天美传媒

Centrifugal pumps represent 70% of all kinds of pumps and are ubiquitous in the industrial world especially in heating, air conditioning and sewage applications. Although modern pumps can last for many years, their sudden failure can lead to undesirable or catastrophic disruptions.

The purpose of this project is to develop a low-cost IoT based predictive maintenance solution to continuously monitor the pump health using motor current signature analysis (MCSA) and predict failures using a combination of signal processing and machine-learning algorithms. An MCSA monitoring system is deployed by attaching current clamps, used as transducers, to power supply wires without requiring direct physical access to the pump itself. The proposed system consists of custom hardware modules that stream the pump data to the cloud, and a back-end for storage, visualisation and intelligent analysis.

The project is a collaboration with Uptime Systems Ltd and was funded by Innovate UK (2019-2023).

Contact:听Vladan Velisavljevic

More details on the Early Detection of Electric Pump Failures project page

This project was funded internally under the England Call fund scheme to use innovative models and methodologies for participatory research. One of the project aims was to start a new research theme within the engineering cluster, around the filtration of industrial sludge. This is now one of the active themes of research activities within IRESE, and continues to expand collaboration with industrial partners through new investigation, bid writing and consultancy work.

Contact: Rostand Tayong

REDIA project was designed to apply the expertise of 天美传媒 (UOB) in aquaculture sector to South Africa to incorporate resilience in the farm management for Abagold. The farm faces various challenges but a significant technical challenge is to deal with harmful algal blooms (HAB). With the main aim to use sensor data from the farm to predict red tide well in advance, in addition to ensuring that the essential balance with environmental, economic, and social aspects is maintained. The project also included capacity building workshops by engaging all spheres of stakeholders especially women and ethnic groups.

Partners: Abagold (South Africa) for the Aquaculture industry in South Africa

Contact: Tahmina Ajmal

Innovate UK IUK Reference: 10004655 2022-2024

Insight project was developed around using a Scouting robot to address labour shortage and yield production in fruit fields. Robot requires advanced technologies (e.g., autonomous navigation, artificial intelligence) at an affordable level 鈥� a key factor for wide adoption. Combining IoT real-time data with accurate geo-spatial data using a long-range wireless network, a digital image of the farm is created and then robot takes images of crop and using advanced image processing, it predicts the yield.

Partners: Antobot (Overall PI) AgriEPI Centre, Bardsley Fruit Framing Limited, Place UK Limited.

Contact:听Tahmina Ajmal

Interreg North-West Europe Project Reference: N WE831 2019- 2023

REAMIT used IoT sensors deployed over 10 case studies across NW Europe with data being transmitted in real time to a dashboard for analysis.

Partners from UK, France, Netherland, Ireland and Germany.

Contact: Tahmina Ajmal

Innovate UK Reference: 86204028 BB/S020896/1 March 2019 鈥� February 2022

ADPAC project aim was to advance digital precision aquaculture in China towards 鈥淎quaculture 4.0鈥�, which is a highly connected and automated cyber-physical system using digital technologies. It will apply and integrate the latest technologies of advanced sensors, 5G-based Internet of Things, Big Data analytics and automation to pilot highly digital precision aquaculture in China. This aim was achieved by the application of new multiparameter anti-fouling aquaculture sensors for real-time monitoring, diagnosis and control, a capability that was lacking in existing aquaculture production; the project saw first application of data analytics and automation for aquaculture over IoT and the development of an integrated system of Aquaculture 4.0.

Partners: Chelsea Technology Group (Overall PI), Perceptive Engineering Limited and 天美传媒 of Surrey

Contact: Tahmina Ajmal

Interreg Northwest Europe Project Reference: NWE553 Sept 2017- Sept 2022

Partners from France, Netherland, Germany, and UK

Contact: Tahmina Ajmal

Newton Fund- British Council, FAPEC Reference: 332387020, from April 2018

TAF project demonstrated application of low-cost sensor systems in artisanal farms in Brazil, the data was then available through a dashboard for easy visualisation, hence supporting farm management.

Partners: Instituto Federal de Educa莽茫o, Ci锚ncia e Tecnologia Catarinense, Brazil

Contact: Tahmina Ajmal

RESORCS is a joint technical research project carried out by university and industry partners in both the UK and in Sri Lanka. The project involves technical innovations, system design, proof of concept, laboratory testing, implementation of the proposed technology and testing in the target country. The outcomes of the project, an innovative system that uses waste and solar energy to generate renewable power, applicable in a tropical country where there is abundant solar incidence.

