Hybrid biodiesel production from optimised novel ternary oil mixture (simplex lattice mixture design) using heterogeneous river shell catalyst

ABSTRACT

In the current study, a mixture of three distinct oil is taken as feedstock to produce hybrid biodiesel. The simplex lattice mixture design has been applied to obtain an optimum volume proportion of each oil in the mixture with improved oil mixture characteristics. In the mixture design, waste cooking oil, Ricinus communis oil, and Melia azedarach oil are taken as the input component and viscosity, cloud point, calorific value, and FFA as responses. The mixture design contains 17 test runs. The ANOVA analysis of model shows that the regression coefficient found better (close to 1) for all variables. The desirability of the mixture design reached its maximum value of 0.619 at the optimum mixture ratio (v%) of 0.6:0.2:0.2 for Waste cooking oil, Ricinus communis oil, and Melia azedarach oil, respectively. Based on the prediction model, optimized ternary oil has viscosity 132.848 mm²/s, cloud point −5.73°C, calorific value 38.898 MJ/kg, and FFA of 3.225%. Calcined river shell heterogeneous catalyst is used in transesterification reaction. The highest peaks 2θ° of 32.2°, 37.3°, 64.1°, and 67.3° in X-ray diffraction patter shows the formation of heterogeneous biocatalyst from river shell. A biodiesel yield of 83.20% is found using the reaction conditions: methanol oil molar ratio (9:1), catalyst dosage (5 wt %), reaction temperature (60°C), reaction time (130 min), and stirring speed (800rpm). The GC-MS test has been conducted a confirmatory test of hybrid biodiesel. Element analysis shows that the produced hybrid biodiesel satisfies ASTM D675 standard, with a carbon content of 76.018 wt% and hydrogen content of 11.435 wt%. The optimized ternary oil mixture used to produce the hybrid biodiesel is of better quality and conforms to ASTM D675/IS 15,607 standards.

KEYWORDS:

• Oil mixture
• mixture design
• optimization
• heterogeneous catalyst
• hybrid biodiesel

Abbreviations

IEA	=	International Environmental Agency
STEPS	=	Stated Policies Scenario
FAME	=	Fatty Acid Methyl Ester
XRD	=	X-ray diffraction
JCPDS	=	Joint Committee on Powder Diffraction Standards
BBD	=	Box-Behnken design
ELM	=	Extreme Learning Machine
RSM	=	Response Surface Methodology
ANN	=	Artificial Neural Network
FFA	=	Free fatty acid
°2Th/2θ°	=	Diffraction angle in degree
2D	=	Two dimensions
3D	=	Three dimensions

Acknowledgement

Authors would like to acknowledge Center for Alternative and Renewable Energy, Mechanical Engineering Department, Rajkiya Engineering College Azamgarh Uttar Pradesh and National Institute of Technology Patna, Bihar India for the work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used to support the findings of this study are included within the article.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Brihaspati Singh

Brihaspati Singh is a student in Mechanical Engineering Department at National Institute of Technology Patna, Bihar, India.

Anmesh Kumar Srivastava

Anmesh Kumar Srivastava is working as an Assistant Professor in Mechanical Engineering Department at National Institute of Technology Patna, Bihar, India.

Om Prakash

Om Prakash is working as a Professor in Mechanical Engineering Department at National Institute of Technology Patna, Bihar, India.