Article
Biotechnology & Applied Microbiology
Iara Barbosa Magalhaes, Alexia Saleme Aona de Paula Pereira, Thiago Abrantes Silva, Natalia dos Santos Renato
Summary: This study proposed equations to predict the higher heating value (HHV) of microalgae biomass based on proximate and ultimate analysis. The equation combining these analyses showed the highest statistical fit and accuracy.
ALGAL RESEARCH-BIOMASS BIOFUELS AND BIOPRODUCTS
(2022)
Article
Agricultural Engineering
Yurany Camacho Ardila, Jaiver Efren Jaimes Figueroa, Maria Regina Wolf Maciel
Summary: The higher heating value (HHV) and elemental composition are important properties of biomass, and their prediction using proximate and ultimate analysis is accurate.
INDUSTRIAL CROPS AND PRODUCTS
(2024)
Article
Thermodynamics
Xiaoling Chen, Yongxing Zhang, Baoshen Xu, Yifan Li
Summary: This study aims to develop correlations based on proximate and ultimate analysis to predict the HHV of oily sludge accurately and easily. The correlations based on ultimate analysis show higher accuracy compared to those based on proximate analysis, especially for samples with higher HHV intended for use as a fuel source. Further validation with samples collected from a local oilfield yielded similar results to the regression analysis.
Article
Energy & Fuels
Satyajit Pattanayak, Chanchal Loha, Lalhmingsanga Hauchhum, Lalsangzela Sailo
Summary: India has abundant bamboo biomass resources, especially in the north-east states. Three artificial neural network models have been developed to accurately predict the higher heating values of bamboo biomass, with the model based on combined proximate-ultimate analysis showing the best accuracy. The ANN models outperform correlation-based models in predicting the actual HHVs with an average error of 2.49%.
BIOMASS CONVERSION AND BIOREFINERY
(2021)
Article
Energy & Fuels
Fatih Gulec, Direnc Pekaslan, Orla Williams, Edward Lester
Summary: This study applies artificial neural network (ANN) models to predict the higher heating value (HHV) of biomass feedstocks and comprehensively analyzes the factors that affect the prediction, including activation functions, algorithms, hidden layers, dataset, and randomization. The results show that using ANN models trained by the combination of ultimate-proximate analyses (UAPA) datasets, with sigmoidal activation functions (tansig and logsig), and with Levenberg-Marquardt (lm) or Bayesian Regularization (br) algorithms as training activation functions, can provide accurate HHV prediction.
Article
Energy & Fuels
Yury Maksimuk, Zoya Antonava, Vladimir Krouk, Alina Korsakova, Vera Kursevich
Summary: Experimental determination of HHV, as well as various carbon, hydrogen, sulphur, nitrogen, chlorine, ash, lignin and extractives contents in different samples, was conducted. Recommendations for HHV of cellulose, hemicellulose, lignin, and extractives for different types of biomass were made. Through analysis, two equations were proposed for predicting biomass HHV based on lignin and carbon contents with an error no worse than 2.7%.
Article
Agricultural Engineering
Augusto C. L. de Oliveira, Natalia dos S. Renato, Rogerio S. Ribeiro, Luna L. H. Ildefonso
Summary: This study aims to estimate the higher heating value (HHV) of biomass based on its proximate and ultimate chemical analysis. A database with 142 samples was created, and 14 formulas were tested. The samples were classified using the k-means algorithm, and regression models were developed for different composition types. The proposed method shows potential in optimizing and reducing costs for determining the HHV of biomass.
ENGENHARIA AGRICOLA
(2023)
Article
Computer Science, Artificial Intelligence
Olga Jaksic, Zoran Jaksic, Koushik Guha, Ana G. Silva, Naushad Manzoor Laskar
Summary: A new set of software tools is introduced for predicting the higher heating values of various biomass species. Twelve different algorithms for training artificial neural networks are compared and analyzed, and their results in predicting the higher heating values are evaluated. The study shows that several algorithms have a good correlation with the measured values of higher heating values.
