Evaluation and Products Characterization of Mango Seed Shell and Kernel Conventional Pyrolysis

EVALUATION AND PRODUCTS CHARACTERIZATION OF MANGO SEED SHELL AND KERNEL CONVENTIONAL PYROLYSIS

Thesis By JUAN CAMILO MAHECHA RIVAS

Presented in the Engineering Faculty of Universidad de los Andes In fulfillment of the requirements for the Degree of

CHEMICAL ENGINEER

Approved by: Advisor, Rocio Sierra Ramírez, Ph.D.

Chemical Engineering Department Bogotá, Colombia January 2020

Evaluation and products characterization of mango seed shell and kernel conventional pyrolysis Juan C. Mahecha-Rivas Department of Chemical Engineering, University of Los Andes, Bogotá, Colombia

GENERAL OBJECTIVE To characterize mango seed’s conventional pyrolysis products at optimal conditions for further valorization

SPECIFICS OBJECTIVES - To evaluate the influence of temperature of conventional pyrolysis in bio-oil, biochar and biogas yields - To compare the pyrolysis’s yields from kernel, shell and kernel/shell mixture fed. - To characterize biochar, bio-oil, and biogas from mango seed’s kernel and shell pyrolysis - To determine the feasibility of mango seed bio-oil as a biodiesel precursor or additive

TABLE OF CONTENTS

Abstract ...... 1 1. Introduction ...... 1 2. Methods ...... 4 2.1. Materials and sample preparation ...... 4 2.2. Biomass characterization ...... 4 2.2.1. Ultimate analysis ...... 4 2.2.2. Fourier transform infrared spectroscopy analysis (FTIR) ...... 5 2.2.3. Simultaneous thermogravimetric and differential scanning calorimetry analysis (TGA-DSC) ...... 5 2.3. Pyrolysis Process ...... 5 2.3.1. Tubular furnace...... 5 2.3.2. Laboratory-scale pyrolysis ...... 6 2.4. Bio-oil characterization ...... 8 2.4.1. Gas chromatography couple with mass spectrometry analysis (GC-MS) ...... 8 2.4.2. Fourier transform infrared spectroscopy analysis (FTIR) ...... 8 2.4.3. Calorific value determination ...... 8 2.4.4. Distillation curve ...... 8 2.5. Biochar characterization ...... 9 3. Results and discussion ...... 9 3.1. Biomass characterization ...... 9 3.1.1. Biomass ultimate and compositional analysis ...... 9 3.1.2. Biomass FTIR ...... 10 3.1.3. Biomass TGA-DSC analysis ...... 11 3.2. Pyrolysis Process ...... 12 3.2.1. Tubular Furnace ...... 12 3.2.2. Laboratory-scale pyrolysis ...... 16 3.3. Bio-oil characterization ...... 19 3.3.1. Bio-oil GC-MS ...... 19 3.3.2. Bio-oil FTIR ...... 24

3.3.3. Calorific value determination ...... 25 3.3.4. Distillation curve ...... 25 3.4. Biochar characterization ...... 26 3.4.1. Elemental and HHV analysis ...... 26 3.4.2. Biochar FTIR ...... 27 4. Conclusions ...... 28 5. Recommendations and future work ...... 29 6. References ...... 30 7. Annexes ...... 36 7.1. Tubular pyrolysis statistical analysis ...... 36 7.1.1. Yields ...... 36 7.1.2. Mass losses ...... 38 2.1.1. Mass losses ...... 39 7.2. Laboratory scale pyrolysis ...... 40 7.3. Bio-oils characterization ...... 41

iii

LIST OF FIGURES Figure 1. Tubular furnace system for small-scale pyrolysis ...... 5 Figure 2. Laboratory-scale pyrolysis equipment (0 = flowmeter, 1 = nitrogen inlet with 6 thermocouples, 2 = reactor chamber, 3 = first SS condenser, 4 = bio-oil amber jar, 5 = second condenser, 6 = heat exchanger, 7 = gas analyser, 8 = gas burner flame/extraction hood) ..... 7 Figure 3. Simple distillation equipment for distillation curve ...... 9 Figure 4. Biomass (Kernel and Shell) elemental composition and higher heating value ..... 10 Figure 5. Biomass FTIR analysis ...... 11 Figure 6. Biomass TGA-DSC analysis ...... 12 Figure 7. Tubular furnace pyrolysis conversions ...... 13 Figure 8. Mass Losses ...... 15 Figure 9. Biochar mass gain by factor ...... 16 Figure 10. Yield obtained in laboratory-scale pyrolyzes test ...... 16 Figure 11. Biogas analysis ...... 18 Figure 12. TGA-DSC analysis for mass deposits ...... 19 Figure 13. Bio-oils FTIR ...... 24 Figure 14. Bio-oils distillation curves ...... 25 Figure 15. Distillation curve of 4 alternative diesel fuels (Gough & Bruno, 2012) ...... 26 Figure 16. Biochar elemental analysis ...... 27 Figure 17. Biochar FTIR ...... 28 Figure 18. Main effects of temperature and biomass on bio-oil production yields ...... 36 Figure 19. Main effects of temperature and biomass on biochar production yields ...... 36 Figure 20. Main effects of temperature and biomass on biogas production yields ...... 37 Figure 21. Mean comparison of bio-oil production yield according to biomass by Sidak test ...... 37 Figure 22. Mean comparison of bio-oil production yield according to temperature by Sidak test ...... 37 Figure 23. Mean comparison of biochar production yield according to biomass by Sidak test ...... 37 Figure 24. Mean comparison of biochar production yield according to temperature by Sidak test ...... 37

Figure 25. Mean comparison of biogas production yield according to biomass by Sidak test ...... 38 Figure 26. Mean comparison of biogas production yield according to temperature by Sidak test ...... 38 Figure 27. Main effects of temperature and biomass on biochar losses ...... 38 Figure 28. Main effects of temperature and biomass on bio-oil losses ...... 38 Figure 29. Mean comparison of bio-oil mass losses according to biomass by Sidak test .... 39 Figure 30. Mean comparison of bio-oil mass losses according to temperature by Sidak test ...... 39 Figure 31. Discriminated biochar mass gain ...... 39 Figure 32. Thermocouple distribution (Ortiz et al., 2017) ...... 40 Figure 33. Register pyrolysis temperature in the laboratory-scale reactor during kernel test ...... 41 Figure 34. Chromatogram for shell bio-oil ...... 41 Figure 35. Chromatogram for kernel bio-oil ...... 42 Figure 36. Chromatogram for mixture bio-oil ...... 42

LIST OF TABLES Table 1. Total solids and compositional analysis of mango kernel and shell in dried basis by Durán (2019) ...... 2 Table 2. Typical product yields (dry wood basis) obtained by different pyrolysis types. From Bridgwater (2004)...... 3 Table 3. Compounds identified by GC–MS of mango shell, kernel and mix bio-oil ...... 20 Table 4. Uses of the most abundant compounds identified by GC–MS in mango shell, kernel and mix bio-oil ...... 22 Table 5. Yields statistical analysis ...... 36 Table 6. Mass losses statistical analysis ...... 38 Table 7. Biochar Mass Gain statistical analysis ...... 40

Evaluation and products characterization of mango seed shell and kernel conventional pyrolysis Juan C. Mahecha-Rivas Department of Chemical Engineering, University of Los Andes, Bogotá, Colombia Abstract With a national production of 361 tons in 2018, mango is a fundamental crop for the development of Colombia, with annual production growth of 20% (Casa Editorial El Tiempo, 2019; MinAgricultura & Agronet, 2019). However, as production increases, so does mango by-products such as seeds. Conventional pyrolysis comes out as a high potential treatment for the conversion of low degradability biomass, such as mango seeds, into added-value products. In this study, the conversion of mango seeds in biochar, bio-oil, and biogas by conventional pyrolysis was evaluated at a wide temperature range (400, 500, 600 and 700°C) and for the different components of the seeds (shell, kernel, and 1:2 mixture). Characterization of biomass (ultimate analysis, FTIR and calorific value) was made. Pyrolytic products were obtained at the optimum temperatures found. Characterization of biochar (ultimate analysis, FTIR and calorific value), bio-oil (GC-MS, FTIR, calorific value and distillation curve) and biogas (composition and calorific value) was also carried out at the optimal temperatures. Biomass fed had a significant effect in production yields, whether temperature had no significant effect over the bio-oil production. At optimum temperatures, bio-oil composition differed between biomasses due to its composition, biogas of kernel pyrolysis had a greater energy value due to the higher temperature set and biochars of the three biomasses fed had similar properties and compositions. High moisture content in bio- oil made it unsuitable for biofuels production unless further treatment is made. Keywords: Conventional pyrolysis, mango seed waste, kernel, shell, bio-oil. 1. Introduction Agriculture is one of the fundamental pillars of the Colombian economic framework. It represented 6.27% of the GDP in 2018, almost doubling the word average (World Bank & OECD, 2019). One of the main agriculture products is mango, a tropical fruit deeply rooted in the Colombian culture, known by its sweet and refreshing taste, with an incredible highly worldwide importance and value (Kittiphoom, 2012). Just in 2018 in Colombia, mango production reached 321 thousand tons. Even more, from 2017 to 2018, there was an annual production growth of 20% and 55.6% in mango exportations (Casa Editorial El Tiempo, 2019; MinAgricultura & Agronet, 2019). Despite the benefits of its success, as mango production increases, so does the residues generated in the industrial processing of the fruit. Only between 35% to 60% of the mango total weight is used (Chandrasekaran, 2012). The rest, which is composed of the peel

