Grass silage for biorefinery A meta-analysis of silage factors affecting liquid-solid separation

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1 Photo: Luke / Marketta Rinne Grass silage for biorefinery A meta-analysis of silage factors affecting liquid-solid separation Marcia Franco, Erika Winquist & Marketta Rinne Natural Resources Institute Finland Luke, Finland

2 Innofeed project 31/07/ Biorefining ensiled grass into inventive feed products Ensiled grass Potential to be biorefined Variety of innovative products Silage All year round Improves the opportunity of a more efficient and sustainable use of green biomass Photo: Luke / Marketta Rinne

3 Concept of a green biorefinery 3 Green biorefinery can produce multiple end products and include various processes Separation Protein Grassland production Harvesting and preservation of grass Proteins soluble sugars Mechanical Mechanical fractionation Liquid fraction Separation, fermentation Anaerobic digestion Direct use of juice Drying Lactic acids Biogas Feed Fermentation medium Fodder pellet or as such Cellulose lignocellulose Solid fraction Anaerobic digestion Fibre production Solid fuel Biogas, fertilizer Materials Adapted from: Xiu, S. & Shahbai, A Development of green biorefinery for biomass utilization: A review. Trends in Renewable Energy 1: Fermentation, gasification, Hydrothermal Biofuels

4 The objective of the current work 4 Evaluate the effect of silage quality on liquid yield, liquid composition and retained compounds in liquid through a meta-analytical approach

5 5 Photo: Luke / Marketta Rinne Photo: Luke / Marketta Rinne Photo: Luke / Marcia Franco

6 Materials and Methods 31/07/ studies 32 mean values of silage that was separated into liquid and solid fractions Meta-analysis: how silage characteristics affect liquid yield, chemical composition and compounds retained in the liquid fraction Species Grass Legume Additive Control Biological Acid Harvest Primary growth Regrowth

7 Materials and Methods 31/07/2018 First step of biorefinery liquid-solid separation 7 Farm scale 1. Farm scale twin screw press (Haarslev) 2. Farm scale single screw press (Pellon) Photos: Luke / M. Franco & M. Rinne

8 Materials and Methods 31/07/2018 First step of biorefinery liquid-solid separation 8 Laboratory scale 3. Laboratory scale twin screw press (Angel Juicer) 4. Laboratory scale pneumatic press (Luke) Photos: Luke / M. Franco & T. Stefański

9 Materials and Methods 31/07/ Chemical composition and in vitro organic matter digestibility (IVOMD) Response variables: liquid yield, composition and retained compounds Silage characteristics with highest correlation to liquid yield Equations were developed using a mixed model regression analysis (individual experiments = random effect) Adjustment: 1. Coefficient of determination (R 2 ) 2. Akaike s information criterion (AIC) 3. Root mean square error (RMSE)

10 10 Photo: Eeva Saarisalo

11 Descriptive statistics of the data set for liquid-solid separation 11 Variable n Mean SD Min Max Regard silage Silage DM, g kg Silage ash, g kg -1 DM Silage CP, g kg -1 DM Silage NDF, g kg -1 DM Silage IVOMD, g kg -1 OM Regard liquid Mean: 321 g kg -1 Salo et al., 2014 Liquid yield Liquid DM, g kg -1 DM Liquid CP, g kg -1 DM DM retained in liquid CP retained in liquid

12 12 Pearson correlation coefficients between liquid yield and silage quality for different liquid-solid separation methods Variable Liquid yield correlation coefficients LPP FSS FTS LTS First Silage DM, g kg Silage ash, g kg -1 DM Silage CP, g kg -1 DM Second Silage NDF, g kg -1 DM Silage IVOMD, g kg -1 OM LPP: laboratory scale pneumatic press FSS: farm scale single screw press FTS: farm scale twin screw press LTS: laboratory scale twin screw press Correlation coefficients bolded are significant at 5% probability

13 13 Effect of silage quality on prediction of liquid yield, composition and retained compounds in liquid Laboratory Scale Pneumatic Press (LPP; n = 22) Y = dep variable X = indep variables α β 1 β 2 AIC R 2 RMSE Liquid yield Liquid DM DM retained in liquid Liquid CP CP retained in liquid X 1 = DM X 1 = DM; X 2 = NDF X 1 = DM r = 0.83; P< X 1 = DM; X 2 = IVOMD X 1 = NDF X 1 = NDF; X 2 = IVOMD r = 0.41; P<0.07 X 1 = DM X 1 = DM; X 2 = NDF X 1 = NDF X 1 = NDF; X 2 = CP Coefficients bolded are significant at 5% probability

14 Effect of silage quality on prediction of liquid yield, composition and retained compounds in liquid for different separation methods 14 Press Y = dep variable X = indep variables α β 1 β 2 AIC R 2 RMSE X 1 = DM FSS (n = 10) Liquid yield X 1 = DM; X 2 = NDF X 1 = IVOMD FTS (n = 4) Liquid yield X 1 = IVOMD; X 2 = NDF X 1 = DM LTS (n = 11) Liquid yield X 1 = DM; X 2 = CP FSS; farm scale single screw press FTS: farm scale twin screw press LTS: laboratory scale twin screw press Coefficients bolded are significant at 5% probability

15 Liquid yield 31/07/ Prediction of liquid yield using regression equations based on silage dry matter for different separation methods Photo: Luke/Niina Pitkänen LPP y = x; R 2 = 0.31 FSS y = x; R 2 = Silage DM, g/kg LTS y = x; R 2 = 0.71 LPP: laboratory scale pneumatic press FSS: farm scale single screw press LTS: laboratory scale twin screw press

16 Conclusion 31/07/ The high correlation between silage quality and liquid yield and composition provides prospective to predict the biorefinery potential of a particular silage batch based on these equations This information can also be used to modify the silage production systems so that they best meet the requirements of a green biorefinery process

17 More information about Innofeed project 17 Project home page: Facebook: Press release: ineries-turn-grass-into-new-feed-products

18 18 Marcia Franco Teppo Tutkija