Tech Bundle

Sustainability

Sustainability is no longer a buzzword, but an environmental, economic and social driver that is changing our daily lives. In the business community, committing to sustainable practices is vital as the negative impacts of climate change have become more prevalent, with the potential to affect everything from supply chain to profitability.

To achieve sustainable development, the Singapore Green Plan sets bold targets to accelerate decarbonisation and sustainability efforts. Technology is shaping sustainability and enabling advanced levels of productivity, efficiency, resource and cost savings, all of which can help to minimise the impact on the environment.

To enable enterprises’ sustainability journey, IPI have curated technological innovations and co-creation opportunities in four areas: Circular Economy, Food Security, Green Energy and Emissions Management, and Safety, Health and Well-being.

AI-enabled Virtual Modelling for Reduction of Energy, Carbon Dioxide Emission
Manufacturing plants constantly seek opportunities to save energy, reduce cost, and be more environmentally sustainable. However, achieving these goals often requires heavy expenditure in the form of hiring teams of experienced engineers, who then perform cost-reduction tasks manually - this method is time-consuming, costly, and prone to inaccuracies due to the risk of human error.  This technology offer provides a no-code Artificial Intelligence (AI) powered platform that monitors energy consumption, carbon dioxide(CO2) emission, and operational expenditures (OPEX) in real-time. The AI engine builds a virtual cognitive model (digital twin) of a physical asset, e.g. a manufacturing plant or a piece of machinary. Simulations are carried out on the model to predict operational inefficiency i.e. high energy usage, equipment breakdown, etc. Upon detection of inefficiencies, the engine is able to suggest the best operating parameters to resolve the inefficiency.
Deep Neural Network (DNN) Approach for Non-Intrusive Load Monitoring (NILM)
Existing methods for load monitoring typically focus primarily on residential building data, while few look at the effectiveness of such systems for industrial or commercial buildings. Apart from the use of this technology for real-time supply-demand response, such methods can be extended for use in anomaly detection, small-scale load change detection, or an estimation of energy usage, without the associated high costs of sub-metering equipment. The proliferation of neural networks for such demanding tasks solves the computationally expensive problem of traditional methods like Hidden Markov Models (HMM) and fuzzy clustering algorithms. This technology offer is a neural network solution for residential and industrial energy management. It utilises a time-series forecasting tool to predict load, renewable energy generation, and electricity prices, without the need for costly sub-metering equipment. It is based on reinforcement learning algorithms which are trained by rewarding and penalising neural network algorithms for good or bad decisions respectively, the solution is a non-intrusive technique that helps residential and commercial end-users save on energy costs in the open energy market by scheduling their load demand for heating, ventilation, air conditioning (HVAC) systems, washing machines, and charging of their Electric Vehicles (EVs).
Rapid Screening of Heavy Metals in Food/Feed Powders
The presence of heavy metals in food or feed powders involves contamination of the food chain and potential harm to public health, as such, rapid detection is a time-critical issue. The uncertainty about food safety caused by the possible presence of heavy metals is of concern to consumers and regulatory authorities and this is typically addressed by increasing the testing frequency of food or feed samples. However, existing testing methods are often time-consuming and require highly skilled laboratory personnel to perform the testing. This technology employs spectroscopic imaging methods and machine learning techiniques to rapidly detect heavy metals in food or feed samples. The machine learning model can perform a multi-class differentiation of the various heavy metals based on spectroscopic measurements. It is also able to predict the concentration of heavy metals present in food or feed powders using spectroscopic measurements. Minimal sample preparation is required for this method, allowing for the rapid screening of food or feed powder samples. The technology owner is interested in collaboration with companies working with food powders, with an interest in heavy metal content within food powders.   
Hydrometallurgy for Recovery of Critical Metals and Graphite in Lithium-Ion Batteries
Electric Vehicles (EVs) are the up-and-coming alternative to internal combustion engines and the market have been projected to grow at an average of around 50% per year. Due to the rapid development and commercialization of EVs, the lithium-ion battery (LIB) market is growing exponentially along with the metals used in the batteries, such as lithium, cobalt, manganese, nickel. However, one major challenge is that the indispensable battery metals are in extremely short supply, and there is a need for a cost-effective and environmentally friendly way of obtaining these materials. This technology offer is a hydrometallurgical recycling method that can recover 80% of the battery value, which is 10% more efficient than current conventional recycling methods. It can recover over 95% of critical cathode metals such as lithium, nickel, cobalt and manganese in a quality and format for direct reuse and manufacturing of new LIBs. It is able to recover high-value non-metallic components such as graphite for the battery anode as well. In addition, for every 1 kg of spent battery recycled, 2.5 kg of carbon dioxide (CO2) is saved allowing for carbon credits generation as well. The technology owner is seeking partners who are interested to further co-develop or license this technology, especially those in the recycling, mining, commodity trading companies and automotive OEM/battery manufactures. They are also looking for companies with an interest to invest in or build a LIB recycling plant.
