Open Access

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.

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.

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.