Process modelling and life cycle assessment of algal biochar- bioenergy system

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GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning

Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance trade-offs, and fast technological advancements. Although there has been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black-box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search, and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient.Comment: Accepted to the 57th Design Automation Conference (DAC 2020); 6 pages, 8 figure

Comparative characterisation and phytotoxicity assessment of biochar and hydrochar derived from municipal wastewater microalgae biomass

Microalgae, originating from a tertiary treatment of municipal wastewater, is considered a sustainable feedstock for producing biochar and hydrochar, offering great potential for agricultural use due to nutrient content and carbon storage ability. However, there are risks related to contamination and these need to be carefully assessed to ensure safe use of material from wastewater microalgae. Therefore, this study compared the properties and phototoxicity of biochar and hydrochar produced via pyrolysis and hydrothermal carbonisation (HTC) of microalgae under different temperatures and residence times. While biochar promoted germination and seedling growth by up to 11.0% and 70.0%, respectively, raw hydrochar showed strong phytotoxicity, due to the high content of volatile matter. Two post-treatments, dichloromethane (DCM) washing and further pyrolysis, proved to be effective methods for mitigating phytotoxicity of hydrochar. Additionally, biochar had 35.8–38.6% fixed carbon, resulting in higher carbon sequestration potential compared to hydrochar