| 研究生: |
朱彥仕 Chu, Yen-Shih |
|---|---|
| 論文名稱: |
生化溶液前處理對竹子特性之影響及進化計算對微藻熱降解之分析 Influence of bio-solution pretreatment on bamboo properties and thermal degradation characteristics of microalgae analyzed by evolutionary computation |
| 指導教授: |
陳維新
Chen, Wei-Hsin |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 航空太空工程學系 Department of Aeronautics & Astronautics |
| 論文出版年: | 2017 |
| 畢業學年度: | 105 |
| 語文別: | 英文 |
| 論文頁數: | 84 |
| 中文關鍵詞: | 生質物與生質能源 、生化溶液 、前處理 、點燃與燃盡溫度 、焙燒 、X-光繞射 、生質物與微藻 、熱降解溫度 、裂解 、粒群演算法(PSO) 、進化計算 、熱重分析與動力式 |
| 外文關鍵詞: | Biomass and bioenergy, Bio-solution, Pretreatment, Ignition and burnout temperatures, Torrefaction, X-ray diffraction (XRD), Biomass and microalgae, Thermal degradation temperature, Pyrolysis, Particle swarm optimization (PSO), Evolutionary computation, TGA and kinetics |
| 相關次數: | 點閱:134 下載:5 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
生質能源為再生能源中之一環,其重要性亦於近幾十年中與時俱增。為了研究生質物於熱降解時之特性,竹子之前處理與微藻之動力式分別於本研究之第一與第二部分進行測試與分析。其中第一部分探討了生化溶液-NOE-7F用於生質物-竹子之前處理上,對於其在結構、反應性以及焙燒等方面之影響。第二部分則藉由建立三種不同微藻之裂解動力式,進而分析其中碳水化合物、蛋白質與脂質之熱降解特性。
本實驗中第一部分為了探討浸泡時顆粒大小之影響,實驗選用兩種模式-Mode 1與Mode 2。Mode 1中竹子先經過破碎之處理,再浸泡於生化溶液中,而Mode 2則是相反之操作順序,兩種模式皆浸泡於五種不同之持續時間。結果顯示於Mode 1之操作下,竹子中之半纖維素被生化溶液消耗,進而改善竹子之均質性。此結果類似於製造生質酒精上之酶水解前處理。而在相同操作天數下,Mode 2之效果未達Mode 1之顯著。前處理後竹子在點燃與燃盡溫度上皆有提升,表示儲存安全性之提升與反應性之降低。另外,生化溶液前處理對於氫碳比、氧碳比與高位發熱質則較無顯著之改變。但纖維素之結晶結構在Mode 1與Mode 2之處理後皆有變化。結果建議若要應用於燃料,生質物需再經過焙燒處理,而Mode 2因其之高焙燒強度則較適用於斯。
第二部分中,動力式之建立乃基於獨立平行反應模型(IPR model),同時藉由粒群演算法(PSO)計算活化能與前置因子等動力式所需參數,以達到預測數據與實驗之最佳符合度。結果顯示與微藻裂解之導數熱重分析(DTG)實驗相比,由動力式在獨立平行反應模型與粒群演算法計算下所建立之曲線可達97.9 %以上之符合度。從計算結果,三種藻類中之碳水化合物、蛋白質與脂質之活化能範圍依序為25-53 kJ mol^-1、96-188 kJ mol^-1與40-59 kJ mol^-1,而此三種物質之裂解溫度範圍依序為130-574 °C、209-373 °C與200-766 °C。由結果可證明在獨立平行反應模型與粒群演算法之計算下,可以建立一定符合度之裂解反應動力式。
Bioenergy, a type of renewable energy, has been getting more important along the last few decades. To investigate the characteristics of biomass thermal degradation, the pretreatment of bamboo and kinetics of microalgae are tested and analyzed in the present study, which are divided into two parts. In the first part, a bio-solution of natural organic enzyme-7F (NOE-7F) is used to pretreat bamboo, with emphasis on the influence of the pretreatment upon the structure, reactivity, and torrefaction of the biomass. In the second part, the kinetics of microalgae pyrolysis is investigated to analyze the thermal degradation of carbohydrates, proteins and lipids in different species of microalgae.
