Adaptive neuro fuzzy predictive models of agricultural biomass standard entropy and chemical exergy based on principal component analysis
Tóm tắt
In order to effectively utilize energy of agricultural biomass, there is a need to evaluate energy potential. For such a purpose, chemical exergy and standard entropy of typical agricultural biomass were examined analytically. Element compositions of the exergy and entropy were acquired for further statistical evaluation. Adaptive neuro fuzzy inference system (ANFIS) was used as the statistical methodology for data analyzing. ANFIS is an efficient estimation model among machine learning techniques. The main weakness of the ANFIS is its dimensionality problem with large inputs. Therefore, the main goal in this study was to estimate the parameters’ influence on the chemical exergy and standard entropy prediction in order to reduce the number of inputs. Principal component analysis was used for presentation of the obtained ANFIS predictive models. Obtained results have shown the best predictive performances for standard entropy based on hydrogen as composite element of the agricultural biomass. Exergy prediction was the best for oxygen as composite element of the agricultural biomass. ANFIS coefficient of determination for standard entropy prediction based on hydrogen is 0.9832 and for chemical exergy prediction is 0.919. The results show the high predictive accuracy of ANFIS models.
Tài liệu tham khảo
Zhang Y, Xu P, Liang S, Liu B, Shuai Y, Li B (2019) Exergy analysis of hydrogen production from steam gasification of biomass: a review. Int J Hydrog Energy 44(28):14290–14302
Yan L, Cao Y, He B (2019) Energy, exergy and economic analyses of a novel biomass fueled power plant with carbon capture and sequestration. Sci Total Environ 690:812–820
Sahoo U, Kumar R, Singh SK, Tripathi AK (2018) Energy, exergy, economic analysis and optimization of polygeneration hybrid solar-biomass system. Appl Therm Eng 145:685–692
Mehrpooya M, Khalili M, Sharifzadeh MMM (2018) Model development and energy and exergy analysis of the biomass gasification process (based on the various biomass sources). Renew Sust Energ Rev 91:869–887
Yang K, Zhu N, Ding Y, Chang C, Wang D, Yuan T (2019) Exergy and exergoeconomic analyses of a combined cooling, heating, and power (CCHP) system based on dual-fuel of biomass and natural gas. J Clean Prod 206:893–906
Qian H, Zhu W, Fan S, Liu C, Lu X, Wang Z, Huang D, Chen W (2017) Prediction models for chemical exergy of biomass on dry basis from ultimate analysis using available electron concepts. Energy 131:251–258
Liu F, Chen G, Yan B, Ma W, Cheng Z (2017) Exergy analysis of a new lignocellulosic biomass-based polygeneration system. Energy 140:1087–1095
Saidur R, BoroumandJazi G, Mekhilef S, Mohammed HA (2012) A review on exergy analysis of biomass based fuels. Renew Sust Energ Rev 16(2):1217–1222
Ojeda K, Sánchez E, Kafarov V (2011) Sustainable ethanol production from lignocellulosic biomass–application of exergy analysis. Energy 36(4):2119–2128
Qian H, Zhu W, Liu C, Lu X, Kontogeorgis GM, Gani R (2018) Exergy efficiency based design and analysis of utilization pathways of biomasses. Computer Aided Chemical Engineering 43:857–862 Elsevier
Rupesh S, Muraleedharan C, Arun P (2016) Energy and exergy analysis of syngas production from different biomasses through air-steam gasification. Frontiers in Energy:1–13 doi: https://doi.org/10.1007/s11708-016-0439-1
Roy D, Ghosh S (2017) Energy and exergy analyses of an integrated biomass gasification combined cycle employing solid oxide fuel cell and organic Rankine cycle. Clean Techn Environ Policy 19(6):1693–1709
Nasiri F, Aghbashlo M, Rafiee S (2017) Exergy analysis of an industrial-scale ultrafiltrated (UF) cheese production plant: a detailed survey. Heat Mass Transf 53(2):407–424
Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans On Systems, Man, and Cybernetics 23:665–685
Petković D, Issa M, Pavlović ND, Pavlović NT, Zentner L (2012) Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties. Expert Systems with Applications, ISSN 0957-4174(39):9477–9482
Petković D, Ćojbašić Ž (2012) Adaptive neuro-fuzzy estimation of automatic nervous system parameters effect on heart rate variability. Neural Comput Applic 21(8):2065–2070
Kurnaz S, Cetin O, Kaynak O (2010) Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles. Expert Syst Appl 37:1229–1234
Petković D, Issa M, Pavlović ND, Zentner L, Ćojbašić Ž (2012) Adaptive neuro fuzzy controller for adaptive compliant robotic gripper. Expert Systems with Applications, ISSN 0957-4174(39):13295–13304
Huang YW, Chen MQ, Li Y, Guo J (2016) Modeling of chemical exergy of agricultural biomass using improved general regression neural network. Energy 114:1164–1175