past two decades. However, a systematic review of these approaches and findings is lacking. Artificial neural networks have been the most commonly used method due to their ability to learn highly non-linear relationships. Machine learning models offer advantages over existing models in incorporating non-numerical parameters and generating multiple solutions for various input parameters. There is a need to emphasize model interpretability.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2022)

添加到收藏夹

Article Engineering, Environmental

Online-learning-aided optimization and interpretation of sugar production from oil palm mesocarp fibers with analytics for industrial applications

Song Han Lee et al.

Summary: A study developed an online framework using orthogonal experimental design and machine learning algorithm to optimize critical conditions for the conversion of oil palm mesocarp fibers into fermentable sugar. The optimized strategy can significantly increase solid reduction and sugar conversion, leading to potential environmental and economic benefits.

RESOURCES CONSERVATION AND RECYCLING (2022)

添加到收藏夹

Article Energy & Fuels

A comprehensive artificial neural network model for gasification process prediction

Simon Ascher et al.

Summary: This study develops a machine learning method to predict the performance of gasification technology, reducing uncertainty in decision-making. The use of an artificial neural network allows for accurate predictions and broad applicability.

APPLIED ENERGY (2022)

添加到收藏夹

Article Agricultural Engineering

Machine learning predicting wastewater properties of the aqueous phase derived from hydrothermal treatment of biomass

Lijian Leng et al.

Summary: Hydrothermal treatment is a potential technology for producing biofuel from wet biomass, but the properties of the generated aqueous phase are not well-studied. In this study, machine learning models were developed to predict the properties of the aqueous phase based on biomass feedstock and hydrothermal treatment parameters. The results showed that the gradient boosting decision tree can accurately predict the properties, and the feature importance analysis provided new insights.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Article Engineering, Environmental

Accuracy of predictions made by machine learned models for biocrude yields obtained from hydrothermal liquefaction of organic wastes

Feng Cheng et al.

Summary: Hydrothermal liquefaction (HTL) has the potential to convert wet organic wastes into renewable fuels, but predicting biocrude yield is difficult. Data-driven methods, specifically Random Forest and eXtreme Gradient Boosting (XGBoost), provide accurate predictions of biocrude yield.

CHEMICAL ENGINEERING JOURNAL (2022)

添加到收藏夹

Article Engineering, Chemical

Hydrogen production optimization from sewage sludge supercritical gasification process using machine learning methods integrated with genetic algorithm

Zeeshan Ul Haq et al.

Summary: The study highlights the importance of understanding the mechanism and optimization methods for hydrogen production through supercritical water gasification. By integrating four machine learning methods and genetic algorithm, the study successfully predicts the hydrogen yield and identifies the influential parameters such as temperature, moisture content, and pressure.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2022)

添加到收藏夹

Article Energy & Fuels

Process optimization of biomass gasification with a Monte Carlo approach and random forest algorithm

Yi Fang et al.

ENERGY CONVERSION AND MANAGEMENT (2022)

添加到收藏夹

Review Engineering, Chemical

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Li-Tao Zhu et al.

Summary: Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep learning and physics-informed ML, is a valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental and computational methods. ML can provide flexible techniques to facilitate the conceptual development of new robust predictive models for multiphase flows and reactors by finding hidden pattern/information/mechanism in a data set.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2022)

添加到收藏夹

Article Multidisciplinary Sciences

Decoupled temperature and pressure hydrothermal synthesis of carbon sub-micron spheres from cellulose

Shijie Yu et al.

Summary: This study presents a hydrothermal system that allows independent control of temperature and pressure, enabling fast synthesis of carbon sub-micron spheres from cellulose. By decoupling temperature and pressure, the degradation temperature of cellulose is significantly reduced, leading to accelerated production of carbon sub-micron spheres and reduced carbon emissions.

NATURE COMMUNICATIONS (2022)

添加到收藏夹

Article Energy & Fuels

Forecast of glucose production from biomass wet torrefaction using statistical approach along with multivariate adaptive regression splines, neural network and decision tree

Wei-Hsin Chen et al.

Summary: This study uses artificial intelligence to predict glucose concentration for bioethanol production and optimizes the experiment using data analysis to find the best operating conditions. Results show that using a neural network for glucose prediction is more effective than using multivariate adaptive regression splines. For biomass pretreatment, sulfuric acid is the key parameter affecting glucose concentration.

