Adaptive edge analytics for distributed networked control of water systems

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.

Combustion of fuel blends containing digestate pyrolysis oil in a multi-cylinder compression ignition engine

Digestate from the anaerobic digestion conversion process is widely used as a farm land fertiliser. This study proposes an alternative use as a source of energy. Dried digestate was pyrolysed and the resulting oil was blended with waste cooking oil and butanol (10, 20 and 30 vol.%). The physical and chemical properties of the pyrolysis oil blends were measured and compared with pure fossil diesel and waste cooking oil. The blends were tested in a multi-cylinder indirect injection compression ignition engine.Engine combustion, exhaust gas emissions and performance parameters were measured and compared with pure fossil diesel operation. The ASTM copper corrosion values for 20% and 30% pyrolysis blends were 2c, compared to 1b for fossil diesel. The kinematic viscosities of the blends at 40 C were 5–7 times higher than that of fossil diesel. Digested pyrolysis oil blends produced lower in-cylinder peak pressures than fossil diesel and waste cooking oil operation. The maximum heat release rates of the blends were approximately 8% higher than with fossil diesel. The ignition delay periods of the blends were higher; pyrolysis oil blends started to combust late and once combustion started burnt quicker than fossil diesel. The total burning duration of the 20% and 30% blends were decreased by 12% and 3% compared to fossil diesel. At full engine load, the brake thermal efficiencies of the blends were decreased by about 3–7% when compared to fossil diesel. The pyrolysis blends gave lower smoke levels; at full engine load, smoke level of the 20% blend was 44% lower than fossil diesel. In comparison to fossil diesel and at full load, the brake specific fuel consumption (wt.) of the 30% and 20% blends were approximately 32% and 15% higher. At full engine load, the CO emission of the 20% and 30% blends were decreased by 39% and 66% with respect to the fossil diesel. Blends CO2 emissions were similar to that of fossil diesel; at full engine load, 30% blend produced approximately 5% higher CO2 emission than fossil diesel. The study concludes that on the basis of short term engine experiment up to 30% blend of pyrolysis oil from digestate of arable crops can be used in a compression ignition engine.

Compression by replication

A recently introduced inference method based on system replication and an online message passing algorithm is employed to complete a previously suggested compression scheme based on a nonlinear perceptron. The algorithm is shown to approach the information theoretical bounds for compression as the number of replicated systems increases, offering superior performance compared to basic message passing algorithms. In addition, the suggested method does not require fine-tuning of parameters or other complementing heuristic techniques, such as the introduction of inertia terms, to improve convergence rates to nontrivial results. © 2014 American Physical Society.