TECHINNOVATION TECH OFFERS

The increasing complexity of plant and factory operations, coupled with growing demands for energy and carbon management, has created an urgent need for integrated solutions. These solutions must not only manage assets and optimize production processes but also measure and reduce carbon emissions, driven by mounting government pressure to achieve smart and green manufacturing with low carbon output. This technology is tailored for manufacturing plants and energy-intensive industries such as oil and gas, petrochemicals, chemicals, and pharmaceuticals - sectors facing significant challenges to lower operational costs, boost production efficiency, and meet carbon neutrality goals. By leveraging 3D visualization, operational simulation, real-time data collection, and big data analytics, this technology reconstructs the entire production and operational processes through a digital twin. It integrates and replicates data from system upgrades, engineering construction, production activities, and process principles. The implementation includes the digital delivery of engineering assets, the creation of a digital twin model based on operational mechanics and historical data, and the seamless integration of operational management with energy and carbon management systems through real-time data access. This technology ultimately establishes a cloud-based digital twin platform that enables comprehensive asset management, production workflow optimization, and precise monitoring of energy consumption and carbon emissions, paving the way for developing low-carbon, smart plants. The technology owner is seeking collaboration with industrial manufacturers, plant operators, energy management companies, engineering design firms, IoT sensor and software providers to co-develop and implement this technology in practical applications, delivering tailored solutions to meet the unique needs of diverse clients.

Insomnia, particularly when caused by stress or anxiety, poses a significant challenge for many individuals. Traditional treatments, including sleeping pills, can be effective but often come with potential side effects and long-term dependency concerns. Addressing this issue, an innovative sleep patch has been developed to offer a non-invasive, drug-free solution for improving sleep hygiene. This sleep patch is designed to tackle common sleep problems such as insomnia and irregular sleep patterns, particularly those triggered by stress and anxiety. Unlike conventional medications, the patch leverages natural products to promote relaxation and support continuous sleep without the adverse effects associated with long-term drug use. Clinical research, including stress experiments and polysomnography studies, has demonstrated the patch’s ability to induce sleep for over 12 hours by alleviating stress. This makes it an ideal choice for individuals seeking a safe, effective alternative to conventional sleep aids. The technology owner is looking for collaboration with local research institutions and universities to validate the effectiveness and safety of the product through comprehensive research and clinical trials.

Indoor vertical farming is pivotal for addressing future food challenges, particularly in arable land-scarce countries. One common method is hydroponics, which uses mineral and nutrient solutions in a water-based platform to grow crops. To optimize the crop yield and to reduce the man work hours required, it is important to automate crop health monitoring and replenishing of specific nutrients. Currently, these tasks are labour-intensive and subjective. While some imaging techniques exist for detecting plant stress and chlorophyll monitoring, a complete system covering all aspects is still lacking. For nutrient analysis, tools like pH and electroconductivity meters can only detect a change in the nutrient composition to start a feedback loop but are unable to determine the specific nutrient component or deficiency level. This technology is a comprehensive quantitative monitoring system integrating imaging spectroscopy and laser-based elemental spectroscopy to quickly identify the crop growth stages, alert crop stresses (tested on several lettuce species) and quantify specific nutrient levels in the nutrient supply. This allows for reduced man work hours and improvement of crop yield.

Access to clean and safe drinking water is essential for health, yet millions of people worldwide still lack this necessity. According to the World Health Organization (WHO), over 2 billion people globally use drinking water sources contaminated with feces, leading to severe health consequences. Unsafe water, along with inadequate sanitation and hygiene, is estimated to cause 485,000 diarrheal deaths each year. Water purification technologies face significant challenges, especially in decentralized systems lacking the efficiencies of large-scale operations. They often have a substantial carbon footprint due to energy-intensive processes and reliance on chemicals. Existing portable devices primarily use filtration and have a limited lifetime on-site, with little opportunity for cleaning to restore its performance.  Developed by a research team, this technology effectively addresses the above challenges by employing electrochemical methods that generates strong oxidizing agents to kill micro-organisms present in raw water and potentially degrade organic pollutants that conventional portable reactors cannot remove via filtration. Due to its working mechanism, the device is self-cleaning and does not need regeneration. By harnessing solar energy and activated carbon, this chemical-free purification approach is not only environmentally friendly but also perfectly suited for deployment in remote areas, developing countries, and disaster-stricken zones where traditional water treatment infrastructure is lacking. The technology owner is looking for collaborations with local SMEs to co-develop scaled systems and deploy it through disaster relief organizations, government agencies and non-profit organizations in selected developing countries. 

Due to the rising demand for industrial automation, autonomous robotic technology is gaining traction across various industries optimising processes and increasing operational efficiency. For these autonomous robots to execute their task with precision, they are required to be constantly aware of their surroundings to response adequately and quickly. Technologies developed from off-the-shelf components (e.g. camera-based perception) faces integration issues while industries with weak or no connectivity to the Global Navigation Satellite System (GNSS) are unable to utilise them. Developed by a Singapore-based startup, the proposed technology herein encompasses a modularised sensor hardware and edge AI software within a compact form factor, addressing fundamental 3D vision problems such as localisation positioning and obstacle detection. This solution can be easily customised, integrated and deployed in GPS-denied or GPD-obstructed areas while maintaining accuracy and reliability in navigation, tracking and monitoring of fleet, autonomous vehicles, etc. The technology solution has engaged with numerous proof-of-concept (POC) projects, including one notable engagement within the defence industry for a drone integration. The technology owner is looking for collaborative partners, such as manufacturers and system integrators within the automation space, who wishes to further enhance their robot’s perception capabilities.