In the first part of this research, two different operating modes accompanied by five different soaking durations are considered. In Mode 1 the bamboo is ground followed by pretreated by the bio-solution, and an inverse procedure is used in Mode 2. The results indicate that, with the operation of Mode 1, NOE-7F removes hemicellulose in the bamboo significantly, thereby improving the homogeneity of the biomass. This pretreated bamboo may be feasible for enzymatic hydrolysis to produce bioethanol. The penetration of the bio-solution into block bamboo becomes the controlling mechanism under Mode 2 operation, and therefore relatively less hemicellulose is consumed from Mode 2. The ignition and burnout temperatures of the pretreated bamboo are higher than those of the raw bamboo, revealing the lower reactivity and higher storage safety of the former. The atomic H/C and O/C ratios as well as the calorific value of the bamboo are insensitive to the pretreatments, whereas the crystalline structure of cellulose is affected by the bio-solution to a certain extent, regardless of Mode 1 or Mode 2 operation. This suggests that torrefaction is required if the pretreated bamboo is employed as a fuel. The pretreated bamboo with Mode 2 is more suitable for torrefaction because of higher torrefaction severity.
In the second part of this research, the pyrolysis process of microalgae is examined by thermogravimetric analysis (TGA). The independent parallel reaction (IPR) model is adopted to obtain the necessary parameters for pyrolysis kinetics, and a kind of evolutionary computation, particle swarm optimization (PSO), is employed to maximize the fit quality of the simulated data. The characteristics of the thermal degradation of different microalgae are compared with each other. The results suggest that the thermal degradation curves of the three microalgae can be predicted with a fit quality of at least 97.4%. The activation energies of carbohydrates, proteins, and lipids in the microalgae are in the ranges of 53.28-53.30, 142.61-188.35, and 40.21-59.23 kJ mol^-1, respectively, while the thermal degradation of carbohydrates, proteins, and lipids are in temperature ranges of 164-497, 209-309, and 200-635 °C, respectively. It is proved in this work that the IPR model and the calculation of the PSO can be used to predict the pyrolysis kinetics of microalgae to a good level of fitness.
[1] De Meyer A, Cattrysse D, Rasinmäki J, Van Orshoven J. Methods to optimise the design and management of biomass-for-bioenergy supply chains: A review. Renew Sust Energ Rev. 2014;31:657-70.
[2] Popp J, Lakner Z, Harangi-Rákos M, Fári M. The effect of bioenergy expansion: Food, energy, and environment. Renew Sust Energ Rev. 2014;32:559-78.
[3] Long H, Li X, Wang H, Jia J. Biomass resources and their bioenergy potential estimation: A review. Renew Sust Energ Rev. 2013;26:344-52.
[4] Sindhu R, Gnansounou E, Binod P, Pandey A. Bioconversion of sugarcane crop residue for value added products – An overview. Renewable Energy. 2016;98:203-15.
[5] Xu Y, Chen B. Investigation of thermodynamic parameters in the pyrolysis conversion of biomass and manure to biochars using thermogravimetric analysis. Bioresource Technol. 2013;146:485-93.
[6] Balat M, Balat M, Kırtay E, Balat H. Main routes for the thermo-conversion of biomass into fuels and chemicals. Part 1: Pyrolysis systems. Energy Conversion and Management. 2009;50:3147-57.
[7] Pang S, Mujumdar AS. Drying of Woody Biomass for Bioenergy: Drying Technologies and Optimization for an Integrated Bioenergy Plant. Drying Technology. 2010;28:690-701.
