I-COSTE 2024 – Page 3 – NPS Australia Submission System

Sustainable Energy for Port Construction with Low Carbon Concrete from Industrial Symbiosis at WESTPORT Kwinana & BANTAENG Sulawesi

At Bantaeng in South Sulawesi and Kwinana in Western Australia new industrial scale ports will be built to serve the industrial precincts at these locations. At both these sites a 1-2Mtpa GPC plant is proposed for precast production of some 1,600 port modules as well as other infrastructure requiring some 750,000 cum of concrete and thereafter the plant can be repurposed for other products for local markets such as reef modules and wall panels. Geopolymer concrete can be the replacement for conventional concrete and be made from waste-derived materials while having a lower carbon footprint. The plant is designed to be operated by renewable energy and an energy audit estimated that a 1Mtpa geopolymer production plant needs up to 200 GWh pa to operate. This could be served by 6-10 on-land wind turbines combined with solar PV farm at a total cost $45-55 million USD. The electricity generated @ say $100/MWh was worth $12-20M pa that could result in a payback of 2-5 years. In Kwinana, planning is already underway for a large wind farm as part of the overall decarbonisation strategy for this industrial precinct. Feedstock materials can be harnessed for use in the geopolymer production plant by means of Circularity Hubs. These hubs can be established through the KIC4 and 6-Capitals models of Industrial Symbiosis and to optimize the proposed geopolymer plant within the industrial precincts at Bantaeng and Kwinana. Such an approach can contribute to Regenerative Development when both of the ports are built.

Low Carbon Concrete for Solid Gravity Energy Storage System and a Sustainable Electricity Grid.

Solid Gravity Energy Storage (SGES) Systems are an innovative way to store energy by using the force of gravity. These systems can use the excess energy from solar photovoltaic power systems to lift large blocks of concrete usually around mid-day and later as the sun sets and power demand is high, the blocks are released and generate gravitational energy which is converted to electricity. Colliecrete is a low-carbon, waste-derived, geopolymer concrete developed in 2021, from the Collie power plants’ flyash, by the Mudlark geopolymer lab at Murdoch University and geopolymer precursors can come from a number of waste-derived materials. Colliecrete can be used in the blocks for SGES. In Australia, most coal power plants will shut by 2030, while in Indonesia, the expectation is to achieve carbon-neutrality by 2060. There are many methods and pathways to achieve this goal with low-carbon geopolymer concrete one of them. Geopolymer precursor material is abundant with flyash available from 200 coal-fired power stations and slag from dozens of steel mills and nickel smelters. Rice husk is disposed of in millions of tonnes by farmers across the archipelago by burning and this ash can be converted to the geopolymer activator. All these make the possibility of an enormous new geopolymer concrete industry to at least partially replace the high-carbon, Portland cement industry. Geopolymer concrete blocks in the SGES system provide long-duration energy storage, assist firming the renewables and reduce carbon emissions while creating a new industry for the energy transition.

The Advent of Metal Additive Manufacturing Technologies – Alternative Options for Small Medium Enterprises

This study aims to explore the latest development made in Metal Additive
Manufacturing Technologies including alternative options which can be readily implemented by Small Medium Enterprises. This research paper also provides a brief overview on the consideration of fused filament fabrication technique based on material extrusion for metal 3D-Printing. Other indispensable processes in this technique, namely debinding and sintering processes are further discussed.

Energy trading in a decentralized blockchain based energy network

Application of new technology (Blockchain) in peer-to-peer energy sharing in smart grids.

Machine Learning-based Active Power Loss Forecasting in Distribution Systems

This paper introduces an optimal model that utilizes machine learning algorithms to predict the
active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The model incorporates the technique of Gradient Boosting Machine Regression (GBMR). This study estimates DG location, bus voltages, DG size, and active
losses without conventional power flow calculations.
The results demonstrate that the suggested estimations of power losses and DG sizing method are effective, practical, and adaptable. The accuracy of the proposed estimation methods has been validated using R-squared and mean absolute percentage error (MAPE) metrics. In the case of fixed load, the GBMR outperforms with a very low (MAPE) (0.9281%), a root mean square error (RMSE) of 1.748, and 0.999 accuracy in predicting
active power losses. When the normalized load variation (NLV), the R-squared 0.9991 value with low MAPE (1.9815%). This approach enables grid operators to effectively manage DG unit integration by providing precise estimates and forecasts of power loss. The effectiveness of the proposed strategy is validated in the IEEE 33 bus test system using MATLAB software.