As global temperatures rise, governments are setting eco-friendly building standards to address concerns about energy consumption and carbon emissions. Improving energy efficiency in buildings, especially in hot climates where cooling demands increase energy use, has become a major challenge. This has driven the need for sustainable and energy-efficient building materials. Aerogels are among the most promising insulation materials due to their large specific surface area (500-1200 m²/g), high porosity (80-99.8%), and ultra-low density (around 0.003 g/cm³). They are amorphous, chemically inert, non-flammable, and exhibit extremely low thermal conductivity (0.01-0.03 W/(m·K)). Silica aerogel (SA) is particularly notable for having the lowest thermal conductivity, making it ideal for building insulation. The technology owner has developed an advanced insulation coating that incorporating in-house fabricated silica aerogel (SA) powders to enhance both thermal and acoustic insulation in buildings. This technology also works with silica aerogel powders purchased externally. Incorporating 20 vol% SA into paint and plaster formulations can reduce surface temperatures by up to 12°C and chamber temperatures by up to 3.3°C, helping to lower air conditioning use and save energy. The coating also improves acoustic insulation, offering a dual benefit. By meeting the growing demand for greener building solutions, this technology offers a competitive edge in reducing energy consumption and improving overall comfort and building performance.   The technology owner is seeking industrial partners for test-bedding and is also open to licensing opportunities for commercialization, especially with construction companies, building material manufacturers, and developers focused on sustainable and energy-efficient construction.

In the construction sector, manual inspections have traditionally been the primary method for detecting water seepage surface defects, a mandatory requirement for construction projects. However, these inspections often suffer from the inherent subjectivity of human judgment, leading to potential inconsistencies and inaccuracies. To overcome these limitations, a handheld water seepage detection system was developed and rigorously tested in collaboration with the Building and Construction Authority (BCA). This innovative system is designed as a portable, intelligent alternative to traditional methods, aiming to enhance the objectivity and reliability of water seepage detection. The system utilizes advanced Long-Wave Infrared (LWIR) thermal sensing technology to accurately detect temperature variations indicative of water seepage. Unlike manual inspections, which can be prone to error, this system offers precise differentiation between genuine water seepage defects and common artifacts found on construction sites, such as glue and paint. By minimizing false alarms, it provides a more dependable and efficient approach to identifying and addressing water-related issues. This advancement not only improves the accuracy of inspections but also ensures that potential water damage is detected early, reducing the risk of costly repairs and enhancing the overall integrity of construction projects.    

Conventional aircrafts have been a preferred mode for fast middle mile delivery within logistics and supply chain. With the increasing traction of drone technology, the usage of drone as a potential alternative mode of transport within the supply chain is greatly considered, given its comparable performance and more sustainable. Current conventional aircraft requires a runway for landing and take-off, limiting their use-case within space constraint areas. For drones, its slow operational speed and short operation range greatly limits it widespread industrial adoption. The technology owner developed a UAV solution which enables faster operational speed (up to four times) and longer flight duration (over 2 hours) compared to incumbent multicopter drones of the same class. This UAV solution comprises of tilt-electric ducted fans, a hybrid energy source and optimised design to harnesses the advantages from both drone and conventional aircraft technologies. Due to these capabilities, the technology solution enables high-risk and high-difficulty missions to be carried out, with the safety of the operator in mind, such as wide-area surveillance and reconnaissance, transportation to remote regions and shore-to-ship deliveries. The technology owner is looking for collaboration partners seeking to further explore drone technologies or integrate drones into existing workflow processes which require longer operational range and faster speed that current traditional multicopter drone is unable to accommodate.

In healthcare, accurately assessing pain is critical, yet it remains a significant challenge, particularly for patients unable to effectively communicate their discomfort. This includes individuals with cognitive impairments, critical care patients, and pre-verbal children. Traditional pain assessment methods rely heavily on verbal communication or subjective observer-based pain assessment, which can lead to missed or poorly managed pain, resulting in either under-treatment or over-analgesia treatment. To address this issue, this advanced AI-based solution automates the process of estimating pain intensity by analyzing facial expressions captured in video data. The technology is a deep learning system consisting of facial landmarks (specific points on a person's face, such as the corners of the eyes, nose, mouth, and other prominent facial features) extraction, 3D normalization and Spatial-Temporal Attention Long Short-Term Memory (STA-LSTM) model. These facial landmarks provide critical information about facial expressions, allowing the system to accurately assess pain levels. By integrating this technology into clinical settings, healthcare providers can significantly enhance patient care, reduce the burden on staff, and improve overall pain management practices. This solution not only ensures timely and appropriate intervention but also protects patient privacy by using non-identifiable facial landmarks, making it a powerful tool for modern healthcare environments.