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Alexandre Rodrigues Torres

Bio: Alexandre Rodrigues Torres is an academic researcher from Rio de Janeiro State University. The author has contributed to research in topics: Partial least squares regression & Alternative energy. The author has an hindex of 4, co-authored 7 publications receiving 308 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, a review on parameters that influence the pyrolysis process, such as temperature, reaction time, heating rate, gas flow rate, feed rate, particle size and biomass composition, is presented.

334 citations

Journal ArticleDOI
01 Feb 2020-Fuel
TL;DR: In this article, multivariate calibration based on Partial Least Squares (PLS), Random Forest (RF) and Support Vector Machine (SVM) methods combined with variable selections tools were used to model the relation between the near-infrared spectroscopy data of biodiesel fuel to its physical-chemical properties.

41 citations

Journal ArticleDOI
15 Sep 2017-Fuel
TL;DR: In this paper, partial least squares regression (PLS) and support vector machine regression (SVM) were used to model the relationship between mid-FT-IR spectroscopic data and the density, refractive index and cold filter plugging point of biodiesel samples and their blends.

35 citations

Journal ArticleDOI
TL;DR: In this article, three technologies were tested (TiO2/UV, H2O2+UV, and TiO 2/H2O+UV/UV) for the degradation and color removal of a 25 mg L-1 mixture of three acid dyes: Blue 9, Red 18, and Yellow 23.
Abstract: Three technologies were tested (TiO2/UV, H2O2/UV, and TiO2/H2O2/UV) for the degradation and color removal of a 25 mg L-1 mixture of three acid dyes: Blue 9, Red 18, and Yellow 23. A low speed rotating disc reactor (20 rpm) and a H2O2 concentration of 2.5 mmol L-1 were used. The dyes did not significantly undergo photolysis, although they were all degraded by the studied advanced oxidation processes. With the TiO2/H2O2/UV process, a strong synergism was observed (color removal reached 100%). Pseudo first order kinetic constants were estimated for all processes, as well as the respective apparent photonic efficiencies.

10 citations

Journal ArticleDOI
TL;DR: In this paper, a multivariate calibration based on partial least squares, random forest, and support vector machine methods, combined with the MissForest imputation algorithm, was used to understand the interaction between ozone and nitrogen oxides, carbon monoxide, wind speed, solar radiation, temperature, relative humidity, and others, the data of which were collected by air quality monitoring stations in the metropolitan area of Rio de Janeiro in four distinct sites between, 2014 and, 2018.
Abstract: Multivariate calibration based on partial least squares, random forest, and support vector machine methods, combined with the MissForest imputation algorithm, was used to understand the interaction between ozone and nitrogen oxides, carbon monoxide, wind speed, solar radiation, temperature, relative humidity, and others, the data of which were collected by air quality monitoring stations in the metropolitan area of Rio de Janeiro in four distinct sites between, 2014 and, 2018. These techniques provide an easy and feasible way of modeling and analyzing air pollutants and can be used when coupled with other methods. The results showed that random forest and support vector machine chemometric techniques can be used in modeling and predicting tropospheric ozone concentrations, with a coefficient of determination for making predictions up to 0.92, a root-mean square error of calibration between 4.66 and 27.15 µg m−3, and a root-mean square error of prediction between 4.17 and 22.45 µg m−3, depending on the air quality monitoring stations and season.

3 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: This paper is devoted to thoroughly reviewing and critically discussing various ML technology applications, with a particular focus on ANN, to solve function approximation, optimization, monitoring, and control problems in biodiesel research.

203 citations

Journal ArticleDOI
TL;DR: In this article, a review scrutinizes the key roles of biochar as an additive and emphasizes the influences of bio-char characteristics on the anaerobic digestion processes and their capability to address the foremost challenges.

203 citations

Journal ArticleDOI
TL;DR: In this article, a critical review of the effect of the key pyrolysis parameters from lignocellulosic biomass to product distribution is presented, and CO2-based benefits, economic assessment, and technical orientation for biofuel production from biomass are included.

195 citations

Journal ArticleDOI
TL;DR: In this article, the progress of catalysts for improving the hydrocarbon compounds in bio-oil obtained from catalytic pyrolysis of biomass was reported, and the effects of other operating conditions, such as temperature, type of biomass, heating rate, vapors residence time, carrier gas, and hydrogen donor on the yield and properties of biooil have been briefly explored.
Abstract: This paper reports the progress of catalysts for improving the hydrocarbon compounds in bio-oil obtained from catalytic pyrolysis of biomass. In addition, the effects of the other operating conditions, such as temperature, type of biomass, heating rate, vapors residence time, carrier gas, and hydrogen donor on the yield and properties of bio-oil have been briefly explored. Temperature and catalysts type were found to have major impact on the bio-oil yield and quality. TGA-DTA analysis of biomass revealed that major biomasses pyrolysis zone for high bio-oil yield is in the range of 400–600 °C. Pilot, semi-pilot and large-scale units reported an average temperature of 500 °C for pyrolysis of biomass. The development of advanced catalysts such as zeolite-based catalysts, supported transition and noble metal catalysts, and metal oxide catalysts have been designed to remove the undesired compounds and to increase the hydrocarbon yield in bio-oil. Noble metal supported catalysts produced bio-oil with a low content of oxygenated compounds compared to non-noble metal catalysts; however, their cost and accessibility favor the utilization of non-noble metal supported catalysts.

160 citations