Mathematical modelling and simulation of the thermo-catalytic decomposition of methane for economically improved hydrogen production

The demand for low-emission hydrogen is set to grow as the world transitions to a future hydrogen economy. Unlike current methods of hydrogen production, which largely derive from fossil fuels with unabated emissions, the thermo-catalytic methane decomposition (TCMD) process is a promising intermediate solution that generates no direct carbon dioxide emissions and can bridge the transition to green hydrogen whilst utilising existing gas infrastructure. This process is yet to see widespread adoption, however, due to the high catalyst turnover costs resulting from the inevitable deactivation of the catalyst, which plays a decisive role in the feasibility of the process. In this study, a feasible TCMD process was identified and a simplified mathematical model was developed, which provides a dynamic estimation for the hydrogen production rate and catalyst turnover costs over various process conditions. The work consisted of a parametric study as well as an investigation into the different process modes. Based on the numerous simulation results it was possible to find the optimal process parameters that maximise the hydrogen pro- duction rate and minimise the catalyst turnover costs, therefore increasing the economic potential of the process and hence its commercial viability.

A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars

Perception in terms of object detection, classification, and dynamic estimation (position and velocity) are fundamental functionalities that autonomous agents (unmanned ground vehicles, unmanned aerial vehicles, or robots) have to navigate safely and autonomously. To date, various sensors have been used individually or in combination to achieve this goal. In this paper, we present a novel method for leveraging millimeter wave radar’s (mmW radar’s) ability to accurately measure position and velocity in order to improve and optimize velocity estimation using a monocular camera (using optical flow) and machine learning techniques. The proposed method eliminates ambiguity in optical flow velocity estimation when the object of interest is at the edge of the frame or far away from the camera without requiring camera–radar calibration. Moreover, algorithms of various complexity were implemented using custom dataset, and each of them successfully detected the object and estimated its velocity accurately and independently of the object’s distance and location in frame. Here, we present a complete implementation of camera–mmW radar late feature fusion to improve the camera’s velocity estimation performance. It includes setup design, data acquisition, dataset development, and finally, implementing a lightweight ML model that successfully maps the mmW radar features to the camera, allowing it to perceive and estimate the dynamics of a target object without any calibration.

Echo State Networks for Estimating Exteroceptive Conditions From Proprioceptive States in Quadruped Robots

We propose a methodology based on reservoir computing for mapping local proprioceptive information acquired at the level of the leg joints of a simulated quadruped robot into exteroceptive and global information, including both the ground reaction forces at the level of the different legs and information about the type of terrain traversed by the robot. Both dynamic estimation and terrain classification can be achieved concurrently with the same reservoir computing structure, which serves as a soft sensor device. Simulation results are presented together with preliminary experiments on a real quadruped robot. They demonstrate the suitability of the proposed approach for various terrains and sensory system fault conditions. The strategy, which belongs to the class of data-driven models, is independent of the robotic mechanical design and can easily be generalized to different robotic structures.

FIELD: A Software Tool That Integrates Harvester Data and Allometric Equations for a Dynamic Estimation of Forest Harvesting Residues

On an international comparison basis, Australia’s utilisation of forest residues remains very low. While there are numerous factors contributing to this low utilisation, this is greatly explained by the limited timely and accurate data on availability, quality, and location of residues generated during harvesting operations. This manuscript reports on the development and testing of a new freeware tool called FIELD (Forest Inventory Electronic Live Data), which supports the real-time monitoring and estimation of forestry harvesting residues. As inputs, FIELD uses StanForD pri files and geo-location data extracted from the harvester’s onboard computer in combination with locally developed species-specific allometric equations. Using a case study, this paper describes how FIELD works operationally and illustrates the range of support features that the tool can provide to decision-makers by producing real-time data on the availability, quality, and location of harvesting residues. In addition, it is discussed how the tool can contribute to supporting decisions during forest operations associated with the feasibility of residue utilisation in specific site conditions. Our results show that it is possible to estimate the availability of harvesting residues at geo-located sites dynamically, although further testing of the tool is required for a more accurate estimation and monitoring of harvesting residues.