APPLIED ENERGY (2022)

添加到收藏夹

Article Agricultural Engineering

Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass

Yize Li et al.

Summary: Biochar production through pyrolysis of organic waste has the potential to reduce dependence on conventional energy sources and mitigate global warming. In this study, data-driven machine learning models were developed to predict biochar yield and compositions, resulting in higher accuracy compared to existing algorithms.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Article Agricultural Engineering

Process water recirculation for catalytic hydrothermal carbonization of anaerobic digestate: Water-Energy-Nutrient Nexus

Mingjing He et al.

Summary: This study proposes a novel strategy to valorize food waste digestate by recirculating the process water in the acid-catalyzed hydrothermal carbonization process. The produced multifunctional hydrochar can be used as a high-quality solid fuel and slow-release fertilizer.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Article Agricultural Engineering

Plant-scale biogas production prediction based on multiple hybrid machine learning technique

Yi Zhang et al.

Summary: In this study, a hybrid extreme learning machine (ELM) model was proposed to improve prediction accuracy of full-scale biogas plants. The model successfully solved the issue of imbalanced data and achieved accurate predictions of biogas production using machine learning and optimization algorithms.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Article Agricultural Engineering

Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass

Lijian Leng et al.

Summary: In this study, machine learning method was successfully utilized to predict and control nitrogen-heterocycles, bio-oil yield, and nitrogen content in bio-oil, achieving good predictive performance. The interpretation and study of the prediction models provided insights into the formation mechanisms and behavior of nitrogen-heterocycles.

BIORESOURCE TECHNOLOGY (2022)

添加到收藏夹

Article Engineering, Environmental

Machine learning predicts and optimizes hydrothermal liquefaction of biomass

Alireza Shafizadeh et al.

Summary: This study applies machine learning to quantify and qualify hydrothermal liquefaction products based on biomass composition and reaction conditions. A universal machine learning model is developed using data patterns compiled from published literature, and Gaussian process regression is found to provide the highest accuracy. Optimal operating conditions and objective functions are developed to maximize biocrude oil yield and minimize byproducts. An easy-to-use software package is also developed to bypass costly and lengthy experiments.

CHEMICAL ENGINEERING JOURNAL (2022)

添加到收藏夹

Article Chemistry, Analytical

Investigation and prediction of co-pyrolysis between oily sludge and high-density polyethylene via in-situ DRIFTS, TGA, and artificial neural network

Zejian Ai et al.

Summary: This study investigated the pyrolysis behavior during co-pyrolysis of oily sludge (OS) and high-density polyethylene (HDPE) and validated the interaction and synergistic effect between the two feedstocks. Kinetic analysis provided the activation energy of co-pyrolysis, and two ANN models were established for prediction. This study offers new insights and strategies for pyrolysis experimental studies.

JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS (2022)

添加到收藏夹

Review Chemistry, Analytical

The influence of key reactions during hydrothermal carbonization of sewage sludge on aqueous phase properties: A review

Zhi-Xiang Xu et al.

Summary: This article discusses the application of hydrothermal carbonization (HTC) technology in treating sludge and generating solid fuel products, with a focus on the process and treatment of the aqueous phase. By discussing the influence of aqueous phase properties on the properties of hydrochar and the mechanism during HTC, new research directions and application prospects in this field are provided for further development.

JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS (2022)

添加到收藏夹

Review Green & Sustainable Science & Technology

A review of computational modeling techniques for wet waste valorization: Research trends and future perspectives

Jie Li et al.

Summary: This review highlights the application of computational modeling techniques, including computational fluid dynamics, process simulation, and machine learning, in waste conversion technologies. The authors summarize the advantages and disadvantages of these modeling techniques and provide case studies. They also emphasize the importance of hybrid frameworks to advance future wet waste valorization strategies.

JOURNAL OF CLEANER PRODUCTION (2022)

添加到收藏夹

Review Green & Sustainable Science & Technology

Synthesis of liquid biofuels from biomass by hydrothermal gasification: A critical review

Hossein Shahbeik et al.

