4.6 Article

Estimation of Compressive Resistance of Briquettes Obtained from Groundnut Shells with Different Machine Learning Algorithms

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 Chemistry, Analytical

Comparison of Artificial Neural Networks and kinetic inverse modeling to predict biomass pyrolysis behavior

Yu Zhong et al.

Summary: The prediction of biomass pyrolysis behavior has attracted significant attention, and inverse modeling based on a specific kinetic scheme is the main predictive tool. However, due to the complex reaction mechanism involved, researchers are exploring the appropriate kinetic scheme for biomass pyrolysis. Artificial neural networks, including the uncommon Elman neural network, are being applied to prediction based on unknown kinetic schemes. Through optimization of the neural network structure and training methods, these neural networks exhibit superior prediction ability, especially in the shoulder and peak regions of mass loss rate curves.

JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS (2023)

Article Energy & Fuels

Experimental investigation of the durability and stability of compressed jojoba cake briquettes

Safa M. M. Aldarabseh

Summary: The study investigated the effects of compression pressure, dwell time, and moisture content on the density, relaxation ratio, and durability of jojoba cake briquettes. The results showed that compression pressure and moisture content had significant effects, while dwell time only had a slight effect. The optimal combination for higher density and lower relaxation ratio was found at a pressure of 40 MPa and moisture content of 35%wb. High-quality briquettes with acceptable durability were obtained at a moisture content of 30-35%wb and 40 MPa pressure.

BIOMASS CONVERSION AND BIOREFINERY (2023)

Article Green & Sustainable Science & Technology

Effects of Feeding Speed and Temperature on Properties of Briquettes from Poplar Wood Using a Hydraulic Briquetting Press

Joseph I. I. Orisaleye et al.

Summary: Biomass has potential as a solution for energy deficit and can be processed into solid fuels for bioenergy conversion. The quality of densified biomass depends on parameters such as die temperature and feeding speed, as demonstrated in this study on poplar biomass briquettes. The study found that temperature, feeding speed, and their interaction significantly influenced the density, mechanical durability, and water resistance of the briquettes. These findings are valuable for optimizing the production of high-quality briquettes using a hydraulic piston press.

RESOURCES-BASEL (2023)

Article Thermodynamics

Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation

Shanke Liu et al.

Summary: Integrated supercritical water gasification of biomass for power generation (ISSCWBPG) is a promising energy conversion technology. A model was established to predict its power generation and heat difference, and an artificial neural network (ANN) was constructed and optimized, providing theoretical guidance for the process design and optimization of ISSCWBPG.

ENERGY (2023)

Article Green & Sustainable Science & Technology

Prediction of instantaneous yield of bio-oil in fluidized biomass pyrolysis using long short-term memory network based on computational fluid dynamics data

Hanbin Zhong et al.

Summary: Computational fluid dynamics (CFD) is effective but time-consuming for biomass fast pyrolysis in fluidized bed reactors. Machine learning (ML) can provide accuracy and efficiency. This study developed a simplified model using LSTM, pooling, and fully connected layers to predict mass flow rates based on historical CFD data, saving computational effort by 30%. The well-predicted fluctuating characteristics and final product yields contribute to improving process simulation accuracy for building smart factories.

JOURNAL OF CLEANER PRODUCTION (2023)

Article Green & Sustainable Science & Technology

Predicting renewable energy production by machine learning methods: The case of Turkey

Ayten Yagmur et al.

Summary: This study created a model by considering the socioeconomic, environmental, and energy time series data of countries to estimate renewable energy production. Turkey was chosen as the case study, and machine learning methods such as support vector regression and artificial neural networks were used for prediction. The results showed that both methods had high predictive power. The successful model can guide the creation of energy policies and contribute to scientific studies.

ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY (2023)

Article Environmental Sciences

Multi-objective optimization of a biomass gasification to generate electricity and desalinated water using Grey Wolf Optimizer and artificial neural network

Farayi Musharavati et al.

Summary: In this research, an innovative biomass-based energy system is proposed for power and desalinated water production. Through a comprehensive thermodynamic and thermoeconomic assessment, the optimal solution with the highest exergy efficiency and the minimum amount of total cost rate is obtained. The artificial neural network plays an important role in decreasing computational time during the optimization process. Additionally, the distribution of key decision variables has a significant impact on the system optimization.

CHEMOSPHERE (2022)

Article Food Science & Technology

Prediction of Pistachio (Pistacia vera L.) Mass Based on Shape and Size Attributes by Using Machine Learning Algorithms

Cevdet Saglam et al.

Summary: The size, mass, and shape attributes play a significant role in the quality assessment and post-harvest technologies of agricultural products. Various pistachio cultivars were analyzed for their physical attributes, with Gaussian processes showing the highest correlation coefficients and lowest RMSE values for mass prediction among the machine learning algorithms used in the study. This suggests the potential of using Gaussian processes for mass prediction of pistachio cultivars.

