000			03118ntm a2200349 i 4500
003			MY-KuUP
005			20260429182458.0
006			a||||fr|||| 000 0
007			ta
008			260429t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010519 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKKP .M39 2025 r Bc.
100	1		_aNur Mawaddah Alia Binti Alias, _eauthor.
245	1	0	_aImproving fish feeding system in aquaponic performance / _cNur Mawaddah Alia Binti Alias
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_ax, 49 pages : _billustrations ;
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Chemical and Process Engineering Technology
502			_aFinal Year Report (Bachelor of Chemical Engineering Technology) -- Universiti Malaysia Pahang Al-Sultan Abdullah - 2025
504			_aInclude bibliographical reference
520	3		_aThis study focuses on enhancing the performance and productivity of an aquaponic system through the redesign of the fish feeder and the implementation of automated monitoring and control. An aquaponic unit was assembled, integrating aquaculture (fish farming) and hydroponics (soil-less plant cultivation) components. This allows for a symbiotic environment whereby fish waste provides nutrients for the plants, and the plants help filter the water for the fish. However, inconsistent dispensing of fish feed from the existing feeder led to suboptimal conditions. To address this, a new fish feeder was digitally modelled using AutoCAD design software and then fabricated using 3D printing technology. The customized feeder allows for calibrated, timely, and reliable feed amounts to promote healthy fish growth. Furthermore, sensors were installed to monitor critical system parameters including pH and turbidity. The real-time sensor data is fed to an Arduino microcontroller which adjusts conditions accordingly to maintain equilibrium. For example, pH outside optimal ranges for either fish or plants triggers the automated system to dose acid or base until reaching the desired pH. A trial run highlighted fluctuations in turbidity and pH, indicating instability. The redesign of the poorly functioning feeder along with automated monitoring and control significantly improved system stability. Output in terms of fish biomass and plant yield is anticipated to increase through these upgrades. There remains ample potential to build on this foundation using techniques such as machine learning algorithms to analyze trends in sensor data and initiate condition adjustments. Multi-cycle testing over extended time periods will help maximize aquaponic productivity and viability as an innovative sustainable food production system.
610	2	0	_aFaculty of Chemical and Process Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
856			_uhttps://umpir.ump.edu.my/id/eprint/47276
942			_2lcc _cPSM
999			_c104798 _d104804