[8] Fagernäs L, Brammer J, Wilén C, Lauer M, Verhoeff F. Drying of biomass for second generation synfuel production. Biomass Bioenerg. 2010;34:1267-77.
[9] Wang X, Tan H, Niu Y, Pourkashanian M, Ma L, Chen E, et al. Experimental investigation on biomass co-firing in a 300 MW pulverized coal-fired utility furnace in China. Proceedings of the Combustion Institute. 2011;33:2725-33.
[10] Tumuluru JS, Wright CT, Hess JR, Kenney KL. A review of biomass densification systems to develop uniform feedstock commodities for bioenergy application. Biofuels, Bioproducts and Biorefining. 2011;5:683-707.
[11] Chen W-H, Peng J, Bi XT. A state-of-the-art review of biomass torrefaction, densification and applications. Renew Sust Energ Rev. 2015;44:847-66.
[12] Oleskowicz-Popiel P, Klein-Marcuschamer D, Simmons BA, Blanch HW. Lignocellulosic ethanol production without enzymes – Technoeconomic analysis of ionic liquid pretreatment followed by acidolysis. Bioresource Technol. 2014;158:294-9.
[13] Chen W-H, Tu Y-J, Sheen H-K. Impact of dilute acid pretreatment on the structure of bagasse for bioethanol production. International Journal of Energy Research. 2010;34:265-74.
[14] Chen W-H, Tu Y-J, Sheen H-K. Disruption of sugarcane bagasse lignocellulosic structure by means of dilute sulfuric acid pretreatment with microwave-assisted heating. Appl Energ. 2011;88:2726-34.
[15] Chen W-H, Ye S-C, Sheen H-K. Hydrolysis characteristics of sugarcane bagasse pretreated by dilute acid solution in a microwave irradiation environment. Appl Energ. 2012;93:237-44.
[16] van der Stelt MJC, Gerhauser H, Kiel JHA, Ptasinski KJ. Biomass upgrading by torrefaction for the production of biofuels: A review. Biomass Bioenerg. 2011;35:3748-62.
[17] Du S-W, Chen W-H, Lucas JA. Pretreatment of biomass by torrefaction and carbonization for coal blend used in pulverized coal injection. Bioresource Technol. 2014;161:333-9.
[18] Chen W-H, Kuo P-C. A study on torrefaction of various biomass materials and its impact on lignocellulosic structure simulated by a thermogravimetry. Energy. 2010;35:2580-6.
[19] Chen W-H, Huang M-Y, Chang J-S, Chen C-Y, Lee W-J. An energy analysis of torrefaction for upgrading microalga residue as a solid fuel. Bioresource Technol. 2015;185:285-93.
[20] Wu Z, Yang W, Tian X, Yang B. Synergistic effects from co-pyrolysis of low-rank coal and model components of microalgae biomass. Energy Conversion and Management. 2017;135:212-25.
[21] Li F, Srivatsa SC, Batchelor W, Bhattacharya S. A study on growth and pyrolysis characteristics of microalgae using Thermogravimetric Analysis-Infrared Spectroscopy and synchrotron Fourier Transform Infrared Spectroscopy. Bioresource Technol. 2017;229:1-10.
[22] Milano J, Ong HC, Masjuki HH, Chong WT, Lam MK, Loh PK, et al. Microalgae biofuels as an alternative to fossil fuel for power generation. Renew Sust Energ Rev. 2016;58:180-97.
[23] Brennan L, Owende P. Biofuels from microalgae—A review of technologies for production, processing, and extractions of biofuels and co-products. Renew Sust Energ Rev. 2010;14:557-77.
[24] Anca-Couce A, Obernberger I. Application of a detailed biomass pyrolysis kinetic scheme to hardwood and softwood torrefaction. Fuel. 2016;167:158-67.
[25] Weerachanchai P, Tangsathitkulchai C, Tangsathitkulchai M. Comparison of pyrolysis kinetic models for thermogravimetric analysis of biomass. Suranaree Journal of Science and Technology. 2010;17:387-400.
