quantification-heavy-metals-other-inorganic-contaminants-on

Quantification of Heavy Metals and Other Inorganic Contaminants on the Productivity of Microalgae

Increasing demand for renewable fuels has researchers investigating the feasibility of alternative feedstocks, such as microalgae. Inherent advantages ...

Utah State University

This study investigates the scaling of photobioreactor productivity based on the growth of Nannochloropsis salina incorporating the effects of direct and diffuse light. The scaling and optimization of photobioreactor geometry was analyzed by determining the growth response of a small-scale system designed to represent a core sample of a large-scale photobioreactor. The small-scale test apparatus was operated at a variety of light intensities on a batch time scale to generate a photosynthetic irradiance (PI) growth dataset, ultimately used to inform a PI growth model. The validation of the scalability of the PI growth model to predict productivity in large-scale systems was done by comparison with experimental growth data collected from two geometrically different large-scale photobioreactors operated at a variety of light intensities. For direct comparison, the small-scale and large-scale experimental systems presented were operated similarly and in such a way to incorporate cultivation relevant time scales, light intensities, mixing, and nutrient loads. Validation of the scalability of the PI growth model enables the critical evaluation of different photobioreactor geometries and design optimization incorporating growth effects from diffuse and direct light. Discussion focuses on the application of the PI growth model to assess the effect of diffuse light growth compared to direct light growth for the evaluation of photobioreactors followed by the use of the model for photobioreactor geometry optimization on the metric of areal productivity.

Scale-Up of flat plate photobioreactors considering diffuse and direct light characteristics.

Although numerous lifecycle assessments (LCA) of microalgae-based biofuels have suggested net reductions of greenhouse gas emissions, limited experimental data exist on direct emissions from microalgae cultivation systems. For example, nitrous oxide (N(2)O) is a potent greenhouse gas that has been detected from microalgae cultivation. However, little quantitative experimental data exist on direct N(2)O emissions from microalgae cultivation, which has inhibited LCA performed to date. In this study, microalgae species Nannochloropsis salina was cultivated with diurnal light-dark cycling using a nitrate nitrogen source. Gaseous N(2)O emissions were quantitatively measured using Fourier transform infrared spectrometry. Under a nitrogen headspace (photobioreactor simulation), the reactors exhibited elevated N(2)O emissions during dark periods, and reduced N(2)O emissions during light periods. Under air headspace conditions (open pond simulation), N(2)O emissions were negligible during both light and dark periods. Results show that N(2)O production was induced by anoxic conditions when nitrate was present, suggesting that N(2)O was produced by denitrifying bacteria within the culture. The presence of denitrifying bacteria was verified through PCR-based detection of norB genes and antibiotic treatments, the latter of which substantially reduced N(2)O emissions. Application of these results to LCA and strategies for growth management to reduce N(2)O emissions are discussed.

Quantitative measurement of direct nitrous oxide emissions from microalgae cultivation.

Produzione commerciale di materie prime rinnovabili energia da microalghe richiedono sistemi di crescita affidabile e scalabile. Due e una met&agrave; anni di dati di produttivit&agrave; di biomassa e lipidi sono stati ottenuti con un photobioreactor all&#39;aperto su scala industriale operato a Fort Collins, Colorado (USA). I tassi di crescita volumetrica annualizzato Nannochloropsis oculata (CCMP 525) e Nannochloropsis salina (CCMP 1776) erano 0.16 g L(-1) d(-1) (picco = 0,37 g L(-1) d(-1)) e d(-1) L(-1) 0,15 g (picco = 0,37 g L(-1) d(-1)) rispettivamente. La produzione collettiva lipidico medio era 10,7 m3 ha(-1) yr(-1) con un valore di picco di 36,3 m3 ha(-1) yr(-1). Risultati di questo studio sono unici basato sulla pubblicazione della biomassa e dei lipidi corrispondenti contenuto combinato con dimostrazione del risparmio energetico realizzato attraverso l&#39;analisi delle esigenze di consegna del gas, da successivi raccolti con nessun effetto sulla produttivit&agrave; e stabilit&agrave; di cultura attraverso dati seriale lignaggio dei lotti e analisi chemotaxonomic di acido grasso contenuto di riciclo dell&#39;acqua.

Nannochloropsis metriche di produzione in un photobioreactor outdoor scalabile per le applicazioni commerciali.