(pericarp), shell (endocarp) and kernel (seed), is extensively incinerated and/or erroneously managed (Meireles et al., 2010). This posed an environmental threat with legal sanctions unless correct waste management is implemented. This could significantly reduce the profit margin of mango production due to the high residue volume (Ayala-Zavala et al., 2011). Thus, is imperative to investigated valorization methods to deal with mango residues and generate additional profits. Specifically, mango seed is composed of an external fibrous cape called shell, which protects the kernel1. The kernel contains the embryo and reserves (Lazzari et al., 2016). The shell has high cellulose, hemicellulose, and lignin proportion, while kernel consists mainly of cellulose and fatty acids (Lazzari et al., 2016). In a previous work by Durán (2019) a complete compositional analysis using the NREL protocols was made to determine proteins, hemicellulose, lignin, pectin, extractives and cellulose fraction in mango shell and kernel (Table 1). Also, in Durán (2019), total solids and ash were determined (Table 1). The low ash content and extended lignocellulosic material make mango shell and kernel ideal biomass for thermal degradation by pyrolysis to obtain added-value products such as biodiesel (Bridgewater, 2004). Furthermore, the pyrolysis of seeds can become a valuable biochemicals source (Ganeshan, Shadangi, & Mohanty, 2016). Table 1. Total solids and compositional analysis of mango kernel and shell in dried basis by Durán (2019)

Component Shell Kernel Total solids 61.23 64.7 Ash 2.4 ± 0.016 2.11 ± 0.04 Protein 1.56 ± 0.05 5.25 ± 0.04 Hemicellulose 18.4 ± 1.5 n. d. Lignin 16.5 ± 0.46 7.79 ± 0.42 Pectin n. d. 5.79 ± 0.08 Extractives 2.1 ± 0.04 15.9 ± 0.02 Cellulose 57.5 ± 1.8 62.76 ± 1.26 Total 98.4 ± 3.8 99.6 ± 1.86 Pyrolysis is a thermal decomposition treatment for the transformation of biomass into added value products in an inert atmosphere without oxygen (Bridgewater, 2004). Pyrolysis transformed biomass into three material states2: solid product, known as biochar, char or carbon; condensable liquid, also known as bio-oil, tar or pyrolytic oil and is usually composed by an organic and aqueous phase; and non-condensable gases refer as biogas, gas or syngas (Al Arni, 2018). These are high energy products, which can be used as solid, liquid and gas fuels or as chemical synthesis feedstocks (Eckert et al., 2016). The distribution of the biomass conversion into these products vary according to the type of pyrolysis used. Slow

1 The botanically correct names are stone, endocarp, and seed, respectively. However, for better understanding and paper comparison, in the rest of the presented work seed will stand for stone, shell for endocarp and kernel for seed. 2 Throughout this work, biochar will refer to the solid pyrolysis product, bio-oil to all liquid pyrolysis products and biogas to the pyrolytic gas. 2 pyrolysis or carbonization is characterized by its low-temperature range (300 - 500 ° C) and high residence times (hours or days). Conventional pyrolysis is characterized by medium temperatures (400 - 600 ºC) and short residence times (5 - 30 min). Fast pyrolysis is characterized by temperatures between 400 and 650 ºC and short residence times (0.5 - 5 s) (Eckert et al., 2016). Gasification is characterized by temperatures above 700°C and high vapor residence times (Yasin et al., 2019). The average percentage of pyrolytic products for each type is shown in Table 2. Table 2. Typical product yields (dry wood basis) obtained by different pyrolysis types. From Bridgwater (2004).

Process Conditions Char Liquid Gas Fast pyrolysis Moderate temperature, short residence time 12% 75% 13% particularly vapor Carbonization Low temperature, very long residence time 35% 30% 35% Gasification High temperature, long residence times 10% 5% 85% Bio-oils are the product of the condensation of gases formed by cracking, decomposition and thermal polymerization of matter in the absence of oxygen. High lignin and extractives content in biomass are related to the formation of bio-oil (Ganeshan et al., 2016). This liquid product is highly unstable and may even be subject to continuous reactions, such as oxidation, once obtained (Diebold, 2000; Eckert et al., 2016). Nonetheless, bio-oil could be used as the feedstock of second-generation bio-fuels or as a starting material for numerous chemicals compounds (Bridgewater, 2004). During pyrolysis, the thermal decomposition of biomass composites produced a wide variety of molecules. For instance, hemicellulose decomposes forming furan and furan derivatives, while lignin produces polymers of aromatic compounds, mainly phenol and methyl phenol derivatives, which constitute the dark oil (Ganeshan et al., 2016). Hence, bio-oils chemical composition is a complex mixture of a great variety of organic compounds such as alcohols, ketones, aldehydes sugars, furans, guaiacols, syringe, phenols, acids, ethers, hydrocarbons and esters (Eckert et al., 2016; Effendi, Gerhauser, & Bridgwater, 2008; Ganeshan et al., 2016; Jahirul, Rasul, Chowdhury, & Ashwath, 2012). A large range of bio-oil chemicals have high industrial importance (Lazzari et al., 2016; Sadaka & Boateng, 2009), for example, phenols in manufacture of resin, sugars as levoglucosan in pharmacologic, biodegradable polymers and surfactants manufacture, and hydroxyacetaldehyde which is one of the most active meat-browning agent (Bridgewater, 2004; Lazzari et al., 2016). As for fuel precursors or additives, bio-oil usually has a high content of oxygenated molecules that restrict its direct use. Moreover, a high oxygenated compound composition made bio-oil an unstable mixture (Czernik & Bridgwater, 2004). In addition, the bio-oil has a high moisture content, solid particles and alkali metals (Diebold, 2000). This composition explains the low calorific value (16 - 19 MJ/kg), acid pH, high density (1.2 g/ml) and low viscosities of the bio-oil (Eckert et al., 2016). As a result of these characteristics, further

3 treatment needs to be applied over bio-oils to reach desirable properties to be incorporated in petroleum productive line (Cardoso & Ataíde, 2013; Eckert et al., 2016; Sadaka & Boateng, 2009). The present work aims to characterize mango seed’s conventional pyrolysis products at optimal conditions for further valorization. Optimal conditions are identified by evaluating the influence of temperature of conventional pyrolysis in bio-oil, biochar and biogas yields. Also, a comparison of the pyrolysis’s yields from kernel, shell, and kernel/shell mixture is made and characterization of biochar, bio-oil and biogas products. Finally, based on bio-oil characterization, the feasibility of mango seed bio-oil as a biodiesel precursor or additive is determined. 2. Methods 2.1. Materials and sample preparation Mango seeds were taken from local groceries and food locals from Bogota, Colombia. The seeds were washed with distillate water to remove all remaining fruit mesocarp. Initially, all seeds were dried at 45°C in a convection oven for 48 h following the NREL sample preparation procedure NREL/TP-510-42620 (Hames et al., 2008). The kernel and shell of the seeds were separated manually crushing the cortex with a hammer and metal scissors. The dried kernel was grinded with an artisanal worm mill and the dried shell with the Universal Cutting Mill Pulverisette 19 Fritsch. All biomass was sieved with a 20-mesh (0.81 mm opening) to remove larger particles. The sieve biomass of kernel and shell were stored in sealed plastic bags at 20°C for further use. 2.2. Biomass characterization 2.2.1. Ultimate analysis Ultimate analysis of the kernel and shell was carried out in Elementar Vario Macro CHNS analyzer. The ASTM D5373-16 protocol was followed to measure carbon (C), hydrogen (H), nitrogen (N) and sulfur (S) concentrations (D05 Committee, n.d.). Oxygen was calculated as the balance of the mass fractions. The higher calorific value for each sample was calculated using the Demirbas formula - Equation 1 (1997). 푀퐽 퐻퐻푉 [ ] = 33.5(퐶) + 142.3(퐻) − 15.4(푂) − 14.5(푁) 푘푔 Equation 1. Higher heating energy for biomass3 (Demirbaş, 1997)

3 C, H, O, and N correspond to the elemental fraction (between 0 to 1) of carbon, hydrogen, oxygen, and nitrogen in dry basis of biomass, respectively. 4

2.2.2. Fourier transform infrared spectroscopy analysis (FTIR) FTIR analysis of the kernel and shell were performed with the Bruker ALPHA FT-IR Eco- ART (attenuated total reflection) spectrometer. The samples IR directly measured at a scan range of 4000 cm-1 to 600 cm-1 with a 4 cm-1 resolution. 2.2.3. Simultaneous thermogravimetric and differential scanning calorimetry analysis (TGA-DSC) TGA-DSC analysis of the kernel and shell were performed with the TA Instruments SDT- Q600 Simultaneous TGA/DSC analyzer. The biomasses were heated form 30°C to 1000°C (983.68) with a 30°C/min heating rate under 60 ml/min UPA nitrogen flow. 2.3. Pyrolysis Process 2.3.1. Tubular furnace 2.3.1.1. Equipment To determine the optimum temperature for each biomass to maximize bio-oil yield, 24 pyrolyzes were conducted to develop a complete factorial design of 2 factors, final temperature (400, 500, 600 and 700 °C) and biomass fed (shell, kernel and 1:2 shell/kernel mixture), by duplicate. The pyrolyzes were carried out in a tubular furnace (Tube Furnace Mini-MiteTM TF55030A Lindberg/Blue with UP150 controller). The tubular furnace was attached to a 50 cm Liebig glass condenser with water at 5°C as a refrigerant (1 l/min). At the end of the condenser, the bio-oil was collected in a 250 ml amber jar previously weighted for each experiment. See Figure 1.

