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1 Roma, 15 maggio 2018 Gli atti dei convegni e più di contenuti su

2 Facoltà di Ingegneria Dipartimento Ingegneria Chimica Materiali Ambiente Processi innovativi per la produzione di biocombustibili in bioraffinerie green ed ecosostenibili Prof. Antonio Zuorro Roma, 15 Maggio 2018

3 Microalgae Microalgae Are photosynthetic microorganisms capable of accumulating considerable amounts of lipids Are capable of synthesizing high added value compounds Do not occupy fertile soil and grow in the most variable water media (fresh water, salt water and waste water) Growth is non-seasonal Plant sources Lipid content ( oil / d.w.) Productivity (L oil / ha) Corn Rapeseed Sunflower Palm oil Microalgae

4 Microalgae Microalgae

5 Introduzione

Microalgae in biorefinery mitigation of greenhouse gas emissions oil for

6 Biorefinery Biorefinery Facility to obtain biofuels, energy and high value products through biomass transformation and process equipment. Microalgae in biorefinery mitigation of greenhouse gas emissions oil for biofuels natural fertilizers high value added compounds high oil productivity (20 50 by dry weight basis)

7 Biodiesel from microalgae Biodiesel from microalgae The oil extracted from microalgae is called 'green crude. The average composition of green crude is triglycerides (90-98), mono- and di- glycerides and free fatty acids (1-5), phospholipids, phosphatides, tocopherols, sulfur compounds, and aqueous residues (> 1).

8 Cell disruption methods Cell disruption methods Mechanical methods can cause: damage to the chemical structure of molecules incomplete cell lysis localised heating and denaturation of molecules

9 Enzymatic pretreatment Biological methods: enzymatic pretreatment Advantages: good lipid recovery disruption cells with minimal damage to the target product due to high selectivity of the reactions efficent catalysts small amounts required effective in mild operating conditions Non-purified and therefore inexpensive commercial multi-enzyme preparations such as cellulase, pectinase, hemicellulase, protease can be used.

10 Lipid extraction extraction from Nannochloropsis Lipid Extraction from Unbroken Microalgal Cells Using Enzymes The suitability of commercial enzyme preparations of relatively low cost to enhance the recovery of lipids from microalgae was investigated. Experimental planning Screening of potentially suitable enzyme preparations. The most effective preparations were then taken as basic components for the formulation of enzyme mixtures. Study of the influence of operating parameters and the characteristics of enzyme preparations by means of "mixture design" techniques and factorial design. Characterization of pretreated microalgae employing transmission electron miscroscopy (TEM) Microalgae Chlorella sorokiniana e Nannochloropsis sp. were used.

$State of the art Chlorella sorokiniana Chlorella sorokiniana (SAG 211-8k) Rigid Fraction Amorphous fraction Cell wall Acetylglucosamine Pectine Hemicellulose$

11 State of the art Chlorella sorokiniana Chlorella sorokiniana (SAG 211-8k) Rigid Fraction Amorphous fraction Cell wall Acetylglucosamine Pectine Hemicellulose Structural proteins Mixotrophic growth with industrial CO 2 and carbon substrates from wastewater (acetic acid and butyrric acid) Lutein and zeaxanthin synthesis

Biomass production 1 Light collector 2 Degasser 1 2 3 3 peristaltic pump Photobioreactor Type: tubolar Total voume: 7 liters Light collector : 66 m Lamps: 60 W Flow rate:

12 Biomass production 1 Light collector 2 Degasser peristaltic pump Photobioreactor Type: tubolar Total voume: 7 liters Light collector : 66 m Lamps: 60 W Flow rate: 0.19 cm/s Growth conditions Temperature: C Growth medium: BBM Standard Photoautotrophic growth Photoperiod: 12 hr of light; 12 hr darkness Sampling in stationary phase

13 Biomass production Cell growth Photoautotrophic growth Maximum concentration Average growth rate 300 g/m 3 20 g/(m 3 d) T=25 C Mixotrophic growth Maximum concentration Average growth rate 1350 g/m g/(m 3 d)

