Document Type : Original Article
Yangon Technological University, Myanmar
In this study, a new optimized version of multi-layer perceptron neural network has been used for modeling achieving an optimized configuration in biofuel production process. 25 semi-pilot fermentation runs are used to determine the best arrangement of percentage selection for the combined substrate of rice bran, cow dung, paper waste, banana stem, and saw dust to develop the biogas generation process efficiency and speed. The neural network is optimized by an improved version of Monarch Butterfly Optimization (DMBO) algorithm and the results have been compared with basic MBO algorithm and GA based algorithm from literature to illustrate the algorithm capability.
 G. Muhammad, M. A. Alam, W. Xiong, Y. Lv, and J.-L. Xu, “Microalgae Biomass Production: An Overview of Dynamic Operational Methods,” in Microalgae Biotechnology for Food, Health and High Value Products: Springer, 2020, pp. 415-432.
 M. Parsaee, M. K. D. Kiani, and K. Karimi, “A review of biogas production from sugarcane vinasse,” Biomass and bioenergy, vol. 122, pp. 117-125, 2019.
 S. Xie, P. G. Lawlor, P. Frost, C. D. Dennehy, Z. Hu, and X. Zhan, “A pilot scale study on synergistic effects of co-digestion of pig manure and grass silage,” Int Biodeterior Biodegrad, vol. 123, pp. 244-250, 2017.
 M. Tišma, M. Planinić, A. Bucić-Kojić, M. Panjičko, G. D. Zupančič, and B. Zelić, “Corn silage fungal-based solid-state pretreatment for enhanced biogas production in anaerobic co-digestion with cow manure,” Bioresour Technol, vol. 253, pp. 220-226, 2018.
 F. Almasi, S. Soltanian, S. Hosseinpour, M. Aghbashlo, and M. Tabatabaei, “Advanced Soft Computing Techniques in Biogas Production Technology,” in Biogas: Springer, 2018, pp. 387-417.
 S. Khishtandar, “Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design,” Applied Energy, vol. 236, pp. 183-195, 2019.
 Y. Chen, Y. n. Wei, G. Tang, and F. Huang, “Study on optimization of biogas purification process based on Fuzzy AHP,” Kezaisheng Nengyuan/Renewable Energy Resources, vol. 35, no. 10, pp. 1423-1430, 2017.
 S. Jacob and R. Banerjee, “Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm,” Bioresour Technol, vol. 214, pp. 386-395, 2016.
 M. I. Oloko-Oba, A. E. Taiwo, S. O. Ajala, B. O. Solomon, and E. Betiku, “Performance evaluation of three different-shaped bio-digesters for biogas production and optimization by artificial neural network integrated with genetic algorithm,” Sustainable Energy Technologies and Assessments, vol. 26, pp. 116-124, 2018.
 M. D. Ghatak and A. Ghatak, “Artificial neural network model to predict behavior of biogas production curve from mixed lignocellulosic co-substrates,” Fuel, vol. 232, pp. 178-189, 2018.
 M. Zaefferer, D. Gaida, and T. Bartz-Beielstein, “Multi-fidelity modeling and optimization of biogas plants,” Applied Soft Computing, vol. 48, pp. 13-28, 2016.
 A. Ramachandran, R. Rustum, and A. J. Adeloye, “Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques,” Processes, vol. 7, no. 12, p. 953, 2019.
 E. G. Kana, J. Oloke, A. Lateef, and M. Adesiyan, “Modeling and optimization of biogas production on saw dust and other co-substrates using artificial neural network and genetic algorithm,” Renewable energy, vol. 46, pp. 276-281, 2012.
 T. Beltramo, M. Klocke, and B. Hitzmann, “Prediction of the biogas production using GA and ACO input features selection method for ANN model,” Information Processing in Agriculture, vol. 6, no. 3, pp. 349-356, 2019.
 A. Miskam, Z. Zainal, and I. Yusof, “Characterization of sawdust residues for cyclone gasifier,” J Appl Sci, vol. 9, no. 12, pp. 2294-2300, 2009.
 P. C. Roy, A. Datta, and N. Chakraborty, “Assessment of cow dung as a supplementary fuel in a downdraft biomass gasifier,” Renewable Energy, vol. 35, no. 2, pp. 379-386, 2010.
 R. Sounders, “Rice bran: Composition and potential food sources,” Food Rev Int, vol. 1, pp. 465-495, 1985.
 N. A. A. Aziz, L.-H. Ho, B. Azahari, R. Bhat, L.-H. Cheng, and M. N. M. Ibrahim, “Chemical and functional properties of the native banana (Musa acuminata× balbisiana Colla cv. Awak) pseudo-stem and pseudo-stem tender core flours,” Food Chemistry, vol. 128, no. 3, pp. 748-753, 2011.
 M. Wilk, A. Magdziarz, K. Jayaraman, M. Szymańska-Chargot, and I. Gökalp, “Hydrothermal carbonization characteristics of sewage sludge and lignocellulosic biomass. A comparative study,” Biomass and bioenergy, vol. 120, pp. 166-175, 2019.
 S. Zhao, G. Li, N. Zheng, J. Wang, and Z. Yu, “Steam explosion enhances digestibility and fermentation of corn stover by facilitating ruminal microbial colonization,” Bioresour Technol, vol. 253, pp. 244-251, 2018.
 J. Filer, H. H. Ding, and S. Chang, “Biochemical methane potential (BMP) assay method for anaerobic digestion research,” Water, vol. 11, no. 5, p. 921, 2019.
 Y. Cao, Y. Li, G. Zhang, K. Jermsittiparsert, and N. Razmjooy, “Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm,” Energy Reports, vol. 5, pp. 1616-1625, 2019.
