Response surface methodology–based extraction optimization with application of ZrCl4 as novel quenching agent for enhancement of bio-oil yield from Jatropha curcas and Chlorella pyrenoidosa

The need for fuel oil continues to increase in line with the increasing number of human populations and the growth rate of dependence on fuel oil. Bio-oil is a condensed-liquid mixture that results from the thermal derivation of biomass containing hemicellulose, lignin, and cellulose. This research developed an optimization of the operation condition of bio-oil from empty palm fruit bunches (OPEFB) using a modified pyrolysis reactor. The temperature and mass of empty palm fruit bunches were the two parameters considered in this study. Optimization was carried out on process parameters using the surface response methodology (RSM) and variance analysis (ANOVA). The significance of the different parameters and the effect of the relationship between parameters on the bio-oil yield is determined using a full factorial central composite design. The optimal operation condition of pyrolysis was found to be 570.71 oC, and the mass of empty palm fruit bunch 420.71 gr. Predictions from the optimum variable of operating conditions produce a bio-oil yield of 5.58%. The actual bio-oil yield on the optimum condition that was be validated is 5.6 %. The chemical composition of bio-oil obtained was evaluated by GCMS to ensure its characterization as a fuel.Keywords: Empty palm fruit bunches, Bio-oil, Pyrolysis, Response Surface Methodology, Optimization

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Thermogravimetric analysis and the optimisation of bio-oil yield from fixed-bed pyrolysis of rice husk using response surface methodology (RSM)

Industrial Crops and Products ◽

10.1016/j.indcrop.2010.10.024 ◽

2011 ◽

Vol 33 (2) ◽

pp. 481-487 ◽

Cited By ~ 62

Author(s):

Khairuddin Md Isa ◽

Suhardy Daud ◽

Nasrul Hamidin ◽

Khudzir Ismail ◽

Saiful Azhar Saad ◽

...

Keyword(s):

Response Surface Methodology ◽

Thermogravimetric Analysis ◽

Response Surface ◽

Rice Husk ◽

Fixed Bed ◽

Oil Yield ◽

Bio Oil

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Comparative assessment of artificial neural network and response surface methodology for evaluation of the predictive capability on bio-oil yield of Tithonia diversifolia pyrolysis

Biomass Conversion and Biorefinery ◽

10.1007/s13399-020-00806-x ◽

2020 ◽

Author(s):

Nilutpal Bhuyan ◽

Rumi Narzari ◽

Satyabrat Malla Bujar Baruah ◽

Rupam Kataki

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Comparative Assessment ◽

Oil Yield ◽

Tithonia Diversifolia ◽

Predictive Capability ◽

Bio Oil ◽

Artificial Neural

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Subcritical Water Extraction of Chlorella pyrenoidosa: Optimization through Response Surface Methodology

BioMed Research International ◽

10.1155/2018/1931634 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Selvakumar Thiruvenkadam ◽

Shamsul Izhar ◽

Yoshida Hiroyuki ◽

Razif Harun

Keyword(s):

Response Surface Methodology ◽

Reaction Time ◽

Response Surface ◽

Reaction Temperature ◽

Subcritical Water ◽

Chlorella Pyrenoidosa ◽

Water Extraction ◽

Biofuel Production ◽

Oil Yield ◽

Subcritical Water Extraction

Subcritical water extraction (SCW) was used to extract oil from Chlorella pyrenoidosa. The operational factors such as reaction temperature, reaction time, and biomass loading influence the oil yield during the extraction process. In this study, response surface methodology was employed to identify the desired extraction conditions for maximum oil yield. Experiments were carried out in batch reactors as per central composite design with three independent factors including reaction temperature (170, 220, 270, 320, and 370°C), reaction time (1, 5, 10, 15, and 20 min), and biomass loading (1, 3, 5, 10, and 15%). A maximum oil yield of 12.89 wt.% was obtained at 320°C and 15 min, with 3% biomass loading. Sequential model tests showed the good fit of experimental data to the second-order quadratic model. This study opens the great potential of SCW to extract algal oil for use in algal biofuel production.

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