14 Development of new preparations Mixture Design Statistical methods for the formulations of enzyme preparations mixtures Allow to study the realation between extraction yield and the composition of the enzyme mixture (Software Design-Expert) Simplex Lattice Design q components Model employed P β, x = q i=1 β i x i + q i<j β ij x i x j Enzymatic preparation Feedlyve AXC 1500L Peclyve V EXTRAROME Feedlyve GMA Lysozime Food Grade Granular Activity Xylanase Pectinase Galcttomannanase Lysozime Experimantal Plan 4 apex points 6 binary mixtures points 1 central point (1/4, 1/4, 1/4, 1/4) 4 internal points with one coordinate of 5/8 and the others of 1/8 5 replications to evaluate the experimental error

61 Peclyve V EXTRAROME 0 Lysozime Food Grade Granular 0 Feedlyve GMA 0.

15 DOE Response surfaces and optimization Response surfaces Lysozime = 0 GMA = 0 Optimization Enzymatic preparation Value Feedlyve AXC 1500L 0.61 Peclyve V EXTRAROME 0 Lysozime Food Grade Granular 0 Feedlyve GMA 0.39 SO y exp y exp mean y exp dev.st Error y calc Deviation PI (95) Opt Opt

16 Nannochloropsis Microalgae Dimension (µm) Lipid content(s/s) Nannochloropsis sp ±1.25

Screening of enzyme preparations Screening of potentially suitable enzyme preparations Operating conditions: T = 50 C, ph = 5, t = 1 h, enzyme dosage = 10 mg g 1 Y lip =

17 Screening of enzyme preparations Screening of potentially suitable enzyme preparations Operating conditions: T = 50 C, ph = 5, t = 1 h, enzyme dosage = 10 mg g 1 Y lip = Lipids weight Biomass weight * 100 CEL, GMA, α-gal and AGL were the most effective preparations These components were grouped into ternary systems: CEL AGL GMA and CEL AGL α-gal.

Mixture Design Enzyme mixtures Enzyme preparation mixture I II III Order Enzyme preparation Enzyme activity A Cellulyve 50LC Cellulasi B Feedlyve AGL β-glucanasi C Feedlyve GMA Galatto-mannanasi A

18 Mixture Design Enzyme mixtures Enzyme preparation mixture I II III Order Enzyme preparation Enzyme activity A Cellulyve 50LC Cellulasi B Feedlyve AGL β-glucanasi C Feedlyve GMA Galatto-mannanasi A Cellulyve 50LC Cellulasi B Feedlyve AGL β-glucanasi C Feedlyve alphagal α-galattosidasi A Lisozima Lisozima B Feedlyve GMA Galatto-mannanasi C Feedlyve alphagal α-galattosidasi Discretization and model Design employed: simple lattice design Model employed: Operating conditions: T = 50 C, ph = 5, t = 1 h, enzyme dosage = 5 mg g 1

19 Optimization of mixtures and validation tests Y = β 1 x 1 + β 2 x 2 + β 3 x 3 + β 12 x 1 x 2 + β 13 x 1 x 3 + β 23 x 2 x 3 Mixtures I II III Enzyme Composition preparation () Cellulyve 50LC 10 Feedlyve AGL 0 Feedlyve GMA 90 Cellulyve 50LC 15 Feedlyve AGL 0 Feedlyve alphagal 85 Lysozime 0 Feedlyve GMA Feedlyve alphagal Y calc () Y sper () Mean error ()

20 Design of experiments (DOE) modifications Central composite rotatable design (one-half fraction factorial design) Factors Code Level Research domain Temperature ( C) T ph ph Time (min) t Dosage Ce-50LC (mg/g) Dosage Fe-GMA (mg/g) D D E = 30 min

