Journal ArticleDOI
Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques
P.J. García Nieto,E. García Gonzalo,F. Sánchez Lasheras,J.P. Paredes–Sánchez,P. Riesgo Fernández +4 more
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TLDR
A new hybrid algorithm, relied on support vector machines (SVMs) combined with the simulated annealing (SA) optimization technique, for predicting the calorific value (HHV) of biomass from operation input parameters determined experimentally during the torrefaction process.About:
This article is published in Journal of Computational and Applied Mathematics.The article was published on 2019-09-01. It has received 52 citations till now. The article focuses on the topics: Torrefaction.read more
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A Survey of Machine Learning Models in Renewable Energy Predictions
TL;DR: This survey attempts to provide a review and analysis of machine-learning models in renewable-energy predictions and depicts procedures, including data pre-processing techniques, parameter selection algorithms, and prediction performance measurements, used in machine- learning models for renewable- energy predictions.
Journal ArticleDOI
Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage
TL;DR: In this paper, a broad view of the current state of the art of ML applications in the manufacturing sectors that have a considerable impact on sustainability and the environment, namely renewable energies (solar, wind, hydropower, and biomass), smart grids, the industry of catalysis and power storage and distribution, is presented.
Journal ArticleDOI
Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes
TL;DR: In this paper , a machine learning approach was applied for the prediction of biocrude yields and higher heating values (HHV) from hydrothermal liquefaction (HTL) of wet biomass and wastes using 17 input features from feedstock characteristics (biological and elemental properties) and operating conditions.
Journal ArticleDOI
Progress in Modeling of Biomass Fast Pyrolysis: A Review
TL;DR: In this article, fast pyrolysis of biomass is an important technology in the conversion of lignocellulosic feedstocks to value-added fuels and chemicals.
Journal ArticleDOI
Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes.
TL;DR: In this article, a machine learning approach was applied for the prediction of biocrude yields and higher heating values (HHV) from hydrothermal liquefaction (HTL) of wet biomass and wastes using 17 input features from feedstock characteristics (biological and elemental properties) and operating conditions.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
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