Optimal Utilization of Renewable Energy in Aquaponic Systems

Aquaponic systems require energy in different forms, heat, solar radiation, electricity etc. Typical actuator components of an aquaponic system include pumps, aerators, heaters, coolers, feeders, propagators, lights, etc., which need electrical energy to operate. Hybrid Energy Systems (HES) can help in improving the economic and environmental sustainability of aquaponic systems with respect to energy aspects. Energy management is one of the key issues in operating the HES, which needs to be optimized with respect to the current and future change in generation, demand, and market price, etc. In this paper, a Decision Support System (DSS) for optimal energy management of an aquaponic system that integrates different energy sources and storage mechanisms according to priorities will be presented. The integrated model consists of photovoltaic and solar thermal modules, wind turbine, hydropower, biomass plant, CHP, gas boiler, energy and heat storage systems and access to the power grid and district heating. The results show that the proposed method can significantly increase the utilization of HES and reduce the exchange with the power grid and district heating and consequently reduce running costs.

Performance Prediction of a Reverse Osmosis Desalination System Using Machine Learning

One of the major challenges that membrane manufacturers, commercial enterprises and the scientific community in the field of membrane-based filtration or reverse osmosis (RO) desalination have to deal with is system performance retardation due to membrane fouling. In this respect, the prediction of fouling or system performance in membrane-based systems is the key to determining the mid and long-term plant operating conditions and costs. Despite major research efforts in the field, effective methods for the estimation of fouling in RO desalination plants are still in infancy, for example, most of the existing methods, neither consider the characteristics of the membranes such as the spacer geometry, nor the efficiency and the day to day chemical cleanings. Furthermore, most studies focus on predicting a single fouling indicator, e.g., flux decline. Faced with the limits of mathematical or numerical approach, in this paper, machine learning methods based on Multivariate Temporal Convolutional Neural networks (MTCN), which take into account the membrane characteristics, feed water quality, RO operation data and management practice such as Cleaning In Place (CIP) will be considered to predict membrane fouling using measurable multiple indicators. The temporal convolution model offers one the capability to explore the temporal dependencies among a remarkably long historical period and has potential use for operational diagnostics, early warning and system optimal control. Data collected from a Desalination RO plant will be used to demonstrate the capabilities of the prediction system. The method achieves remarkable predictive accuracy (root mean square error) of 0.023, 0.012 and 0.007 for the relative differential pressure and permeates Total Dissolved solids (TDS) and the feed pressure, respectively.

Controlling and regulation of integrated aquaponic production systems – An approach for a management execution system (MES)

Manufacturing execution systems(MESs)play a significant role in the manufacturing paradigm.MES is there to link between the Enterprise Resource Planning(ERP)systems and the plant equipment control or Supervisory Control and Data Acquisition(SCADA)applications.In this paper the MES of the INAPRO aquaponics system which was developed to support and advise the aquaponics managers in operating the complex aquaponic farms,will be presented.One important feature of the INAPRO aquaponics system is to minimize fresh water<3%,energy and nutrient supplies.This can only be achieved by appropriate design of the fish and crop mixture,considering the fish to crop ratio,when to sow the crops etc.and to monitor the system to see whether it performing as designed or not.Therefore,the MES has a view to show the designed system with all the material flow(water,energy and nutrients)balances and also how the system will be performing for a given predictive horizon.Knowing the future developments of the system,the operator can taking corrective measures to make sure that the system is behaving as required.An example of water balance of a system with 40 m3 fish tanks coupled with a hydroponic NFT system with 1,000 m2 which can produce five tons of Tilapia and 75 tons of tomato yearly is given.

Deep Learning Based Model Predictive Control for a Reverse Osmosis Desalination Plant

Reverse Osmosis (RO) desalination plants are highly nonlinear multi-input-multioutput systems that are affected by uncertainties, constraints and some physical phenomena such as membrane fouling that are mathematically difficult to describe. Such systems require effective control strategies that take these effects into account. Such a control strategy is the nonlinear model predictive (NMPC) controller. However, an NMPC depends very much on the accuracy of the internal model used for prediction in order to maintain feasible operating conditions of the RO desalination plant. Recurrent Neural Networks (RNNs), especially the Long-Short-Term Memory (LSTM) can capture complex nonlinear dynamic behavior and provide long-range predictions even in the presence of disturbances. Therefore, in this paper an NMPC for a RO desalination plant that utilizes an LSTM as the predictive model will be presented. It will be tested to maintain a given permeate flow rate and keep the permeate concentration under a certain limit by manipulating the feed pressure. Results show a good performance of the system.

How to Use Wind Power Efficiently for Seawater Reverse Osmosis Desalination

Due to water scarcity and the global trends in climate change, winning drinking water through desalination is increasingly becoming an option, especially using reverse osmosis (RO) membrane technology. Operating a reverse osmosis desalination plant is associated with several expenses and energy consumption that take a very large share. Several studies have shown that wind power incurs lower energy costs compared to other renewable energy sources, therefore, should be the first choice to be coupled to an RO desalination system to clean water using sustainable energy. Therefore, in this paper, we investigate the feasibility of driving an RO desalination system using wind power with and without pressure vessel energy storage and small scale energy recovery using Clark pump based on simulation models. The performance of both variants was compared with several scenarios of wind patterns. As expected buffering and energy recovery delivered higher water production and better water quality demonstrating the importance of an energy storage/recovery system for a wind-power-supplied desalination plant.

Intelligent Information Management in Aquaponics to Increase Mutual Benefits

Aquaponics are feedback and two player systems, in which fish and crops mutually benefit from one another and, therefore require close monitoring, management and control. Vast amount of data and information flow from the aquaponics plant itself with its huge amount of smart sensors for water quality, fish and plant growth, system state etc. and from the stakeholder, e.g., farmers, retailers and end consumers. The intelligent management of aquaponics is only possible if this data and information are managed and used in an intelligent way. Therefore, the main focus of this paper is to introduce an intelligent information management (IIM) for aquaponics. It will be shown how the information can be used to create services such as predictive analytics, system optimization and anomaly detection to improve the aquaponics system. The results show that the system enabled full traceability and transparency in the aquaponics processes (customers can follow what is going on at the farm), reduced water and energy use and increased revenue through early fault detection. In this, paper the information management approach will be introduced and the key benefits of the digitized aquaponics system will be given.