Summary: This paper comprehensively reviews and critically discusses the process of producing syngas from biomass using the HTG process and its conversion into liquid biofuels. The paper details the basics and mechanisms of biomass HTG processing, analyzes the effects of main operating parameters on the performance of the HTG process, and evaluates the economic performance and environmental impacts of using the HTG-FT route to produce liquid biofuels. The paper concludes that effective conversion of biomass to syngas using the HTG process and its successful upgrading using the FT process offer a viable route for producing liquid biofuels.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2022)

添加到收藏夹

Article Environmental Sciences

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

Lijian Leng et al.

Summary: This study used machine learning models to predict and optimize the specific surface area, nitrogen content, and yield of biochar, based on the composition of biomass and pyrolysis conditions. The results showed that pyrolysis temperature, residence time, and fixed carbon were the most influential factors in predicting the targets. The findings provide insights for designing biochar with desired properties and targeted applications.

BIOCHAR (2022)

添加到收藏夹

Review Environmental Sciences

Waste-derived biochar for water pollution control and sustainable development

Mingjing He et al.

Summary: This review discusses the application of biochar in municipal wastewater treatment, industrial wastewater decontamination, and stormwater management. Biochar can be engineered to target specific contaminants in industrial wastewater treatment and enhance processes in municipal wastewater treatment and stormwater management. The scalability and commercialization of biochar production need to be investigated to maximize environmental, societal, and economic benefits.

NATURE REVIEWS EARTH & ENVIRONMENT (2022)

添加到收藏夹

Article Engineering, Environmental

An overview of sulfur-functional groups in biochar from pyrolysis of biomass

Lijian Leng et al.

Summary: Biochar is a solid material obtained from the pyrolytic carbonization of biomass in an oxygen-free/limited environment. Sulfur-containing biochar has a wide range of applications, such as adsorptive removal of pollutants (e.g., Hg, Cd, and Ni) and acting as a solid acid catalyst or as an electrode of Li-S battery. To date, many methods have been developed to strengthen the function of biochar by introducing sulfur-containing groups to promote the application and commercialization of biochar.

JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING (2022)

添加到收藏夹

Article Green & Sustainable Science & Technology

The Estimation of the Higher Heating Value of Biochar by Data-Driven Modeling

Jiefeng Chen et al.

Summary: This study developed predictive models for high heating value (HHV) of biochar using proximate and ultimate analysis. The study found strong correlations between HHV and indicators such as ash, fixed carbon, and carbon. Machine learning models showed better performance in predicting HHV compared to multi-linear regression models.

JOURNAL OF RENEWABLE MATERIALS (2022)

添加到收藏夹

Article Chemistry, Analytical

Physico-chemical properties prediction of hydrochar in macroalgae Sargassum horneri hydrothermal carbonisation

Sajjad Rasam et al.

Summary: This study evaluates and compares various machine learning methods in the hydrothermal carbonisation process of macroalgae Sargassum horneri, finding that SVM shows better performance. Additionally, the coupling of MLP and ANFIS with GOA optimization enhances the accuracy in estimating BET, HHV, and energy recovery parameters.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL ANALYTICAL CHEMISTRY (2021)

添加到收藏夹

Article Engineering, Chemical

Machine learning modeling and predictive control of nonlinear processes using noisy data

Zhe Wu et al.

Summary: This work focuses on machine learning modeling and predictive control of nonlinear processes with noisy data, utilizing LSTM networks with Monte Carlo dropout method and co-teaching training method for improved model performance. The proposed approaches were demonstrated using a chemical process example, showing open- and closed-loop performance under a Lyapunov-based model predictive controller.

AICHE JOURNAL (2021)

添加到收藏夹

Article Energy & Fuels

Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network

Hannah O. Kargbo et al.

Summary: The study developed an artificial neural network model to predict and optimize the gasification process using experimental data, reducing time and costs in development and testing. The model accurately predicts gas composition and yield to achieve high carbon conversion, high hydrogen yield, and low carbon dioxide levels in the output.

APPLIED ENERGY (2021)

添加到收藏夹

Article Energy & Fuels

A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification

Jie Li et al.

Summary: Recent advances in machine learning have led to an increased interest in its application in waste to energy conversion. A novel hybrid approach combining data-driven and mechanistic modeling was developed for hydrothermal gasification of wet waste, showing promising results in predicting and optimizing syngas yield. The study identified reaction temperature and feedstock solid content as significant factors in achieving high H-2 yield in syngas production.

