Application of artificial intelligence to maximize methane production from waste paper
Corresponding Author
A.G. Olabi
Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, UAE
Center for Advanced Materials Research, University of Sharjah, Sharjah, UAE
Mechanical Engineering and Design, Aston University, School of Engineering and Applied Science, Birmingham, UK
Correspondence
A.G. Olabi, Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, UAE.
Email: [email protected]
Hegazy Rezk, College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Email: [email protected]
Search for more papers by this authorAhmed M. Nassef
College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
Computers and Automatic Control Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt
Search for more papers by this authorCristina Rodriguez
School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley, UK
Search for more papers by this authorMohammad A. Abdelkareem
Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, UAE
Center for Advanced Materials Research, University of Sharjah, Sharjah, UAE
Chemical Engineering Department, Faculty of Engineering, Minia University, Minya, Egypt
Search for more papers by this authorCorresponding Author
Hegazy Rezk
College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
Electrical Engineering Department, Faculty of Engineering, Minia University, Minya, Egypt
Correspondence
A.G. Olabi, Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, UAE.
Email: [email protected]
Hegazy Rezk, College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Email: [email protected]
Search for more papers by this authorCorresponding Author
A.G. Olabi
Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, UAE
Center for Advanced Materials Research, University of Sharjah, Sharjah, UAE
Mechanical Engineering and Design, Aston University, School of Engineering and Applied Science, Birmingham, UK
Correspondence
A.G. Olabi, Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, UAE.
Email: [email protected]
Hegazy Rezk, College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Email: [email protected]
Search for more papers by this authorAhmed M. Nassef
College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
Computers and Automatic Control Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt
Search for more papers by this authorCristina Rodriguez
School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley, UK
Search for more papers by this authorMohammad A. Abdelkareem
Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, UAE
Center for Advanced Materials Research, University of Sharjah, Sharjah, UAE
Chemical Engineering Department, Faculty of Engineering, Minia University, Minya, Egypt
Search for more papers by this authorCorresponding Author
Hegazy Rezk
College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
Electrical Engineering Department, Faculty of Engineering, Minia University, Minya, Egypt
Correspondence
A.G. Olabi, Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, UAE.
Email: [email protected]
Hegazy Rezk, College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Email: [email protected]
Search for more papers by this authorSummary
This article proposes a methodology based on artificial intelligence to enhance methane production from waste paper. The proposed methodology combines fuzzy logic-based modelling and modern optimization. Firstly, a robust Adaptive Network-based Fuzzy Inference System model of methane production process through fuzzy logic modelling is created using experimental datasets. Second, a particle swarm optimizer was used to obtain the optimal process conditions. During the optimization procedure, the beating time and feedstock/inoculum ratio are employed as decision variables in order to maximize methane production. The obtained resulted from the proposed methodology are compared with those obtained by response surface methodology. The results of the comparison confirmed the superiority of the proposed methodology. The fuzzy model shows a better fitting to the experimental data compared to ANOVA. The fuzzy model showed a higher coefficient of determination and a lower value of root mean squared errors compared to ANOVA. Moreover, the proposed strategy, that is, modelling and optimization, is an effective method for increasing the biomethane yield at extended range conditions.
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