{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/102959"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/102959","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Development of universal stoichiometric coefficients for modeling microalgal cultivation systems","abstract":"Biological treatment processes at water resource recovery facilities (WRRFs; a.k.a. wastewater treatment plants) are approaching the limit of technology for nitrogen and phosphorus removal. Algae treatment technologies have the ability to remove additional nitrogen and phosphorus, thereby lowering the effluent nutrient discharge level at WRRFs. A critical challenge for the adoption of algal technologies, however, is the lack of robust algae modeling platforms for wastewater treatment that can predict process performance under fluctuating reactor conditions and despite the inevitable biodiversity of influent wastewater. One necessary step towards improved modeling capabilities for algae treatment systems is the development of generalizable model parameters, such as stoichiometric parameters – like those used in the International Water Association’s (IWA’s) Activated Sludge Models (ASMs). This work introduces universal stoichiometric coefficients for algal process modeling derived from the conserved enzymatic properties for seven algae species using 11 genome-scale models. The model parameters include yield coefficients for algae grown under various energy inputs (photoautotrophic and heterotrophic), nitrogen sources (ammonia and nitrate), and carbon sources (inorganic, acetate, and glucose) as well as stoichiometric parameters for the accumulation of storage compounds (starch and lipids). Generalizable stoichiometric parameters based on conserved metabolic properties would bolster accuracy and the accessibility of algal process models. This will help promote the use of algal technologies by wastewater design engineers and utilities to improve the effluent quality at water resource recovery facilities.","abstract_html":"Biological treatment processes at water resource recovery facilities (WRRFs; a.k.a. wastewater treatment plants) are approaching the limit of technology for nitrogen and phosphorus removal. Algae treatment technologies have the ability to remove additional nitrogen and phosphorus, thereby lowering the effluent nutrient discharge level at WRRFs. A critical challenge for the adoption of algal technologies, however, is the lack of robust algae modeling platforms for wastewater treatment that can predict process performance under fluctuating reactor conditions and despite the inevitable biodiversity of influent wastewater. One necessary step towards improved modeling capabilities for algae treatment systems is the development of generalizable model parameters, such as stoichiometric parameters – like those used in the International Water Association’s (IWA’s) Activated Sludge Models (ASMs). This work introduces universal stoichiometric coefficients for algal process modeling derived from the conserved enzymatic properties for seven algae species using 11 genome-scale models. The model parameters include yield coefficients for algae grown under various energy inputs (photoautotrophic and heterotrophic), nitrogen sources (ammonia and nitrate), and carbon sources (inorganic, acetate, and glucose) as well as stoichiometric parameters for the accumulation of storage compounds (starch and lipids). Generalizable stoichiometric parameters based on conserved metabolic properties would bolster accuracy and the accessibility of algal process models. This will help promote the use of algal technologies by wastewater design engineers and utilities to improve the effluent quality at water resource recovery facilities.","abstract_has_math":false,"creators":["Schramm, Stephanie Marie"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Environ Engr in Civil Engr","degree_department":null,"school":null,"contributors":["Guest, Jeremy S."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-02-08T18:44:45Z","date_published":"2019-02-08T18:44:45Z","updated_at":"2026-07-22T22:24:42Z","subjects":["metabolic modeling","wastewater","microalgae"],"languages":["en"],"rights":["Copyright 2018 Stephanie Schramm"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/102959","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Guest, Jeremy S."]},{"key":"dc:creator","label":"Author","values":["Schramm, Stephanie Marie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-02-08T18:44:45Z","2021-02-09T10:15:45Z","2018-12-12","2018-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Environ Engr in Civil Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["metabolic modeling","wastewater","microalgae"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Stephanie Schramm"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/102959"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Biological treatment processes at water resource recovery facilities (WRRFs; a.k.a. wastewater treatment plants) are approaching the limit of technology for nitrogen and phosphorus removal. Algae treatment technologies have the ability to remove additional nitrogen and phosphorus, thereby lowering the effluent nutrient discharge level at WRRFs. A critical challenge for the adoption of algal technologies, however, is the lack of robust algae modeling platforms for wastewater treatment that can predict process performance under fluctuating reactor conditions and despite the inevitable biodiversity of influent wastewater. One necessary step towards improved modeling capabilities for algae treatment systems is the development of generalizable model parameters, such as stoichiometric parameters – like those used in the International Water Association’s (IWA’s) Activated Sludge Models (ASMs). This work introduces universal stoichiometric coefficients for algal process modeling derived from the conserved enzymatic properties for seven algae species using 11 genome-scale models. The model parameters include yield coefficients for algae grown under various energy inputs (photoautotrophic and heterotrophic), nitrogen sources (ammonia and nitrate), and carbon sources (inorganic, acetate, and glucose) as well as stoichiometric parameters for the accumulation of storage compounds (starch and lipids). Generalizable stoichiometric parameters based on conserved metabolic properties would bolster accuracy and the accessibility of algal process models. This will help promote the use of algal technologies by wastewater design engineers and utilities to improve the effluent quality at water resource recovery facilities.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-12-01","The student, Stephanie Schramm, accepted the attached license on 2018-12-11 at 15:31.","The student, Stephanie Schramm, submitted this Thesis for approval on 2018-12-11 at 15:44.","This Thesis was approved for publication on 2018-12-12 at 09:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13294 on 2019-02-08 at 11:42:03","Made available in DSpace on 2019-02-08T18:44:45Z (GMT). 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Algae treatment technologies have the ability to remove additional nitrogen and phosphorus, thereby lowering the effluent nutrient discharge level at WRRFs. A critical challenge for the adoption of algal technologies, however, is the lack of robust algae modeling platforms for wastewater treatment that can predict process performance under fluctuating reactor conditions and despite the inevitable biodiversity of influent wastewater. One necessary step towards improved modeling capabilities for algae treatment systems is the development of generalizable model parameters, such as stoichiometric parameters – like those used in the International Water Association’s (IWA’s) Activated Sludge Models (ASMs). This work introduces universal stoichiometric coefficients for algal process modeling derived from the conserved enzymatic properties for seven algae species using 11 genome-scale models. The model parameters include yield coefficients for algae grown under various energy inputs (photoautotrophic and heterotrophic), nitrogen sources (ammonia and nitrate), and carbon sources (inorganic, acetate, and glucose) as well as stoichiometric parameters for the accumulation of storage compounds (starch and lipids). Generalizable stoichiometric parameters based on conserved metabolic properties would bolster accuracy and the accessibility of algal process models. This will help promote the use of algal technologies by wastewater design engineers and utilities to improve the effluent quality at water resource recovery facilities.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-12-01","The student, Stephanie Schramm, accepted the attached license on 2018-12-11 at 15:31.","The student, Stephanie Schramm, submitted this Thesis for approval on 2018-12-11 at 15:44.","This Thesis was approved for publication on 2018-12-12 at 09:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13294 on 2019-02-08 at 11:42:03","Made available in DSpace on 2019-02-08T18:44:45Z (GMT). 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