Optimization of Biofilter Size for Aquaponics Using Genetic Algorithm
Amir A. Bracino*,, Jason L. Española*, Argel A. Bandala**, Elmer P. Dadios*, Edwin Sybingco**, and Ryan Rhay P. Vicerra*
*Manufacturing Engineering and Management Department, De La Salle University
2401 Taft Avenue, Malate, Manila, Manila 1004, Philippines
**Electronics and Communications Engineering Department, De La Salle University
2401 Taft Avenue, Malate, Manila, Manila 1004, Philippines
Unlike a media-filled aquaponic system, the nutrient film technique (NFT) and deep water culture (DWC) require the installation of an external biofilter to provide sufficient area for nitrifying bacteria colonization, which is essential for the conversion of toxic ammonia from fish waste into nitrate that is easily assimilated by plants. Given the importance of biofilters, it is imperative to properly design this tank to effectively support the nitrification process. Several factors need to be considered for the biofilter design. Thus, an optimization algorithm can be used to obtain combinations of the design parameters. The genetic algorithm (GA) is a heuristic solution search or optimization technique based on the Darwinian principle of genetic selection. The main goal of this study was to obtain the optimal biofilter size for a given fishpond volume and the amount of ammonia to be treated. The conversion coefficient in the Michaelis–Menten equation was used as the fitness function in this study. The parameters optimized using GA include the hydraulic loading rate, height of the biofilter, and predicted ammonia concentration. For the given assumption of a 60 kg feed introduced to the system and a 1500 L fishpond, the hydraulic loading rate, biofilter height, and final concentration of ammonia were 0.17437 m, 0.58585 m, and 0.01026 ppm, respectively. Using the values obtained from running the GA, the optimum biofilter volume for the system was 0.4608 m3, whereas the water flow rate was 0.03 L/min. For recommendations, multiple objective GAs can be used to add cost-related variables in the optimization because they have not yet been considered in the computation.
-  S. Wongkiew et al., “Fate of nitrogen in floating-raft aquaponic systems using natural abundance nitrogen isotopic compositions,” Int. Biodeterior. Biodegrad, Vol.125, pp. 24-32, doi: 10.1016/j.ibiod.2017.08.006, 2017.
-  B. Yep and Y. Zheng, “Aquaponic trends and challenges – A review,” J. Clean. Prod, Vol.228, pp. 1586-1599, doi: 10.1016/j.jclepro.2019.04.290, 2019.
-  K. Connolly and T. Trebic, “Optimization of a Backyard Aquaponic Food Production System,” Bioresource Engineering Design Reports, 74pp., 2010.
-  A. A. Bracino et al., “Biofiltration for Recirculating Aquaponic Systems: A Review,” IEEE 12th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), doi: 10.1109/HNICEM51456.2020.9400136, 2020.
-  B. R. L. Nelson, “Aquaponic Equipment, The Bio Filter,” Aquaponics J., Issue 48, pp. 22-23, 2008.
-  T. M. Samocha and D. I. Prangnell, “System Treatment and Preparation,” T. M. Samocha (Ed.), “Sustainable Biofloc Systems for Marine Shrimp,” pp. 119-131, Academic Press, 2019.
-  M. F. Q. Say et al., “A Genetic Algorithm Approach to PID Tuning of a Quadcopter UAV Model,” 2021 IEEE/SICE Int. Symp. on System Integration (SII), pp. 675-678, doi: 10.1109/IEEECONF49454.2021.9382697, 2021.
-  R. G. Baldovino et al., “GA optimization of coconut sugar cooking process: A preliminary study using stochastic universal sampling (SUS) technique,” Proc. of 9th Int. Conf. on Computer and Automation Engineering, pp. 346-349, doi: 10.1145/3057039.3057064, 2017.
-  P. M. Chan et al., “Philippine License Plate Localization Using Genetic Algorithm and Feature Extraction,” IEEE 12th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), doi: 10.1109/HNICEM51456.2020.9400148, 2020.
-  D. G. D. Evangelista, R. R. P. Vicerra, and A. A. Bandala, “Approximate Optimization Model on Routing Sequence of Cargo Truck Operations through Manila Truck Routes using Genetic Algorithm,” 2020 IEEE 12th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), doi: 10.1109/HNICEM51456.2020.9400044, 2020.
