Electricity production by the application of a low voltage DC-DC boost converter to a continuously operating flat-plate microbial fuel cell

An ultra-low voltage customized DC-DC booster circuit was developed using a LTC3108 converter, and used continuously on a flat-plate microbial fuel cell (FPM) system. The boost converter successfully stepped up the microbial fuel cell (MFC) voltage from ~0.5 V to 3.3 and 5.0 V of outputs. The designed circuit and system displayed the dynamic variations of the source FPM as well as the output voltage through the designed three connection points within the booster circuit. The source MFC voltage was interrelated with the booster circuit and its performance, and it adapted to the set points of the booster dynamically. The maximum output power density of the MFC with the DC-DC booster circuit was 8.16 W/m3 compared to the maximum source FPM input power of 14.27 W/m3 at 100 Ω, showing a conversion efficiency of 26–57%, but with a 10-fold higher output than that of the source voltage. The combined LTC3108 with FPM supplied power for electronic devices using synthetic and real domestic wastewater. This report presents a promising strategy for utilizing the electrical energy produced from MFCs, and expands the applicability of bioelectrochemical systems with an improved energy efficiency of the present wastewater treatment system

Sensitivity enriched multi-criterion decision making process for novel railway switches and crossings - a case study

Background: Despite their important role in railway operations, switches and crossings (S&C) have changed little since their conception over a century ago. It stands now that the existing designs for S&C are reaching their maximum point of incremental performance improvement, and only a radical redesign can overcome the constraints that current designs are imposing on railway network capacity. This paper describes the process of producing novel designs for next generation switches and crossings, as part of the S-CODE project. Methods: Given the many aspects that govern a successful S&C design, it is critical to adopt multi criteria decision making (MCDM) processes to identify a specific solution for the next generation of switches and crossings. However, a common shortcoming of these methods is that their results can be heavily influenced by external factors, such as uncertainty in criterium weighting or bias of the evaluators, for example. This paper therefore proposes a process based on the Pugh Matrix method to reduce such biases by using sensitivity analysis to investigate them and improve the reliability of decision making. Results: In this paper, we analysed the influences of three different external factors, measuring the sensitivity of ranking due to (a) weightings, (b) organisational and (c) discipline bias. The order of preference of the results was disturbed only to a minimum while small influences of bias were detected. Conclusions: Through this case study, we believe that the paper demonstrates an effective case study for a quantitative process that can improve the reliability of decision making

Control of microbial fuel cell voltage using a gain scheduling control strategy

Recent microbial fuel cell (MFC) research frequently addresses matters associated with scale and deployability. Modularisation is often needed to reduce ohmic losses with increasing volume. Series/parallel is then often an obvious strategy to enhance power quality during operation, to make best use of generated electricity. Hence, voltage reversal resulting from power and voltage mismatch between cells, become virtually unavoidable. Controlling MFC voltages could be used to stabilise MFC stacks. Here, nonlinear MFCs are controlled using simple gain scheduled Proportional+Integral actions. Parsimonious control may be necessary for implementation in MFC arrays, so minimising costs. Controller parameterisation used several linearised models over the dynamic operating range of the MFCs. Controller gains were then scheduled according to the operating conditions. A digital potentiometer was used to actuate the control, varying the current sourced from the MFC. The results show that the controller was able to control MFC voltages, rejecting the disturbances. It was shown that the controller was transferable between MFCs with different power performances. This study demonstrates that the control of MFCs can be achieved with relatively simple digital approaches, plausibly implementable using low cost microcontrollers, and likely to be useful in the effective deployment of MFCs in large scale arrays• Control of microbial fuel cell (MFC) voltage is demonstrated . • Gain scheduling allows control over the operating range. • Control is transferable between similar MFCs. • Control strategy is parsimonious and hence practical.This work was supported by the Natural Environment Research Council (NERC) [grant number: NE/L014106/1]; through the Resource Recovery from Waste Programme, in the Microbial Electrochemical Technology for Resource Recovery (MeteoRR) project

Detailed list of sub-use cases, applicable forecasting methodologies and necessary output variables, Deliverable D4.4 of the H2020 project LEVITATE.

Work package 4 (WP4) within LEVITATE is concerned with gathering city visions and developing feasible paths of automated vehicles related interventions to achieve policy goals. City visions contributed to the project in assessing the impact indicators that are needed to be addressed for a useful policy support tool (PST). Previous deliverables of WP4 (deliverable 4.2 and 4.3) used backcasting methods to develop feasible pathways to reach these goals by using policy interventions related to connected and automated transport systems (CATS). These were carried out for the city of Vienna, Amsterdam andGreater Manchester.This deliverable summarises the work that has been conducted in the frame of WP4 and sets the scene for the core LEVITATE work packages (WPs 5, 6 and 7), which address the three main use cases of the project: Urban transport, Passenger cars and Freight transport. Further, the goal of this deliverable is to summarise a timewiseimplementation of different sub-use cases, and the forecasting methodologies that need to be employed to assess the direct, wider and systemic impacts of CATS. Discussion on the specific ways to study the impacts of the interventions using micro-simulationtechnique is conducted and the necessary outcome variables of the forecasting models are specified.The main contribution of deliverable 4.4 is a consolidated list of sub-use cases and output variables, and an indicative timewise implementation of the interventions. The list of subuse cases and interventions was evaluated against the available methods by performing a decision-making exercise among the project partners. From this evaluation, downselection was carried out during a plenary project meeting at the Hague in October 2019, to select the most appropriate and feasible sub-use cases and interventions. Later,these items were arranged on a timeline from present (2020) to 2040 to indicate possible arrival of the services, technologies or interventions due to the anticipated arrival of CATS. This gives an insight into what changes are to be expected in a future city.A small extract from Deliverable 3.2 (methods that could be applied to measure societal level impacts from CATS) is included in the current deliverable to provide a short summary of the methods available for forecasting societal level impacts. Since the systemic and wider impacts are somewhat dependent on the direct impact, traffic microsimulation method is the first choice to initially get direct impact. Therefore, this method is described in more detail. Further research is being undertaken in WPs 5, 6 and 7 to assess the impacts from specified sub-use cases in the most efficient way. To determinethese impacts quantitatively, a list of impact indicators is presented as output variables for the various methods that will be employed