Figure 1. Tubular furnace system for small-scale pyrolysis

2.3.1.2. Process The furnace was fed with 15.8±0.7 g of biomass. The pyrolyzes were conducted with a continuous flow of industrial nitrogen (10±2 ml/min) after the system was purged for 20 minutes. A 30 °C/min ramp was used to reach the different final temperatures. When reached, the final temperature was maintained constant for 15 minutes. Due to the lack of an inner refrigeration system, an approximate of 1 to 2 hours were used to cool down the furnace (150°C) with a continuous flow of nitrogen. 2.3.1.3. Yield calculations The yields of bio-oil and biochar were calculated by the percentage of mass obtained of bio- oil/biochar divided by the total mass fed. For each pyrolysis, the bio-oil was collected directly in a purged 250 ml amber jar previously weighted. Also, at the system dismantling, any leakage from the fittings was recovered directly in the jar employing a funnel. After all the system was dismantled, the jar closed was weighted. The difference between the initial and final weight of the jar was the mass of bio-oil produced. For the biochar, the mass obtained was determined by transferring the content of the furnace dial to a previously weighed beaker. The difference between the initial and final weight of the beaker was the mass of biochar obtained. The biogas yield was established as the mass not transformed into biochar or bio- oil. 2.3.1.4. Mass losses For 14 pyrolyzes a mass loss analysis was made as a result of condensed deposits, which were seen in the system’s fittings and parts. All the parts of the system were weighed before and after the pyrolysis. The dial was weighted before feeding, after feeding, after pyrolysis and after biochar transference to the beaker. The glass condenser was weighted also after cleaning. The difference between the weights of the dial corresponds to the biochar losses, and the residual mass of the other parts correspond to bio-oil losses. 2.3.1.5. Biochar mass gain The variation of the weight of biochar in a short period was measured for 12 pyrolyzes. Every 5 minutes from its transference to the beaker, the biochar was weighed for 20 minutes straight. This variable is used as a subrogated variable of the biochar’s humidity adsorption capacity. 2.3.2. Laboratory-scale pyrolysis 2.3.2.1. Equipment To obtain large amounts of biochar and bio-oil, for each biomass (shell, kernel and 1:2 shell/kernel mixture) pyrolysis was carried out at the optimum temperature found in a laboratory-scale fixed bed pyrolysis reactor with a 35 L feeding capacity. The reactor 316L stainless steel (SS) chamber was connected to a one-meter SS condenser (1” diameter) with 2 kg of dry ice as a refrigerant (-70°C). The bio-oil was recovered in a previously weighted

6 amber jar. The gases which were not condensed, passed through another one-meter SS condenser with 2 kg of dry ice, for further bio-oil recover. The uncondensed gasses (biogas) were cooled down in a shell and tube heat exchanger (1°C water-ethanol mixture as a refrigerant) and passed through a series of air filters for compositional analysis by a gas analyzer (Wuhan Cubic Optoelectronics GAS 3100) or discharge. See Figure 2.

Figure 2. Laboratory-scale pyrolysis equipment (0 = flowmeter, 1 = nitrogen inlet with 6 thermocouples, 2 = reactor chamber, 3 = first SS condenser, 4 = bio-oil amber jar, 5 = second condenser, 6 = heat exchanger, 7 = gas analyser, 8 = gas burner flame/extraction hood) 2.3.2.2. Process The reactor was fed with 301.4±0.1 g of biomass. The pyrolyzes were conducted with a continuous flow of industrial nitrogen (1 l/min). The system was purged until no oxygen concentration was detected. To ensure no gas leaks, the reactor and condensation systems were pressurized until zero flow condition was encountered. Also, the same test was performed with the gas analyzer. Additionally, a flow verification test was executed by comparing inlet an outlet flow for leakage identification as well. For the pyrolyzes, 21% input power was used with a set point 70°C above the desired final temperature under the developer specifications (Ortiz, Amaya, & Hernández, 2017). The biogas was analyzed every 5 minutes, ensuring a flow of 1 l/min for the analysis equipment. The pyrolyzes were terminated when no significant changes in the biogas analytes concentrations were observed. The system took around 12 hours to cooled down. Yields were calculated as described in section 2.3.1.3. 2.3.2.3. Mass losses For all 3 pyrolyzes a mass deposits were measured and collected. All the parts of the system were weighed before and after the pyrolysis. The deposits were collected and later storage in a plastic bag at 5°C. The difference between the weights of fittings and parts correspond to bio-oil losses. The deposits were characterized by TGA-DSC as described in section 2.2.3.

2.4. Bio-oil characterization 2.4.1. Gas chromatography couple with mass spectrometry analysis (GC-MS) The presence and absence of volatile compounds were determined by GC-MS. For sample preparation, 250 µl of bio-oil were taken to 1 ml with dichloromethane (DCM). The analysis was performed using Agilent Technology 6890N Network gas chromatographer coupled with the Agilent Technology VLMSD 5975B mass selective spectrometer. An HP5 DB-5 MS column of 30 m length, 0.25 mm diameter and 0.1 µm filter was used. The GC was programmed for a total run time of 30 min at 313 K for 0.5 min with a heating rate of 10 K/min and 1 µl injection (Ganeshan et al., 2016). Helium UAP was used as the carrier at 0.6 ml/min flow. 2.4.2. Fourier transform infrared spectroscopy analysis (FTIR) Due to its low pH (Diebold, 2000), for each sample, 1 ml of bio-oil was diluted in 4 ml of absolute ethanol with 50 µl of 3 N NaOH solution. The obtained solutions were analyzed as in section 2.2.2. 2.4.3. Calorific value determination The Parr 6200 Oxygen Bomb Calorimeter was used to determine the calorific value of the bio-oils following the ASTM E711-87 protocol (D34 Committee, n.d.). Raw bio-oil and dry bio-oil (60°C for 12 h) were tested. 2.4.4. Distillation curve 2.4.4.1. Equipment To obtain the distillation curve, a simple distillation was performed over the bio-oils produced in the laboratory scale pyrolyzes based on the ASTM D86-18 (D02 Committee, n.d.). The system was composed of 250 ml 1-mouth flask over an electrical heating mantel of 240 W. The flask was connected to a 3-way distillation head with a thermocouple at the top. Two Liebig condensers were connected in series to the distillation head, providing a 50 cm condensation line, followed by a receiver-deliver adapter. The distillate was recovered in a 50 ml glass graduated cylinder above a balance. See Figure 3.

Figure 3. Simple distillation equipment for distillation curve 2.4.4.2. Process All bio-oils were filtered, weighted and their volumes measured (300 µm filter). 50 ml of the treated and weighed bio-oil was poured into a 250 ml flask of the distillation system. Once the bio-oil was in the flask, the system was closed. During the bio-oil distillation, the temperature, weight, and volume were recorded from the first distillate drop to the last, every 1 ml (except the initial 5 ml due to lack of graduation marks in the cylinder). 2.5. Biochar characterization Biochars were characterized by applying FTIR and elemental analysis as described in section 2.2.2 and 2.2.1, respectively. 3. Results and discussion 3.1. Biomass characterization 3.1.1. Biomass ultimate and compositional analysis Figure 4 displays the ultimate analysis and Table 1 in Annexes by Duran (Durán Aranguren, 2019) displays the compositional analysis. At first, there are no significant differences between kernel and shell elemental composition. Most of the differences do not exceed a 1% margin. For nitrogen, the difference can be attributed to the portion of proteins, which is a greater portion for kernel. Sulfur content can be associated with ash content in inorganic compounds or proteins. Low nitrogen and sulfur are desirable due to the reduced nitrogen and sulfur oxides emissions by biofuels (Andrade, Barrozo, & Vieira, 2016; Ola & Jekayinfa, 2014). High carbon fraction is a primary indicator of high energy value due to the presence of carbon-carbon bonds (Andrade et al., 2016). Hydrogen fraction is within the usual 9 concentrations for kernel and shell (Andrade et al., 2016; Ganeshan et al., 2016; Ola & Jekayinfa, 2014). Furthermore, low ash composition indicates possible reduced biochar production during pyrolysis for both biomasses.

Biomass Elemental Composition Biomass Higher

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Figure 4. Biomass (Kernel and Shell) elemental composition and higher heating value Also, based on the elemental analysis, in Figure 4 the higher heating value (HHV) of both biomasses is presented. Between kernel and shell, due to their similar elemental composition, there is not a significant difference. Since shell has a slightly bigger oxygen fraction than kernel, the HHV is lower due to the negative influence of oxygen in energy values. Compared to literature (Ganeshan et al., 2016; Lazzari et al., 2016; Ola & Jekayinfa, 2014), the values are below other estimations and measures, probably due to the presences of water traces. 3.1.2. Biomass FTIR In Figure 5, FTIR analysis results for shell and kernel are presented. Low moisture content can be identified between 3200-3400 cm-1 by the axial deformation of O-H group (de Souza et al., 2015; Ganeshan et al., 2016). Also, hemicellulose presence is confirmed by 2850 and 3000 cm-1 peaks (Ganeshan et al., 2016). The 2920 cm-1 peaks are appreciated due to the presence of waxes by the CH2 stretching bands (Nanda et al., 2013). The presences of lignin is confirmed by 1644 cm-1 and 1510 cm-1 peaks due to the stretching of O-H phenolic groups and C=C aromatic skeletal mode bonds, respectively (Andrade et al., 2016; Ganeshan et al., 2016; Sun, Xu, Sun, Fowler, & Baird, 2005; Xiao, Sun, & Sun, 2001). The peak at 1042 cm- 1 band is characteristic of stretching of C-O and C-H in cellulose and hemicellulose (pyranose rings and guaiacyl monomers) (Andrade et al., 2016; Nanda et al., 2013; H. Yang, Yan, Chen, Lee, & Zheng, 2007). C-H scissoring deformation of the aromatic ring at the range 898–678 cm−1 is evident (de Souza et al., 2015). The main difference encounter between the samples is related to the cellulose peak (1042 cm-1) that is greater for the kernel, which agrees with the compositional analysis (Table 1). Also, the 782 cm-1 peak is associated with C–H alkynes bends (Nanda et al., 2013), which can be related to complex molecules such as proteins. 10

Shell Kernel 100%

98%

t i

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92% 4000 3500 3000 2500 2000 1500 1000 500 4000 3500 3000 2500 2000 1500 1000 500 Wave Number (cm-1) Wave Number (cm-1)

Figure 5. Biomass FTIR analysis 3.1.3. Biomass TGA-DSC analysis In Figure 6 TGA-DSC analysis results for kernel and shell are displayed. The thermal degradation can be divided into 4 stages. First, from 30 to 150°C there are water and CO2 lost (Andrade et al., 2016; Lazzari et al., 2016). From 200°C to 300°C the hemicellulose suffered thermal degradation (de Souza et al., 2015; Nanda et al., 2013), which is evident only the shell biomass due to its high concentration (Table 1), whereas no presence is identified in kernel sample. The third stage goes from 250°C to 350°C where the cellulose is decomposed (de Souza et al., 2015; Nanda et al., 2013; H. Yang et al., 2007). Finally, the fourth stage which ranges from 200°C to 500°C is characterized by the decomposition of lignin (Nanda et al., 2013). The weight loss is higher for this stage as the percentage of lignin is higher. Regarding the heat flow, it appears that the decomposition of lignin provides an important amount of energy, based on the comparison between kernel and shell DSC lines.