Leapfrogging Aquaculture Side-stream Wastage
In the aquaculture industry, tons of inedible components and by-products are being generated and discarded as waste every day. Some of these side-streams, such as skins, bone and scales contain numerous bioactive compounds. The research team found that skin from farmed American bullfrog is highly enriched with type I collagen. Despite being a rich source of collagen, current extraction process and the technological utility of American bullfrog collagen is scant. Using a patent-pending mechano-chemical process, they were able to extract up to 70% (w/w) of type I collagen from the bullfrog skin. Additionally, the process is straightforward, cost-effective, scalable and the extraction time could be shortened by 40% compared to traditional acid solubilisation method. The extracted collagen was found to be highly soluble and stable in its tropocollagen-like state for easy tailoring of its chemistries and properties. As proof-of-concept, the lab has successfully processed bullfrog collagen-based products that are amendable for wound healing and bone grafting applications, thereby demonstrating its utility as a renewable, sustainable, and valuable “waste-to-resource” biomaterial. They hope to bring their entire technological pipeline closer to commercialisation by partnering with potential manufacturers and/or offtakers of the products for non wound healing applications.
Smart Recyclable Waste Sorting Platform
In Singapore, recyclables are collected at decentralised collection sites and transported by dedicated recycling trucks to centralised Materials Recovery Facility (MRF), where they are sorted out into paper, glass, metal and plastic. Subsequently, each type of waste is packed into bundles of the same material where they are sent to the respective recycling plant for material-specific recycling. This technology offer is a recyclable waste sorting platform that is based on Artificial Intelligence (AI) image recognition technology and a human-behavioral algorithm. It includes a mechanical separation system to automatically identify and segregate between recyclables and non-recyclables.The platform is intended as a modular solution to be incorporated into new bins or retrofitted into existing infrastructure to address pain points associated with waste collection.
Blockchain-Enabled Material Traceability and Greenhouse Gas Emissions Tracking Platform
Majority of global greenhouse gas emissions stems from industrial activities, over 70% of which can be attributed to manufacturing, energy, and transportation. Companies are under increasing regulatory, investor and public pressure to demonstrate responsible sourcing, recycling and reduce carbon footprint across their supply chains, but many do not know where to start due to a lack of usable data and visibility across their supply chains. This technology offer enables the organisation to achieve end-to-end visibility of supply chains that provides actionable and reliable data to make informed decisions and meet compliance requirements. The solution also dynamically tracks emissions at each supply chain step based on the actual flow of materials for their Scope 1, Scope 2 and Scope 3 emissions. By using blockchain, each component of the supply chain can be made immutable, and possibly be used for carbon credits generation, offsetting and trading. The technology owner is seeking partners and collaborators especially those in mining, battery recycling and electric vehicles (EVs) industries to test bed their solution.
Liquid-phase Electro-conversion of Carbon Dioxide to Syngas
In recent years, carbon dioxide (CO2) levels in the atmosphere have reached unacceptably high levels. The CO2 in the air can be captured but a method of effectively utilising or sequestrating it remains to be found. Therefore, there is a need for a new technology that can cost effectively, and energy efficiently utilise or sequestrate CO2. This technology offer uses a natural, low-temperature, liquid-phase processes to electrochemically convert captured CO2 into syngas, a mixture of hydrogen (H2) and carbon monoxide (CO). This is highly desired as syngas is the precursor of many useful materials such as plastics, ammonia, methane and methanol and is typically made from fossil fuel.  Any emitters of CO2 could integrate this technology into their plants to both reduce their carbon emissions and increase overall system efficiency.  The projected costs show that the syngas will be able to compete with fossil sources based on price, in many cases, without the need for a carbon subsidy or relying on any value from the oxygen (O2) produced as a co-product. The technology owner is looking for partners in various CO2 emitting industries for further co-development and test bedding of the solution. The technology owner is also keen to license this technology as well.
Eco-pesticides Development Platform
Chemical substances used in agriculture (agrochemicals) have a significant impact on the quality of soil, surface and subsurface water. Their extensive and prolonged use over time causes negative effects on ecosystems. This technology proposes the use of natural nano and micromaterials and food-grade polymers to develop smart agrochemicals with a lower environmental impact and associated economic advantages. This technological platform is applicable to a broad spectrum of pesticides for which controlling leaching, volatilisation and premature degradation is a priority. The university is looking for agrochemical companies to further test the platform and develop custom products.