21 Design of experiments (DOE) Model employed y = β 0 + β i x i + β ii x 2 i + i=1 i=1 i=1 j =i+1 β ij x i x j Analysis of variance (ANOVA) Coefficient Term Coefficient estimate Standard error F-value p-value β 0 Intercept β 1 T < β 2 ph < β 3 P < β 4 D < β 5 D < β 12 T x ph β 15 T x D β 11 T x T < β 22 ph x ph < β 33 P x P β 44 D 1 x D β 55 D 2 x D Source SS df MS F-value p-value Model < Residual Lack of Fit Pure Error Total R R 2 adjusted R 2 predicted 0.933

22 DOE Response surfaces Response surfaces Pretreatment time: 150 min Dosage Ce-50LC: 10 mg/g Dosage Fe-GMA: 1 mg/g Temperature: 45 C ph: 5 Pretreatment time: 150 min

23 Ypred DOE Validation andoptimization Optimization Factor Units Value SO y exp y exp mean y exp dev.st Error y calc Deviation PI (95) Temperature C 53 ph Time min 210 Ce-50LC load mg/g 13.8 Fe-GMA load mg/g 1.5 Opt Opt Validation SO T ( C) ph Time (min) D 1 (mg/g) D 2 (mg/g) y exp y calc Deviation PI (95) 30 V V V Yexp

24 Results Extraction time Extraction yield vs extraction time Asymptotic yield value close to 90 was achieved under optimal pretreatment conditions after 3.5 h of extraction Asymptotic value for the blank around 55 Average difference of 35 of average between optimal conditions and blank.

25 Y () Results Economic considerations SO y exp Ce50LC /kg y calc Deviation FeGMA /kg PI (95) OptEc Factor Units Value Temperature C 47 ph Time min 210 Ce-50LC load mg/g 10 Fe-GMA load mg/g 0.5 Factor y exp Mixture cost ( /ton) Opt OptEc Reuse cycle Low cost of commercial enzymatic preparations Cost of economically optimized mixture 2.5 times lower Possibility of mixture reuse

26 TEM characterization Untreated Nannochloropsis Treated Nannochloropsis

27 Conclusions The lipids contained in the microalgae can be effectively recovered by an enzymatic biomass pretreatment The combined use of cellulase and mannanase can significantly improve lipid recovery from the Nannochloropsis microalgae. Extraction yields close to 90 were obtained under optimized conditions with a 3.5 h extraction. The specificity of the enzymes used allows to preserve the activity of easily degradable compounds, such as proteins and carotenoids, which are produced by Nannochloropsis and could be co-extracted with lipids.

28 References Zuorro A., Lavecchia R., Maffei G., Marra F., Miglietta S., Petrangeli A., Familiari G., Valente T., Enhanced lipid extraction from unbroken microalgal cells using enzymes, Chem. Eng. Trans. 43: Zuorro A., Maffei G., Lavecchia R., Optimization of enzyme-assisted lipid extraction from Nannochloropsis microalgae, J. Taiwan Inst. Chem. Eng. 67: Zuorro A., Miglietta S., Familiari G., Lavecchia R., Enhanced lipid recovery from Nannochloropsis microalgae by treatment with optimized cell wall degrading enzyme mixtures. Bioresource Technology. 212:

29 Facoltà di Ingegneria Dipartimento Ingegneria Chimica Materiali Ambiente Processi innovativi per la produzione di biocombustibili in bioraffinerie green ed ecosostenibili Prof. Antonio Zuorro Roma, 15 Maggio 2018

Evaluation of Dissolved Organic Carbon (DOC) as a Measure of Cell Wall Degradation During Enzymatic Treatment of Microalgae

Evaluation of Dissolved Organic Carbon (DOC) as a Measure of Cell Wall Degradation During Enzymatic Treatment of Microalgae 373 A publication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 49, 216 Guest Editors: Enrico Bardone, Marco Bravi, Tajalli Keshavarz Copyright 216, AIDIC Servizi S.r.l., ISBN 978-88-9568-4-2; ISSN 2283-9216