Shell Kernel

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e 0.2 e

D -6

0.0 -8 0.0 -8 0 200 400 600 800 1000 0 200 400 600 800 1000 Temperature (°C) Temperature (°C) Figure 6. Biomass TGA-DSC analysis 3.2. Pyrolysis Process 3.2.1. Tubular Furnace 3.2.1.1. Optimum conversion temperature Figure 7 shows the yields of biochar, bio-oil and biogas production for each temperature and feeding. The error bars correspond to the standard deviation of the duplicates, which for all cases were below 5%. A 2-way ANOVA statistical analysis was made to determine the influence of the temperature and biomass type over the production yields with a 95% confidence. Normality and homoscedasticity assumptions were verified. In Table 5 in Annexes, all results of the analysis and assumptions verification are presented. Also, Sidak test was used to compare the different groups means4.

4 Sidak mean comparison test was used since it is the most conservative method with false-positives protection (Minitab, 2019a, 2019b) 12

Detailed Tubular Furnace Pyrolysis

Biochar Bio-oil Biogas

. 4

60% .

6 1

40% 6

Yield (%)

1 7

20% 1

6 7 Shell Kernel Mix Temperature (°C)

Figure 7. Tubular furnace pyrolysis conversions For bio-oils, there is statistical evidence of the biomass influence over the production yields (0.000 p-value). The highest bio-oil yields are presented in shell pyrolysis, follow by the mixture and last the kernel (Figure 18). Furthermore, kernel, shell, and mix yields are statistically different (mean comparison by Sidak test - Figure 21). The bio-oil yield’s difference is consistent with the literature (Ganeshan et al., 2016; Lazzari et al., 2016). The higher content of lignin of shell (Table 1) explains the higher bio-oil production at high temperatures (Imam & Capareda, 2012; Onay, 2007). Also, the yields obtained are lower than those reported in Ganeshan et al. (2016) as a result of the formation of deposits of high- viscosity/easy-solidified liquids in the condensation system. In terms of the temperature, there is no statistical evidence of its influence over bio-oil production (0.619 p-value). In literature, the influence of temperature in bio-oil production is clearly evident (Ganeshan et al., 2016; Ola & Jekayinfa, 2014; Onay, 2007), with a specific optimum which is determined by the competition between the formation of pyrolytic vapors and secondary cracking of them. The lack of more replicates and the large discretization of the temperature range could explain the non-statistical significance of temperature. In addition, physically, only one aqueous phase was appreciated in bio-oil for all three biomasses. Two-phases in mango bio- oil are not reported in the literature. For biochar, biomass has a statistical influence in the biochar production yields (0.000 p- value). Based only on the means, kernel did get the higher yields, followed by the mixture and finally, shell (Figure 19), which is consistent with the literature (Ganeshan et al., 2016; Lazzari et al., 2016). Statistically, kernel and the mixture behave equally for biochar yield (mean comparison by Sidak test - Figure 23). Since the mixture is mostly kernel, this behavior is consistent. Kernel was expected to have a lower biochar production based on the ash

13 content (Table 1), however, a greater biochar production in shell was observed. In contrast to the compositional analysis of Table 1 made with NREL protocols, kernel ash content and fixed carbon by ASTM reported elsewhere (Ganeshan et al., 2016; Lazzari et al., 2016) was higher than shell, thus the production of biochar in kernel must be greater as experimented. About temperature, it has a statistical influence over bio-char production (0.001 p-value). Based only on the means, the production of biochar decreases with the rise of temperature until 600°C, then it increases (Figure 19). Statistically, there is no difference between 400°C and 500°C, and between 500, 600 and 700 °C (Figure 24). With the temperature increment, biochar yield was supposed to decrease as reported in the literature. Based on the means this behavior is presented except for 700°C. However, the yield’s decrease is not statistical evidence. Both observations could be a consequence of high-boiling-point gases which condensate rapidly over biochar at lower temperature spots increasing its mass and, thus, its yield. This phenomenon is suspected to increase with temperature. Also, the lack of more replicates explains non-statistical significance. For biogas, the biomass fed also has a statistical influence in its production yields (0.012 p- value). Based only on the means, kernel has the higher yields, followed by the mixture and finally, shell (Figure 20), which is consistent with the literature (Ganeshan et al., 2016; Lazzari et al., 2016). Statistically, kernel and the mixture behave equally for biogas yield (mean comparison by Sidak test - Figure 25), which is consistent considering the mixture composition. The temperature has a statistical influence over biogas production (0.003 p- value). Based only on the means, the production of biochar increased with the rise of temperature (Figure 20). Statistically, there is no difference between 400°C and 500°C, and between 500, 600 and 700 °C (Figure 26). The behavior of the means is as expected and reported on literature. Again, the lack of more replicates explains non-statistical significance. It is important to acknowledge that the mixture behaved approximately as a pondered mean of the relation of shell/kernel fed for biochar, bio-oil and biogas production. Also, for all cases, biomass-temperature interaction did not have statistical influence over biochar, bio-oil and biogas production (0.080, 0.132 & 0.121 p-values, respectively - Table 5). Although no temperature influence was evidenced, due to the lack of replicates, the optimal temperature for each biomass was used for laboratory-scale pyrolyzes (Shell = 500 °C, Kernel = 600°C & Mix = 600 °C). 3.2.1.2. Mass losses For 12 pyrolyzes, mass deposits were measured and taken as mass losses. Figure 8 presents the results obtained. Also, an ANOVA statistical analysis was made to determine the influence of the temperature and biomass type over mass losses with a 95% confidence. Normality and homoscedasticity assumptions were verified. In Table 6 all results of the analysis and assumptions verification are presented. Also, Sidak test was used to compare the different groups’ means.

Losses By Biomass Losses By Temperature

Biochar Bio-oil Biochar Bio-oil

25% 25%

. .

20% 1 20%

)

(

% %

15% 15% %

s .

10% 10% 7

. 5 5% 5%

0% 0% Shell Kernel Mix 400°C 500°C 600°C 700°C Figure 8. Mass Losses For biochar, there was no statistical difference between the pyrolysis temperatures and the biomass used (0.248 & 0.223 p-values, respectively - Table 6). This can be easily identified as the error intervals are overlapped in Figure 8. Temperature and biomass should not have an influence over losses since the extraction method of biochar for all experiments was the same. On other aspects, for bio-oil mass losses, both temperature and biomass have a statistical influence (0.017 & 0.006 p-values, respectively - Table 6). As shown in Figure 8, bio-oil mass losses are greater in the kernel test, followed by the mixture (Figure 28). Statistically, kernel and mix losses are equal (Figure 29), which is consistent with the mixture composition. Furthermore, based on the means, the mass losses are greater for 700°C, followed by 600°C, then 400°C and finally 500°C (Figure 28). Nonetheless, there are no statistical differences between 400°C - 500°C, 400°C – 600 °C and 600°C – 700 °C (Figure 30). These deposits (losses) can be attributed to an organic phase of high viscosity and low solidification point, thus, an increase in temperature causes an increased yield of this organic phase, as experimented. Further study needs to be done about these deposits. 3.2.1.3. Biochar Mass Gain The sudden increase of mass of biochar was recorded for 12 samples as a subrogated variable of the biochar’s humidity adsorption capacity. Since no BET or SEM analysis was made, this variable gives us a rough idea of the surface of biochar and the difference between the influence of biomass and temperature. For every sample, a linear regression was applied to the water adsorption (mass gain percentage) (Figure 31). The slopes of the regressions were taken as the water adsorption velocity for each biochar. Results classified by biomass types and temperatures are displayed in Figure 9. An ANOVA statistical analysis was made to determine the influence of the temperature and biomass type over water adsorption velocity with a 95% confidence. Normality and homoscedasticity assumptions were verified. In Table 7 results of the analysis and assumptions verification are presented. For both biomass type and temperature have no statistical influence over the biochar water adsorption velocity

(0.918 & 0.262 p-value, respectively - Table 7). This implies that is possible that the surface areas of biochars are similar between different pyrolysis temperatures and biomasses. However, based on the means, the greater mass loss velocity is for the mixture, possibly due to the heterogeneous morphology cause by the pyrolysis of different biomasses mixture.