APPLIED ENERGY (2021)

添加到收藏夹

Article Chemistry, Applied

Dietary fiber extracted from pomelo fruitlets promotes intestinal functions, both in vitro and in vivo

Huifan Liu et al.

Summary: Pomelo fruitlets have beneficial effects on glucose metabolism and blood sugar control due to their high dietary fiber content. The fibers also have the ability to decrease total cholesterol content and influence gut microbiota composition.

CARBOHYDRATE POLYMERS (2021)

添加到收藏夹

Article Agricultural Engineering

Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics

Qinghui Tang et al.

Summary: This study aimed to predict pyrolytic gas yield and compositions based on pyrolysis conditions and biomass characteristics using machine learning algorithms and feature reduction. The results showed that six features were sufficient for accurate yield prediction, while compositions only required three. The study revealed the higher relative contribution of pyrolysis conditions to yield, CO2, and H2 compared to biomass characteristics.

BIORESOURCE TECHNOLOGY (2021)

添加到收藏夹

Article Engineering, Environmental

Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening

Jie Li et al.

Summary: A unified machine learning framework was developed to predict syngas composition from wet organic wastes through supercritical water gasification, offering a more efficient method for catalyst screening and experimental optimization.

CHEMICAL ENGINEERING JOURNAL (2021)

添加到收藏夹

Article Green & Sustainable Science & Technology

Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: Application of machine learning on waste-to-resource

Jie Li et al.

Summary: Hydrothermal carbonization is a promising technology for recovering valuable resources from high-moisture wastes, while machine learning tools can accelerate experiments and improve product preparation efficiency.

JOURNAL OF CLEANER PRODUCTION (2021)

添加到收藏夹

Article Thermodynamics

A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage

Oraleou Sangue Djandja et al.

Summary: A neural network model was successfully used to predict the nitrogen content of hydrochar, with sewage sludge-N identified as the main contributor, predicting a conversion rate of 40-70%.

ENERGY (2021)

添加到收藏夹

Article Thermodynamics

Prediction of three-phase product distribution and bio-oil heating value of biomass fast pyrolysis based on machine learning

Erwei Leng et al.

Summary: By establishing regression prediction models, this study shows that the random forest algorithm is suitable for predicting three-phase product distribution and bio-oil heating value, with significant impacts from factors such as pyrolysis temperature, carbon, and hydrogen content.

ENERGY (2021)

添加到收藏夹

Article Engineering, Environmental

Machine learning for the selection of carbon-based materials for tetracycline and sulfamethoxazole adsorption

Xinzhe Zhu et al.

Summary: This study developed machine learning models to predict tetracycline and sulfamethoxazole adsorption on carbon-based materials, with random forest outperforming other methods. Specific surface area was found to play a critical role in adsorption, while other material properties had varying influences.

CHEMICAL ENGINEERING JOURNAL (2021)

添加到收藏夹

Article Energy & Fuels

Effect of reaction temperature on the conversion of algal biomass to bio-oil and biochar through pyrolysis and hydrothermal liquefaction

Kathirvel Brindhadevi et al.

Summary: This review focuses on the conversion of various microalgal and cyanobacterial biomasses into bio-oil and solid char products through pyrolysis and hydrothermal liquefaction. The impact of reaction temperature on the quantity and quality of bio-oil and solid char obtained from pyrolysis and hydrothermal liquefaction is comprehensively discussed in the review, providing opportunities for further research in this area.

FUEL (2021)

添加到收藏夹

Article Chemistry, Physical

Gasification of food waste in supercritical water: An innovative synthesis gas composition prediction model based on Artificial Neural Networks

Shribalaji Shenbagaraj et al.

Summary: This study developed FFBPNN models based artificial neural network to predict the compositions and yields of synthesis gas in supercritical water gasification. The models showed good prediction accuracy and performance when trained and tested with experimental datasets.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2021)

添加到收藏夹

Article Chemistry, Analytical

The comparison study of multiple biochar stability assessment methods

Jiefeng Chen et al.

Summary: Biochar produced from thermochemical processing biomass is being developed as an effective way to mitigate climate change, and its stability is crucial for its potential in climate change mitigation. There is currently no standard method available for assessing biochar stability, and results from different methods show significant differences. Research findings show that the volatile matter/(fixed carbon + volatile matter) has high correlations with other indicators, aiding in the evaluation of biochar stability.

JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS (2021)

添加到收藏夹

Article Chemistry, Physical

Molecular dynamic investigation on nitrogen migration during hydrogen production by indole gasification in supercritical water

Shanke Liu et al.

Summary: This study investigated the nitrogen migration mechanism during supercritical water gasification using indole as a model compound. It revealed the competitive relationship among different ring-opening ways and the promoting effect of supercritical water on the separation of nitrogen atoms from the carbon skeleton. The study provided theoretical support for nitrogen regulation in biomass gasification.

JOURNAL OF MOLECULAR LIQUIDS (2021)

添加到收藏夹

Article Engineering, Chemical

Improving pressure drops estimation of fresh cemented paste backfill slurry using a hybrid machine learning method

Chongchong Qi et al.

Summary: Estimation of pressure drops for fresh cemented paste backfill slurry using a hybrid machine learning method combining artificial neural network and differential evolution has shown significant improvement in estimation performance. The most influential variables on pressure drops were found to be solids content, inlet velocity, SiO2, CaO, and Fe2O3.

MINERALS ENGINEERING (2021)

添加到收藏夹

Review Environmental Sciences

A review on nitrogen transformation in hydrochar during hydrothermal carbonization of biomass containing nitrogen

Lijian Leng et al.

Summary: Biomass is a renewable and sustainable resource used for the production of fuels, chemicals, and materials. The manipulation of nitrogen in biomass during processing can lead to reduced or enriched nitrogen content in the resulting hydrochar. Further research is needed to fully understand the transformation of nitrogen during hydrothermal carbonization.

SCIENCE OF THE TOTAL ENVIRONMENT (2021)

添加到收藏夹

Article Agricultural Engineering

A comparative study of machine learning methods for bio-oil yield prediction-A genetic algorithm-based features selection

Zahid Ullah et al.

Summary: A novel genetic algorithm-based feature selection approach was used in this study to predict bio-oil yield using four different machine learning methods. The findings suggested that Random Forest model performed the best and was able to reliably predict bio-oil yield. The research provided new insights into the pyrolysis process of biomass and offered a new approach to improve bio-oil yield.

BIORESOURCE TECHNOLOGY (2021)

添加到收藏夹

Article Energy & Fuels

Effect of biomass type and pyrolysis temperature on nitrogen in biochar, and the comparison with hydrochar

Siyu Xu et al.

Summary: This study investigated the impacts of biomass type and pyrolysis temperature on the physical and chemical properties of biochar, especially focusing on nitrogen content and composition. The results showed a positive correlation between biochar nitrogen content and biomass nitrogen content, and a negative correlation with pyrolysis temperature. Pyrolysis process converted biomass nitrogen mainly existed as protein-N and inorganic-N to more stable structures in biochar, which had more kinds of nitrogen-containing species, more aromatic structures, and higher stability compared with hydrochar.

FUEL (2021)

添加到收藏夹

Review Green & Sustainable Science & Technology

Valorization of the aqueous phase produced from wet and dry thermochemical processing biomass: A review

Lijian Leng et al.

Summary: This review compares the production and characteristics of aqueous phase (AP) generated after thermochemical processing, and discusses various pathways for converting AP into energy and chemicals, including direct utilization, separation purification, and conversion. Strategies for promoting future research are proposed to advance the industrialization of biomass processing technologies.

JOURNAL OF CLEANER PRODUCTION (2021)

添加到收藏夹

Article Environmental Sciences

An overview on engineering the surface area and porosity of biochar

Lijian Leng et al.

Summary: Surface area and porosity are crucial physical properties of biochar that significantly impact its applications, with biomass composition and pyrolysis temperature being major influencing factors. Activation, particularly through chemical means, is the most effective way to enhance biochar surface area and porosity, while other treatment methods like carbonaceous materials coating and ball milling can also contribute to improvement. Future research should focus on developing treatment technologies to simultaneously enhance the functionality and porous structure of biochar.

SCIENCE OF THE TOTAL ENVIRONMENT (2021)

添加到收藏夹

Review Energy & Fuels

Modeling of thermochemical conversion of waste biomass - a comprehensive review

Sinhara M. H. D. Perera et al.

