Development of a Cobalt Electrochemical Sensor for Measuring Phosphate in Municipal Wastewaters

The introduction of the Water Framework directive sets stringent limits on phosphorous discharge from wastewater treatment plants to maintain the complex interdependent relationship between water tributaries and the ecosystem. This paper studies a cobalt based electrochemical sensor for phosphate detection in wastewater. An evaluation of the sensors operational envelope, impact of pH, detection limits, linearity of response, accuracy and reproducibility in a single ion solution was conducted. An indirect method was employed to assess the effect of all of these parameters; the parameter was kept constant, while the phosphate concertation was varied. Tests on real wastewater samples verified the effect of the interfering factors, as phosphate measurements from three different sampling points (influent, activated sludge mixed liquors and effluent) did not correlate favourably with measurements acquired from a specialised laboratory. The success of this sensor is probably dependent on the simultaneous measurement of, or the calibration for, interfering parameters. However, the former approach would most likely require additional probes to measure these interfering parameters and the latter would probably require a complex calibrating matrix to account for all the interfering parameters. Nonetheless, variations of such sensors reviewed in this paper and their encouraging results offer an optimistic field of improvement on the design of the sensor studied in this paper for it to be employed on real wastewater systems.

Using Reinforcement Learning for Demand Response of Domestic Hot Water Buffers: a Real-Life Demonstration

This paper demonstrates a data-driven control approach for demand response in real-life residential buildings. The objective is to optimally schedule the heating cycles of the Domestic Hot Water (DHW) buffer to maximize the self-consumption of the local photovoltaic (PV) production. A model-based reinforcement learning technique is used to tackle the underlying sequential decision-making problem. The proposed algorithm learns the stochastic occupant behavior, predicts the PV production and takes into account the dynamics of the system. A real-life experiment with six residential buildings is performed using this algorithm. The results show that the self-consumption of the PV production is significantly increased, compared to the default thermostat control.