IRESE Research Projects
The research projects in IRESE are focused on engineering and environmental applications. You can find below a list of past and ongoing projects and also potential project ideas for future student applicants.
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: 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 University of Bedfordshire (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 – 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: 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 – February 2022
ADPAC project aim was to advance digital precision aquaculture in China towards “Aquaculture 4.0”, 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 University 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 Educação, Ciência e Tecnologia Catarinense, Brazil
Contact: Tahmina Ajmal
Potential Student Projects
We are always looking for enthusiastic candidates for Masters by Research and Doctoral (PhD) studies. If you are interested in developing your research skills and getting a valuable highest degree with IRESE and have some perspective insights in the topics given below, please contact the provided researchers and apply through our Research Graduate School
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’s 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: 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’s 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: 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’s 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