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Sha Liu

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2 papers

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EAAI Journal 2025 Journal Article

An integrated exergy efficiency and machine learning method for optimizing organic solid waste gasification process

Wenni Chen
Xianan Xiang
Sha Liu
Jun Guo
Tao Li
Xuehua Zhou
Deyong Peng
Zhiya Deng

Organic solid waste (OSW) gasification is a critical pathway toward sustainable energy utilization. This study develops an integrated prediction model by combining exergy efficiency-based analytic hierarchy process-fuzzy comprehensive evaluation (AHP-FCE) with machine learning techniques. The model aims to select the optimal gasifier type and operational parameters based on OSW characteristics and processing capacities. Exergy efficiency derived from experimental data is used to construct AHP-FCE scores, which are then predicted using eight machine learning algorithms. Gradient boosting decision tree (GBDT) achieves the best performance. The prediction model is applied to three practical cases. For a project with an annual processing capacity of 2000 tons of refuse-derived fuel (RDF), the model consistently recommends the downdraft fixed-bed gasifier (DBG). In a corn straw gasification project processing 11, 000 tons per year, the bubbling fluidized-bed gasifier (BBG) is identified as the optimal choice. For a bamboo chip gasification project with an annual capacity of 150, 000 tons, the model suggests using the circulating fluidized-bed gasifier (CFBG) for reduction objectives and the dual fluidized-bed gasifier (DFBG) for hydrogen production goals. Additionally, the model shows significant potential. It can also be applied to optimize other complex systems that require balancing multiple influencing factors.

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YNIMG Journal 2014 Journal Article

Visualization of mouse barrel cortex using ex-vivo track density imaging

Nyoman D. Kurniawan
Kay L. Richards
Zhengyi Yang
David She
Jeremy F.P. Ullmann
Randal X. Moldrich
Sha Liu
Javier Urriola Yaksic

We describe the visualization of the barrel cortex of the primary somatosensory area (S1) of ex vivo adult mouse brain with short-tracks track density imaging (stTDI). stTDI produced much higher definition of barrel structures than conventional fractional anisotropy (FA), directionally-encoded color FA maps, spin-echo T 1- and T 2-weighted imaging and gradient echo T 1/T 2*-weighted imaging. 3D high angular resolution diffusion imaging (HARDI) data were acquired at 48micron isotropic resolution for a (3mm)3 block of cortex containing the barrel field and reconstructed using stTDI at 10micron isotropic resolution. HARDI data were also acquired at 100micron isotropic resolution to image the whole brain and reconstructed using stTDI at 20micron isotropic resolution. The 10micron resolution stTDI maps showed exceptionally clear delineation of barrel structures. Individual barrels could also be distinguished in the 20micron stTDI maps but the septa separating the individual barrels appeared thicker compared to the 10micron maps, indicating that the ability of stTDI to produce high quality structural delineation is dependent upon acquisition resolution. Close homology was observed between the barrel structure delineated using stTDI and reconstructed histological data from the same samples. stTDI also detects barrel deletions in the posterior medial barrel sub-field in mice with infraorbital nerve cuts. The results demonstrate that stTDI is a novel imaging technique that enables three-dimensional characterization of complex structures such as the barrels in S1 and provides an important complementary non-invasive imaging tool for studying synaptic connectivity, development and plasticity of the sensory system.

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