[26] Bach Q-V, Chen W-H. A comprehensive study on pyrolysis kinetics of microalgal biomass. Energy Conversion and Management. 2017;131:109-16.
[27] Lin T, Goos E, Riedel U. A sectional approach for biomass: Modelling the pyrolysis of cellulose. Fuel Processing Technology. 2013;115:246-53.
[28] Soria-Verdugo A, Goos E, García-Hernando N. Effect of the number of TGA curves employed on the biomass pyrolysis kinetics results obtained using the Distributed Activation Energy Model. Fuel Processing Technology. 2015;134:360-71.
[29] Bui H-H, Tran K-Q, Chen W-H. Pyrolysis of microalgae residues – A kinetic study. Bioresource Technol. 2016;199:362-6.
[30] Eiben AE, Smith J. From evolutionary computation to the evolution of things. Nature. 2015;521:476-82.
[31] Lin YC, Lee Wj Fau - Chen C-C, Chen Cc Fau - Chen C-B, Chen CB. Saving energy and reducing emissions of both polycyclic aromatic hydrocarbons and particulate matter by adding bio-solution to emulsified diesel.
[32] Lin Y-C, Lee W-J, Chao H-R, Wang S-L, Tsou T-C, Chang-Chien G-P, et al. Approach for Energy Saving and Pollution Reducing by Fueling Diesel Engines with Emulsified Biosolution/Biodiesel/Diesel Blends. Environmental Science & Technology. 2008;42:3849-55.
[33] Pedersen M, Meyer AS. Influence of substrate particle size and wet oxidation on physical surface structures and enzymatic hydrolysis of wheat straw. Biotechnology Progress. 2009;25:399-408.
[34] Bhavanam A, Sastry RC. Kinetic study of solid waste pyrolysis using distributed activation energy model. Bioresource Technol. 2015;178:126-31.
[35] Chen Z, Zhou S, Luo J. A robust ant colony optimization for continuous functions. Expert Systems with Applications. 2017;81:309-20.
[36] Luo J, Liu Q, Yang Y, Li X, Chen M-r, Cao W. An artificial bee colony algorithm for multi-objective optimisation. Applied Soft Computing. 2017;50:235-51.
[37] Zuccolotto M, Pereira CE, Fasanotti L, Cavalieri S, Lee J. Designing an Artificial Immune Systems for Intelligent Maintenance Systems. IFAC-PapersOnLine. 2015;48:1451-6.
[38] de Araujo AF, Constantinou CE, Tavares JMRS. New artificial life model for image enhancement. Expert Systems with Applications. 2014;41:5892-906.
[39] Mazitov T, Božek P, Abramov A, Nikitin Y, Abramov I. Using Bee Algorithm in the Problem of Mapping. Procedia Engineering. 2016;149:305-12.
[40] Liu W-Y, Lin C-C. Spatial forest resource planning using a cultural algorithm with problem-specific information. Environmental Modelling & Software. 2015;71:126-37.
[41] Sheth PN, Babu BV. Differential Evolution Approach for Obtaining Kinetic Parameters in Nonisothermal Pyrolysis of Biomass. Materials and Manufacturing Processes. 2008;24:47-52.
[42] Paperin G, Green DG, Sadedin S. Dual-phase evolution in complex adaptive systems. Journal of the Royal Society Interface. 2011;8:609-29.
[43] Bilal S, Abdelouahab M. Evolutionary algorithm and modularity for detecting communities in networks. Physica A: Statistical Mechanics and its Applications. 2017;473:89-96.
[44] Domínguez-Isidro S, Mezura-Montes E, Osorio-Hernández L-G. Evolutionary programming for the length minimization of addition chains. Engineering Applications of Artificial Intelligence. 2015;37:125-34.