&#x5546;&#x4E1A;&#x5316;&#x751F;&#x4EA7;&#x7684;&#x53EF;&#x518D;&#x751F;&#x80FD;&#x6E90;&#x539F;&#x6599;&#x4ECE;&#x5FAE;&#x85FB;&#x5C06;&#x9700;&#x8981;&#x53EF;&#x9760;&#x548C;&#x53EF;&#x4F38;&#x7F29;&#x7684;&#x751F;&#x957F;&#x7CFB;&#x7EDF;&#x3002;&#x4E0E;&#x5DE5;&#x4E1A;&#x89C4;&#x6A21;&#x7684;&#x6237;&#x5916;&#x53CD;&#x5E94;&#x5668;&#x64CD;&#x4F5C;&#x5728;&#x79D1;&#x7F57;&#x62C9;&#x591A;&#x5DDE;&#x67EF;&#x6797;&#x65AF;&#x5821; (&#x7F8E;&#x56FD;) &#x83B7;&#x5F97;&#x4E86;&#x4E24;&#x4E2A;&#x534A;&#x5E74;&#x7684;&#x751F;&#x7269;&#x91CF;&#x548C;&#x8102;&#x8D28;&#x751F;&#x4EA7;&#x7387;&#x6570;&#x636E;&#x3002;&#x5FAE;&#x7EFF;&#x7403;&#x85FB; (CCMP 525) &#x548C;&#x5FAE;&#x85FB; &#xFF08;CCMP 1776) &#x7684;&#x4F53;&#x79EF;&#x7684;&#x5E74;&#x5747;&#x7684;&#x589E;&#x957F;&#x7387;&#x7387;&#x662F; 0.16 g L(-1) d(-1) &#xFF08;&#x5CF0;&#x503C; = 0.37 g L(-1) d(-1)) &#x548C; 0.15 g L(-1) d(-1) &#xFF08;&#x5CF0;&#x503C;&#x5206;&#x522B; = 0.37 g L(-1) d(-1))&#x3002;&#x96C6;&#x4F53;&#x5E73;&#x5747;&#x8102;&#x751F;&#x4EA7;&#x662F; 10.7 m3 ha(-1) yr(-1) &#x4E0E;&#x5CF0;&#x503C;&#x4E3A; 36.3 m3 ha(-1) yr(-1)&#x3002;&#x4ECE;&#x8FD9;&#x9879;&#x7814;&#x7A76;&#x7684;&#x7ED3;&#x679C;&#x662F;&#x552F;&#x4E00;&#x7684;&#x57FA;&#x4E8E;&#x51FA;&#x7248;&#x7269;&#x7684;&#x751F;&#x7269;&#x91CF;&#x548C;&#x76F8;&#x5E94;&#x5185;&#x5BB9;&#x7684;&#x8102;&#x8D28;&#x7ED3;&#x5408;&#x793A;&#x8303;&#x901A;&#x8FC7;&#x5206;&#x6790;&#x6C14;&#x4F53;&#x7684;&#x9001;&#x8D27;&#x8981;&#x6C42;&#xFF0C;&#x6C34;&#x5FAA;&#x73AF;&#x4ECE;&#x8FDE;&#x7EED;&#x6536;&#x83B7;&#x4E0D;&#x5F71;&#x54CD;&#x751F;&#x4EA7;&#x6548;&#x7387;&#x548C;&#x6587;&#x5316;&#x7A33;&#x5B9A;&#x901A;&#x8FC7;&#x4E32;&#x884C;&#x6279;&#x5904;&#x7406;&#x6CBF;&#x88AD;&#x6570;&#x636E;&#x548C;&#x5206;&#x7C7B;&#x5B66;&#x7684;&#x8102;&#x80AA;&#x9178;&#x542B;&#x91CF;&#x5206;&#x6790;&#x5B9E;&#x73B0;&#x7684;&#x80FD;&#x6E90;&#x8282;&#x7EA6;&#x3002;

&#x53EF;&#x6269;&#x5C55;&#xFF0C;&#x5E94;&#x7528;&#x4E8E;&#x5546;&#x4E1A;&#x6237;&#x5916;&#x5149;&#x751F;&#x7269;&#x53CD;&#x5E94;&#x5668;&#x5FAE;&#x751F;&#x4EA7;&#x5EA6;&#x91CF;&#x6807;&#x51C6;&#x3002;