Summary: Thermochemical processes are perceived to be more efficient in converting waste biomass to energy and value-added products compared to biochemical processes. However, the complexity of these processes makes reactor and process modeling challenging. The success of commercialization relies on optimized designs achieved through modeling and simulation. Models developed for specific applications need further exploration to understand their applicability, limitations, accuracy, and special features.

BIOFUEL RESEARCH JOURNAL-BRJ (2021)

添加到收藏夹

Article Agricultural Engineering

Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae

Weijin Zhang et al.

Summary: Machine learning algorithms were applied to predict and optimize bio-oil production with algae compositions and HTL conditions as inputs. Gradient boosting regression showed better performance than random forest for prediction tasks. The importance of operating conditions was higher than algae characteristics for the three targets according to model-based interpretation.

BIORESOURCE TECHNOLOGY (2021)

添加到收藏夹

Article Chemistry, Physical

New insights into hydrogen uptake on porous carbon materials via explainable machine learning

Muhammad Irfan Maulana Kusdhany et al.

Summary: The study found that pressure and BET surface area are the two strongest predictors of excess hydrogen uptake in porous carbon materials, with oxygen content also showing a positive correlation. Additionally, ultramicropores are more positively correlated with excess uptake than micropores, although the effect is relatively small compared to other factors such as BET surface area and total pore volume.

CARBON (2021)

添加到收藏夹

Review Environmental Sciences

Wet organic waste treatment via hydrothermal processing: A critical review

Mojtaba Hedayati Marzbali et al.

Summary: This paper reviews recent literature on hydrothermal carbonization, liquefaction and supercritical water gasification of lignocellulosic biomass and algae, as well as the production and applications of hydrochar, bio-oil, and synthesis gas. It also critically examines the comprehensive review of hydrothermal treatment for wet wastes, including municipal solid waste, food waste, sewage sludge, and algae, while establishing a comparison of different treatment options. Additionally, the study delves into the role of catalysts and the synthesis of catalysts using hydrothermal treatment of biomass, and summarizes findings on modeling works and techno-economic assessments in this area for the first time.

CHEMOSPHERE (2021)

添加到收藏夹

Article Energy & Fuels

Leaching Char with Acidic Aqueous Phase from Biomass Pyrolysis: Removal of Alkali and Alkaline-Earth Metallic Species and Uptakes of Water-Soluble Organics

Tianlong Liu et al.

Summary: The study introduces a new method of leaching char with pyrolytic AP for the removal of AAEM species and the uptake of water-soluble organics. Experimental results show that the majority of K, Mg, and Ca are leached within the first hour, with nonacidic compounds in the pyrolytic AP slightly hindering the leaching of K but having no impacts on that of Mg and Ca.

ENERGY & FUELS (2021)

添加到收藏夹

Article Green & Sustainable Science & Technology

Interpretable machine learning for predicting and evaluating hydrogen production via supercritical water gasification of biomass

Sheng Zhao et al.

Summary: This study established four machine learning models to predict hydrogen production via SCWG of biomass, interpreted the inner workings of the optimal model and evaluated the performance of SCWG. The results suggested that the random forest (RF) model outperformed other models for predicting H2 yield (R2 = 0.9782).

JOURNAL OF CLEANER PRODUCTION (2021)

添加到收藏夹

Article Thermodynamics

Predictive modeling of biomass gasification with machine learning-based regression methods

Furkan Elmaz et al.

ENERGY (2020)

添加到收藏夹

Article Engineering, Environmental

Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials

Gabriel Sigmund et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2020)

添加到收藏夹

Review Thermodynamics

Valorization of hydrothermal liquefaction aqueous phase: pathways towards commercial viability

Jamison Watson et al.

PROGRESS IN ENERGY AND COMBUSTION SCIENCE (2020)

添加到收藏夹

Article Energy & Fuels

Classification of solid fuels with machine learning

Furkan Elmaz et al.

FUEL (2020)

添加到收藏夹

Review Environmental Sciences

Algae as potential feedstock for the production of biofuels and value-added products: Opportunities and challenges

Manish Kumar et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2020)

添加到收藏夹

Review Biotechnology & Applied Microbiology

Algal biorefinery to value-added products by using combined processes based on thermochemical conversion: A review

Liangliang Fan et al.