[45] Kashan AH, Akbari AA, Ostadi B. Grouping evolution strategies: An effective approach for grouping problems. Applied Mathematical Modelling. 2015;39:2703-20.
[46] Jafari S, Mahini SS. Lightweight concrete design using gene expression programing. Construction and Building Materials. 2017;139:93-100.
[47] Saha B, Reddy PK, Ghoshal AK. Hybrid genetic algorithm to find the best model and the globally optimized overall kinetics parameters for thermal decomposition of plastics. Chemical Engineering Journal. 2008;138:20-9.
[48] Z-Flores E, Abatal M, Bassam A, Trujillo L, Juárez-Smith P, El Hamzaoui Y. Modeling the adsorption of phenols and nitrophenols by activated carbon using genetic programming. Journal of Cleaner Production. 2017.
[49] Kang S, Chae J. Harmony search for the layout design of an unequal area facility. Expert Systems with Applications. 2017;79:269-81.
[50] Cobos C, Estupiñán D, Pérez J. GHS+LEM: Global-best Harmony Search using learnable evolution models. Applied Mathematics and Computation. 2011;218:2558-78.
[51] Ravichandran B, Gandhe A, Smith R, Mehra R. Robust automatic target recognition using learning classifier systems. Information Fusion. 2007;8:252-65.
[52] Ramadan HS, Bendary AF, Nagy S. Particle swarm optimization algorithm for capacitor allocation problem in distribution systems with wind turbine generators. International Journal of Electrical Power & Energy Systems. 2017;84:143-52.
[53] Chang WL, Pang LM, Tay KM. Application of self-organizing map to failure modes and effects analysis methodology. Neurocomputing. 2017;249:314-20.
[54] Nebti S, Boukerram A. Swarm intelligence inspired classifiers for facial recognition. Swarm and Evolutionary Computation. 2017;32:150-66.
[55] Ding Y, Wang C, Chaos M, Chen R, Lu S. Estimation of beech pyrolysis kinetic parameters by Shuffled Complex Evolution. Bioresource Technol. 2016;200:658-65.
[56] Li K-Y, Huang X, Fleischmann C, Rein G, Ji J. Pyrolysis of Medium-Density Fiberboard: Optimized Search for Kinetics Scheme and Parameters via a Genetic Algorithm Driven by Kissinger’s Method. Energy & Fuels. 2014;28:6130-9.
[57] James K, Russell E. Particle Swarm Optimization. IEEE International Conference on Neural Networks. 1995:1942-8.
[58] Liu J-L, Lin J-H. Evolutionary computation of unconstrained and constrained problems using a novel momentum-type particle swarm optimization. Engineering Optimization. 2007;39:287-305.
[59] Serani A, Leotardi C, Iemma U, Campana EF, Fasano G, Diez M. Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems. Applied Soft Computing. 2016;49:313-34.
[60] Mehdinejad M, Mohammadi-Ivatloo B, Dadashzadeh-Bonab R, Zare K. Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms. International Journal of Electrical Power & Energy Systems. 2016;83:104-16.
[61] Rousset P, Aguiar C, Labbé N, Commandré J-M. Enhancing the combustible properties of bamboo by torrefaction. Bioresource Technol. 2011;102:8225-31.
[62] Chen C-C. Novel Technologies for energy saving and carbon reduction of water emulsified fuel and carbon containing fly ashes. Environmental Engineering: National Cheng Kung University; 2009.
[63] Liu Z, Hu W, Jiang Z, Mi B, Fei B. Investigating combustion behaviors of bamboo, torrefied bamboo, coal and their respective blends by thermogravimetric analysis. Renewable Energy. 2016;87, Part 1:346-52.
[64] Li M-F, Chen C-Z, Li X, Shen Y, Bian J, Sun R-C. Torrefaction of bamboo under nitrogen atmosphere: Influence of temperature and time on the structure and properties of the solid product. Fuel. 2015;161:193-6.