Commercial production of renewable energy feedstocks from microalgae will require reliable and scalable growth systems. Two and one half years of biomass and lipid productivity data were obtained with an industrial-scale outdoor photobioreactor operated in Fort Collins, Colorado (USA). The annualized volumetric growth rates for Nannochloropsis oculata (CCMP 525) and Nannochloropsis salina (CCMP 1776) were 0.16 g L(-1) d(-1) (peak=0.37 g L(-1) d(-1)) and 0.15 g L(-1) d(-1) (peak=0.37 g L(-1) d(-1)) respectively. The collective average lipid production was 10.7 m3 ha(-1) yr(-1) with a peak value of 36.3 m3 ha(-1) yr(-1). Results from this study are unique based on publication of biomass and corresponding lipid content combined with demonstration of energy savings realized through analysis of gas delivery requirements, water recycling from successive harvests with no effect on productivity, and culture stability through serial batch lineage data and chemotaxonomic analysis of fatty acid contents.

Nannochloropsis production metrics in a scalable outdoor photobioreactor for commercial applications.

La production commerciale de mati&egrave;res premi&egrave;res renouvelables de microalgues peuvent requ&eacute;rir un syst&egrave;me fiable et &eacute;volutive de la croissance. Deux et demi ans de donn&eacute;es de productivit&eacute; de la biomasse et les lipides ont &eacute;t&eacute; obtenue avec un photobior&eacute;acteur ext&eacute;rieure &eacute;chelle industrielle exploit&eacute; &agrave; Fort Collins, Colorado (USA). Les taux de croissance annualis&eacute; de volum&eacute;trique pour Nannochloropsis oculata (525 CCMP) et Nannochloropsis salina (CCMP 1776) &eacute;taient 0,16 g L(-1) d(-1) (pic = 0,37 g L(-1) d(-1)) et d(-1) L(-1) 0,15 g (max. = 0,37 g L(-1) d(-1)) respectivement. La production de lipides moyenne collective &eacute;tait de 10,7 m3 ha(-1) yr(-1) avec une valeur de cr&ecirc;te de 36,3 m3 ha(-1) yr(-1). Les r&eacute;sultats de cette &eacute;tude sont uniques bas&eacute; sur la publication de la biomasse et lipides correspondants combin&eacute;e avec d&eacute;monstration des &eacute;conomies d&#39;&eacute;nergie r&eacute;alis&eacute;es gr&acirc;ce &agrave; l&#39;analyse des besoins de livraison de gaz, recyclage des r&eacute;coltes successives sans effet sur la productivit&eacute; et la stabilit&eacute; de la culture par le biais de donn&eacute;es lineage s&eacute;rie lot et chimiotaxonomique analyse des teneurs en acide gras de l&#39;eau.

Nannochloropsis m&eacute;triques de production dans un photobior&eacute;acteur ext&eacute;rieure &eacute;volutive pour les applications commerciales.

Kommerzielle Produktion von erneuerbaren Rohstoffen aus Mikroalgen ben&ouml;tigen zuverl&auml;ssige und skalierbare Systeme. Zwei und eine H&auml;lfte Jahre Biomasse und Lipid-Produktivit&auml;t-Daten wurden mit einer industriellen Ma&szlig;stab im freien Photobioreaktor betrieben in Fort Collins, Colorado (USA) erzielt. Die annualisierte Volumetrische Wachstumsraten Nannochloropsis Oculata (CCMP 525) und Nannochloropsis Salina (CCMP 1776) wurden 0,16 g L(-1) d(-1) (Spitzenwert = 0,37 g L(-1) d(-1)) und 0,15 g L(-1) d(-1) (Spitzenwert = 0,37 g L(-1) d(-1)) bzw.. Die kollektive durchschnittliche Lipid-Produktion war 10.7 m3-ha(-1)-yr(-1) mit einem Spitzenwert von 36,3 m3 ha(-1) yr(-1). Ergebnisse dieser Studie sind einzigartig, basierend auf Ver&ouml;ffentlichung von Biomasse und entsprechenden Lipid Inhalt kombiniert mit Demonstration von Energieeinsparungen durch Analyse der Lieferanforderungen ab Gas, Wasser-recycling von aufeinander folgenden Ernten keine Auswirkungen auf Produktivit&auml;t und Kultur Stabilit&auml;t durch serielle Chargendaten &Uuml;bertragungslinie und chemotaxonomic Analyse der Fetts&auml;ure Inhalt realisiert.