Improved Energy Management for Hybrid Systems via Dual Predator Optimization

A novel hybrid renewable energy microgrid optimization algorithm supported with wind, solar, and backup diesel generators is suggested in this research work. The proposed Dual Predator Optimization (DPO) algorithm combines the Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO). This algorithm integrates with a hybrid microgrid to optimize the use of renewable resources, reduce reliance on fossil fuel, and increase the cost-effectiveness by adjusting these parameters over time. The DPO is flexible and more suitable for hybrid energy management taking into consideration the exploration (exploitation) of system-level energy behaviors simultaneously in large-scale problems. The results show that the DPO is efficient in handling hybrid systems by significantly reducing electricity costs and decreasing probabilities of non-supply. It was determined that in comparison to the current GWO and WOA, the Cost of Energy (COE) of the proposed DPO algorithm is decreased to an average of 20%, while Loss of Power Supply Probability (LPSP) increases to an average of 7.5%.

Learning through Research: The Impact of Pattern Extraction on Neural Networks Architectures

Meeting today’s learners learning styles. Demonstrate how research influences learning new concepts to the level of mastering.

Machine Learning-based Active Power Loss Forecasting in Distribution Systems

This paper introduces an optimal model that utilizes machine learning algorithms to predict the
active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The model incorporates the technique of Gradient Boosting Machine Regression (GBMR). This study estimates DG location, bus voltages, DG size, and active
losses without conventional power flow calculations.
The results demonstrate that the suggested estimations of power losses and DG sizing method are effective, practical, and adaptable. The accuracy of the proposed estimation methods has been validated using R-squared and mean absolute percentage error (MAPE) metrics. In the case of fixed load, the GBMR outperforms with a very low (MAPE) (0.9281%), a root mean square error (RMSE) of 1.748, and 0.999 accuracy in predicting
active power losses. When the normalized load variation (NLV), the R-squared 0.9991 value with low MAPE (1.9815%). This approach enables grid operators to effectively manage DG unit integration by providing precise estimates and forecasts of power loss. The effectiveness of the proposed strategy is validated in the IEEE 33 bus test system using MATLAB software.

A Novel Approach to Verification of Neural Cyber-physical Systems

The verification of Cyber-physical System (CPS),
particularly those incorporating neural networks for safety-critical
functions remains an ongoing challenge due to the lack
of advanced verification and validation frameworks. Current
methods are often limited in their scalability and ability to
comprehensively verify system-level and component-level properties,
leading to potential vulnerabilities in these systems. This
issue becomes even more pronounced when dealing with hybrid
systems that integrate physical processes and neural network-based
controllers. We propose a novel verification framework
tailored for complete CPS verification using a decomposition based
approach to address this challenge. Our method performs
sequential-distributed verification, ensuring each component
adheres to compositional Metric Interval Temporal Logic
(MITL). By applying this framework to a model (CPS), we use
backward induction to verify that component-level and system-level
properties remain within predefined operational ranges,
derived from simulation data. Implemented in MATLAB,
this approach enhances the verification process by identifying
potential failure points across subsystems, providing a scalable
solution for verifying complex hybrid systems. This method
significantly improves verification accuracy and enables precise
identification of faulty components, making it a highly effective
tool for robust system design and analysis.

Leveraging Web Applications for Enhanced Transportation Mobility: Integrating Taxi Booking and Volunteer Ride Services in Fiji’s

The significant research contribution of this project is the development of a web-based platform that integrates real-time taxi booking, ride-sharing, and volunteer ride services tailored for Fiji. This innovative solution addresses key challenges in Fiji’s urban transportation, such as traffic congestion, vehicle overuse, and lack of affordable transport for low-income individuals. By promoting environmental sustainability, fostering community engagement through volunteer rides, and leveraging secure online payment systems, this platform contributes to enhancing mobility and reducing greenhouse gas emissions in a unique socio-economic context.

Conference Site: I-COSTE 2024