Optimization Model for Recirculating Aquaculture Systems (RASs) for Nile Tilapia in Kenya
AbstractSimulation in aquaculture necessitates dynamic modeling that provides a deeper insight into the aquaculture performance. The development towards the use of simulation models in aquaculture has been witnessed in the last few years. Most of the simulation models originate from ecological modeling and applies to through-flow systems. On the other hand, studies on Recirculating Aquaculture Systems (RAS) which consider wastewater treatment, use basic steady-state models of the treatment processes, where the efficiency is set to either a fixed percentage removal or a fixed removal rate. For a farmer, the major target is to improve the efficiency and predictability of intensive aquaculture operations. These improvements are subject to accurate quantification of metabolic rates of the fish coupled with the relationships between water quality and fish growth. Moreover, improvements and refinement of water reuse technologies are inevitable towards the improvement of intensive aquaculture systems. Computer models are effective tools for analyzing water treatment units and fish metabolic response effects on overall system performance. This study aimed at developing a RAS simulation and optimization model to help in predicting water quality and cost optimization of recirculating aquaculture systems. A general standard model development procedure was used in the development of the RAS model. Matrix Laboratory (MATLAB) programming language was used to accomplish the model development task. The data collected on energy consumption, running costs, biofilter efficiency, and flow rates through the connection pipes were used to calibrate and validate the model. From the model evaluation, the Nash-Sutcliff Efficiency (NSE) values for ammonia, pH, Dissolved Oxygen (DO), Electrical conductivity (EC) and Energy were, -4.26, 0.97, 0.77, 0.59 and 0.94 respectively. Similarly, the coefficient of determination (R2) for ammonia, pH, Dissolved Oxygen (DO), Electrical conductivity (EC) and Energy were, 0.96, 0.89, 0.23, 0.87 and 0.85 respectively. The model also showed that low stocking densities (2.3 kg/m3 to 5.0 kg/m3) led to longer payback periods as compared to higher stocking densities (7.0 kg/m3 to 10.0 kg/m3). The model gave a good prediction for most water quality parameters. On profit optimization, most of the good cost scenarios did not coincide with the best water quality conditions. More sub-models to the models to be added to capture aspects of different water treatment and other fish species other than Nile Tilapia.
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