-  X.-S. Yang, “Chapter 5 – Genetic Algorithms,” “Nature-Inspired Optimization Algorithms,” pp. 77-87, Elsevier, 2014.
-  J. L. Espanola et al., “Deign of a Fuzzy-Genetic Controller for an Articulated Robot Gripper,” TENCON 2018 – 2018 IEEE Region 10 Conf., pp. 1701-1706, doi: 10.1109/TENCON.2018.8650431, 2019.
-  A. L. P. De Ocampo and E. P. Dadios, “Energy cost optimization in irrigation system of smart farm by using genetic algorithm,” 2017IEEE 9th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), doi: 10.1109/HNICEM.2017.8269497, 2017.
-  J. C. V. Puno et al., “Soil Nutrient Detection using Genetic Algorithm,” 2019 IEEE 11th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), doi: 10.1109/HNICEM48295.2019.9072689, 2019.
-  Q. Ren et al., “A method for predicting dissolved oxygen in aquaculture water in an aquaponics system,” Comput. Electron. Agric, Vol.151, pp. 384-391, doi: 10.1016/j.compag.2018.06.013, 2018.
-  M. G. B. Palconit et al., “Towards Tracking: Investigation of Genetic Algorithm and LSTM as Fish Trajectory Predictors in Turbid Water,” 2020 IEEE Region 10 Conf. (TENCON), pp. 744-749, doi: 10.1109/TENCON50793.2020.9293730, 2020.
-  V. J. D. Almero et al.,“Genetic algorithm-based dark channel prior parameters selection for single underwater image dehazing,” 2020 IEEE Region 10 Conf. (TENCON), pp. 1153-1158, doi: 10.1109/TENCON50793.2020.9293849, 2020.
-  R. S. Concepcion et al., “Estimation of photosynthetic growth signature at the canopy scale using new genetic algorithm-modified visible band triangular greenness index,” 2020 Int. Conf. on Advanced Robotics and Intelligent Systems (ARIS), doi: 10.1109/ARIS50834.2020.9205787, 2020.
-  R. Concepcion et al., “Genetic algorithm-based visible band tetrahedron greenness index modeling for lettuce biophysical signature estimation,” 2020 IEEE Region 10 Conf. (TENCON), pp. 679-684, doi: 10.1109/TENCON50793.2020.9293916, 2020.
-  A. L. P. De Ocampo et al., “Estimation of Triangular Greenness Index for Unknown PeakWavelength Sensitivity of CMOS-acquired Crop Images,” 2019 IEEE 11th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), doi: 10.1109/HNICEM48295.2019.9072796, 2019.
-  I. Valenzuela et al., “Pre-Harvest Factors Optimization using Genetic Algorithm for Lettuce,” J. Telecommun. Electron. Comput. Eng., Vol.10, No.1, pp. 159-163, 2018.
-  R. M. C. Santiago et al., “Multiple objective optimization of LED lighting system design using genetic algorithm,” 2017 5th Int. Conf. on Information and Communication Technology (ICoIC7), doi: 10.1109/ICoICT.2017.8074698, 2017.
-  O.-I. Lekang, “Aquaculture Engineering,” 2nd edition, John Wiley & Sons, Ltd., 2013.
-  E. A. Van Os, T. H. Gieling, and J. Heinrich Lieth, “Technical equipment in soilless production systems,” “Soilless Culture: Theory and Practice,” 2nd edition, pp. 587-635, Elsevier, 2019.
-  T. Losordo and D. DeLong, “Estimating biofilter size for RAS systems,” Global Aquaculture Advocate, September 11, 2015, https://www.aquaculturealliance.org/advocate/estimating-biofilter-size-for-ras-systems/ [accessed February 10, 2021]
-  D. R. Sallenave, “Important Water Quality Parameters in Aquaponics Systems,” Circular 680, New Mexico State University, 2016, https://aces.nmsu.edu/pubs/_circulars/CR680/welcome.html [accessed July 13, 2021]
-  S. Wongkiew, “Nitrogen Cycle in Floating-Raft Aquaponic Systems,” Ph.D. Thesis, University of Hawai‘i (Manoa), 2018.
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