ALGAL RESEARCH-BIOMASS BIOFUELS AND BIOPRODUCTS (2020)

添加到收藏夹

Article Engineering, Environmental

Reaction kinetics and characterisation of species in renewable crude from hydrothermal liquefaction of monomers to represent organic fractions of biomass feedstocks

Reem Obeid et al.

CHEMICAL ENGINEERING JOURNAL (2020)

添加到收藏夹

Review Environmental Sciences

Biochar technology in wastewater treatment: A critical review

Wei Xiang et al.

CHEMOSPHERE (2020)

添加到收藏夹

Article Energy & Fuels

Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning

Jie Li et al.

APPLIED ENERGY (2020)

添加到收藏夹

Review Computer Science, Information Systems

Julia language in machine learning: Algorithms, applications, and open issues

Kaifeng Gao et al.

COMPUTER SCIENCE REVIEW (2020)

添加到收藏夹

Article Thermodynamics

A deep learning approach for prediction of syngas lower heating value from CFB gasifier in Aspen plus®

Furkan Kartal et al.

ENERGY (2020)

添加到收藏夹

Review Agricultural Engineering

The effect of aqueous phase recirculation on hydrothermal liquefaction/carbonization of biomass: A review

Songqi Leng et al.

BIORESOURCE TECHNOLOGY (2020)

添加到收藏夹

Review Engineering, Environmental

Nitrogen in bio-oil produced from hydrothermal liquefaction of biomass: A review

Lijian Leng et al.

CHEMICAL ENGINEERING JOURNAL (2020)

添加到收藏夹

Review Environmental Sciences

Bioenergy recovery from wastewater produced by hydrothermal processing biomass: Progress, challenges, and opportunities

Lijian Leng et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

On hyperparameter optimization of machine learning algorithms: Theory and practice

Li Yang et al.

NEUROCOMPUTING (2020)

添加到收藏夹

Article Agricultural Engineering

Experimental and model enhancement of food waste hydrothermal liquefaction with combined effects of biochemical composition and reaction conditions

Aersi Aierzhati et al.

BIORESOURCE TECHNOLOGY (2019)

添加到收藏夹

Article Energy & Fuels

Molecular Structure and Formation Mechanism of Hydrochar from Hydrothermal Carbonization of Carbohydrates

Ning Shi et al.

ENERGY & FUELS (2019)

添加到收藏夹

Article Engineering, Environmental

The application of machine learning methods for prediction of metal sorption onto biochars

Xinzhe Zhu et al.

JOURNAL OF HAZARDOUS MATERIALS (2019)

添加到收藏夹

Article Agricultural Engineering

Correlations between the physicochemical properties of hydrochar and specific components of waste lettuce: Influence of moisture, carbohydrates, proteins and lipids

Yang Li et al.

BIORESOURCE TECHNOLOGY (2019)

添加到收藏夹

Review Green & Sustainable Science & Technology

A review on the hydrothermal processing of microalgal biomass to bio-oil - Knowledge gaps and recent advances

Thangavel Mathimani et al.

JOURNAL OF CLEANER PRODUCTION (2019)

添加到收藏夹

Review Environmental Sciences

Biochar stability assessment methods: A review

Lijian Leng et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2019)

添加到收藏夹

Article Agricultural Engineering

Quantifying the sensitivity of feedstock properties and process conditions on hydrochar yield, carbon content, and energy content

Liang Li et al.

BIORESOURCE TECHNOLOGY (2018)

添加到收藏夹

Article Thermodynamics

Hydrothermal carbonization of Miscanthus x giganteus: Structural and fuel properties of hydrochars and organic profile with the ecotoxicological assessment of the liquid phase

Marija Mihajlovic et al.

ENERGY CONVERSION AND MANAGEMENT (2018)

添加到收藏夹

Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

添加到收藏夹

Article Chemistry, Multidisciplinary

Synergistic and Antagonistic Interactions during Hydrothermal Liquefaction of Soybean Oil, Soy Protein, Cellulose, Xylose, and Lignin

Jianwen Lu et al.

ACS SUSTAINABLE CHEMISTRY & ENGINEERING (2018)

添加到收藏夹

Article Thermodynamics

Deep learning based monitoring of furnace combustion state and measurement of heat release rate

Zhenyu Wang et al.

ENERGY (2017)

添加到收藏夹

Article Thermodynamics

Hydrothermal carbonisation of sewage sludge for char production with different waste biomass: Effects of reaction temperature and energy recycling

Yunbo Zhai et al.

