Biofuel Research Team BRTeam

Karaj, Iran

Biofuel Research Team BRTeam

Karaj, Iran

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Mirzajanzadeh M.,Islamic Azad University at Tehran | Tabatabaei M.,Biofuel Research Team BRTeam | Tabatabaei M.,Agricultural Biotechnology Institute of Iran ABRII | Ardjmand M.,Islamic Azad University at Tehran | And 7 more authors.
Fuel | Year: 2014

This study was aimed at synthesizing a novel soluble hybrid nanocatalyst to decrease emissions i.e., nitrogen oxide compounds (NOx), carbon monoxide (CO), unburned hydrocarbons (HC) and soot, of a DI engine fueled with diesel-biodiesel blends. Moreover, enhancement of performance parameters i.e. power, torque and fuel consumption was also simultaneously targeted. The hybrid nanocatalyst containing cerium oxide on amide-functionalized multiwall carbon nanotubes (MWCNT) was investigated using two types of diesel-biodiesel blends (B5 and B20) at three concentrations (30, 60 and 90 ppm). The results obtained revealed that high surface area of the soluble nano-sized catalyst particles and their proper distribution along with catalytic oxidation reaction resulted in significant overall improvements in the combustion reaction specially in B20 containing 90 ppm of the catalyst B20(90 ppm). More specifically, all pollutants i.e., NOx, CO, HC and soot were reduced by up to 18.9%, 38.8%, 71.4% and 26.3%, respectively, in B20(90 ppm) compared to neat B20. The innovated fuel blend also increased engine performance parameters i.e., power and torque by up to 7.81%, 4.91%, respectively, and decreased fuel consumption by 4.50%. © 2014 Elsevier Ltd. All rights reserved.


Khoshnevisan B.,University of Tehran | Khoshnevisan B.,Biofuel Research Team BRTeam | Shafiei M.,University of Isfahan | Rajaeifar M.A.,Biofuel Research Team BRTeam | And 3 more authors.
Energy | Year: 2016

In this study, the lignocellulosic biofuel production from pinewood, pretreated with steam explosion and N-methylmorpholine-N-oxide (NMMO), was investigated from a life cycle perspective in Sweden. To perform this study four scenarios, i.e. ethanol and biogas production by NMMO (Sc-1) and steam explosion (Sc-3) pretreatments, and biogas production by NMMO (Sc-2) and steam explosion (Sc-4) pretreatments, were developed. The consequential life cycle assessment (CLCA) methodology with a cradle to gate approach was employed and two functional units, i.e. 105,263 tonnes pinewood input to the biofuel plant and 1 MJ energy produced, were selected in order to assess the environmental impacts of pinewood-based biofuel production. The results revealed that bioenergy production with NMMO-based pretreatment method was more environmentally friendly than steam explosion process in terms of human health, ecosystem quality, resources and climate change. Moreover, it was shown that the Sc-2 in which methane was the single outcome of the plant (the main product) outperformed the other scenarios in terms of environmental performance and energy balance. © 2016 Elsevier Ltd


Hosseini S.S.,University of Tehran | Aghbashlo M.,University of Tehran | Tabatabaei M.,Agricultural Biotechnology Research Institute of Iran | Tabatabaei M.,Biofuel Research Team BRTeam | And 2 more authors.
International Journal of Hydrogen Energy | Year: 2015

This paper proposes a thermodynamic framework based on exergy and eco-exergy concepts for biological hydrogen production from CO-enriched gas via a locally isolated photosynthetic bacterium Rhodopseudomonas palustris PT. In order to achieve a deeper understanding on the bioreactor performance, exergetic parameters like exergy destruction, exergy efficiency, and sustainability index for the bioreactor were determined using both concepts at different acetate concentrations as a carbon source ranging from 0 to 3 g/ L. The exergetic results based on both concepts remarkably diverged from each other due to the inclusion of the work of information carried by the genomes of living organisms in the eco-exergy concept. The sustainable dosage of sodium acetate was found to be 1.5 g/L for efficient and eco-friendly bioconversion of harmful carbon monoxide to hydrogen and carbon dioxide through the water-gas shift (WGS) reaction. The methodologies applied herein revealed the benefits of applying exergy analysis for the design and optimization of industrial-scale bioreactors to attain more cost-effective and eco-friendly biohydrogen production. Consequently, the photobiological hydrogen production can be taken into account as a sustainable alternative fuel to the non-renewable fossil resources by minimizing the thermodynamics irreversibilities. © 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.