 N. Razmjooy, F. R. Sheykhahmad, and N. Ghadimi, “A hybrid neural network–world cup optimization algorithm for melanoma detection,” Open Medicine, vol. 13, no. 1, pp. 9-16, 2018.
 D. Yu, Y. Wang, H. Liu, K. Jermsittiparsert, and N. Razmjooy, “System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm,” Energy Reports, vol. 5, pp. 1365-1374, 2019.
 P. Moallem and N. Razmjooy, “A multi layer perceptron neural network trained by invasive weed optimization for potato color image segmentation,” Trends Appl. Sci. Res., vol. 7, no. 6, p. 445, 2012.
 K. Roy, K. K. Mandal, and A. C. Mandal, “Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system,” Energy, vol. 167, pp. 402-416, 2019.
 M. Saeedi, M. Moradi, M. Hosseini, A. Emamifar, and N. Ghadimi, “Robust optimization based optimal chiller loading under cooling demand uncertainty,” Applied Thermal Engineering, vol. 148, pp. 1081-1091, 2019.
 N. Razmjooy, B. S. Mousavi, F. Soleymani, and M. H. Khotbesara, “A computer-aided diagnosis system for malignant melanomas,” Neural Comput Appl, vol. 23, no. 7-8, pp. 2059-2071, 2013.
 M. H. Firouz and N. Ghadimi, “Wind Energy Uncertainties in Multi-objective Environmental/Economic Dispatch Based on Multi-objective Evolutionary Algorithm,” UCT Journal of Research in Science, Engineering and Technology, vol. 3, no. 3, pp. 8-15, 2015.
 N. Ghadimi, “A method for placement of distributed generation (DG) units using particle swarm optimization,” International Journal of Physical Sciences, vol. 8, no. 27, pp. 1417-1423, 2013.
 H. Manafi, N. Ghadimi, M. Ojaroudi, and P. Farhadi, “Optimal placement of distributed generations in radial distribution systems using various PSO and DE algorithms,” Elekt.Elektrotech., vol. 19, no. 10, pp. 53-57, 2013.
 M. Mir, M. Shafieezadeh, M. A. Heidari, and N. Ghadimi, “Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction,” Evolving Systems, pp. 1-15, 2019.
 A. Namadchian, M. Ramezani, and N. Razmjooy, “A New Meta-Heuristic Algorithm for Optimization Based on Variance Reduction of Guassian Distribution,” Majlesi Journal of Electrical Engineering, vol. 10, no. 4, p. 49, 2016.
 N. Razmjooy, M. Khalilpour, and M. Ramezani, “A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System,” J. Control Autom. Elect. Syst., vol. 27, no. 4, pp. 419-440, 2016.
 X. Fei, R. Xuejun, and N. Razmjooy, “Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1-21, 2019.
 M. Salehi Maleh, S. Soleymani, R. Rasouli Nezhad, and N. Ghadimi, “Using particle swarm optimization algorithm based on multi-objective function in reconfigured system for optimal placement of distributed generation,” Journal of Bioinformatics and Intelligent Control, vol. 2, no. 2, pp. 119-124, 2013.
 M. Hamian, A. Darvishan, M. Hosseinzadeh, M. J. Lariche, N. Ghadimi, and A. Nouri, “A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm,” Engineering Applications of Artificial Intelligence, vol. 72, pp. 203-212, 2018.
 M. Arif and G. Wang, “Fast curvelet transform through genetic algorithm for multimodal medical image fusion,” Soft Computing, pp. 1-22, 2019.
 J. H. Holland, “Genetic algorithms,” Sci Am, vol. 267, no. 1, pp. 66-73, 1992.
 B. S. Mousavi and F. Soleymani, “Semantic image classification by genetic algorithm using optimised fuzzy system based on Zernike moments,” Signal Image Video Process., vol. 8, no. 5, pp. 831-842, 2014.
 J. Pierezan and L. D. S. Coelho, “Coyote optimization algorithm: a new metaheuristic for global optimization problems,” in 2018 IEEE Congress on Evolutionary Computation (CEC), 2018: IEEE, pp. 1-8.
 G. Brammya, S. Praveena, N. Ninu Preetha, R. Ramya, B. Rajakumar, and D. Binu, “Deer Hunting Optimization Algorithm: A New Nature-Inspired Meta-heuristic Paradigm,” The Computer Journal, 2019.
 G. Dhiman and V. Kumar, “Emperor penguin optimizer: A bio-inspired algorithm for engineering problems,” Knowledge-Based Systems, vol. 159, pp. 20-50, 2018.
 G.-G. Wang, S. Deb, and Z. Cui, “Monarch butterfly optimization,” Neural Comput Appl, vol. 31, no. 7, pp. 1995-2014, 2019.
 H. R. Tizhoosh, “Opposition-based learning: a new scheme for machine intelligence,” in International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06), 2005, vol. 1: IEEE, pp. 695-701.
 E. Çelik, “A powerful variant of symbiotic organisms search algorithm for global optimization,” Engineering Applications of Artificial Intelligence, vol. 87, p. 103294, 2020.
 S. Rahnamayan, H. R. Tizhoosh, and M. M. Salama, “Quasi-oppositional differential evolution,” in 2007 IEEE congress on evolutionary computation, 2007: IEEE, pp. 2229-2236.
 D. Yang, G. Li, and G. Cheng, “On the efficiency of chaos optimization algorithms for global optimization,” Chaos, Solitons & Fractals, vol. 34, no. 4, pp. 1366-1375, 2007.
 C. Rim, S. Piao, G. Li, and U. Pak, “A niching chaos optimization algorithm for multimodal optimization,” Soft Computing, vol. 22, no. 2, pp. 621-633, 2018.