Article
Energy & Fuels
Richa Dubey, Velmathi Guruviah
Summary: This study proposes a new approach using machine learning models to optimize the higher heating value of agricultural biomass. The models show high accuracy in prediction and the validity of the approach is verified.
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
(2022)
Article
Green & Sustainable Science & Technology
Abolfazl Sajadi Noushabadi, Amir Dashti, Farhad Ahmadijokani, Jinguang Hu, Amir H. Mohammadi
Summary: The utilization of biomass fuels as a potential renewable energy is favored for its advantages in sustainable economy and environment. Various accurate correlation methods like multivariate nonlinear regression and genetic algorithm-radial basis function are effective in estimating the higher heating value (HHV) of biomass fuels. The study provides reliable results that can be used by researchers to design and optimize biomass combustion systems.
Article
Multidisciplinary Sciences
Richa Dubey, Velmathi Guruviah
Summary: In this study, machine learning approaches were used to estimate the heating value of biochars, and a combinational method was proposed to improve prediction accuracy. Experimental results showed that pairing Bagging and CVR meta-classifiers with the RF model can achieve good prediction performance.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2023)
Article
Energy & Fuels
Seyed Hashem Samadi, Barat Ghobadian, Mohsen Nosrati
Summary: In this research, a machine learning tool based on gradient boosted regression trees (GBRT) was used to predict the HHV of biomass. The developed model showed high precision in HHV prediction compared to previous models in the literature.
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
(2021)
Article
Energy & Fuels
Phuris Khunphakdee, Krittin Korkerd, Chaiwat Soanuch, Benjapon Chalermsinsuwan
Summary: In this study, data-driven correlation models were developed to assess the higher heating value (HHV) of biomass and waste solid fuels. Artificial neural networks (ANN) were used to connect inputs and outputs, and the models were tested using large datasets. The results showed high accuracy in predicting the HHV, and the findings are important for the design and operation of thermochemical conversion processes.
Article
Energy & Fuels
Na Xu, Zhiwei Wang, Yuchen Dai, Qiang Li, Wei Zhu, Ru Wang, Robert B. Finkelman
Summary: A gradient boosting regression tree model was established to predict the higher heating value of coal based on the correlation between proximate analysis and higher heating value. Experimental results showed that the model achieved high accuracy compared to existing methods, with coefficients of determination of 0.9862 and 0.9541 for two databases.
INTERNATIONAL JOURNAL OF COAL GEOLOGY
(2023)
Article
Engineering, Environmental
Jatuporn Parnthong, Supaporn Nualyai, Wasawat Kraithong, Anan Jiratanachotikul, Pongtanawat Khemthong, Kajornsak Faungnawakij, Sanchai Kuboon
Summary: This study measured the higher heating value (HHV) of sugarcane leaf and giant leucaena wood after hydrothermal carbonization (HTC) and proposed empirical correlations based on ultimate and proximate analysis to predict HHV during the process. The results showed that the accuracy of the HHV prediction was influenced by the types of biomass feedstock, HTC operating conditions, compositions of hydrochar, and the scopes of ultimate and proximate variables.
JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING
(2022)
Article
Agricultural Engineering
John O'Loughlin, Kevin McDonnell, John Finnan
BIOMASS & BIOENERGY
(2018)
Article
Agricultural Engineering
Gary D. Gillespie, Aoife A. Gowen, John M. Finnan, John P. Carroll, Damien J. Farrelly, Kevin P. McDonnell
BIOSYSTEMS ENGINEERING
(2019)
Article
Energy & Fuels
Gary D. Gillespie, Aoife A. Gowen, John M. Finnan, John P. Carroll, Damien J. Farrelly, Kevin P. McDonnell
Article
Food Science & Technology
Mukaddes Kilic Bayraktar, Niamh B. Harbourne, Colette C. Fagan
LWT-FOOD SCIENCE AND TECHNOLOGY
(2019)
Article
Energy & Fuels
Vinay Virupaksha, Mary Harty, Kevin McDonnell
Article
Energy & Fuels
Kevin McDonnell, Levente Molnar, Mary Harty, Fionnuala Murphy
Article
Agricultural Engineering
Gary D. Gillespie, Kevin P. McDonnell
BIOSYSTEMS ENGINEERING
(2020)
Article
Environmental Sciences
Ciara Beausang, Kevin McDonnell, Fionnuala Murphy
SCIENCE OF THE TOTAL ENVIRONMENT
(2020)
Article
Food Science & Technology
Rosa C. Sullivan, Colette C. Fagan, Jane K. Parker
Summary: New data show that even low levels of fat can significantly reduce the recovery of volatile compounds. Therefore, selecting the appropriate extraction technique is crucial when extracting volatile compounds from high-fat foods.
FOOD ANALYTICAL METHODS
(2021)
Article
Computer Science, Artificial Intelligence
Gary D. Gillespie, Kevin P. McDonnell, Gregory M. P. O'Hare
Summary: Machine learning algorithms were found to be more accurate in classifying leaf wetness measurements compared to empirical models, with an average increase in classification accuracy of 4.85%. Increasing the relative humidity threshold improved the accuracy of the empirical models by 1.12%. Regional subsets of data had a greater impact on model accuracy than temporal subsets.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Green & Sustainable Science & Technology
Gary D. Gillespie, Oyinlola Dada, Kevin P. McDonnell
Summary: Suppressed wool prices in Ireland have caused farmers to experience net losses per animal, as the cost of shearing exceeds the value of the wool. A study suggests that hydrolysed sheep wool could provide up to 15.8% of the nitrogen needed for Ireland's cereal crops, along with significant amounts of sulfur, zinc, and copper. The main cost associated with this process is purchasing wool at an economically favorable level. Based on the distribution of sheep, Athlone is identified as the most suitable location for a processing facility.
Article
Agronomy
Isabella Donnelly, Kevin McDonnell, John Finnan
Article
Agronomy
Michael G. O'Flynn, John M. Finnan, Edna M. Curley, Kevin P. McDonnell
Article
Energy & Fuels
Yingna Du, Chen Huang, Wei Jiang, Qiangwei Yan, Yongfei Li, Gang Chen
Summary: In this study, anionic surfactants modified hydrotalcite was used as a flow improver for crude oil under low-temperature conditions. The modified hydrotalcite showed a significant viscosity reduction effect on crude oil. The mechanism of the modified hydrotalcite on viscosity and pour point of crude oil was explored through characterization and analysis of the modified hydrotalcite and oil samples.
Article
Energy & Fuels
Mohammad Saeid Rostami, Mohammad Mehdi Khodaei
Summary: In this study, a hybrid structure, MIL-53(Al)@MWCNT, was synthesized by combining MIL-53(Al) particles and -COOH functionalized multi-walled carbon nanotube (MWCNT). The hybrid structure was then embedded in a polyethersulfone (PES) polymer matrix to prepare a mixed matrix membrane (MMM) for CO2/CH4 and CO2/N2 separation. The addition of MWCNTs prevented MIL-53(Al) aggregation, improved membrane mechanical properties, and enhanced gas separation efficiency.
Article
Energy & Fuels
Yunlong Li, Desheng Huang, Xiaomeng Dong, Daoyong Yang
Summary: This study develops theoretical and experimental techniques to determine the phase behavior and physical properties of DME/flue gas/water/heavy oil systems. Eight constant composition expansion (CCE) tests are conducted to obtain new experimental data. A thermodynamic model is used to accurately predict saturation pressure and swelling factors, as well as the phase boundaries of N2/heavy oil systems and DME/CO2/heavy oil systems, with high accuracy.
