Welcome to Scholar Publishing Group

Academic Journal of Energy, 2022, 3(4); doi: 10.38007/RE.2022.030405.

System Integration and Method for High-efficiency Utilization of Solar Energy and Biomass Energy Thermochemically Based on Neural Network Algorithm


Jing Zhou

Corresponding Author:
Jing Zhou

East China University of Science and Technology, Shanghai, China


With the rapid development of economy and society, many problems such as environmental pollution and shortage of fossil energy have become increasingly prominent. It is urgent to develop and vigorously utilize renewable and clean energy such as solar energy. The purpose of this work is to study the system integration and biomass thermochemistry based on neural network algorithm. The proposed methanol and electricity cogeneration system is used to complete the step-by-step and orderly conversion of fuel chemical energy, and analyze the thermal characteristics of the cogeneration. Analysis and discussion are made for the academic characteristics and economic performance under different operating modes. The performance evaluation results of the solar-biomass gasification polygeneration system show that the thermal energy loss or exergy loss of the system is 63885kW and 15187.9kW respectively, of which the solar heat collection part has the largest loss, accounting for 43.2% of the total heat loss of the system and 43.2%. The total exergy loss accounts for 48.6%. Based on this, the collaborative optimization of the operating parameters of these devices will greatly promote the effective utilization of system energy.


Neural Network, Solar Energy, Biomass Energy, System Integration

Cite This Paper

Jing Zhou. System Integration and Method for High-efficiency Utilization of Solar Energy and Biomass Energy Thermochemically Based on Neural Network Algorithm. Academic Journal of Energy (2022), Vol. 3, Issue 4: 42-50. https://doi.org/10.38007/RE.2022.030405.


[1] Delaney N ,  Deegan J ,  Escudero M V , et al. Innovative system services for facilitating the integration of high levels of renewable generation in Ireland and Northern Ireland. IET Renewable Power Generation, 2020, 14(19):3954-3960. https://doi.org/10.1049/iet-rpg.2020.0614

[2] Vandone A ,  Baraldo S ,  Anastassiou D , et al. 3D vision system integration on Additive Manufacturing machine for in-line part inspection. Procedia CIRP, 2020, 95(2):72-77. https://doi.org/10.1016/j.procir.2020.01.191

[3] Aoa B ,  Ys A ,  Hz A , et al. Observer-based interval type-2 fuzzy PID controller for PEMFC air feeding system using novel hybrid neural network algorithm-differential evolution optimizer. Alexandria Engineering Journal, 2022, 61( 9):7353-7375. https://doi.org/10.1016/j.aej.2021.12.072

[4] Skuratov V ,  Kuzmin K ,  Nelin I , et al. Creation Of A Neural Network Algorithm For Automated Collection And Analysis Of Statistics Of Exchange Quotes Graphics. Eureka Physics and Engineering, 2020, 3(3):22-29.

[5] M Dubé,  Shultz J ,  Barnes S , et al. Goals, Recommendations, and the How-To Strategies for Developing and Facilitating Patient Safety and System Integration Simulations:. HERD: Health Environments Research & Design Journal, 2020, 13(1):94-105. https://doi.org/10.1177/1937586719846586

[6] Domashova J V ,  Emtseva S S ,  Fail V S , et al. Selecting an optimal architecture of neural network using genetic algorithm. Procedia Computer Science, 2021, 190(14):263-273.

[7] Bhamidipati S ,  Kim K J ,  Sun H , et al. Artificial-Intelligence-Based Distributed Belief Propagation and Recurrent Neural Network Algorithm for Wide-Area Monitoring Systems. IEEE Network, 2020, 34(3):64-72.

[8] Lynd L R ,  Beckham G T ,  Guss A M , et al. Toward low-cost biological and hybrid biological/catalytic conversion of cellulosic biomass to fuels. Energy & Environmental Science, 2022, 15(3):938-990. https://doi.org/10.1039/D1EE02540F

[9] Szczyglewska P ,  Feliczak-Guzik A ,  Jaroniec M , et al. Catalytic role of metals supported on SBA-16 in hydrodeoxygenation of chemical compounds derived from biomass processing. RSC Advances, 2021, 11(16):9505-9517. https://doi.org/10.1039/D0RA06696F

[10] Kannan R ,  Marinacci F ,  Vogelsberger M , et al. Simulating the interstellar medium of galaxies with radiative transfer, non-equilibrium thermochemistry, and dust. Monthly Notices of the Royal Astronomical Society, 2020, 499(4):5732-5748. https://doi.org/10.1093/mnras/staa3249

[11] Armentrout P B ,  Peterson K A . Guided Ion Beam and Quantum Chemical Investigation of the Thermochemistry of Thorium Dioxide Cations: Thermodynamic Evidence for Participation of f Orbitals in Bonding. Inorganic Chemistry, 2020, 59(5):3118-3131.

[12] Sakano M N ,  Hamed A ,  Kober E M , et al. Unsupervised Learning-Based Multiscale Model of Thermochemistry in 1,3,5-Trinitro-1,3,5-triazinane (RDX). The Journal of Physical Chemistry A, 2020, 124(44):9141-9155. https://doi.org/10.1021/acs.jpca.0c07320

[13] Kuzhanthaivelan, S, Rajakumar, et al. Thermochemistry and Kinetic Studies on the Autoignition of 2-Butanone: A Computational Study. The journal of physical chemistry, A. Molecules, spectroscopy, kinetics, environment, & general theory, 2018, 122(29):6134-6146.

[14] Tobias, Roland, Csaszar, et al. Definitive thermochemistry and kinetics of the interconversions among conformers of n-butane and n-pentane. Journal of Computational Chemistry: Organic, Inorganic, Physical, Biological, 2018, 39(7-8):424-437. https://doi.org/10.1002/jcc.25130

[15] Leinders G ,  Cardinaels T ,  Binnemans K , et al. Low-Temperature Oxidation of Fine UO2 Powders: Thermochemistry and Kinetics. Inorganic Chemistry, 2018, 57(7):4196-4204.

[16] Fouad, N, Ajeel, et al. Electronic, Thermochemistry and Vibrational Properties for Single-walled Carbon Nanotubes. Nanoscience and Nanotechnology - Asia, 2018, 8(2):233-239.

[17] Covert, Kyle, J, et al. Thermochemistry of the smallest QOOH radical from the roaming fragmentation of energy selected methyl hydroperoxide ions. Physical chemistry chemical physics: PCCP, 2018, 20(32):21085-21094. https://doi.org/10.1039/C8CP03168A

[18] Schamm S ,  Rabaidel L ,  Grannec I , et al. Partial phase diagram of the ternary reciprocal system KF-AlF3-Al2O3-K2O. Calphad-computer Coupling of Phase Diagrams & Thermochemistry, 2018, 14(4):385-402. https://doi.org/10.1016/0364-5916(90)90006-L