Hosseini S.S.,University of Tehran | Aghbashlo M.,University of Tehran | Tabatabaei M.,Agricultural Biotechnology Research Institute of Iran | Tabatabaei M.,Biofuel Research Team BRTeam | And 2 more authors.
Energy | Year: 2015

In this study, exergy analysis of batch biohydrogen production through WGS (water-gas shift) reaction using an anaerobic photosynthetic bacteria Rhodospirillum rubrum was carried out for the first time. Various carbon sources including formate, acetate, malate, glucose, fructose, and sucrose were applied to support microbial growth in the presence of CO-rich syngas. The microorganisms utilized carbon monoxide and produced molecular hydrogen concurrently. The process was analyzed based on both conventional exergy and eco-exergy concepts for determining the exergetic parameters i.e., exergy destruction and exergy efficiency. Unlike the exergy efficiency, the exergy destruction based on the eco-exergy concept was remarkably lower than what obtained via the conventional exergy theory. Minimum normalized exergy destruction values of 189.67 and 181.40 kJ/kJ H2 were achieved for acetate as substrate using the exergy and eco-exergy approaches, respectively. In better words, acetate was identified as the most appropriate carbon source for biohydrogen production from the exergy point of view. Finally, the findings of this study confirmed that exergy analysis could be employed as an adaptable framework to determine and compare the renewability of biological hydrogen production using different routes in order to decide on the most suitable approach and conditions. © 2015 Elsevier Ltd.


Hosseinpour S.,University of Tehran | Aghbashlo M.,University of Tehran | Tabatabaei M.,Agricultural Biotechnology Research Institute of Iran | Tabatabaei M.,Biofuel Research Team BRTeam | And 2 more authors.
Energy Conversion and Management | Year: 2016

Cetane number (CN) is among the most important properties of biodiesel because it quantifies combustion speed or in better words, ignition quality. Experimental measurement of biodiesel CN is rather laborious and expensive. However, the high proportionality of biodiesel fatty acid methyl esters (FAMEs) profile with its CN is very appealing to develop straightforward and inexpensive computerized tools for biodiesel CN estimation. Unfortunately, correlating the chemical structure of biodiesel to its CN using conventional statistical and mathematical approaches is very difficult. To solve this issue, partial least square (PLS) adapted by artificial neural network (ANN) was introduced and examined herein as an innovative approach for the exact estimation of biodiesel CN from its FAMEs profile. In the proposed approach, ANN paradigm was used for modeling the inner relation between the input and the output PLS score vectors. In addition, the capability of the developed method in predicting the biodiesel CN was compared with the basal PLS method. The accuracy of the developed approaches for computing the biodiesel CN was assessed using three statistical criteria, i.e., coefficient of determination (R2), mean-squared error (MSE), and percentage error (PE). The ANN-adapted PLS method predicted the biodiesel CN with an R2 value higher than 0.99 demonstrating the fidelity of the developed model over the classical PLS method with a markedly lower R2 value of about 0.85. In order to facilitate the use of the proposed model, an easy-to-use computer program was also developed on the basis of ANN-adapted PLS method for determining the biodiesel CN from its FAMEs profile. © 2016 Elsevier Ltd


Aghbashlo M.,University of Tehran | Hosseinpour S.,University of Tehran | Tabatabaei M.,Agricultural Biotechnology Research Institute of Iran | Tabatabaei M.,Biofuel Research Team BRTeam | And 2 more authors.
Energy | Year: 2016

The aim of this work was to exergetically optimize the performance of a continuous photobioreactor for hydrogen production from syngas via water gas shift reaction by Rhodospirillum rubrum. To achieve this, a new multi-objective hybrid optimization technique was developed by coupling the elitist NSGA-II (non-dominated sorting genetic algorithm) with the ANFIS (adaptive neuro-fuzzy inference system) to optimize the operational conditions of the photobioreactor. The syngas flow rate and culture agitation speed were independent variables, while rational and process exergy efficiencies as well as normalized exergy destruction were dependent variables. The ANFIS was used to establish an objective function for each dependent variable individually based on the independent variables. The developed ANFIS model was then utilized by the NSGA-II approach to find the optimal operating conditions simultaneously leading to the highest rational and process exergy efficiencies and the lowest normalized exergy destruction. Consequently, the best operating conditions for the photobioreactor were extracted using a Pareto optimal front set consisting of seven optimum points. Accordingly, syngas flow rate of 13.34 mL/min and culture agitation speed of 383.33 rpm yielding process exergy efficiency of 21.66%, rational exergy efficiency of 85.64%, and normalized exergy destruction of 1.55 were found as the best operating conditions. © 2015 Elsevier Ltd.