Water adsortion by biomass Water adsortion by temperature 40

) 40

)

c c

g g

 20

(

s s

d 10

a W 0 W 0 Shell Kernel Mix 400°C 500°C 600°C 700°C

Figure 9. Biochar mass gain by factor 3.2.2. Laboratory-scale pyrolysis 3.2.2.1. Yields Three pyrolyzes were carried out, one for each biomass at the optimum temperature obtained in the tubular furnace, regardless of the statistical analysis. In Figure 10 the yields are shown.

Laboratory Scale Pyrolysis

Biochar Bio-oil Losses Biogas

50%

. 3

25% 2

Yield (%)

. 3 0% Shell (500 °C) Kernel (600 °C) Mix (600 °C)

Figure 10. Yield obtained in laboratory-scale pyrolyzes test

Overall, higher bio-oils yields were obtained in comparison with the tubular furnace due to the cooling capacity and the position of the condensers which take advantage of gravitational force. In the tubular furnace, a great portion of condensable gases was lost because of the insufficient condensation force. Also, is important to acknowledge that the pyrolysis specifications change because of system limitations. For instance, the ramp in these tests was around 9 to 12 °C/min compared to the stable 30°C/min in the tubular furnace. Also, the retention time was almost double. These and other variations explain the difference with the tubular pyrolysis yields. Nevertheless, mass losses maintain the behavior observed in tubular pyrolysis. It is unclear whether these deposits are not affected by the variations of the system or are different deposits that behave similarly. Based only on appearance, it is feasible that are different deposits with similar properties. Further investigation needs to be done to answer this question. 3.2.2.2. Biogas analysis This laboratory-scale reactor allows the analysis of the exit biogas. In Figure 11 the concentrations of CO2, CO, CnHm, CH4, H2, and O2 of kernel and shell biogas produced by pyrolysis are presented in a time series. Also, the evolution of the temperature inside the reactor chamber was measured and is presented in Figure 11 (second row). The system used 6 thermocouples as distributed as shown in Figure 32. To reduce complexity, the mean of the temperature measured was considered as the representative temperature of the reaction system (black line in Figure 11). Before the mixture pyrolysis was made, the analyzer presented a series of failures, thus the gas analysis was not carried out. However, in Figure 32 you can see the temperature behavior of the mixture test.

Shell Biogas Kernel Biogas 30% 2000 30% 2000

n n

o o

i i

f t t 1500 1500

c c

a 20% a 20%

r r

c i i 1000 1000

t t

(

e e

c m m 10% 10%

l l

500 / 500

o o

V V

)

) 0% 0 0% 0 0 15 30 45 60 75 90 0 15 30 45 60 75 90 105 Time (min) Time (min)

CO2 CnHm O2 CO2 CnHm O2 Calorifc Value Calorifc Value CO CH4 H2 CO CH4 H2

Shell Pyrolysis Temperature Kernel Pyrolysis Temperature

600 600

)

(

400 400

e p

200 p 200

T T

0 0 0 15 30 45 60 75 90 0 15 30 45 60 75 90 105 Time (min) Time (min)

T1 T2 T3 T4 T5 T6 Mean T1 T2 T3 T4 T5 T6 Mean

Figure 11. Biogas analysis Using shell as biomass and at a maximum temperature of 500°C, significant biogas production starts at 200°C. At this temperature the hemicellulose stars to discomposed (Andrade et al., 2016; H. Yang et al., 2007). From this point to 470°C, significant changes in the different concentrations were presented while the temperature increased. Carbon dioxide and monoxide dominated the first stages of the pyrolysis, with an increasing methane concentration. A slight increase in the hydrocarbons is identifiable, nonetheless, it does not exceed the 3% fraction. The concentrations started to decrease as the temperature got to the setpoint and stabilize. Since the 500°C temperature was not achieved, lignin did not discompose completely, thus, a higher calorific value could be obtained. Not oxygen nor hydrogen showed up during the test. Using kernel as biomass and at a maximum temperature of 600°C, significant biogas production starts at 200°C as in shell test. Higher heating rate seams to increase the rate of methane and carbon dioxide and monoxide. Possibly, most hemicellulose and cellulose were consumed when the temperature reached the maximum. In this case, hydrogen was obtained because of the complete decomposition of lignin and the thermal cracking of pyrolytic vapors above 500 °C (H. Yang et al., 2007). A slight increase in the hydrocarbons was also identified. The concentrations started to decrease as the temperature got to the setpoint and stabilize. The kernel test did get a greater calorific value due to the high temperature used,

18 which is in concordance with literature revision (Eckert et al., 2016). The oxygen peak at the end of kernel’s pyrolysis was caused by the beginning of failures in the gas analyzer. 3.2.2.3. TGA-DSC mass losses analysis In Figure 12, TGA-DSC curves of mass deposits (losses) are displayed. For both, the water lost from 25 to 110 °C is at least 50% of the total mass. This indicates that the deposits are rich in water and possible hydrophilic. For kernel, it appears that lower boiling point compounds are presented in the sample (15%). For the mixture, it is clear that the majority is water, as the TGA curve descends strongly from almost 95% to 55%.

Kernel Losses Mix Losses

Weight Heat Flow Weight Heat Flow

100% 1 100% 1

H H 80%

80% e e 0

0 a

) )

F %

F (

( 60%

60% l

o t

w h

h -1 -1 g

g i

(

W e

W e 40% 40%

/ W

) -2 ) -2 20% 20%

0% -3 0% -3 0 200 400 600 800 1000 0 200 400 600 800 1000 Temperature (°C) Temperature (°C)

Deriv. Weight Deriv. Weight

5 4 5 4

) )

H 1

2 1 2 - 4 - 4

C C

( (

t t

F h 3 h 3

i i

w -2 w

e -2 e

(

( W

W W

2 2 W

. .

-4 /

v -4 v

g i

r r

)

) e

1 e 1 D -6 D -6

0 -8 0 -8 0 200 400 600 800 1000 0 200 400 600 800 1000 Temperature (°C) Temperature (°C) Figure 12. TGA-DSC analysis for mass deposits After dehydration, a controlled weight loss occurs. Due to its form and the temperature range, the dehydrated part could be compared to diesel or other fuels. This statement relies also upon the DSC curve, which after dehydration shows increasing heat flow from the samples. 3.3. Bio-oil characterization 3.3.1. Bio-oil GC-MS The characterization of mango’s kernel, shell and mix bio-oils by gas chromatography coupled with mass spectrometry is presented Table 3, where the compounds found are organized according to the retention time in the column, with their CAS identification number and the peak area percentage. Also in Annexes, the chromatograms of mango’s kernel, shell, and mix bio-oils are shown in Figure 34, Figure 35 and Figure 36, respectively. All compounds were reported with at least 70% identification quality. It is important to

19 acknowledge that this analysis is limited to the presence and absence of volatile compounds. Sugars, fats or highly complex molecules cannot be determined by this analytic procedure due to the thermal instability of the molecules and the CG-MS limitations. Also, the peak’s area and height could be used as a semiquantitative indicator of the abundance but is not a concentration measure or related. Table 3 correspond to bio-oils characterization. As shown, in general, all bio-oils are a mixture of a great variety of compounds such as acids, alcohols, ethers, esters, ketones, aldehydes, phenols, and aromatic molecules. Most species identified were oxygenated compounds, as in literature (Bridgwater, 2012; Eckert et al., 2016). Organic oxygen in bio- oils promotes its instability, low viscosity and low energy density (HHV) (Czernik & Bridgwater, 2004). Also, the bio-oils obtain had an acid pH (2.59 for shell bio-oil, 4.31 for kernel bio-oil and 3.81 for mix bio-oil). Acid pH is a consequence of organic acids and alcohols presences. Low pH affects negatively its application on combustion motors due to corrosive behavior (Diebold, 2000). Also, it is important to acknowledge that, as exposed in Czernik & Bridgwater (2004), water is the single most abundant oxygenated compound, which can be verified with the subsequent analyses made (Calorific value determination and Distillation curve). Only two nitrogenated compounds (NC) and four deoxygenated hydrocarbons (DHC) were identified in the three bio-oils. Shell bio-oil contains one NC (2-hydroxy-(2,4- dinitrophenyl)hydrazine-benzaldehyde) and three DHC (toluene, 1,4-dihydro-1H-fluorene and Tricyclo[9.2.2.2(4,7)]heptadeca-1(14),2,4(17),5,7(16),11(15),12-heptaene). Only one DHC (toluene) was found in kernel bio-oil and none NC. In mix bio-oil toluene and 4a,9- dihydro-2H-fluorene were the only DHC, and 9-amino-1-methyl-3,6-diazahomoadamantane was the only NC. These indicate that nitrogen tends to concentre mostly in biochar. Also, if using bio-oils as biofuels is desirable, further treatment such as hydrodeoxygenation, zeolites cracking and decoupled liquid bio-oil upgrading needs to be done to deoxygenated the compounds (Bridgwater, 2012; Eckert et al., 2016; Ganeshan et al., 2016). Table 3. Compounds identified by GC–MS of mango shell, kernel and mix bio-oil

RT (min) CAS Compound Area (%) Shell bio-oil 2.6643 000108-88-3 Toluene 2.1171 3.6267 000098-01-1 Furfural 10.634 6.6387 000765-70-8 1,2-Cyclopentanedione, 3-methyl- 3.9586 7.1029 000090-05-1 Phenol, 2-methoxy- 5.4177 8.122 000105-67-9 Phenol, 2,4-dimethyl- 0.3634 8.6089 000093-51-6 Phenol, 2-methoxy-4-methyl- 2.4641 9.3109 000120-80-9 1,2-Benzenediol 1.1116 10.4659 000067-47-0 2-Furancarboxaldehyde, 5-(hydroxymethyl)- 0.7755 11.066 000091-10-1 Phenol, 2,6-dimethoxy- 6.8836 11.6435 000097-53-0 Eugenol 0.5081 11.8926 032933-07-6 1-(2,4-Dimethyl-furan-3-yl)-ethanone 1.0359 12.3228 000121-34-6 Benzoic acid, 4-hydroxy-3-methoxy- 2.3478 13.0362 000498-02-2 Ethanone, 1-(4-hydroxy-3-methoxyphenyl)- 0.334 13.2853 006443-69-2 Benzene, 1,2,3-trimethoxy-5-methyl- 1.9539

