Detection of Patterns in Pressure Signal of Compressed Air System Using Wavelet Transform

This paper investigates detecting patterns in the pressure signal of a compressed air system (CAS) with a load/unload control using a wavelet transform. The pressure signal of a CAS carries useful information about operational events. These events form patterns that can be used as ‘signatures’ for event detection. Such patterns are not always apparent in the time domain and hence the signal was transformed to the time-frequency domain. Three different CAS operating modes were considered: idle, tool activation and faulty. The wavelet transforms of the CAS pressure signal reveal unique features to identify events within each mode. Future work will investigate creating machine learning tools for that utilize these features for fault detection in CAS.

Integration of Catalytic Biofuel Production and Anaerobic Digestion for Biogas Production

The drive towards a low carbon economy will lead to an increase in new lignocellulosic biorefinery activities. Integration of biorefinery waste products into established bioenergy technologies could lead to synergies for increased bioenergy production. In this study, we show that solid residue from the acid hydrolysis production of levulinic acid, has hydrochar properties and can be utilised as an Anaerobic Digestion (AD) supplement. The addition of 6 g/L solid residue to the AD of ammonia inhibited chicken manure improved methane yields by +14.1%. The co-digestion of biorefinery waste solids and manures could be a promising solution for improving biogas production from animal manures, sustainable waste management method and possible form of carbon sequestration.

Development of an Advanced Solar Tracking Energy System

This paper describes the design of an advanced solar tracking system development that can be deployed for a range of applications. The work focused on the design and implementation of an advanced solar tracking system that follow the trajectory of the sun’s path to maximise the power capacity generated by the solar panel. The design concept focussed on reliability, cost effectiveness, and scalability. System performance is of course a key issue and is at the heart of influencing the hardware, software and mechanical design. The result ensured a better system performance achieved. Stability issues were also addressed, in relation to optimisation and reliability. The paper details the physical tracker device developed as a prototype, as well as the proposed advanced control system for optimising the tracking.

Enhancing Methane Production from Spring-Harvested Sargassum muticum

Sargassum muticum is a brown seaweed which is invasive to Europe and currently treated as waste. The use of S. muticum for biofuel production by anaerobic digestion (AD) is limited by low methane (CH4) yields. This study compares the biochemical methane potential (BMP) of S. muticum treated in three different approaches: aqueous methanol (70% MeOH) treated, washed, and untreated. Aqueous MeOH treatment of spring-harvested S. muticum was found to increase CH4 production potential by almost 50% relative to the untreated biomass. The MeOH treatment possibly extracts AD inhibitors which could be high-value compounds for use in the pharmaceutical industry, showing potential for the development of a biorefinery approach; ultimately exploiting this invasive seaweed species.

Solute Driven Transient Convection in Layered Porous Media

CO2 geological sequestration has been proposed as a climate change mitigation strategy that can contribute towards meeting the Paris Agreement. A key process on which successful injection of CO2 into deep saline aquifer relies on is the dissolution of CO2 in brine. CO2 dissolution improves storage security and reduces risk of leakage by (i) removing the CO2 from a highly mobile fluid phase and (ii) triggering gravity-induced convective instability which accelerates the downward migration of dissolved CO2. Our understanding of CO2 density-driven convection in geologic media is limited. Studies on transient convective instability are mostly in homogeneous systems or in systems with heterogeneity in the form of random permeability distribution or dispersed impermeable barriers. However, layering which exist naturally in sedimentary geological formations has not received much research attention on transient convection. Therefore, we investigate the role of layering on the onset time of convective instability and on the flow pattern beyond the onset time during CO2 storage. We find that while layering has no significant effect on the onset time, it has an impact on the CO2 flux. Our findings suggest that detailed reservoir characterisation is required to forecast the ability of a formation to sequester CO2.

A Novel Approach for U-Value Estimation of Buildings’ Multi-layer Walls Using Infrared Thermography and Artificial Intelligence

Estimating the U-value of walls of buildings is a key process to evaluate the overall thermal performance. Low U-value in buildings is desired in order to keep heat within the envelop and consume less energy in heating. Addressing the limitations in the currently used U-value estimation techniques, this paper proposes a novel approach for estimating the U-value of the envelop of buildings using infrared thermography and Artificial Neural Network (ANN) with the application of a point heat source. The novel system is calibrated by training the ANN in a lab environment using a wide range of samples with multi-layers to be able to estimate the in situ U-value of walls in real buildings during field work with relatively high accuracy.

Integration of Building Information Modelling and Augmented Reality for Building Energy Systems Visualisation

Buildings consist of numerous energy systems, including heating, ventilation, and air conditioning (HVAC) systems and lighting systems. Typically, such systems are not fully visible in operational building environments, as some elements remain built into the walls, or hidden behind false ceilings. Fully visualising energy systems in buildings has the potential to improve understanding of the systems’ performance and enhance maintenance processes. For such purposes, this paper describes the process of integrating Building Information Modelling (BIM) models with Augmented Reality (AR) and identifies the current limitations associated with the visualisation of building energy systems in AR using BIM. Testing of the concept included creating and superimposing a BIM model of a room in its actual physical environment and performing a walk-in analysis. The experimentation concluded that the concept could result in effective visualisation of energy systems with further development on the establishment of near real-time information.

Innovative Strategy for Addressing the Challenges of Monitoring Off-Shore Wind Turbines for Condition-Based Maintenance

Off-shore wind energy technology is considered to be one of the most important renewable energy source in the 21st century towards reducing carbon emission and providing the electricity needed to power our cities. However, due to being installed away from the shore, ensuring availability and performing maintenance procedures could be an expensive and time consuming task. Condition Based Maintenance (CBM) could play an important role in enhancing the payback period on investment and avoiding unexpected failures that could reduce the available capacity and increase maintenance costs. Due to being at distance from the shore, it is difficult to transfer high frequency data in real time and because of this data transferring issue, only low frequency-average SCADA data (Supervisory Control And Data Acquisition) is available for condition monitoring. Another problem when monitoring wind energy is the massive variation in weather conditions (e.g. wind speed and direction), which could produce a wide range of operational alerts and warnings. This paper presents a novel case study of integrated event-based wind turbine alerts with time-based sensory data from the SCADA system to perform a condition monitoring strategy to categorise health conditions. The initial results presented in this paper, using vibration levels of the drive train, indicate that the suggested monitoring strategy could be implemented to develop an effective condition monitoring system.

A Brief Review on Nano Phase Change Material-Based Polymer Encapsulation for Thermal Energy Storage Systems

In recent years, considerable attention has been given to phase change materials (PCMs) that is suggested as a possible medium for thermal energy storage. PCM encapsulation technology is an efficient method of enhancing thermal conductivity and solving problems of corrosion and leakage during a charging process. Moreover, nanoencapsulation of phase change materials with polymer has several benefits as a thermal energy storage media, such as small-scale, high heat transfer efficiency and large specific surface area. However, the lower thermal conductivity (TC) of PCMs hinders the thermal efficiency of the polymer based nano-capsules. This review covers the effect of polymer encapsulation on PCMs while concentrating on providing solutions related to improving the thermal efficiency of system.

The Impact of Data Segmentation in Predicting Monthly Building Energy Use with Support Vector Regression

Advances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.