Nannochloropsis Produktion Metriken in einer skalierbaren drau&szlig;en Photobioreaktor f&uuml;r kommerzielle Anwendungen.

&#x5FAE;&#x7D30;&#x85FB;&#x985E;&#x7531;&#x6765;&#x306E;&#x518D;&#x751F;&#x53EF;&#x80FD;&#x30A8;&#x30CD;&#x30EB;&#x30AE;&#x30FC;&#x306E;&#x5546;&#x696D;&#x751F;&#x7523;&#x306F;&#x3001;&#x4FE1;&#x983C;&#x6027;&#x3068;&#x62E1;&#x5F35;&#x6027;&#x306E;&#x6210;&#x9577;&#x30B7;&#x30B9;&#x30C6;&#x30E0;&#x5FC5;&#x8981;&#x304C;&#x3042;&#x308A;&#x307E;&#x3059;&#x3002;2 &#x5206;&#x306E; 1 &#x5E74;&#x306E;&#x30D0;&#x30A4;&#x30AA;&#x30DE;&#x30B9;&#x3068;&#x8102;&#x8CEA;&#x751F;&#x7523;&#x6027;&#x30C7;&#x30FC;&#x30BF; &#x30D5;&#x30A9;&#x30FC;&#x30C8;&#x30B3;&#x30EA;&#x30F3;&#x30BA;&#x3001;&#x30B3;&#x30ED;&#x30E9;&#x30C9;&#x5DDE; &#xFF08;&#x30A2;&#x30E1;&#x30EA;&#x30AB;&#xFF09; &#x3092;&#x904B;&#x55B6;&#x3059;&#x308B;&#x5DE5;&#x696D;&#x898F;&#x6A21;&#x306E;&#x5C4B;&#x5916;&#x30D5;&#x30A9;&#x30C8;&#x30D0;&#x30A4;&#x30AA;&#x30EA;&#x30A2;&#x30AF;&#x30BF;&#x30FC;&#x5F97;&#x3089;&#x308C;&#x307E;&#x3057;&#x305F;&#x3002;0.16 G L(-1) d(-1) &#x5E74;&#x7387;&#x4F53;&#x7A4D;&#x6210;&#x9577;&#x7387;&#x30CA;&#x30CE;&#x30AF;&#x30ED;&#x30ED;&#x30D7;&#x30B7;&#x30B9; (CCMP 525&#xFF09; &#x304A;&#x3088;&#x3073;&#x30CA;&#x30CE;&#x30AF;&#x30ED;&#x30ED;&#x30D7;&#x30B7;&#x30B9; &#x30B5;&#x30EA;&#x30FC;&#x30CA; &#xFF08;CCMP 1776 &#x5E74;&#xFF09; &#x3067;&#x3042;&#x3063;&#x305F; &#xFF08;&#x30D4;&#x30FC;&#x30AF; = 0.37 g L(-1) d(-1)) &#x3068; 0.15 g L(-1) d(-1) (&#x30D4;&#x30FC;&#x30AF; 0.37 g L(-1) d(-1)) &#x3092;&#x305D;&#x308C;&#x305E;&#x308C; =&#x3002;&#x96C6;&#x56E3;&#x306E;&#x5E73;&#x5747;&#x8102;&#x8CEA;&#x751F;&#x7523; 10.7 m3 ha(-1) yr(-1) &#x30D4;&#x30FC;&#x30AF;&#x5024;&#x304C; 36.3 m3 ha(-1) yr(-1) &#x306E;&#x3060;&#x3063;&#x305F;&#x3002;&#x3053;&#x306E;&#x7814;&#x7A76;&#x306E;&#x7D50;&#x679C;&#x306F;&#x3001;&#x30D0;&#x30A4;&#x30AA;&#x30DE;&#x30B9;&#x3068;&#x5BFE;&#x5FDC;&#x3059;&#x308B;&#x8102;&#x8CEA;&#x542B;&#x6709;&#x306E;&#x751F;&#x7523;&#x6027;&#x3001;&#x304A;&#x3088;&#x3073;&#x30B7;&#x30EA;&#x30A2;&#x30EB; &#x30D0;&#x30C3;&#x30C1;&#x7CFB;&#x5217;&#x30C7;&#x30FC;&#x30BF;&#x3068;&#x30B3;&#x30A6;&#x30DC;&#x8102;&#x80AA;&#x9178;&#x5185;&#x5BB9;&#x5206;&#x6790;&#x3092;&#x901A;&#x3058;&#x3066;&#x6587;&#x5316;&#x306E;&#x5B89;&#x5B9A;&#x6027;&#x306B;&#x5F71;&#x97FF;&#x3092;&#x4E0E;&#x3048;&#x306A;&#x3044;&#x9023;&#x7D9A;&#x53CE;&#x7A6B;&#x304B;&#x3089;&#x30EA;&#x30B5;&#x30A4;&#x30AF;&#x30EB;&#x6C34;&#x30AC;&#x30B9;&#x306E;&#x914D;&#x4FE1;&#x8981;&#x4EF6;&#x306E;&#x5206;&#x6790;&#x3092;&#x901A;&#x3057;&#x3066;&#x5B9F;&#x73FE;&#x30A8;&#x30CD;&#x30EB;&#x30AE;&#x30FC; &#x30B3;&#x30B9;&#x30C8;&#x524A;&#x6E1B;&#x306E;&#x5B9F;&#x8A3C;&#x3068;&#x7D44;&#x307F;&#x5408;&#x308F;&#x305B;&#x3066;&#x51FA;&#x7248;&#x306B;&#x57FA;&#x3065;&#x3044;&#x3066;&#x4E00;&#x610F;&#x3067;&#x3059;&#x3002;