13.5571 002503-46-0 2-Propanone, 1-(4-hydroxy-3-methoxyphenyl)- 1.7393 14.8592 006627-88-9 Phenol, 2,6-dimethoxy-4-(2-propenyl)- 0.5792 15.0743 000134-96-3 Benzaldehyde, 4-hydroxy-3,5-dimethoxy- 0.6018 15.9575 002478-38-8 Ethanone, 1-(4-hydroxy-3,5-dimethoxyphenyl)- 1.2163 16.3765 000437-72-9 Desaspidinol 1.9174 17.7126 041593-21-9 Fluorene, 1,4-dihydro- 0.1505 18.3014 000084-74-2 Dibutyl phthalate 0.1001 18.426 000143-07-7 Dodecanoic acid 0.1118 18.6864 087345-53-7 3,5-Dimethoxy-4-hydroxycinnamaldehyde 0.6233 20.0678 049576-90-1 Tricyclo[9.2.2.2(4,7)]heptadeca-1(14),2,4(17),5,7(16),11(15),12-heptaene 0.0496 20.4528 000057-11-4 Octadecanoic acid 0.1014 21.7323 001035-77-4 Estra-1,3,5(10)-trien-17-ol, 3-methoxy-, (17.beta.)- 0.0279 24.7102 1000242-80-7 4-Methoxy-4',5'-methylenedioxybiphenyl-2-carboxylic acid 0.0673 25.2424 015640-40-1 Phenol, 4,4'-methylenebis[2,6-dimethoxy- 0.0171 26.7371 001160-76-5 Benzaldehyde, 2-hydroxy-, (2,4-dinitrophenyl)hydrazone 0.0254 Kernel bio-oil 2.6303 000108-88-3 Toluene 1.2884 3.8476 000098-00-0 2-Furanmethanol 20.143 5.4724 000108-95-2 Phenol 3.8094 6.1858 000080-71-7 2-Cyclopenten-1-one, 2-hydroxy-3-methyl- 2.4277 6.3783 006124-79-4 4-Methyl-5H-furan-2-one 0.2478 6.5028 000095-48-7 Phenol, 2-methyl- 1.0213 6.8651 000108-39-4 Phenol, 3-methyl- 2.667 7.4879 000118-71-8 Maltol 4.1228 7.8842 000526-75-0 Phenol, 2,3-dimethyl- 0.3499 7.9748 028564-83-2 4H-Pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl- 0.4316 9.0279 000120-80-9 1,2-Benzenediol 1.4257 10.4432 000452-86-8 1,2-Benzenediol, 4-methyl- 3.2038 10.5904 000108-46-3 Resorcinol 0.6562 10.9188 000608-25-3 1,3-Benzenediol, 2-methyl- 0.5503 11.2245 000504-15-4 3,5-Dihydroxytoluene 1.7759 11.5416 032933-07-6 1-(2,4-Dimethyl-furan-3-yl)-ethanone 0.7115 13.3193 002503-46-0 2-Propanone, 1-(4-hydroxy-3-methoxyphenyl)- 0.253 14.0326 029668-44-8 1,4-Benzodioxan-6-carboxaldehyde 0.3741 15.2216 000079-77-6 3-Buten-2-one, 4-(2,6,6-trimethyl-1-cyclohexen-1-yl)-, (E)- 0.1676 16.2067 1000116-22-3 2-Pentanone, 1-(2,4,6-trihydroxyphenyl) 0.2296 Mix bio-oil 2.653 000108-88-3 Toluene 1.6506 3.038 005371-52-8 2-Furanol, tetrahydro- 0.5516 3.9212 000098-00-0 2-Furanmethanol 7.2574 5.4838 000108-95-2 Phenol 3.1921 6.2878 000080-71-7 2-Cyclopenten-1-one, 2-hydroxy-3-methyl- 2.7234 6.5369 000095-48-7 Phenol, 2-methyl- 0.8285 6.8879 000106-44-5 Phenol, 4-methyl- 3.2643 7.5559 000118-71-8 Maltol 2.735 7.9749 028564-83-2 4H-Pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl- 0.3579 8.4165 000093-51-6 Phenol, 2-methoxy-4-methyl- 0.424 8.9487 000120-80-9 1,2-Benzenediol 3.3524 10.4546 000452-86-8 1,2-Benzenediol, 4-methyl- 2.3069 10.6131 000123-31-9 Hydroquinone 0.4802 10.8736 000091-10-1 Phenol, 2,6-dimethoxy- 1.7114 11.2586 000095-71-6 1,4-Benzenediol, 2-methyl- 1.8418 11.5756 002896-60-8 1,3-Benzenediol, 4-ethyl- 0.8161 11.8813 000608-43-5 2,3-Dimethylhydroquinone 0.9155 13.1608 1000296-12-2 3-Isopropyl-1-methyl-4-methylamino-pyrrole-2,5-dione 0.7237 14.9499 000708-76-9 4,6-Dimethoxysalicylaldehyde 0.3349 15.4028 006627-88-9 Phenol, 2,6-dimethoxy-4-(2-propenyl)- 0.4705 15.7991 002478-38-8 Ethanone, 1-(4-hydroxy-3,5-dimethoxyphenyl)- 0.482 16.8295 1000216-24-2 9-Amino-1-methyl-3,6-diazahomoadamantane 0.1074 17.5995 059247-36-8 Fluorene, 2,4a-dihydro- 0.098

Comparing the bio-oils composition (Table 3), shell has a greater variety of compounds due to the formation of additional products derived from the decomposition of hemicellulose (Silva et al., 2014). Kernel bio-oil contains a little less identified compounds and, according to Figure 35 chromatogram, lighter compounds. However, it is possible that kernel bio-oil is richer in complex molecules as a result of its high organic content of proteins, cellulose, and extractives, which cannot be measured by CG-MS. Besides, the difference between the bio- oils compositions can be related to its biomass composition. For instance, since shell has a greater percentage of lignin material, its bio-oils are richer in phenolic species (Ganeshan et al., 2016). Also, due to the presence of long-chain organic acids such as dodecanoic and octadecanoic acid, shell bio-oil is more suitable for biofuels production than kernel and mix bio-oils (Lazzari et al., 2016). On the other hand, kernel has a higher percentage of cellulose and pectin which leads to the formation of furans and related compounds (Silva et al., 2014). The mix bio-oil, due to its composition and pyrolysis temperature, is closer to kernel’s bio- oil. However, 43% of the compounds are different from shell and kernel compounds, which proves that reactions between kernel and shell vapors are occurring to a certain degree, producing unique molecules. Further analysis needs to be done to determine how these reactions are changing the composition of the mixture’s bio-oil and if it is statistically significant. Furthermore, identification of predominant valuable compounds is imperative, since separation technics of bio-oil compounds are complex and expensive (Bridgewater, 2004; Cardoso & Ataíde, 2013). Despite oxygenated compounds are not desirable in bio-oils for biofuels, they have an enormous potential for industrial applications and manufacture. The usages of the most abundant species in the three bio-oils, selected based on the area and height of the peaks, are described in Table 4. Table 4. Uses of the most abundant compounds identified by GC–MS in mango shell, kernel and mix bio-oil

Compound Usages References

Shell bio-oil

Furfural furan-2-carbaldehyde Production of medicines, resins, food and fuel additives. (Lu et al., 2011; Consider one of the 30 most valuable chemical compounds Werpy & Petersen, from biomass. 2004) Syringol 2,6-dimethoxyphenol Use as fragrance, food additive and flavor. Use in (PubChem & EPA cigarettes, personal care, and consumer use products. CPDat, 2005e) Guaiacol 2-methoxyphenol Use as a drug, food additive, fragrance and active pesticide (PubChem & EPA ingredient. Use in cigarettes, medicines, personal care CPDat, 2005a) products, pesticides, chemical manufacture, and food manufacture. Creosol 2-methoxy-4-methylphenol Use as food additive flavor, fragrance, pesticide, and (PubChem & EPA preservative. Use in adhesives, paints, pesticides, cigarettes, CPDat, 2005b) and industrial manufacturing - 3-methylcyclopentane-1,2-dione Use as fragrance and food additive. Use in consumer use (PubChem & EPA products. CPDat, 2005f) Vanillic 4-hydroxy-3-methoxybenzoic Use as drug, food additive and food flavor. Use in (PubChem & EPA acid acid medicines. CPDat, 2004e)

Toluene Toluene Use as solvent and degreaser, incorporated in products such (Fox, 2015) as paints and glues, with a high presence in gasoline and other fuels.

Kernel bio-oil

Furfuryl 2-furanmethanol Use as binding agent, fluid property modulator, solvent, (EPA CPDat & alcohol food additive, food additive flavor, fragrance, chemical DSSTox process regulator, iron casting agent, and raw material. Use Substance Id, n.d.) in paints, consumer use tools and construction. Use in manufacture of building wood, computers, machines, metals, adhesives, rubber, furniture, radios and TVs. Maltol 3-hydroxy-2-methylpyran-4-one Use as absorbent, food additive and flavor, industrial (PubChem & EPA fragrance, lubricant, and raw material. Use in cigarettes, CPDat, 2005d) medicines and pesticides (inactive ingredient), industrial washing, industrial manufacture, cosmetics, air treatment, chemical manufacture, food manufacture, and construction. Phenol Phenol Use in the production of phenolic resin, synthetic fibers, (ATSDR, 2011) disinfectants and medicines 4-methyl- 4-methylbenzene-1,2-diol Use as drug, cancer and neuroprotective medicine and (PubChem & EPA catechol antioxidant. CPDat, 2004a) m-Cresol 3-methylphenol Use as drug inactive ingredient, food additive and flavor, (PubChem & EPA antimicrobial agent, pesticides active and inert ingredient, CPDat, 2004c) solvent and fragrance. Use in manufacture of chemicals, leathers, textiles, plastics, raw material, paints, adhesives, and metals. Cyclotene 2-hydroxy-3-methylcyclopent-2- Use as pesticide active ingredient, biocide agent and food (PubChem & EPA en-1-one additive and flavor. Use in cigarettes and air treatment CPDat, 2005c) products.

