Bio-oil yield maximization and characteristics of neem based biomass at optimum conditions along with feasibility of biochar through pyrolysis

Author:

Singh Yashvir1ORCID,Singh Nishant Kumar1ORCID,Sharma Abhishek2,Lim Wei Hong3ORCID,Palamanit Arkom4,Alhussan Amel Ali5ORCID,El-kenawy El-Sayed M.67ORCID

Affiliation:

1. Department of Mechanical Engineering, Harcourt Butler Technical University 1 , Kanpur, Uttar Pradesh 208002, India

2. Department of Computer Science and Engineering, Graphic Era Deemed to be University 2 , Dehradun 248002, India

3. Faculty of Engineering, Technology and Built Environment, UCSI University 3 , 56000 Cheras Kuala Lumpur, Malaysia

4. Energy Technology Program, Department of Specialized Engineering, Faculty of Engineering, Prince of Songkla University 4 , Hat Yai, Songkhla 90110, Thailand

5. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University 5 , P.O. Box 84428, Riyadh 11671, Saudi Arabia

6. Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology 6 , Mansoura 35111, Egypt

7. MEU Research Unit, Middle East University 7 , Amman 11831, Jordan

Abstract

There is a growing need for a more streamlined and automated method of refining biofuels, as there are currently no universally applicable process inspection instruments on the market. All process variables in bio-oil upgrading operations are maintained according to the offline specifications of the products and intermediates. Failure of the process and loss of resources can result from batch-wise monitoring not having real-time product standards. Consequently, in order to cut down on waste and lessen the chances of process failure, a quick and accurate tool for specifying intermediates and products is required. To resolve this issue, we created a model using response surface methodology and an artificial neural network that can increase the bio-oil yield involving parameters, i.e., biomass particle size (mm), temperature (°C), and residence time (min). The maximum bio-oil production (47.0883%) was achieved at 3 mm particle size, 523°C temperature, and 20 min residence time. All results are “better” for root mean squared error (∼1), and the highest coefficient of regression for bio-oil production is in the range of 0.97–0.99. Temperature is the most significant factor in bio-oil yield, followed by particle size and residence time. Based on physicochemical properties, bio-oil has the maximum kinematic viscosity (11.3 Cst) and water content (18.7%). Making bio-oil precious compounds allows it to be used as boiler feedstock and steam generation fuel.

Funder

Princess Nourah Bint Abdulrahman University

Publisher

AIP Publishing

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