Die Idee zu unse­rem Unter­neh­men ent­stand im Rah­men ver­schie­de­ner For­schungs­pro­jek­te, daher haben wir For­schung und Inno­va­ti­on in unse­rer DNA fest ver­an­kert. Wir davon über­zeugt, dass für eine siche­re und lebens­wer­te Zukunft ein Umden­ken in der Lebens­mit­tel­ver­sor­gung erfor­der­lich ist. Zusam­men mit ver­schie­de­nen Part­nern arbei­ten wir Hand in Hand an der Erfor­schung neu­er Wege im Bereich Aqua­kul­tur sowie an neu­en tech­no­lo­gi­schen Ent­wick­lun­gen, Stu­di­en und Innovationsthemen.





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2019 | Aquaponics Systems Modelling

Karel J. Kees­man, Oli­ver Kör­ner, Kai Wag­ner, Jan Urban, Divas Kari­man­zi­ra, Tho­mas Rau­schen­bach, and Simon Goddek

Mathe­ma­ti­cal models can take very dif­fe­rent forms and very dif­fe­rent levels of com­ple­xi­ty. A sys­te­ma­tic way to pos­tu­la­te, cali­bra­te and vali­da­te, as pro­vi­ded by sys­tems theo­ry, can the­r­e­fo­re be very hel­pful. In this chap­ter, dyna­mic sys­tems model­ling of aqua­po­nic (AP) sys­tems, from a sys­tems theo­re­ti­cal per­spec­ti­ve, is con­side­red and demons­tra­ted to each of the sub­sys­tems of the AP sys­tem, such as fish tanks, anae­ro­bic diges­ter and hydro­po­nic (HP) green­house. It fur­ther shows the links bet­ween the sub­sys­tems, so that in prin­ci­ple a com­ple­te AP sys­tems model can be built and inte­gra­ted into dai­ly prac­ti­ce with respect to manage­ment and con­trol of AP sys­tems. The main chall­enge is to choo­se an appro­pria­te model com­ple­xi­ty that meets the expe­ri­men­tal data for esti­ma­ti­on of para­me­ters and sta­tes and allows us to ans­wer ques­ti­ons rela­ted to the model­ling objec­ti­ve, such as simu­la­ti­on, expe­ri­ment design, pre­dic­tion and control.

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2019 | Development of denitrification in semi-automated moving bed biofilm reactors operated in a marine recirculating aquaculture system

Ores­tis Stav­ra­ki­dis-Zachou, Anne­lie­se Ernst, Chris­ti­an Stein­bach, Kai Wag­ner, Uwe Waller

This stu­dy exami­ned the per­for­mance of three inde­pendent­ly ope­ra­ted deni­tri­fy­ing moving bed bio­film reac­tors (MBBRs) in a zero-exch­an­ge mari­ne recir­cu­la­ting aquacul­tu­re sys­tem (RAS) sto­cked with Euro­pean sea­bass (Dicen­trarch­us labrax). A semi-auto­ma­ted con­trol stra­tegy was appli­ed to fos­ter spon­ta­neous deni­tri­fi­ca­ti­on. Pro­cess auto­ma­ti­on con­sis­ted of a pul­sed car­bon sup­p­ly and an inflow of nitra­te-rich, aer­ated pro­cess water con­trol­led by the oxi­da­ti­on-reduc­tion poten­ti­al (ORP) in the MBBR. Car­bon dosing fre­quen­cy was adjus­ted manu­al­ly if the pro­cess pro­du­ced unwan­ted pro­ducts (i.e., nitri­te or ammo­nia). OPR-con­trol­led inflow sti­mu­la­ted bac­te­ri­al acti­vi­ties in the MBBRs until inflow rea­ched the pre-set maxi­mum at a hydrau­lic reten­ti­on time (HRT) of 0.75 h. This allo­wed for a quick start-up of the deni­tri­fi­ca­ti­on pro­ces­ses in spi­te of high initi­al varia­bi­li­ty of pro­cess water inflow and of nitra­te rem­oval effi­ci­en­cy (NRE). A start-up with gly­ce­rol did not indu­ce a sta­ble deni­tri­fi­ca­ti­on pro­cess; howe­ver, after the pro­cess had been estab­lished with ace­tate, gly­ce­rol pro­mo­ted effi­ci­ent deni­tri­fi­ca­ti­on with NRE clo­se to one. The suc­ces­si­ve appli­ca­ti­on of the two car­bon sources resul­ted in a high nitra­te rem­oval rate (NRR) of 2 kg nitrate‑N m−3 day−1 in the bio­fil­ters. This dimi­nis­hed the con­cen­tra­ti­on of nitra­te-nitro­gen (nitrate‑N) in the RAS (volu­me 9 m³) from176 to 36g m−3 in 42 days with bio­fil­ters com­pri­sing only 1% of the RAS volu­me. The impli­ca­ti­ons for the deve­lo­p­ment of an auto­ma­ted deni­tri­fi­ca­ti­on pro­cess are discussed.

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