Article
Energy & Fuels
Morteza Afkhamipour, Ebad Seifi, Arash Esmaeili, Mohammad Shamsi, Tohid N. Borhani
Summary: Non-conventional amines are being researched worldwide to overcome the limitations of traditional amines like MEA and MDEA. Adequate process and thermodynamic models are crucial for understanding the applicability and performance of these amines in CO2 absorption, but studies on process modeling for these amines are limited. This study used rate-based modeling and Deshmukh-Mather method to model CO2 absorption by DETA solution in a packed column, validated the model with experimental data, and conducted a sensitivity analysis of mass transfer correlations. The study also compared the CO2 absorption efficiency of DETA solution with an ionic solvent [bmim]-[PF6] and highlighted the importance of finding optimum operational parameters for maximum absorption efficiency.
Article
Energy & Fuels
Arastoo Abdi, Mohamad Awarke, M. Reza Malayeri, Masoud Riazi
Summary: The utilization of smart water in EOR operations has gained attention, but more research is needed to understand the complex mechanisms involved. This study investigated the interfacial tension between smart water and crude oil, considering factors such as salt, pH, asphaltene type, and aged smart water. The results revealed that the hydration of ions in smart water plays a key role in its efficacy, with acidic and basic asphaltene acting as intrinsic surfactants. The pH also influenced the interfacial tension, and the aged smart water's interaction with crude oil depended on asphaltene type, salt, and salinity.
Article
Energy & Fuels
Dongao Zhu, Kun Zhu, Lixian Xu, Haiyan Huang, Jing He, Wenshuai Zhu, Huaming Li, Wei Jiang
Summary: In this study, cobalt-based metal-organic frameworks (Co-based MOFs) were used as supports and co-catalysts to confine the NHPI catalyst, solving the leaching issue. The NHPI@Co-MOF with carboxyl groups exhibited stronger acidity and facilitated the generation of active oxygen radicals O2•, resulting in enhanced catalytic activity. This research provides valuable insights into the selection of suitable organic linkers and broadens the research horizon of MOF hybrids in efficient oxidative desulfurization (ODS) applications.
Article
Energy & Fuels
Edwin G. Hoyos, Gloria Amo-Duodu, U. Gulsum Kiral, Laura Vargas-Estrada, Raquel Lebrero, Rail Munoz
Summary: This study investigated the impact of carbon-coated zero-valent nanoparticle concentration on photosynthetic biogas upgrading. The addition of nanoparticles significantly increased microalgae productivity and enhanced nitrogen and phosphorus assimilation. The presence of nanoparticles also improved the quality of biomethane produced.
Article
Energy & Fuels
Yao Xiao, Asma Leghari, Linfeng Liu, Fangchao Yu, Ming Gao, Lu Ding, Yu Yang, Xueli Chen, Xiaoyu Yan, Fuchen Wang
Summary: Iron is added as a flocculant in wastewater treatment and the hydrothermal carbonization (HTC) of sludge produces wastewater containing Fe. This study investigates the effect of aqueous phase (AP) recycling on hydrochar properties, iron evolution and environmental assessment during HTC of sludge. The results show that AP recycling process improves the dewatering performance of hydrochar and facilitates the recovery of Fe from the liquid phase.
Article
Energy & Fuels
He Liang, Tao Wang, Zhenmin Luo, Jianliang Yu, Weizhai Yi, Fangming Cheng, Jingyu Zhao, Xingqing Yan, Jun Deng, Jihao Shi
Summary: This study investigated the influence of inhibitors (carbon dioxide, nitrogen, and heptafluoropropane) on the lower flammability limit of hydrogen and determined the critical inhibitory concentration needed for complete suppression. The impact of inhibitors on explosive characteristics was evaluated, and the inhibitory mechanism was analyzed with chemical kinetics. The results showed that with the increase of inhibitor quantity, the lower flammability limit of hydrogen also increased. The research findings can contribute to the safe utilization of hydrogen energy.