[65] Lu J-J, Chen W-H. Investigation on the ignition and burnout temperatures of bamboo and sugarcane bagasse by thermogravimetric analysis. Appl Energ. 2015;160:49-57.
[66] Chen W-H, Huang M-Y, Chang J-S, Chen C-Y. Torrefaction operation and optimization of microalga residue for energy densification and utilization. Appl Energ. 2015;154:622-30.
[67] Chen C-Y, Chen Y-C, Huang H-C, Ho S-H, Chang J-S. Enhancing the production of eicosapentaenoic acid (EPA) from Nannochloropsis oceanica CY2 using innovative photobioreactors with optimal light source arrangements. Bioresource Technol. 2015;191:407-13.
[68] Chen W-H, Wu Z-Y, Chang J-S. Isothermal and non-isothermal torrefaction characteristics and kinetics of microalga Scenedesmus obliquus CNW-N. Bioresource Technol. 2014;155:245-51.
[69] Rueda-Ordóñez YJ, Tannous K, Olivares-Gómez E. An empirical model to obtain the kinetic parameters of lignocellulosic biomass pyrolysis in an independent parallel reactions scheme. Fuel Processing Technology. 2015;140:222-30.
[70] Manara P, Vamvuka D, Sfakiotakis S, Vanderghem C, Richel A, Zabaniotou A. Mediterranean agri-food processing wastes pyrolysis after pre-treatment and recovery of precursor materials: A TGA-based kinetic modeling study. Food Research International. 2015;73:44-51.
[71] Vyazovkin S, Burnham AK, Criado JM, Pérez-Maqueda LA, Popescu C, Sbirrazzuoli N. ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data. Thermochimica Acta. 2011;520:1-19.
[72] Sfakiotakis S, Vamvuka D. Development of a modified independent parallel reactions kinetic model and comparison with the distributed activation energy model for the pyrolysis of a wide variety of biomass fuels. Bioresource Technol. 2015;197:434-42.
[73] Vamvuka D, Sfakiotakis S. Effects of heating rate and water leaching of perennial energy crops on pyrolysis characteristics and kinetics. Renewable Energy. 2011;36:2433-9.
[74] Bach Q-V, Tran K-Q, Skreiberg Ø, Khalil RA, Phan AN. Effects of wet torrefaction on reactivity and kinetics of wood under air combustion conditions. Fuel. 2014;137:375-83.
[75] Lopez-Velazquez MA, Santes V, Balmaseda J, Torres-Garcia E. Pyrolysis of orange waste: A thermo-kinetic study. Journal of Analytical and Applied Pyrolysis. 2013;99:170-7.
[76] Ma Z, Sun Q, Ye J, Yao Q, Zhao C. Study on the thermal degradation behaviors and kinetics of alkali lignin for production of phenolic-rich bio-oil using TGA–FTIR and Py–GC/MS. Journal of Analytical and Applied Pyrolysis. 2016;117:116-24.
[77] Hu M, Chen Z, Wang S, Guo D, Ma C, Zhou Y, et al. Thermogravimetric kinetics of lignocellulosic biomass slow pyrolysis using distributed activation energy model, Fraser–Suzuki deconvolution, and iso-conversional method. Energy Conversion and Management. 2016;118:1-11.
[78] Rousset P, Davrieux F, Macedo L, Perré P. Characterisation of the torrefaction of beech wood using NIRS: Combined effects of temperature and duration. Biomass Bioenerg. 2011;35:1219-26.
[79] Chen W-H, Kuo P-C. Torrefaction and co-torrefaction characterization of hemicellulose, cellulose and lignin as well as torrefaction of some basic constituents in biomass. Energy. 2011;36:803-11.
[80] Tapasvi D, Khalil R, Skreiberg Ø, Tran K-Q, Grønli M. Torrefaction of Norwegian Birch and Spruce: An Experimental Study Using Macro-TGA. Energy & Fuels. 2012;26:5232-40.