Mix bio-oil Furfuryl 2-furanmethanol See Kernel bio-oil alcohol Catechol 1,2-Benzenediol Use as colorant, fluid property modulator, food additive, (PubChem & EPA food additive flavor, and chemical process regulator. Use in CPDat, 2004b) drugs and hair dye. Use in manufacture of leather, raw material, metals, chemicals, TVs and radios. p-Cresol 4-methylphenol Use as laboratory chemical, drug, solvent, food additive, (PubChem & EPA food additive flavor, and fragrance. Use in paints, industrial CPDat, 2004d) colorants, adhesives, furniture washing products, and textiles. Use in manufacture of metals, raw material, apparels, leathers, and plastics. Phenol Phenol See Kernel bio-oil Maltol 3-hydroxy-2-methylpyran-4-one See Kernel bio-oil Cyclotene 2-hydroxy-3-methylcyclopent-2- See Kernel bio-oil en-1-one 4-methyl- 4-methylbenzene-1,2-diol See Kernel bio-oil catechol

As observed in Table 4, all bio-oils count with highly commercial molecules with endless industrial applications, ranging from food manufacture to medicine synthesis. Thus, it is imperative to develop, search and apply methodologies for the selective production and recovery of these added-value compounds. Specifically, the majority species in Table 4 are phenolic compounds. Phenolic compounds are formed by thermal degradation of lignin material (Ganeshan et al., 2016; Kim, 2015; Lazzari et al., 2016; Silva et al., 2014). They are widely used in the production of resins, in the food industry as flavorings, in the manufacture

of adhesives and polymers and as intermediates in pharmaceutical syntheses (Andrade et al., 2016; McGrath, Brown, Meruva, & Chan, 2009). The production of phenolic resin is used predominantly in wood composite and computer industries (Kim, 2015). To improve phenolic compounds production, catalytic pyrolysis or microwave-assisted pyrolysis can be implemented (Kim, 2015; Lu et al., 2011). Furthermore, phenols-rich bio-oils are commercially used as a replacement of phenolic compounds up to 50% in the manufacture of phenolic resins (Bridgwater, 2012; Kim, 2015). No reports of commercialization of simple phenols are known, possibly due to the obstacles in separating these species (Kim, 2015). Nonetheless, various experimental methods for selective separation of phenols are presented in Kim (2015). Other highly abundant compounds found in mango bio-oils (Table 3 & Table 4) are furfurals. These compounds are formed during the thermal decomposition of hemicellulose and cellulose at around 350°C (Andrade et al., 2016; Ganeshan et al., 2016; Lu et al., 2011). Furfural and its variations are highly value molecules in the industry. Furfural is one of the 30 most valuable chemicals derived from biomass, and furfuryl alcohol is an important furan resin monomer (Hoydonckx, Rhijn, Rhijn, Vos, & Jacobs, 2007; Werpy & Petersen, 2004). To further increase furfural production, catalytic pyrolysis could be applied as Lu et al. (2011). Separation of furfural and related compounds can be obtained as in Yang, Zhao, Wu, & Yang (2013). 3.3.2. Bio-oil FTIR Bio-oils FTIR analysis results are presented in Figure 13. The results have a non-common behavior, with transmittance above 100%. Thus, it is possible that the FTIR crystal was not clean thoughtfully between measurements, or the environmental blank was not taken correctly.

Shell Bio-oil Kernel Bio-oil Mix Bio-oil 140% 140% 140%

120% 120% 120%

t i

m 100% 100% 100%

a r

T 80% 80% 80%

60% 4000 3500 3000 2500 2000 1500 1000 500 4000 3500 3000 2500 2000 1500 1000 500 4000 3500 3000 2500 2000 1500 1000 500 Wave Number (cm-1) Wave Number (cm-1) Wave Number (cm-1) Figure 13. Bio-oils FTIR However, some important details can be recovered from the results. All three samples have the same peaks; thus, it can be predicted that the chemical speciation in the bio-oils is similar. Furthermore, the molecules will share similar functional groups (Ganeshan et al., 2016), having equivalent chemical behaviors. Also, coupled with the next analysis, 3260 cm-1, 1635 cm-1 and 490 cm -1 peaks, which correspond to O-H stretching, indicate the presence of moisture (water) in all the samples (Sirendi, 2015).

3.3.3. Calorific value determination Due to the high moisture content (>5%), the bomb was incapable of measure the calorific value of the samples without treatment. This means that the calorific value was below 1000 BTU/lb. Of all the dry samples, only the mixture dry sample calorific value was above the detection limit. Dry mixture bio-oil has 8,617 BTU/lb (20,043.14 J/g) gross calorific value (higher heating value). In comparison with the initial biomass, the calorific value obtain is greater, thus dry bio-oil has more potential for energy production than the seed. In comparison with other biomasses, the mixture dry bio-oil has a slightly larger calorific value than the average, similar to the sugarcane bagasse bio-oil (Onay, 2007). In comparison with commercial fuels, bio-oils tend to have a low energy power (between 16 and 19 MJ/kg) even lower than diesel (~45 MJ/kg) (Eckert et al., 2016; Onay, 2007). 3.3.4. Distillation curve Figure 14 display the distillation curves for the three bio-oils. Figure 15 shows the distillation curve of diesel.

Shell Bio-oil Distillation Kernel Bio-oil Distillation Mix Bio-oil Distillation

120

)

° (

110

r e

p 100

e T

90 0% 20% 40% 60% 80% 100%100% / 0% 20% 40% 60% 80% 100%100% / 0% 20% 40% 60% 80% 100% Recovered (%) Recovered (%) Recovered (%)

Figure 14. Bio-oils distillation curves At first look, one can identify the high moisture content. For all the samples, the curve is primarily stabilized at 100 °C. All exhibit similar behaviors. Water in bio-oils comes from moisture in feedstock and dehydration reactions (Czernik & Bridgwater, 2004). Based on the curve, the moisture content is around 36% for shell, 34% for kernel and 48% for mix bio-oil. This moisture content is above the reported in other bio-oils and strongly affects is feasibility as biofuel (Czernik & Bridgwater, 2004). For usage in combustion engines, water in bio-oil have some benefits such as lowering viscosity, enhance a more uniform temperature profile in the cylinder, reduce NOx and PM emissions and increase pH (low corrosion), however, it lowers the heating value and the flame temperature and decrease its stability affecting the combustion properties of the fuel (Bridgwater, 2012; Czernik & Bridgwater, 2004; Diebold, 2000; Vardon et al., 2013). Around 10% of the samples are composed of lighter species. Also, around 34-44% correspond to the heavier fraction of the bio-oils components, some of them presented in Table 3. Between 8% to 12% of the sample distillate was a solid residue, possibly inorganic content rich in carbon, metals, and salts. This inorganic content has a negative impact on combustion applications due to blockage formation in engine filters and

25 injectors and the promotion of the aging process (Bridgwater, 2012; Diebold, 2000; Miles, Baxter, Bryers, Jenkins, & Oden, 1996). Considering the high-water content and inorganic residues, the bio-oils obtain are not suitable as biofuels or biofuels additives, unless further treatment is made.

Figure 15. Distillation curve of 4 alternative diesel fuels (Gough & Bruno, 2012) Comparing with Figure 15, the bio-oils obtained are far from the alternative diesel curve. Distillation temperatures in diesel are never reached in bio-oils. A high drying method needs to be developed to remove the water content of the samples. Nonetheless, the mass deposits TGA-DSC (losses), show that after dehydration they can be compared with the diesel distillation curve. 3.4. Biochar characterization 3.4.1. Elemental and HHV analysis Figure 16 represents the results of the biochar elemental analysis. As expected, the carbon fraction increase compared to the biomass initial values. For nitrogen, kernel increased the fraction implicating a concentration of the atomic species in the biochar. This implies that protein nitrogen tends to stay in biochar rather than forming species in the bio-oil, as observed in section 2.4.1, or emitting gaseous NOx. Sulfur did not suffer grate changes, thus, the content of sulfur in bio-oil could be significant as inorganic salts, decreasing the potential quality of the biofuel. Hydrogen and oxygen fraction diminished. Due to the variety of reactions in a pyrolysis process (Diebold, 2000), it is feasible that part of the hydrogen and oxygen fraction ended up producing water.

Biochar Higher

Biochars Elemental Compositions Heating Value

) s

i 99.9% 30

a 7

99.0% 2

0 7

2 .

8 8

90.0% 6

D 2

V 26

70.0% %

% %

[ %

(

50.0% 3

. .

7 7

5 o

30.0% k

i 1

1 24

% t

c %

]

% a

10.0% .

F 1

. 22

. .

1.0% .