Article
Energy & Fuels
Zonghui Liu, Zhongze Zhang, Yali Zhou, Ziling Wang, Mingyang Du, Zhe Wen, Bing Yan, Qingxiang Ma, Na Liu, Bing Xue
Summary: In this study, high-performance solid catalysts based on phosphotungstic acid (HPW) supported on Zr-SBA-15 were synthesized and evaluated for the one-pot conversion of furfural (FUR) to γ-valerolactone (GVL). The catalysts were characterized using various techniques, and the ratio of HPW and Zr was found to significantly affect the selectivity of GVL. The HPW/Zr-SBA-15 (2-4-15) catalyst exhibited the highest GVL yield (83%) under optimized reaction conditions, and it was determined that a balance between Bronsted acid sites (BAS) and Lewis acid sites (LAS) was crucial for achieving higher catalytic performance. The reaction parameters and catalyst stability were also investigated.
Article
Energy & Fuels
Michael Stoehr, Stephan Ruoff, Bastian Rauch, Wolfgang Meier, Patrick Le Clercq
Summary: As part of the global energy transition, an experimental study was conducted to understand the effects of different fuel properties on droplet vaporization for various conventional and alternative fuels. The study utilized a flow channel to measure the evolution of droplet diameters over time and distance. The results revealed the temperature-dependent effects of physical properties, such as boiling point, liquid density, and enthalpy of vaporization, and showed the complex interactions of preferential vaporization and temperature-dependent influences of physical properties for multi-component fuels.
Article
Energy & Fuels
Yuan Zhuang, Ruikang Wu, Xinyan Wang, Rui Zhai, Changyong Gao
Summary: Through experimental validation and optimization of the chemical kinetic model, it was found that methanol can accelerate the oxidation reaction of ammonia, and methanol can be rapidly oxidized at high concentration. HO2 was found to generate a significant amount of OH radicals, facilitating the oxidation of methanol and ammonia. Rating: 7.5/10.
Article
Energy & Fuels
Radwan M. EL-Zohairy, Ahmed S. Attia, A. S. Huzayyin, Ahmed I. EL-Seesy
Summary: This paper presents a lab-scale experimental study on the impact of diethyl ether (DEE) as an additive to waste cooking oil biodiesel with Jet A-1 on combustion and emission features of a swirl-stabilized premixed flame. The addition of DEE to biodiesel significantly affects the flame temperature distribution and emissions. The W20D20 blend of DEE, biodiesel, and Jet A-1 shows similar flame temperature distribution to Jet A-1 and significantly reduces UHC, CO, and NOx emissions compared to Jet A-1.
Article
Energy & Fuels
Jiang Bian, Ziyuan Zhao, Yang Liu, Ran Cheng, Xuerui Zang, Xuewen Cao
Summary: This study presents a novel method for ammonia separation using supersonic flow and develops a mathematical model to investigate the condensation phenomenon. The results demonstrate that the L-P nucleation model accurately characterizes the nucleation process of ammonia at low temperatures. Numerical simulations also show that increasing pressure and concentration can enhance ammonia condensation efficiency.
Article
Energy & Fuels
Shiyuan Pan, Xiaodan Shi, Beibei Dong, Jan Skvaril, Haoran Zhang, Yongtu Liang, Hailong Li
Summary: Integrating CO2 capture with biomass-fired combined heat and power (bio-CHP) plants is a promising method for achieving negative emissions. This study develops a reliable data-driven model based on the Transformer architecture to predict the flowrate and CO2 concentration of flue gas in real time. The model validation shows high prediction accuracy, and the potential impact of meteorological parameters on model accuracy is assessed. The results demonstrate that the Transformer model outperforms other models and using near-infrared spectral data as input features improves the prediction accuracy.