The total technical concept and the Organic Rankine Cycle based rotary turbine are the main technically innovative elements of the project. The proposed system uses heat absorption and propulsion via an innovative Rankine cycle based rotary turbine in transforming energy to electric power. Energy is primarily stored in a thermal energy storage which is nearly 20 times cost effective than battery storage. The technology is suitable for use as a standalone application or to feed harnessed electricity to the grid. This is expected to meet the energy demand of a cluster of dwellings, a small factory or an enterprise irrespective of whether they are grid connected or not. The project aims to prove this innovative and cost-effective technology, implement a working unit of the system at a selected site in Sri Lanka and test performance to demonstrate the technology.

The project team comprises of four academic partners: 天美传媒, City 天美传媒 of London, Bristol 天美传媒 and 天美传媒 of Moratuwa from Sri Lanka, and two industry partners: FeTu from the UK and Regen Renewables from Sri Lanka.

This project aims to develop an optimal control strategy for wind turbines using a method called Model Predictive Control (MPC). Wind turbines are complex machines that convert wind energy into electricity, and their performance is influenced by factors such as wind speed, rotor speed, and turbine settings. The challenge is ensuring that these turbines operate efficiently, safely, and reliably, especially under varying conditions like changing wind speeds and gusts.

The core idea of this project is to use a predictive approach where the system continuously forecasts how the turbine will behave and adjusts its settings to achieve optimal performance. By doing so, we can maximize energy production while reducing mechanical wear and tear, which leads to longer-lasting turbines and more stable energy output.

However, a key challenge is that the model used to predict the turbine鈥檚 behavior might not always match real-world conditions, resulting in discrepancies and errors in control. This project will focus on developing a truly effective model predictive control strategy for wind turbines and explore novel approaches to address these discrepancies. The ultimate goal is to make wind energy generation more efficient, reliable, and sustainable, supporting the growing demand for clean energy with minimal environmental impact.

Contact:听Imran Ghous at Imran.Ghous@beds.ac.uk

This project aims to develop a smarter control system for robotic arms, which are commonly used in industries like manufacturing, healthcare, and logistics. Robotic arms are complex machines that often face challenges like unexpected changes in their surroundings, carrying different weights, or handling unknown objects. These factors can affect how precisely and reliably they work.

The goal is to design a control system that adjusts itself in real-time to keep the robotic arm steady and precise, even in uncertain conditions. This system will use a method called fractional-order sliding mode control, which is better at handling sudden changes and complex movements compared to traditional methods. It also helps reduce unnecessary movements, improving accuracy and efficiency while reducing wear and tear on the robotic arm.

One of the main challenges is dealing with the unpredictable nature of the robotic arm鈥檚 environment, like unknown forces or unexpected changes. The project focuses on developing a control system that can automatically adapt to these changes, ensuring the robotic arm works reliably and enjoys a longer lifespan. Ultimately, this will make robotic arms more versatile and practical in real-world applications.

Contact:听Imran Ghous at Imran.Ghous@beds.ac.uk

This project aims to develop a robust human activity recognition (HAR) system leveraging artificial intelligence (AI) and radio frequency (RF) sensing. The focus will be on designing machine learning models to classify activities such as sitting, standing, walking, and falls, using contactless RF technologies. This research will explore advanced techniques to enhance the system鈥檚 accuracy, scalability, and usability in real-world scenarios such as elderly healthcare and smart home environments.

Contact: Umer Saeed at Umer.Saeed@beds.ac.uk

This study seeks to develop a non-invasive respiratory monitoring system using contactless RF sensing technologies such as RADAR/SDR and machine learning algorithms. The project will investigate the use of RF signals to capture subtle respiratory patterns and analyze them with AI-driven models for accurate and continuous monitoring. Potential applications include early detection of respiratory conditions, remote health monitoring and integration into wearable or smart home systems.

