Abstract
Biogas production from organic wastes is a complex, dynamic, nonlinear, multivariable, and uncertain biological process whose underlying mechanisms are still unclear. Accordingly, this process is not amenable to conventional mathematical and phenomenological modeling and optimization approaches. Advanced soft computing techniques are considered as powerful tools for dealing with the complexity, nonlinearity, dimensionality, and uncertainties of complicated ill-defined biological processes like biogas production. For this reason, advanced soft computing techniques are extensively employed in biogas applications due to their higher efficiency, generalization, and simplicity. In this chapter, after introducing the soft computing techniques and briefly describing their theoretical backgrounds, an overview is presented of the most important applications of these approaches for modeling and optimization of biogas production processes. This chapter is arranged into four main sections. In the first section, artificial neural network (ANN) is introduced and its applications in biogas production processes are reviewed and discussed. In the second part, fuzzy logic systems like Sugeno and Mamdani systems are presented in detail and their related applications in biogas production processes are summarized and analyzed. The third section covers evolutionary algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) and their applications for optimizing biogas production processes. Hybrid models like Neuro-fuzzy, Fuzzy-Neural, and Neuro-Evolutionary approaches are discussed in the last section and their applications in anaerobic digestion systems are also summarized and scrutinized.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdallah M, Warith M, Narbaitz R, Petriu E, Kennedy K (2011) Combining fuzzy logic and neural networks in modeling landfill gas production. World Acad Sci Eng Technol 78:559–565
Akbaş H, Bilgen B, Turhan AM (2015) An integrated prediction and optimization model of biogas production system at a wastewater treatment facility. Biores Technol 196:566–576
Almasi F, Jafari A, Akram A, Nosrati M, Afazeli H (2014) New method of Artificial Neural Networks (ANN) in modeling broiler production energy index in Alborz Province. Int J Adv Biol Biomed Res 2(5):1707–1718
Antwi P, Li J, Boadi PO, Meng J, Shi E, Deng K, Bondinuba FK (2017) Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network. Biores Technol 228:106–115
Arumugam T, Parthiban L, Rangasamy P (2015) Two-phase anaerobic digestion model of a tannery solid waste: experimental investigation and modeling with ANFIS. Arab J Sci Eng 40(2):279–288
Bullnheimer B, Hartl RF, Strauss C (1997) A new rank based version of the Ant System. A computational study
Carrère H, Dumas C, Battimelli A, Batstone D, Delgenès J, Steyer J, Ferrer I (2010) Pretreatment methods to improve sludge anaerobic degradability: a review. J Hazard Mater 183(1):1–15
Dai X, Duan N, Dong B, Dai L (2013) High-solids anaerobic co-digestion of sewage sludge and food waste in comparison with mono digestions: stability and performance. Waste Manag 33(2):308–316
Dibaba OR, Lahiri SK, T’Jonck S, Dutta A (2016) Experimental and artificial neural network modeling of a Upflow Anaerobic Contactor (UAC) for biogas production from Vinasse. Int J Chem Reactor Eng 14(6):1241–1254
Dorigo M, Stützle T (2010) Ant colony optimization: overview and recent advances. In: Handbook of metaheuristics. Springer, Berlin, pp 227–263
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on Micro Machine and Human Science. MHS’95, IEEE, pp 39–43
Elnekave M, Celik SO, Tatlier M, Tufekci N (2012) Artificial neural network predictions of Up-Flow Anaerobic Sludge Blanket (UASB) reactor performance in the treatment of citrus juice wastewater. Pol J Environ Stud 21(1)
Gazi V, Passino KM (2011) Swarm stability and optimization. Springer Science & Business Media, Berlin
Horváth IS, Tabatabaei M, Karimi K, Kumar R (2016) Recent updates on biogas production—a review. Biofuel Res J 3(2):394–402
Jacob S, Banerjee R (2016) Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm. Biores Technol 214:386–395