FOOD ANALYTICAL METHODS (2022)

Article Green & Sustainable Science & Technology

Prediction of torrefied biomass properties from raw biomass

Furkan Kartal et al.

Summary: In this study, the torrefaction process was modeled using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Based on a large dataset, the carbon, hydrogen, oxygen content, and higher heating value of torrefied biomass were successfully estimated with good accuracy. The results show that the developed ANN model is a useful tool for obtaining the desired torrefied biomass.

RENEWABLE ENERGY (2022)

Review Energy & Fuels

A Review of Biomass Briquette Binders and Quality Parameters

Okey Francis Obi et al.

Summary: The adverse effects of fossil fuels have led to increased research and development in renewable energy, including the use of loose biomass to produce solid fuels. This review surveys the current state of research on binders in briquette production and examines the parameters used to assess biomass briquette quality, highlighting the need for standardization. The review also addresses factors hindering the widespread production and adoption of biomass briquettes and proposes solutions.

ENERGIES (2022)

Article Engineering, Environmental

Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR)

Fatima Ezzahra Yatim et al.

Summary: Circular economy is a global trend that can be addressed by waste-to-energy, which is an effective solution for the increasing waste generation and energy demand. This research developed new models to estimate the energy value of biomass waste and found that artificial neural network (ANN) is the best model. Therefore, this study provides attractive options for designing and optimizing combustion reactors.

WASTE MANAGEMENT (2022)

Article Energy & Fuels

Quality Assessment of Biofuel Briquettes Obtained from Greenhouse Waste Using a Mobile Prototype Briquetting Machine with PTO Drive

Onder Kabas et al.

Summary: Turkey has a large agricultural area and abundant biomass waste resources. This study used a mobile briquetting machine to obtain bio-briquettes from dried greenhouse wastes and conducted physical tests on the briquettes. The results showed that greenhouse waste biomass is an excellent feedstock for the production of high quality bio-briquettes.

ENERGIES (2022)

Article Green & Sustainable Science & Technology

Plant Biomass Conversion to Vehicle Liquid Fuel as a Path to Sustainability

Aleksandr Ketov et al.

Summary: This paper explores the possibility of synthesizing high-energy liquid vehicle fuels from renewable sources of plant origin and produces high-energy biofuel from sawdust and linseed oil using slow pyrolysis. The proposed approach not only preserves existing high-tech energy sources, but also reduces carbon footprint and aims to achieve carbon neutrality.

RESOURCES-BASEL (2022)

Article Energy & Fuels

Modeling of biogas production from food, fruits and vegetables wastes using artificial neural network (ANN)

Joao Goncalves Neto et al.

Summary: The study investigated the influence of different conditions on biogas production using experimental and numerical models, finding that the highest production was achieved under thermophilic or mesophilic conditions. Additionally, optimizing the biodigestion process can lead to higher volatile solid content and increased biogas production.
Article Thermodynamics

Prediction of solar energy guided by pearson correlation using machine learning

Imane Jebli et al.

Summary: This paper presents a solar energy forecasting approach based on machine and deep learning techniques, evaluating its relevance and performance in real-time and short-term solar energy predictions. The study found that RF and ANN provided higher accuracy compared to LR and SVR, with ANN performing well in both real-time and short-term predictions.

ENERGY (2021)

Article Computer Science, Interdisciplinary Applications

Prediction of pellet quality through machine learning techniques and near-infrared spectroscopy

Manuela Mancini et al.

COMPUTERS & INDUSTRIAL ENGINEERING (2020)

Article Energy & Fuels

Application of artificial neural networks in predicting biomass higher heating value: an early appraisal

Joshua O. Ighalo et al.

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS (2020)

Proceedings Paper Green & Sustainable Science & Technology

A survey of Artificial Neural Network-based Prediction Models for Thermal Properties of Biomass

Olatunji Obafemi et al.

SUSTAINABLE MANUFACTURING FOR GLOBAL CIRCULAR ECONOMY (2019)

Article Chemistry, Physical

Assessment of producer gas composition in air gasification of biomass using artificial neural network model

Joel George et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2018)

Article Biochemistry & Molecular Biology

RFAmyloid: A Web Server for Predicting Amyloid Proteins

Mengting Niu et al.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2018)

Article Green & Sustainable Science & Technology

Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees

Muhammad Waseem Ahmad et al.

JOURNAL OF CLEANER PRODUCTION (2018)

Article Agricultural Engineering

Artificial neural network prediction of the biogas flow rate optimised with an ant colony algorithm

Tetyana Beltramo et al.

BIOSYSTEMS ENGINEERING (2016)

Article Agricultural Engineering

Artificial neural network models for biomass gasification in fluidized bed gasifiers

Maria Puig-Arnavat et al.

BIOMASS & BIOENERGY (2013)

Review Agricultural Engineering

An overview of second generation biofuel technologies

Ralph E. H. Sims et al.

BIORESOURCE TECHNOLOGY (2010)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)