&#x30CA;&#x30CE;&#x30AF;&#x30ED;&#x30ED;&#x30D7;&#x30B7;&#x30B9;&#x751F;&#x7523;&#x30E1;&#x30C8;&#x30EA;&#x30C3;&#x30AF; &#x30B9;&#x30B1;&#x30FC;&#x30E9;&#x30D6;&#x30EB;&#x306A;&#x5C4B;&#x5916;&#x30D5;&#x30A9;&#x30C8;&#x30D0;&#x30A4;&#x30AA;&#x30EA;&#x30A2;&#x30AF;&#x30BF;&#x30FC;&#x5546;&#x7528;&#x30A2;&#x30D7;&#x30EA;&#x30B1;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x306E;&#x305F;&#x3081;&#x306B;&#x3002;

&#xC0C1;&#xC5C5; &#xC0DD;&#xC0B0; &#xBBF8;&#xC138;&#xC5D0;&#xC11C; &#xC2E0; &#xC7AC;&#xC0DD; &#xC5D0;&#xB108;&#xC9C0; &#xC6D0;&#xB8CC;&#xC758; &#xC548;&#xC815;&#xC801;&#xC774; &#xACE0; &#xD655;&#xC7A5; &#xAC00;&#xB2A5;&#xD55C; &#xC131;&#xC7A5; &#xC2DC;&#xC2A4;&#xD15C;&#xC744; &#xC694;&#xAD6C;&#xD560; &#xAC83; &#xC774;&#xB2E4;. &#xBC14;&#xC774;&#xC624; &#xB9E4;&#xC2A4; &#xBC0F; &#xC9C0;&#xC9C8; &#xC0DD;&#xC0B0;&#xC131; &#xB370;&#xC774;&#xD130;&#xC758; &#xB450; &#xBC0F; &#xC808;&#xBC18; &#xB144; &#xD3EC;&#xD2B8; &#xCF5C;&#xB9B0;&#xC2A4;, &#xCF5C;&#xB85C;&#xB77C;&#xB3C4; (&#xBBF8;&#xAD6D;)&#xC5D0;&#xC11C; &#xC6B4;&#xC601; &#xD558; &#xACE0; &#xC0B0;&#xC5C5; &#xADDC;&#xBAA8; &#xC57C;&#xC678; photobioreactor&#xB85C; &#xC5BB;&#xC740; &#xD588;&#xB2E4;. Nannochloropsis oculata (CCMP 525) &#xBC0F; Nannochloropsis &#xC0B4; &#xB9AC; &#xB098; (CCMP 1776)&#xC5D0; &#xB300; &#xD55C; &#xC5F0;&#xAC04;&#xB41C; &#xCCB4;&#xC801; &#xC99D;&#xAC00;&#xC728;&#xC740; 0.16 g L(-1) d(-1) (&#xD53C;&#xD06C; = 0.37 g L(-1) d(-1))&#xC640; 0.15 g L(-1) d(-1) (&#xD53C;&#xD06C; &#xAC01;&#xAC01; 0.37 g L(-1) d(-1)) =. &#xC9D1;&#xB2E8; &#xD3C9;&#xADE0; &#xC9C0;&#xC9C8; &#xC0DD;&#xC0B0; 36.3 m 3 ha(-1) yr(-1)&#xC758; &#xD53C;&#xD06C; &#xAC12;&#xC73C;&#xB85C; 10.7 m 3 ha(-1) yr(-1) &#xD588;&#xB2E4;. &#xC774; &#xC5F0;&#xAD6C;&#xC758; &#xACB0;&#xACFC; &#xC0DD;&#xC0B0;&#xC131; &#xBC0F; &#xC9C1;&#xB82C; &#xBC30;&#xCE58; &#xD608;&#xD1B5; &#xB370;&#xC774;&#xD130; &#xBC0F; &#xC9C0;&#xBC29;&#xC0B0; &#xB0B4;&#xC6A9;&#xC758; chemotaxonomic &#xBD84;&#xC11D;&#xC744; &#xD1B5;&#xD574; &#xBB38;&#xD654; &#xC548;&#xC815;&#xC131;&#xC5D0; &#xC544;&#xBB34;&#xB7F0; &#xC601;&#xD5A5;&#xC73C;&#xB85C; &#xC5F0;&#xC18D; &#xC218;&#xD655;&#xC5D0;&#xC11C; &#xC7AC;&#xD65C;&#xC6A9; &#xBB3C; &#xAC00;&#xC2A4; &#xBC30;&#xB2EC; &#xC694;&#xAD6C;&#xC758; &#xBD84;&#xC11D;&#xC744; &#xD1B5;&#xD574; &#xC2E4;&#xD604; &#xD558;&#xB294; &#xC5D0;&#xB108;&#xC9C0; &#xC808;&#xC57D;&#xC758; &#xB370;&#xBAA8;&#xC640; &#xD568;&#xAED8; &#xBC14;&#xC774;&#xC624; &#xB9E4;&#xC2A4; &#xBC0F; &#xD574;&#xB2F9; &#xC9C0;&#xC9C8; &#xCF58;&#xD150;&#xCE20; &#xAC8C;&#xC2DC; &#xAE30;&#xBC18; &#xB3C5;&#xD2B9;&#xD569;&#xB2C8;&#xB2E4;.

&#xC0C1;&#xC6A9; &#xC560;&#xD50C; &#xB9AC; &#xCF00;&#xC774; &#xC158;&#xC5D0; &#xB300; &#xD55C; &#xD655;&#xC7A5; &#xAC00;&#xB2A5;&#xD55C; &#xC57C;&#xC678; photobioreactor&#xC5D0; Nannochloropsis &#xC0DD;&#xC0B0; &#xD1B5;&#xACC4;.

Producci&oacute;n comercial de materias primas renovables de microalgas requerir&aacute; sistemas de crecimiento escalable y confiable. Se obtuvieron dos y medio a&ntilde;os de datos de productividad de biomasa y de l&iacute;pidos con un fotobiorreactor al aire libre de escala industrial operado en Fort Collins, Colorado (Estados Unidos). La tasa de crecimiento anualizada volum&eacute;trica para Nannochloropsis oculata (CCMP 525) y Nannochloropsis salina (CCMP 1776) eran 0.16 g L(-1) d(-1) (pico = 0,37 g L(-1) d(-1)) y 0.15 g L(-1) d(-1) (pico = 0,37 g L(-1) d(-1)) respectivamente. La producci&oacute;n de l&iacute;pidos promedio colectivo fue de 10,7 m3 ha(-1) yr(-1) con un valor m&aacute;ximo de 36,3 m3 ha(-1) yr(-1). Resultados de este estudio son &uacute;nicos basados en la publicaci&oacute;n de biomasa y contenido de l&iacute;pidos correspondiente combinado con demostraci&oacute;n de ahorro de energ&iacute;a realizado a trav&eacute;s de an&aacute;lisis de requisitos de entrega de gas, agua, reciclaje de cosechas sucesivas con ning&uacute;n efecto sobre la productividad y la estabilidad de la cultura a trav&eacute;s de datos de linaje lote serial y chemotaxonomic an&aacute;lisis de contenido de &aacute;cidos grasos.

M&eacute;tricas de producci&oacute;n de Nannochloropsis en un fotobiorreactor exterior escalable para aplicaciones comerciales.

