Smart Low-Cost Control System for Fish Farm Facilities
Abstract
:1. Introduction
2. Identification of Critical Factors for Control Systems for Aquaculture
2.1. Factors Affecting Fish Wellbeing and Performance
- Production Domain: Factors derived from the way in which fish are produced in the facilities. Most of those factors are fixed and depend on the design and location of the production plant. Other factors are more flexible, such as feeding-related ones, and might be changed or improved.
- Biotic Domain: Factors dependent on the biotic part of the environment in which the facilities are located. All these factors are independent of the facilities and include water, atmosphere, and light factors. If facilities are located in open skies, barely any control or modification can be performed on these factors. Nonetheless, it is possible to modify these factors when facilities are indoors. More information is provided in the section titled Control Domain.
- Species Biotic Domain: Factors that depend on the grown species, including the growing stage and physiological and social factors. Even though most of them cannot be controlled or modified, some factors can be altered.
- External Biotic Domain: Factors depending on the presence and effects of other living beings found in the surroundings of the aquaculture facilities. It includes the effects of animals, plants, and bacteria, among others, on the fish. In general terms, the presence of the effect of external biotic factors can be controlled, minimized, or avoided.
- Control Domain: Factors referring to the control actions that can be conducted on fish farms, including monitoring and modification of biotic, abiotic, and production domains. This is the core of aquaculture facilities’ smart, low-cost control systems. In the Domain, only the aspects related to the control of other domains are listed since the rest of the factors do not affect fish performance or well-being. These factors, such as monitoring, telecommunication, or computing technology, are defined in subsequent sections.
2.1.1. Factors of the Production Domain
2.1.2. Factors of the Species Abiotic Domain
2.1.3. Factors of the Species Biotic Domain
2.1.4. Factors of the External Biotic Domain
2.1.5. Factors of the Control Domain
2.2. Interaction between Factors
3. Selected Factors for Smart Low-Cost Control Systems for Aquaculture
3.1. Selected Control Factors for Aquaculture Facilities with Cages in the Sea
3.2. Selected Control Factors for RAS
4. Proposal for Smart Low-Cost Control Systems for Aquaculture
4.1. Proposal for Smart Low-Cost Control Systems for Aquaculture Facilities with Cages in the Sea
4.1.1. General Aspects
4.1.2. Sensors and Actuators
4.1.3. Nodes
4.1.4. Communication Protocol and Communication Technologies
4.1.5. DB, AI, and Other Information Sources
4.2. Adaptations for Smart Low-Cost Control Systems for Aquaculture Facilities with RAS
4.2.1. General Aspects
4.2.2. Sensors and Actuators
4.2.3. Communication Protocol and Communication Technologies
5. Smart Control Algorithm for Aquaculture
5.1. Included Algorithms in the Complete System
5.1.1. Predefined Control Algorithms
5.1.2. Energy Efficiency Algorithms
5.1.3. Fault-Tolerance Algorithms
- The algorithms relative to the failure in the network include using 3G/4G in the gateway of sea cages to ensure connectivity with the DB in case of failure of the LoRa network. If the gateway node does not receive any response from the DB when data are sent it supposes that the connectivity through LoRa is lost and the backup system based on 3G/4G starts to operate. Then, data are sent again with the new communication technology.
- If connectivity with DB is not restored after changing the wireless technology, the gateway assumes that the failure is in the DB itself. Therefore, a message is sent to all the sensor and actuator nodes in order to run the predefined control algorithm and save all the gathered information.
- Another way in which a failure in the connectivity with the DB can be prevented from affecting the regular operation of the actuators is when a message from the DB is expected and not received. In that case, the gateway body assumes that the connectivity is lost and alerts the local nodes to use the predefined control algorithm and save all the gathered information.
- If a node does not receive the response when data are forwarded to the gateway, it automatically assumes that the connectivity with the gateway is lost and attempts to connect with the backup gateway. The redundancy of critical elements such as the gateway buoy is another example of fault tolerance mechanisms.