Jafari A, Rafiee S, Nosrati M, Almasi F (2014) Investigation yield and energy balances for biogas production from cow and poultry manure. Int J Renew Energy Res (IJRER) 4(2):312–320
Kana EG, Oloke J, Lateef A, Adesiyan M (2012) Modeling and optimization of biogas production on saw dust and other co-substrates using artificial neural network and genetic algorithm. Renew Energy 46:276–281
Kanat G, Saral A (2009) Estimation of biogas production rate in a thermophilic UASB reactor using artificial neural networks. Environ Model Assess 14(5):607–614
Khalid A, Arshad M, Anjum M, Mahmood T, Dawson L (2011) The anaerobic digestion of solid organic waste. Waste Manag 31(8):1737–1744
Lin Y, Ge X, Li Y (2014) Solid-state anaerobic co-digestion of spent mushroom substrate with yard trimmings and wheat straw for biogas production. Biores Technol 169:468–474
Macias-Corral M, Samani Z, Hanson A, Smith G, Funk P, Yu H, Longworth J (2008) Anaerobic digestion of municipal solid waste and agricultural waste and the effect of co-digestion with dairy cow manure. Biores Technol 99(17):8288–8293
McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133
Nair VV, Dhar H, Kumar S, Thalla AK, Mukherjee S, Wong JW (2016) Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor. Biores Technol 217:90–99
Nguyen HT, Sugeno M (2012) Fuzzy systems: modeling and control, vol 2. Springer Science & Business Media, Berlin
Qdais HA, Hani KB, Shatnawi N (2010) Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resour Conserv Recycl 54(6):359–363
Robles A, Latrille E, Ruano M, Steyer J-P (2017) A fuzzy-logic-based controller for methane production in anaerobic fixed-film reactors. Environ Technol 38(1):42–52
Saha M, Eskicioglu C, Sadiq R (2014) A fuzzy rule-based approach for modelling effects of bench-scale microwave pre-treatment on solubilisation and anaerobic digestion of secondary sludge. Int J Environ Eng 6(2):183–204
Sewsynker-Sukai Y, Faloye F, Kana EBG (2016) Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review). Biotechnol Biotechnol Equip 2818:1–15. https://doi.org/10.1080/13102818.2016.1269616
Sewsynker-Sukai Y, Faloye F, Kana EBG (2017) Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review). Biotechnol Biotechnol Equip 31(2):221–235
Siddique N, Adeli H (2013) Computational intelligence: synergies of fuzzy logic, neural networks and evolutionary computing. Wiley, Hoboken
Sosnowski P, Wieczorek A, Ledakowicz S (2003) Anaerobic co-digestion of sewage sludge and organic fraction of municipal solid wastes. Adv Environ Res 7(3):609–616
Strik DP, Domnanovich AM, Zani L, Braun R, Holubar P (2005) Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB neural network toolbox. Environ Model Softw 20(6):803–810
Stützle T, Hoos H (1997) MAX-MIN ant system and local search for the traveling salesman problem. In: IEEE international conference on evolutionary computation, IEEE, pp 309–314
Stützle T, Hoos HH (2000) MAX–MIN ant system. Future Gener Comput Syst 16(8):889–914
Tay J-H, Zhang X (2000) A fast predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems. Water Res 34(11):2849–2860
Turkdogan-Aydınol FI, Yetilmezsoy K (2010) A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater. J Hazard Mater 182(1):460–471
Varne AL, Macwan J (2012) Fuzzy rule based approach for modeling biogas production rate in a real scale UASB reactor treating distillery wastewater. J Environ Res Dev 6(3A)
Verdaguer M, Molinos-Senante M, Poch M (2016) Optimal management of substrates in anaerobic co-digestion: an ant colony algorithm approach. Waste Manag 50:49–54
Waewsak C, Nopharatana A, Chaiprasert P (2010) Neural-fuzzy control system application for monitoring process response and control of anaerobic hybrid reactor in wastewater treatment and biogas production. J Environ Sci 22(12):1883–1890
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Almasi, F., Soltanian, S., Hosseinpour, S., Aghbashlo, M., Tabatabaei, M. (2018). Advanced Soft Computing Techniques in Biogas Production Technology. In: Tabatabaei, M., Ghanavati, H. (eds) Biogas. Biofuel and Biorefinery Technologies, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-77335-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-77335-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77334-6
Online ISBN: 978-3-319-77335-3
eBook Packages: EnergyEnergy (R0)