In the current literature, the life cycle, technoeconomic, and resource assessments of microalgae-based biofuel production systems have relied on growth models extrapolated from laboratory-scale data, leading to a large uncertainty in results. This type of simplistic growth modeling overestimates productivity potential and fails to incorporate biological effects, geographical location, or cultivation architecture. This study uses a large-scale, validated, outdoor photobioreactor microalgae growth model based on 21 reactor- and species-specific inputs to model the growth of Nannochloropsis. This model accurately accounts for biological effects such as nutrient uptake, respiration, and temperature and uses hourly historical meteorological data to determine the current global productivity potential. Global maps of the current near-term microalgae lipid and biomass productivity were generated based on the results of annual simulations at 4,388 global locations. Maximum annual average lipid yields between 24 and 27 m(3)·ha(-1)·y(-1), corresponding to biomass yields of 13 to 15 g·m(-2)·d(-1), are possible in Australia, Brazil, Colombia, Egypt, Ethiopia, India, Kenya, and Saudi Arabia. The microalgae lipid productivity results of this study were integrated with geography-specific fuel consumption and land availability data to perform a scalability assessment. Results highlight the promising potential of microalgae-based biofuels compared with traditional terrestrial feedstocks. When water, nutrients, and CO2 are not limiting, many regions can potentially meet significant fractions of their transportation fuel requirements through microalgae production, without land resource restriction. Discussion focuses on sensitivity of monthly variability in lipid production compared with annual average yields, effects of temperature on productivity, and a comparison of results with previous published modeling assumptions.

Global evaluation of biofuel potential from microalgae.

Oleaginous microbes can upgrade carbon to lipids, which can be used as a feedstock to produce renewable replacements for petroleum-based compounds. Efficient extraction of lipids from oleaginous microbes typically involves dewatering and drying of the biomass. Problematically, drying often requires an amount of energy approaching that available from the cells. Here, we report an approach for the high efficiency extraction of triacylglycerides (TAG) from wet oleaginous microbes, bypassing the drying process. Solvent candidates for extraction of wet oleaginous biomass were identified using ASPEN's databases to determine an activity based selectivity coefficient. Optimal extraction conditions were determined which resulted in >91% extraction of TAG from yeast, bacteria, and microalgae. Experimental data was integrated into system models to evaluate the energetics of the processes compared to traditional extraction methods. The net energy ratio (NER) of a traditional dry solvent extraction is 0.84, whereas the approach presented here has a NER of 0.34 for yeast.

Improving energetics of triacylglyceride extraction from wet oleaginous microbes.

This study presents experimental measurements of the biochemical methane production for whole and lipid extracted Nannochloropsis salina. Results show whole microalgae produced 430 cm(3)-CH4 g-volatile solids(-1) (g-VS) (σ=60), 3 times more methane than was produced by the LEA, 140 cm(3)-CH4 g-VS(-1) (σ=30). Results illustrate current anaerobic modeling efforts in microalgae to biofuel assessments are not reflecting the impact of lipid removal. On a systems level, the overestimation of methane production is shown to positively skew the environmental impact of the microalgae to biofuels process. Discussion focuses on a comparison results to those of previous anaerobic digestion studies and quantifies the corresponding change in greenhouse gas emissions of the microalgae to biofuels process based on results from this study.

Microalgae to biofuels: life cycle impacts of methane production of anaerobically digested lipid extracted algae.

Microalgae biofuel production has been extensively evaluated through resource, economic and life cycle assessments. Resource assessments consistently identify land as non-limiting and highlight the need to consider siting based on combined geographical constraints of land and other critical resources such as water and carbon dioxide. Economic assessments report a selling cost of fuel that ranges between $1.64 and over $30 gal(-1) consistent with large variability reported in the life cycle literature, -75 to 534 gCO2-eq MJ(-1). Large drivers behind such variability stem from differences in productivity assumptions, pathway technologies, and system boundaries. Productivity represents foundational units in these assessments with current assumed yields in various assessments varying by a factor of 60. A review of the literature in these areas highlights the need for harmonized assessments such that direct comparisons of alternative processing technologies can be made on the metrics of resource requirements, economic feasibility, and environmental impact.

Jason C. Quinn

Mechanical and Aerospace Engineering

Utah State University

Quantification of Heavy Metals and Other Inorganic Contaminants on the Productivity of Microalgae