5.1.4. Data Management and Smart Control Algorithm
- The sensor-gathered data contain information on the value of the different monitored factors such as water temperature, swimming depth, and feed falling, among others. This can be considered the primary source of information.
- The actuator data indicate the operation modes of different actuators in the fish farm facilities. They might indicate the periods in which actuators were deactivated or activated. Since some actuators might operate at different velocities or ratios, data include the value in those cases. The schedule of feeders or photoperiod of lights is another type of information. Finally, the activation of the alarm is important information for the system. This can be considered the second information source, which is particularly important in the case of RAS facilities.
- The remote sensing data offer reliable information on water quality in areas beyond the fish farm facilities. This information is only useful in marine cages and can be essential for pollution monitoring. It is the third information source.
5.2. Example 1: Control Algorithm Operation in Sea Cages
- A reduction in the cost of feed, since 14.25 kg/m3 is used with the smart control algorithm against 15 kg /m3 with the predefined control algorithm. Considering that feed represents 70% of the costs of aquaculture production, saving 5% of the feed is an important economic saving.
- An improvement in feed utilization, considering that fish consumed the feed in a ratio of 13.82 kg /m3 when the fish farm received the information from the DB and the AI compared with the 13.18 kg /m3 when it did not receive it. This difference in feed consumption supposes better fish growth, which implies a better fish size at the harvest moment resulting in a higher market price of the produced fish.
- A reduction in environmental pollution due to uneaten food pellets, since 0.45 kg /m3 are deposited in the seabed when the system is fully operative. The value increases to 1.82 kg /m3 when predefined control algorithms regulate the feeding process. Uneaten feed is considered one of the most relevant environmental impacts of the aquaculture industry. A reduction in environmental impact supposes a greener and more sustainable production, better usage of resources, and cleaner oceans. In addition, the uneaten feed might unleash several pollution problems, which can affect fish, causing a decrease in fish welfare.
5.3. Example 2: Control Algorithm Operation in RAS
- An increase in fish welfare, since the water quality conditions in the tank in the first example are much better than in the second case. The reduced DO and high turbidity cause stress and alterations in the swimming patterns of fish. The turbidity in the first case represents 12.5% of the turbidity in the second case. The DO in the first case dropped by less than 2%. Meanwhile, the DO reduction is supposed to be 8% in the second case.
- An improvement in energy use, since the use of aeration, is more efficient in the first case. Even though aeration is activated for more time, 19 min in the first case compared with 7 min in the second case, the power consumption of the aeration system is much higher in the second case. The energy is reduced by 35% when the AI of the control system controls the situation compared with the predefined control algorithms.
- Finally, the fast action of workers due to the alarm helped to avoid further problems in the fish farm facilities in the first case. In the second case, no activation of the alarm is triggered by the AI. Thus, the turbidity reaches different parts of the water conditioning steps and the tanks. This supposes more maintenance tasks and can even cause the necessity of replacing elements of the facilities. Adequate maintenance based on the prediction of failures, thanks to the Smart Control System, reduces the time workers must dedicate to maintenance tasks. In this case, the use of the control system facilitates that the solid filters are fixed in the first minutes by simple action with no need to replace elements. This supposes economic savings for the company and better working conditions for operators.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parra, L.; Sendra, S.; Garcia, L.; Lloret, J. Smart Low-Cost Control System for Fish Farm Facilities. Appl. Sci. 2024, 14, 6244. https://doi.org/10.3390/app14146244
Parra L, Sendra S, Garcia L, Lloret J. Smart Low-Cost Control System for Fish Farm Facilities. Applied Sciences. 2024; 14(14):6244. https://doi.org/10.3390/app14146244
Chicago/Turabian StyleParra, Lorena, Sandra Sendra, Laura Garcia, and Jaime Lloret. 2024. "Smart Low-Cost Control System for Fish Farm Facilities" Applied Sciences 14, no. 14: 6244. https://doi.org/10.3390/app14146244
APA StyleParra, L., Sendra, S., Garcia, L., & Lloret, J. (2024). Smart Low-Cost Control System for Fish Farm Facilities. Applied Sciences, 14(14), 6244. https://doi.org/10.3390/app14146244