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 ...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.展开更多
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 mathematicall...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.展开更多
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 p...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 <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">be</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> 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 permeate</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> Total Dissolved solids (TDS) and the feed pressure, respectively.</span></span></span></span>展开更多
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, ...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.展开更多
Due to water scarcity and the global trends in climate change, winning drinking </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span ...Due to water scarcity and the global trends in climate change, winning drinking </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">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 </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">that </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">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 </span><span style="font-family:Verdana;">paper</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> we investigate the feasibility of driving a</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">n</span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;"> RO desalination system using wind power with and without pressure vessel energy storage and small scale energy recovery us</span><span><span style="font-family:Verdana;">ing </span><span style="font-family:Verdana;">Clark</span><span style="font-family:Verdana;"> pump based on simulation models. The performance of both variants </span><span style="font-family:Verdana;">w</span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">as</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> compared </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">with</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> several scenarios of wind patterns. As expected buffering and energy recovery delivered higher water production </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">and better water quality demonstrating the importance of an energy storage/recovery system for a wind-power-supplied desalination plant.展开更多
Modern aquaponic systems can be highly successful,but they require intensive monitoring,control and management.Consequently,the Automation Pyramid(AP)with its layers of Supervisory Control and Data Acquisition(SCADA),...Modern aquaponic systems can be highly successful,but they require intensive monitoring,control and management.Consequently,the Automation Pyramid(AP)with its layers of Supervisory Control and Data Acquisition(SCADA),Enterprise Resource Planning(ERP)and Manufacturing Execution System(MES)is applied for process control.With cloudbased IoT-based Predictive Analytics at the fore marsh,it is worth finding out if IoTwill make these technologies obsolete,or they can work together to gain more beneficial results.In this paper,we will discuss the enhancement of SCADA,ERP and MES with IoT in aquaponics and likewise how IoT-based Predictive Analytics can help to get more out of it.An example use case of an aquaponics project with five demonstration sites in different geographical locations will be presented to show the benefits of IoT on example Predictive Analytics services.Innovative is the collection of data from the five demonstration sites over IoT to make the models of fish,tomatoes,technical components such as filters used for remote monitoring,predictive remote maintenance and economical optimization of the individual plants robust.Robustness of the various models,fish and crop growth models,models for econometric optimization were evaluated using Monte Carlo Simulations revealing as expected the superiority of the IoT-based models.Our analysis suggest that the models are generally tolerant to the temperature coefficient variations of up to 15%and the econometric models tolerated a variation of for example feed ration size for fish of up to 4%and by the energy optimization models a tolerance of up to 14%by variations of solar radiation could be noticed.Furthermore,from the analysis made,it can be concluded that MES has several capabilities which cannot be replaced by IoT such as responsiveness to trigger changes on anomalies.It act as proxy when there is no case for sensors and reliably ensure correct execution in the aquaponics plants.IoT systems can produce unprecedented improvements in many areas but need MES to leverage their true potential and benefits.展开更多
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 Contr...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.展开更多
文摘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.
文摘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.
文摘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 <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">be</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> 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 permeate</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> Total Dissolved solids (TDS) and the feed pressure, respectively.</span></span></span></span>
文摘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.
文摘Due to water scarcity and the global trends in climate change, winning drinking </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">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 </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">that </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">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 </span><span style="font-family:Verdana;">paper</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> we investigate the feasibility of driving a</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">n</span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;"> RO desalination system using wind power with and without pressure vessel energy storage and small scale energy recovery us</span><span><span style="font-family:Verdana;">ing </span><span style="font-family:Verdana;">Clark</span><span style="font-family:Verdana;"> pump based on simulation models. The performance of both variants </span><span style="font-family:Verdana;">w</span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">as</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> compared </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">with</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> several scenarios of wind patterns. As expected buffering and energy recovery delivered higher water production </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">and better water quality demonstrating the importance of an energy storage/recovery system for a wind-power-supplied desalination plant.
基金The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP7-ENV-2013-WATER-INNO-DEMO under Grant agreement N°619137.
文摘Modern aquaponic systems can be highly successful,but they require intensive monitoring,control and management.Consequently,the Automation Pyramid(AP)with its layers of Supervisory Control and Data Acquisition(SCADA),Enterprise Resource Planning(ERP)and Manufacturing Execution System(MES)is applied for process control.With cloudbased IoT-based Predictive Analytics at the fore marsh,it is worth finding out if IoTwill make these technologies obsolete,or they can work together to gain more beneficial results.In this paper,we will discuss the enhancement of SCADA,ERP and MES with IoT in aquaponics and likewise how IoT-based Predictive Analytics can help to get more out of it.An example use case of an aquaponics project with five demonstration sites in different geographical locations will be presented to show the benefits of IoT on example Predictive Analytics services.Innovative is the collection of data from the five demonstration sites over IoT to make the models of fish,tomatoes,technical components such as filters used for remote monitoring,predictive remote maintenance and economical optimization of the individual plants robust.Robustness of the various models,fish and crop growth models,models for econometric optimization were evaluated using Monte Carlo Simulations revealing as expected the superiority of the IoT-based models.Our analysis suggest that the models are generally tolerant to the temperature coefficient variations of up to 15%and the econometric models tolerated a variation of for example feed ration size for fish of up to 4%and by the energy optimization models a tolerance of up to 14%by variations of solar radiation could be noticed.Furthermore,from the analysis made,it can be concluded that MES has several capabilities which cannot be replaced by IoT such as responsiveness to trigger changes on anomalies.It act as proxy when there is no case for sensors and reliably ensure correct execution in the aquaponics plants.IoT systems can produce unprecedented improvements in many areas but need MES to leverage their true potential and benefits.
基金This research was supported by the European Union’s Seventh Framework Programme FP7-ENV-2013-WATERINNO-DEMO under Grant agreement N619137.
文摘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.