Aghbashlo M.,University of Tehran | Shamshirband S.,University of Malaya | Tabatabaei M.,Agricultural Biotechnology Research Institute of Iran | Tabatabaei M.,Biofuel Research Team BRTeam | And 3 more authors.
Energy | Year: 2016

In this study, a novel method based on Extreme Learning Machine with wavelet transform algorithm (ELM-WT) was designed and adapted to estimate the exergetic performance of a DI diesel engine. The exergetic information was obtained by calculating mass, energy, and exergy balance equations for the experimental trials conducted at various engine speeds and loads as well as different biodiesel and expanded polystyrene contents. Furthermore, estimation capability of the ELM-WT model was compared with that of the ELM, GP (genetic programming) and ANN (artificial neural network) models. The experimental results showed that an improvement in the exergetic performance modelling of the DI diesel engine could be achieved by the ELM-WT approach in comparison with the ELM, GP, and ANN methods. Furthermore, the results showed that the applied algorithm could learn thousands of times faster than the conventional popular learning algorithms. Obviously, the developed ELM-WT model could be used with a high degree of confidence for further work on formulating novel model predictive strategy for investigating exergetic performance of DI diesel engines running on various renewable and non-renewable fuels. © 2015 Elsevier Ltd.


Aghbashlo M.,University of Tehran | Tabatabaei M.,Agricultural Biotechnology Research Institute of Iran | Tabatabaei M.,Biofuel Research Team BRTeam | Karimi K.,Isfahan University of Technology
Energy | Year: 2016

This paper presents an in-depth exergy analysis of the ethanol fermentation process with various forms of fungus Mucor indicus under aerobic and anaerobic conditions to select the most productive and sustainable conditions. Various carbon sources including fructose, glucose, and sucrose as well as the whole and inverted sugar beet and sugarcanes molasses were used during the fermentation. The rational and process exergetic efficiencies were found to be in the range of 65.21%-88.54% and 0.00%-44.31%, respectively. Overall, the exergy-based parameter based on the process outputs could provide useful information about the sustainability and productivity of the fermentation process compared to the rational analysis. More specifically, the inverted sugar beet molasses with MF (mostly filamentous) form of M. indicus under anaerobic cultivation was shown to be the best option for industrial production phase with respect to the productivity and sustainability issues. The results obtained confirmed that the process yield alone cannot perfectly reflect the exact sustainability parameters of the renewable ethanol production systems. Finally, the developed exergetic framework could help engineers to couple biochemical and physical concepts more robustly for achieving the most cost-effective and eco-friendly pathways for bioethanol production. © 2016 Elsevier Ltd.


Shamshirband S.,University of Malaya | Tabatabaei M.,Agricultural Biotechnology Research Institute of Iran | Tabatabaei M.,Biofuel Research Team BRTeam | Aghbashlo M.,University of Tehran | And 2 more authors.
Applied Thermal Engineering | Year: 2016

In the present study, four Support Vector Machine-based (SVM-based) approaches and the standard artificial neural network (ANN) model were designed and compared in modelling the exergetic parameters of a DI diesel engine running on diesel/biodiesel blends containing expanded polystyrene (EPS) wastes. For this aim, the SVM was coupled with discrete wavelet transform (SVM-WT), firefly algorithm (SVM-FFA), radial basis function (SVM-RBF) and quantum particle swarm optimization (SVM-QPSO). The exergetic data were computed using mass, energy, and exergy balance equations for the engine at different speeds and loads as well as various biodiesel and EPS wastes quantities. Three statistical indicators namely root means square error, coefficient of determination and Pearson coefficient were used to access the capability of the developed approaches for exergetic performance modelling of the DI diesel engine. The modelling results indicated that the SVM-WT approach was more efficient in exergetic modelling of the engine than the other three approaches. Moreover, the results obtained confirmed the effectiveness of the SVM-WT model in identifying the most exergy-efficient combustion conditions and the best fuel composition for achieving the most cost-effective and eco-friendly combustion process. © 2015 Elsevier Ltd. All rights reserved.


Hajjari M.,Islamic Azad University at South Tehran | Ardjmand M.,Islamic Azad University at South Tehran | Tabatabaei M.,Biofuel Research Team BRTeam
RSC Advances | Year: 2014

Nano cerium oxide, a combustion-improving fuel additive, was investigated for its impact on biodiesel oxidative stability. The findings of the present study revealed for the first time that nano cerium oxide addition at the concentrations generally used to improve combustion (<100 ppm) severely reduced the oxidative stability of biodiesel. This journal is © the Partner Organisations 2014.

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