Contact: Umer Saeed at Umer.Saeed@beds.ac.uk

Since 1990 the UK has almost halved the greenhouse gas emission; with the aim to bring greenhouse gas emission to net zero by 2050. As such the net zero and topics related to that are among the hottest topics within the scientific and political communities. Carbon capture technologies as means of removing carbon dioxide from atmosphere have been known for a while and they are now considered as one of the main tools to adverse global warming consequences. However, the current carbon capture processes are high energy demand. The aim of this project is to investigate and optimise low energy carbon capture processes and improve utilisation technologies.

Contact: Mina Mortazavi at mina.mortazavi@beds.ac.uk

The freshwater crisis is now as urgent as making the transition to zero carbon. The worldwide water demand is expected to increase by 55%. According to a study by the Organization for Economic and Cooperative Development (OECD), the three leading reasons for the increase will be manufacturing, thermal electricity, and domestic use between 2000 and 2050.

The main objective of this project is to design and optimise industrial wastewater management systems with an interdisciplinary and low-cost method. The project is to develop a sustainable water management system concerning industrial wastewater and mineral re-use.

Contact: Mina Mortazavi at mina.mortazavi@beds.ac.uk

Thermal energy storage options and hydro based technologies have become interesting due to their environmental benefits compared to conventional galvanic battery technologies. This project aims to design, develop, and test a large-scale thermal energy storage solution for a designated community, including residential, commercial, and industrial units. Energy will be harnessed from multiple renewable sources鈥攕olar PV, solar thermal, wind, geothermal, and waste-derived energy. Stored thermal energy will be maintained at a controlled temperature within a modular system, ensuring flexibility in storage and use. Energy will be used on demand for electricity generation, as well as space and water heating.

There are a few research projects within this topic; one is to develop and test the technology, another is to develop an optimised AI based control system for the system.

Contact: Rohitha Weerasinghe at Rohihta.Weerasinghe@beds.ac.uk

This proposal is aimed at developing smart agricultural equipment that leverages technologies like IoT (Internet of Things), Artificial Intelligence (AI), Machine Learning (ML), and Robotics to enhance agricultural productivity and sustainability. There is a trilateral partnership with a 天美传媒 in Sri Lanka and an agricultural company which will combine academic expertise, industrial capabilities, and practical applications to address pressing challenges in agriculture.

There are a few research projects that are available in this area to develop new mechanical and robotic technologies, use of AI and machine learning and new applications in the area

Contact: Rohitha Weerasinghe at Rohihta.Weerasinghe@beds.ac.uk

The overall cost of global warming has been estimated to losing between 5% and 20% GDP growth every year. The UK has committed to be a NetZero country by 2050. The legal framework for this process is covered by the Environment act (2021) and the Climate Change Act (2008). The NetZero Strategy (2021) sets out the government鈥檚 vision for a market-led, technology driven transition to decarbonise the UK. However, neither a complete technology package nor a fool-proof strategy to become NetZero is present on this day. A new project has been initiated to provide solutions for Net Zero strategies. The process starts with calculating the emissions, followed by setting up science based targets, emission reduction and emission removal.

Contact: Rohitha Weerasinghe at Rohihta.Weerasinghe@beds.ac.uk

The biggest problem with transport data is the sheer volume of the data and the variety. Net0MobLTN will run a pilot project to assess the feasibility of handling this volume. When data are not available, as mentioned earlier, artificially generated data will be used to represent a real scenario. The project aims to develop and use a data optimisation engine that will use the existing scattered data sets, for example the transport data that LLA owns and data that LBC has access to, and use a computational tool (a computer program ) to assess the feasibility of optimal data exchange. At this stage, when real data are not available, generated data can be used to demonstrate the efficacy and the usefulness of the concept.

Contact: Rohitha Weerasinghe at Rohihta.Weerasinghe@beds.ac.uk

An approach with a total renewable energy based supply, eliminating transmission losses can be achieved through a distributed energy supply system. If solar based energy generation can be used with minimum feed to the grid, but with maximum local consumption and a consumption matched generation profile, the transmission losses and storage losses can be minimised. This can be demonstrated via a pilot self-sufficient energy supply system in representative localities in the respective countries. Demand-side management and demand based generation is necessary to minimise losses, and in turn achieve Net Zero.

With the proposed energy generation and demonstration model, it is aimed to use next generation solar energy harnessing and storage systems, modern cooking technologies with induction cooking and modern biomass based systems. The system proposed needs to have a low initial cost and a fast return on investment.

Contact: Rohitha Weerasinghe at Rohihta.Weerasinghe@beds.ac.uk