Biofuel Research Team BRTeam

Karaj, Iran

Biofuel Research Team BRTeam

Karaj, Iran
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Aghbashlo M.,University of Tehran | Tabatabaei M.,Biofuel Research Team BRTeam | Tabatabaei M.,Agricultural Biotechnology Research Institute of Iran | Mohammadi P.,Biofuel Research Team BRTeam | And 3 more authors.
Energy Conversion and Management | Year: 2017

In the present study, a DI diesel engine operating on various diesel/biodiesel blends containing different amounts of polymer waste was thermodynamically scrutinized using two exergy-based methods, i.e., exergoeconomic and exergoenvironmental analyses for the first time. Exergoeconomic and exergoenvironmental parameters were calculated for five fuel blends utilized throughout this study at different engine loads and speeds. These approaches were used to make decisions on fuel composition and engine operational conditions by taking into account the financial and environmental issues. The results showed that the exergoeconomic and exergoenvironmental parameters varied profoundly with engine load and speed. In general, increasing engine load remarkably decreased the unit cost and the unit environmental impact of the shaft work exergy, while enhancing engine speed acted oppositely. More specifically, the lowest unit cost and unit environmental impact of full load work exergy were found to be 36.08 USD/MJ and 32.03 mPts/GJ for neat diesel and B5 containing 75 g EPS/L biodiesel, respectively, both at engine speed of 1600 min−1. Moreover, the exergoeconomic and exergoenvironmental factors of the diesel engine were very poor due to the higher thermodynamic losses occurring during the combustion process. Although the maximum exergetic efficiency of the diesel engine was obtained for B5 including 50 g EPS/L biodiesel, the exergoeconomic and exergoenvironmental analyses could not detect any spectacular differences among the fuel blends applied. Overall, using biodiesel in neat or blended form appeared to be less attractive strategy from the exergoeconomic and exergoenvironmental perspectives considering the current biodiesel prices and production technologies. © 2017 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 | Mehrpooya M.,University of Tehran
Energy | Year: 2017

The higher heating value (HHV) of biomass fuels is a crucial factor in the techno-economic analysis and subsequent development of bioenergy projects. In this study, iterative neural network-adapted partial least squares (INNPLS) was applied to estimate the HHV of biomass fuels as a function of fixed carbon (FC), volatile matters (VM), and ash content. The ANN paradigm was used to correlate the inputs and the outputs of PLS score vectors thorough iterative training procedure. The prediction capability of the proposed model was compared with those of the classical PLS, the coupled principle component analysis and ANN paradigm (PCA-ANN), and the neural network-adapted partial least squares (NNPLS). The presented models were developed, trained, and tested using 350 data points obtained from the published literature. According to the results obtained, the INNPLS showed an excellent capability to model the HHV of biomass fuels over the other methods. This approach was then embedded into a simple and user-friendly software for estimating the HHV of biomass fuels on the basis of their proximate data. The developed software can be utilized for reliable and accurate estimation of biomass HHV based on only three input parameters as an alternative to the lengthy and costly laboratorial measurements. © 2017 Elsevier Ltd


Zahed O.,Agricultural Biotechnology Research Institute of Iran | Zahed O.,University of Tehran | Jouzani G.S.,Agricultural Biotechnology Research Institute of Iran | Abbasalizadeh S.,Agricultural Biotechnology Research Institute of Iran | And 3 more authors.
Folia Microbiologica | Year: 2016

The present study was set to develop a robust and economic biorefinery process for continuous co-production of ethanol and xylitol from rice straw in a membrane bioreactor. Acid pretreatment, enzymatic hydrolysis, detoxification, yeast strains selection, single and co-culture batch fermentation, and finally continuous co-fermentation were optimized. The combination of diluted acid pretreatment (3.5 %) and enzymatic conversion (1:10 enzyme (63 floating-point unit (FPU)/mL)/biomass ratio) resulted in the maximum sugar yield (81 % conversion). By concentrating the hydrolysates, sugars level increased by threefold while that of furfural reduced by 50 % (0.56 to 0.28 g/L). Combined application of active carbon and resin led to complete removal of furfural, hydroxyl methyl furfural, and acetic acid. The strains Saccharomyces cerevisiae NCIM 3090 with 66.4 g/L ethanol production and Candida tropicalis NCIM 3119 with 9.9 g/L xylitol production were selected. The maximum concentrations of ethanol and xylitol in the single cultures were recorded at 31.5 g/L (0.42 g/g yield) and 26.5 g/L (0.58 g/g yield), respectively. In the batch co-culture system, the ethanol and xylitol productions were 33.4 g/L (0.44 g/g yield) and 25.1 g/L (0.55 g/g yield), respectively. The maximum ethanol and xylitol volumetric productivity values in the batch co-culture system were 65 and 58 % after 25 and 60 h, but were improved in the continuous co-culture mode and reached 80 % (55 g/L) and 68 % (31 g/L) at the dilution rate of 0.03 L per hour, respectively. Hence, the continuous co-production strategy developed in this study could be recommended for producing value-added products from this hugely generated lignocellulosic waste. © 2015, Institute of Microbiology, Academy of Sciences of the Czech Republic, v.v.i.


Aghbashlo M.,University of Tehran | Hosseinpour S.,University of Tehran | Tabatabaei M.,Agricultural Biotechnology Research Institute of Iran | Tabatabaei M.,Biofuel Research Team BRTeam | Dadak A.,University of Tehran
Energy | Year: 2017

This study was aimed at performing a multi-objective fuzzy modeling and optimization of a low power, high frequency piezo-ultrasonic reactor applied for biodiesel production from waste cooking oil (WCO). To achieve this, three different fuzzy optimization methods were interfaced with adaptive neuro-fuzzy inference system (ANFIS) as modeling system to minimize the specific energy consumption of the reactor and to satisfy the ASTM standard on yield, i.e., conversion efficiency of >96.5%. Two ANFIS models were applied to correlate two output variables (conversion efficiency and specific energy consumption) individually with three input variables (reaction temperature, ultrasonic irradiation time, and methanol/oil molar ratio). The multi-objective optimization techniques included the fuzzy systems with independent, interdependent, and locally-modified interdependent objectives. Based on the results achieved, both ANFIS models excellently tracked the output parameters. Furthermore, the fuzzy system with locally-modified interdependent objectives outperformed the other two fuzzy systems in optimizing the transesterification process of WCO. The optimal WCO transesterification process for biodiesel production in the developed reactor corresponded to the methanol/oil molar ratio of 6.1:1, ultrasonic irradiation time of 10 min, and reaction temperature of 59.5 °C, leading to a conversion efficiency of 96.63% and a specific energy consumption of 373.87 kJ/kg. © 2017 Elsevier Ltd


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.


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.


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 | 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.


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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