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Membrane distillation (MD) is a thermal separation process which possesses a hydrophobic, microporous
membrane as vapor space. A high potential application for MD is the concentration of hypersaline brines, such as
e.g. reverse osmosis retentate or other saline effluents to be concentrated to a near saturation level with a Zero
Liquid Discharge process chain. In order to further commercialize MD for these target applications, adapted MD
module designs are required along with strategies for the mitigation of membrane wetting phenomena. This
work presents the experimental results of pilot operation with an adapted Air Gap Membrane Distillation
(AGMD) module for hypersaline brine concentration within a range of 0–240 g NaCl /kg solution. Key performance
indicators such as flux, GOR and thermal efficiency are analyzed. A new strategy for wetting mitigation
by active draining of the air gap channel by low pressure air blowing is tested and analyzed. Only small reductions
in flux and GOR of 1.2% and 4.1% respectively, are caused by air sparging into the air gap channel.
Wetting phenomena are significantly reduced by avoiding stagnant distillate in the air gap making the air blower
a seemingly worth- while additional system component.
With the need for automatic control based supervisory controllers for complex energy systems, comes the need for reduced order system models representing not only the non-linear behaviour of the components but also certain unknown process dynamics like their internal control logic. At the Institute of Energy Systems Technology in Offenburg we have built a real-life microscale trigeneration plant and present in this paper a rational modelling procedure that satisfies the necessary characteristics for models to be applied in model predictive control for grid-reactive optimal scheduling of this complex energy system. These models are validated against experimental data and the efficacy of the methodology is discussed. Their application in the future for the optimal scheduling problem is also briefly motivated.
Am 1. Juli 2022 trafen sich im Rahmen des Abschlusskolloquiums des Projekts ACA-Modes rund 60 Teilnehmende aus Forschung, Lehre und Industrie zu einer internationalen Konferenz an der Hochschule Offenburg. Hier wurden die Projektergebnisse rund um die erfolgreiche Implementierung modellprädiktiver Regelstrategien vorgestellt, aktuelle Fragestellungen diskutiert und Entwicklungspfade hin zu einem netzdienlichen Betrieb von Energieverbundsystemen skizziert.
Microscale trigeneration systems are highly flexible in their operation and thus offer the technical possibility for peak load shifting in building demand side management. However to harness their potential modern control methods such as model predictive control must be implemented for their optimal scheduling. In literature the need for experimental investigation of microscale trigeneration systems to identify typical characteristics of the components and their interactions has been identified. On a real-life setup control specific information of the components is collected and lessons learnt during commissioning of the equipment is shared. The data is analysed to draw the vital characteristics of the system and it will be used for creating models of the components that can be utilised for optimal control.
The transformation of the building energy sector to a highly efficient, clean, decentralised and intelligent system requires innovative technologies like microscale trigeneration and thermally activated building structures (TABS) to pave the way ahead. The combination of such technologies however presents a scientific and engineering challenge. Scientific challenge in terms of developing optimal thermo-electric load management strategies based on overall energy system analysis and an engineering challenge in terms of implementing these strategies through process planning and control. Initial literature research has pointed out the need for a multiperspective analysis in a real life laboratory environment. To this effect an investigation is proposed wherein an analytical model of a microscale trigeneration system integrated with TABS will be developed and compared with a real life test-rig corresponding to building management systems. Data from the experimental analysis will be used to develop control algorithms using model predictive control for achieving the thermal comfort of occupants in the most energy efficient and grid reactive manner. The scope of this work encompasses adsorption cooling based microscale trigeneration systems and their deployment in residential and light commercial buildings.
The energy system of the future will transform from the current centralised fossil based to a decentralised, clean, highly efficient, and intelligent network. This transformation will require innovative technologies and ideas like trigeneration and the crowd energy concept to pave the way ahead. Even though trigeneration systems are extremely energy efficient and can play a vital role in the energy system, turning around their deployment is hindered by various barriers. These barriers are theoretically analysed in a multiperspective approach and the role decentralised trigeneration systems can play in the crowd energy concept is highlighted. It is derived from an initial literature research that a multiperspective (technological, energy-economic, and user) analysis is necessary for realising the potential of trigeneration systems in a decentralised grid. And to experimentally quantify these issues we are setting up a microscale trigeneration lab at our institute and the motivation for this lab is also briefly introduced.
Cooling towers or recoolers are one of the major consumers of electricity in a HVAC plant. The implementation and analysis of advanced control methods in a practical application and its comparison with conventional controllers is necessary to establish a framework for their feasibility especially in the field of decentralised energy systems. A standard industrial controller, a PID and a model based controller were developed and tested in an experimental set-up using market-ready components. The characteristics of these controllers such as settling time, control difference, and frequency of control actions are compared based on the monitoring data. Modern controllers demonstrated clear advantages in terms of energy savings and higher accuracy and a model based controller was easier to set-up than a PID.
Drawing off the technical flexibility of building polygeneration systems to support a rapidly expanding renewable electricity grid requires the application of advanced controllers like model predictive control (MPC) that can handle multiple inputs and outputs, uncertainties in forecast data, and plant constraints amongst other features. In this original work, an economic-MPC-based optimal scheduling of a real-world building energy system is demonstrated and its performance is evaluated against a conventional controller. The demonstration includes the steps to integrate an optimisation-based supervisory controller into a standard building automation and control system with off-the-shelf HVAC components and usage of state-of-art algorithms for solving complex nonlinear mixed integer optimal control problems. With the MPC, quantitative benefits in terms of 6–12% demand-cost savings and qualitative benefits in terms of better controller adaptability and hardware-friendly operation are identified. Further research potential for improving the MPC framework in terms of field-level stability, minimising constraint violations, and inter-system communication for its deployment in a prosumer-network is also identified.
Optimisation based economic despatch of real-world complex energy systems demands reduced order and continuously differentiable component models that can represent their part-load behaviour and dynamic responses. A literature study of existing modelling methods and the necessary characteristics the models should meet for their successful application in model predictive control of a polygeneration system are presented. Deriving from that, a rational modelling procedure using engineering principles and assumptions to develop simplified component models is applied. The models are quantitatively and qualitatively evaluated against experimental data and their efficacy for application in a building automation and control architecture is established.
Since 2003, most European countries established heat health warning systems to alert the population to heat load. Heat health warning systems are based on predicted meteorological conditions outdoors. But the majority of the European population spends a substantial amount of time indoors, and indoor thermal conditions can differ substantially from outdoor conditions. The German Meteorological Service (Deutscher Wetterdienst, DWD) extended the existing heat health warning system (HHWS) with a thermal building simulation model to consider heat load indoors. In this study, the thermal building simulation model is used to simulate a standardized building representing a modern nursing home, because elderly and sick people are most sensitive to heat stress. Different types of natural ventilation were simulated. Based on current and future test reference years, changes in the future heat load indoors were analyzed. Results show differences between the various ventilation options and the possibility to minimize the thermal heat stress during summer by using an appropriate ventilation method. Nighttime ventilation for indoor thermal comfort is most important. A fully opened window at nighttime and the 2-h ventilation in the morning and evening are more sufficient to avoid heat stress than a tilted window at nighttime and the 1-h ventilation in the morning and the evening. Especially the ventilation in the morning seems to be effective to keep the heat load indoors low. Comparing the results for the current and the future test reference years, an increase of heat stress on all ventilation types can be recognized.