ENERGY (2017)

添加到收藏夹

Review Green & Sustainable Science & Technology

Lignocellulosic biomass pyrolysis: A review of product properties and effects of pyrolysis parameters

Tao Kan et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2016)

添加到收藏夹

Article Statistics & Probability

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

Alex Goldstein et al.

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS (2015)

添加到收藏夹

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

添加到收藏夹

Review Multidisciplinary Sciences

Machine learning: Trends, perspectives, and prospects

M. I. Jordan et al.

SCIENCE (2015)

添加到收藏夹

Article Agricultural Engineering

Effect of operating conditions on yield and quality of biocrude during hydrothermal liquefaction of halophytic microalga Tetraselmis sp.

B. E. Eboibi et al.

BIORESOURCE TECHNOLOGY (2014)

添加到收藏夹

Article Engineering, Chemical

Hydrothermal Carbonization of Biomass: Major Organic Components of the Aqueous Phase

Roland Becker et al.

CHEMICAL ENGINEERING & TECHNOLOGY (2014)

添加到收藏夹

Review Green & Sustainable Science & Technology

Hydrothermal gasification of sewage sludge and model compounds for renewable hydrogen production: A review

Chao He et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2014)

添加到收藏夹

Proceedings Paper Construction & Building Technology

Simulated Annealing Algorithm Improved BP Learning Algorithm

Yingjian Lin et al.

APPLIED SCIENCE, MATERIALS SCIENCE AND INFORMATION TECHNOLOGIES IN INDUSTRY (2014)

添加到收藏夹

Review Biochemistry & Molecular Biology

Hydrothermal conversion of biomass to fuels and energetic materials

Andrea Kruse et al.

CURRENT OPINION IN CHEMICAL BIOLOGY (2013)

添加到收藏夹

Review Energy & Fuels

An overview of the composition and application of biomass ash. Part 2. Potential utilisation, technological and ecological advantages and challenges

Stanislav V. Vassilev et al.

FUEL (2013)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Gradient boosting machines, a tutorial

Alexey Natekin et al.

FRONTIERS IN NEUROROBOTICS (2013)

添加到收藏夹

Review Green & Sustainable Science & Technology

Bio-oil production and upgrading research: A review

Shuangning Xiu et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2012)

添加到收藏夹

Review Multidisciplinary Sciences

Valorization of Biomass: Deriving More Value from Waste

Christopher O. Tuck et al.

SCIENCE (2012)

添加到收藏夹

Article Agricultural Engineering

Alkaline hydrothermal conversion of cellulose to bio-oil: Influence of alkalinity on reaction pathway change

Sudong Yin et al.

BIORESOURCE TECHNOLOGY (2011)

添加到收藏夹

Article Energy & Fuels

Hydrothermal Carbonization (HTC) of Lignocellulosic Biomass

S. Kent Hoekman et al.

ENERGY & FUELS (2011)

添加到收藏夹

Review Biotechnology & Applied Microbiology

Hydrothermal carbonization of biomass: A summary and discussion of chemical mechanisms for process engineering

Axel Funke et al.

BIOFUELS BIOPRODUCTS & BIOREFINING-BIOFPR (2010)

添加到收藏夹

Article Agricultural Engineering

Energy recovery from secondary pulp/paper-mill sludge and sewage sludge with supercritical water treatment

Linghong Zhang et al.

BIORESOURCE TECHNOLOGY (2010)

添加到收藏夹

Article Computer Science, Information Systems

GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

Adriano L. I. Oliveira et al.

INFORMATION AND SOFTWARE TECHNOLOGY (2010)

添加到收藏夹

Review Green & Sustainable Science & Technology

Review of catalytic supercritical water gasification for hydrogen production from biomass

Y. Guo et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2010)

添加到收藏夹

Review Chemistry, Physical

Hydrothermal biomass gasification

Andrea Kruse

JOURNAL OF SUPERCRITICAL FLUIDS (2009)

添加到收藏夹

Review Chemistry, Multidisciplinary

Thermochemical biofuel production in hydrothermal media: A review of sub- and supercritical water technologies

Andrew A. Peterson et al.

ENERGY & ENVIRONMENTAL SCIENCE (2008)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.