0 0

a 0.1% 20 M Shell Kernel Mix Shell Kernel Mix

N C S H O and rest HHV

Figure 16. Biochar elemental analysis Also, in Figure 16, the HHVs of the biochars are presented. Again, since there are not significant differences in the elemental composition, there are no significant variations in HHV between the biomasses fed. The variations are associated mainly with the divergence in hydrogen fraction, which has a positive effect over the HHV. In comparison with the initial biomass, the HHV obtain is greater, thus biochar has more potential for energy production than the seed without treatment. In comparison with other biochars, all 3 biochars have an HHV slightly above the average (eucalyptus, bamboo, coconut fibers & rice husk) (Malucelli et al., 2019). In comparison with commercial fuels, biochars HHV is lower but close to vegetal carbon calorific value (~29 MJ/kg) (García, Pizarro, Lavín, & Bueno, 2014). As follows, due to its HHV, it can be used as a solid fuel with nearly the same performance as a commercial fuel or carbon. 3.4.2. Biochar FTIR Biochar FTIR analysis results are presented in Figure 17. The results have a non-common behavior, with transmittance above 100%. Thus, it is possible that the FTIR crystal was not clean thoughtfully between measurements, or the environmental blank was not taken correctly.

Shell Biochar Kernel Biochar 100.5% 100.5%

100.0% 100.0%

e c

c 99.5%

n a

99.5% a

i m

m 99.0%

s n

99.0% n

a r

r 98.5%

T T 98.5% 98.0%

98.0% 97.5% 4000 3500 3000 2500 2000 1500 1000 500 4000 3500 3000 2500 2000 1500 1000 500 Wave Number (cm-1) Wave Number (cm-1) Figure 17. Biochar FTIR However, as in bio-oils, some important details can be recovered from the results. The two samples have the same peaks; thus, it can be predicted that the chemical speciation in the biochar is similar. Furthermore, the molecules will share similar functional groups (Ganeshan et al., 2016), having equivalent chemical behaviors. About its peaks, the O-H stretching bonds for water identification (3260 cm-1, 1635 cm-1, and 490 cm -1 (Nanda et al., 2013)) are not presented since the samples have low water content. 4. Conclusions Conventional pyrolysis proved to be a valid thermal treatment of high lignocellulosic biomass. Mango seed shell and kernel have low nitrogen and sulfur content making them desirable feedstocks for biofuels production. Also, both biomasses have similar elemental composition, HHV and FTIR spectrum. Small variations in the properties are attributed to compositional differences. At pyrolysis, biomass fed had a significant effect over the yields’ distribution, with shell as the maximum bio-oil producer (29.6%) and statistically equal behavior between kernel (18.8%) and mixture (24.0%) yields. The temperature had a statistical effect only over biochar and biogas yields with various tendencies behaviors. Losses were encounter as a result of bio-oil and vapors properties such as viscosity and solidification point, varying for each biomass and with no significant effect of temperature. Kernel had greater losses (16.06%). Additionally, biochar’s humidity adsorption capacity was around 17 µg H2O/g char/min with no significant effect of temperature and biomass. Yields in laboratory-scale pyrolysis change due to variations in flow nitrogen, heating rate and condensation system (28% biochar, 35% bio-oil: 35% and 38% biogas), however losses yields did not change despite these variations. TGA of the losses showed high moisture content and possibly high biofuel potential. The biogas analysis established that higher heating rate seams to increase methane, carbon dioxide and monoxide production, and at temperatures above 500°C, hydrogen was obtained due to the complete decomposition of lignin and the thermal cracking of pyrolytic vapors. Since kernel pyrolysis had a higher temperature and heating rate, the calorific value of its biogas was greater than shell.

In regard to bio-oil, they are composed mostly of oxygenated compounds, reducing its potential as biofuels. Shell bio-oil has a greater variety of compounds with a higher phenolic species fraction, while in kernel and mix bio-oil, furans and related compounds are presented in large proportions. These are caused by biomass composition. Also, the presence of different compounds in mix bio-oil is proof of reactions between kernel and shell vapors producing unique molecules. Moreover, high commercial value compounds were identified in all bio-oils. Besides that, high moisture was identified by FTIR and calorific bomb analysis. Only dry-mix bio-oil had a measurable higher heating value of 20 MJ/kg, above the detection limit, which is slightly above the average. Additionally, bio-oils have a similar FTIR spectrum which indicates comparable properties and functional group presence. Furthermore, the distillation curve confirmed high moisture content and high solids content. Thus, the bio-oils obtained are not suitable as biofuels or additives until further treatment is made to deoxygenate them. Finally, biochar showed similar elemental composition. Nitrogen fraction increased, implying a concentration of nitrogen products in biochar. The sulfur fraction was similar to the initial biomass, thus, biofuels from bio-oil could potentially have sulfur traces, increasing predicted sulfur oxide emissions. Comparable presences and content of functional groups are expected since biochars FTIR spectrums were similar. Also, the HHV of biochars was greater than HHV of the initial biomass. Moreover, comparison between other biochars from different fruit biomasses showed that mango biochars have a higher HHV, posing them as better solid fuels. 5. Recommendations and future work For similar works with the intention of evaluating biomass pyrolysis, it is highly recommended applying fast pyrolysis, ideally in a fluidified bed reactor, to increase bio-oil production and lower moisture content. For tubular reactors (fixed bed) use a high nitrogen flow and large vertical Liebig condensers (> 1 m and below 0°C refrigeration temperatures) to increase bio-oil recovery, diminish losses and obtain undisrupted oil composition. Also, a central composite design centered at previously reported optimums with at least 3 replicates is desirable to evaluate temperature and mixture compositions to reduce uncertainty. Besides that, use a small temperature range. If possible, consider other factors such as retention time, heating rate (for fixed bed reactors), mass fed, addition of catalytic agents, addition of peal in biomass, and nitrogen flow and couple it with other response variables such as biochar surface area, HHV and composition (proximate and ultimate analysis with metals); bio-oil properties (pH, viscosity, HHV, etc) and composition (volatiles, complex molecules, metals) and biogas composition and energy value. It is important to address these factors if they are not reported, or as a hole (enormous but strong experimental design), to join it with financial feasibility analysis. For future works over the bio-oils obtained, dehydration and deoxygenation techniques must be evaluated to produce biofuels with complete fuel

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7. Annexes 7.1. Tubular pyrolysis statistical analysis 7.1.1. Yields Table 5. Yields statistical analysis

Conversion to ANOVA 2-ways (p-value) Biochar Bio-oil Biogas Temperature 0.001 0.619 0.003 Biomass 0.000 0.000 0.012 Temperature + Biomass 0.080 0.132 0.121 Conversion to Coeficient of Determination Biochar Bio-oil Biogas R2 89.24% 87.43% 80.66% Adjusted R2 79.38% 75.91% 62.92% Predicted R2 56.97% 49.72% 22.52% Conversion to ANOVA assumptions (p-value) Biochar Bio-oil Biogas Normality (By Anderson-Darling) 0.150 0.866 0.818 Homoscedasticity (By Bartlett) 0.205 0.159 0.753

Figure 18. Main effects of temperature and biomass on bio-oil production yields

Figure 19. Main effects of temperature and biomass on biochar production yields

Figure 20. Main effects of temperature and biomass on biogas production yields

Figure 21. Mean comparison of bio-oil production Figure 22. Mean comparison of bio-oil production yield according to biomass by Sidak test yield according to temperature by Sidak test

Figure 23. Mean comparison of biochar production Figure 24. Mean comparison of biochar production yield according to biomass by Sidak test yield according to temperature by Sidak test

Figure 25. Mean comparison of biogas production Figure 26. Mean comparison of biogas production yield according to biomass by Sidak test yield according to temperature by Sidak test

7.1.2. Mass losses Table 6. Mass losses statistical analysis

Losses ANOVA (p-value) Biochar Bio-oil Temperature 0.248 0.017 Biomass 0.223 0.006 Losses Coeficient of Determination Biochar Bio-oil R2 51.45% 90.75% Adjusted R2 21.11% 84.96% Predicted R2 0.00% 74.82% Losses ANOVA assumptions (p-value) Biochar Bio-oil Normality (By Anderson-Darling) 0.758 0.573 Homoscedasticity (By Bartlett) 0.524 0.896

Figure 27. Main effects of temperature and biomass Figure 28. Main effects of temperature and biomass on biochar losses on bio-oil losses

Figure 29. Mean comparison of bio-oil mass losses Figure 30. Mean comparison of bio-oil mass losses according to biomass by Sidak test according to temperature by Sidak test

2.1.1. Mass losses

Biochar Mass Gain

0.08% )

% 0.06%

(

c r

e 0.04%

s s

a 0.02% M

0.00% 5 10 15 20 Time (min)

Shell (400°C - 1) Kernel (400 °C - 1) Mix (400 °C - 2) Shell (400°C - 2) Kernel (400 °C - 2) Mix (400 °C - 1) Shell (500 °C) Kernel (600 °C) Mix (600°C - 1) Kernel (700 °C - 1) Mix (600°C - 2) Kernel (700 °C - 2)

Figure 31. Discriminated biochar mass gain

Table 7. Biochar Mass Gain statistical analysis

Biochar Mass Gain ANOVA (p-value) Temperature 0.918 Biomass 0.262 Coeficient of Determination R2 50.31% Adjusted R2 8.90% Predicted R2 0.00% ANOVA assumptions (p-value) Normality (By Anderson-Darling) 0.432 Homoscedasticity (By Bartlett) 0.480 7.2. Laboratory scale pyrolysis

Figure 32. Thermocouple distribution (Ortiz et al., 2017)

Kernel Pyrolysis Temperature

600

)

(

e r

u 400

e p

m 200

e T

0 0 15 30 45 60 75 Time (min)

T1 T2 T3 T4 T5 T6 Mean

Figure 33. Register pyrolysis temperature in the laboratory-scale reactor during kernel test 7.3. Bio-oils characterization

Figure 34. Chromatogram for shell bio-oil

Figure 35. Chromatogram for kernel bio-oil

Figure 36. Chromatogram for mixture bio-oil