title: Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm.
abstract: A novel approach to overcome the acidification problem has been attempted in the present study by codigesting industrial potato waste (PW) with Pistia stratiotes (PS, an aquatic weed). The effectiveness of codigestion of the weed and PW was tested in an equal (1:1) proportion by weight with substrate concentration of 5g total solid (TS)/L (2.5gPW+2.5gPS) which resulted in enhancement of methane yield by 76.45% as compared to monodigestion of PW with a positive synergistic effect. Optimization of process parameters was conducted using central composite design (CCD) based response surface methodology (RSM) and artificial neural network (ANN) coupled genetic algorithm (GA) model. Upon comparison of these two optimization techniques, ANN-GA model obtained through feed forward back propagation methodology was found to be efficient and yielded 447.4±21.43LCH4/kgVSfed (0.279gCH4/kgCODvs) which is 6% higher as compared to the CCD-RSM based approach.
authors.affiliation: Microbial Biotechnology and Downstream Processing Laboratory, Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, West Bengal, India.
authors.forename: Samuel
authors.initials: S
authors.lastname: Jacob
authors.affiliation: Microbial Biotechnology and Downstream Processing Laboratory, Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, West Bengal, India. Electronic address: rb@iitkgp.ac.in.
authors.forename: Rintu
authors.initials: R
authors.lastname: Banerjee
mesh_terms: D000465:Algorithms; D000693:Anaerobiosis; D000704:Analysis of Variance; D059001:Aquatic Organisms / Q000737:chemistry*; D056804:Biofuels / Q000032:analysis; D001709:Biotechnology / Q000379:methods*; D005232:Fatty Acids, Volatile / Q000032:analysis; D006863:Hydrogen-Ion Concentration; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D057685:Plant Weeds / Q000737:chemistry*; D012044:Regression Analysis; D015203:Reproducibility of Results; D011198:Solanum tuberosum / Q000737:chemistry*; D014866:Waste Products
chemical_list: D056804:Biofuels; D005232:Fatty Acids, Volatile; D014866:Waste Products
keywords: Anaerobic codigestion; Methane; Optimization; Pistia stratiotes; Potato wastes