[81] Mi B, Liu Z, Hu W, Wei P, Jiang Z, Fei B. Investigating pyrolysis and combustion characteristics of torrefied bamboo, torrefied wood and their blends. Bioresource Technol. 2016;209:50-5.
[82] Li H, Liu X, Legros R, Bi XT, Lim CJ, Sokhansanj S. Torrefaction of sawdust in a fluidized bed reactor. Bioresource Technol. 2012;103:453-8.
[83] Saelee K, Yingkamhaeng N, Nimchua T, Sukyai P. An environmentally friendly xylanase-assisted pretreatment for cellulose nanofibrils isolation from sugarcane bagasse by high-pressure homogenization. Industrial Crops and Products. 2016;82:149-60.
[84] Li M-F, Chen L-X, Li X, Chen C-Z, Lai Y-C, Xiao X, et al. Evaluation of the structure and fuel properties of lignocelluloses through carbon dioxide torrefaction. Energy Conversion and Management. 2016;119:463-72.
[85] Kirtania K, Bhattacharya S. Application of the distributed activation energy model to the kinetic study of pyrolysis of the fresh water algae Chlorococcum humicola. Bioresource Technol. 2012;107:476-81.
[86] De Filippis P, de Caprariis B, Scarsella M, Verdone N. Double Distribution Activation Energy Model as Suitable Tool in Explaining Biomass and Coal Pyrolysis Behavior. Energies. 2015;8.
[87] López-González D, Fernandez-Lopez M, Valverde JL, Sanchez-Silva L. Pyrolysis of three different types of microalgae: Kinetic and evolved gas analysis. Energy. 2014;73:33-43.
[88] Gai C, Zhang Y, Chen W-T, Zhang P, Dong Y. Thermogravimetric and kinetic analysis of thermal decomposition characteristics of low-lipid microalgae. Bioresource Technol. 2013;150:139-48.
[89] Agrawal A, Chakraborty S. A kinetic study of pyrolysis and combustion of microalgae Chlorella vulgaris using thermo-gravimetric analysis. Bioresource Technol. 2013;128:72-80.
[90] Zhao B, Wang X, Yang X. Co-pyrolysis characteristics of microalgae Isochrysis and Chlorella: Kinetics, biocrude yield and interaction. Bioresource Technol. 2015;198:332-9.
[91] Soria-Verdugo A, Goos E, Morato-Godino A, García-Hernando N, Riedel U. Pyrolysis of biofuels of the future: Sewage sludge and microalgae – Thermogravimetric analysis and modelling of the pyrolysis under different temperature conditions. Energy Conversion and Management. 2017;138:261-72.
[92] Fagerson IS. Thermal degradation of carbohydrates; a review. Journal of Agricultural and Food Chemistry. 1969;17:747-50.
[93] Pavlath AE, Gregorski KS. Atmospheric pyrolysis of carbohydrates with thermogravimetric and mass spectrometric analyses. Journal of Analytical and Applied Pyrolysis. 1985;8:41-8.
[94] Ross AB, Jones JM, Kubacki ML, Bridgeman T. Classification of macroalgae as fuel and its thermochemical behaviour. Bioresource Technol. 2008;99:6494-504.
[95] Kebelmann K, Hornung A, Karsten U, Griffiths G. Intermediate pyrolysis and product identification by TGA and Py-GC/MS of green microalgae and their extracted protein and lipid components. Biomass Bioenerg. 2013;49:38-48.
[96] Tibbetts SM, Milley JE, Lall SP. Chemical composition and nutritional properties of freshwater and marine microalgal biomass cultured in photobioreactors. Journal of Applied Phycology. 2015;27:1109-19.
[97] Brown MR, Jeffrey SW. Biochemical composition of microalgae from the green algal classes Chlorophyceae and Prasinophyceae. 1. Amino acids, sugars and pigments. Journal of Experimental Marine Biology and Ecology. 1992;161:91-113.