Recovery of bioactives from kānuka leaves using subcritical water extraction: Techno-economic analysis, environmental impact assessment and technology readiness level

Essien, Sinemobong; Udugama, Isuru A.; Young, Brent R.; Baroutian, Saeid

doi:10.1016/j.supflu.2020.105119

Cited by 18 publications

(1 citation statement)

References 36 publications

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“…Subcritical water extraction (SWE) was compared with conventional techniques using ethanol extraction (EE) in yielding bioactive kanuka leaf extract [9]. The assessment involves TRL valuation dealing with process units, total capital expenditure (CAPEX), and profitability.…”

Section: Introductionmentioning

confidence: 99%

Automatic Assessment of Technology Readiness Level Using LLDA-Helmholtz for Ranking University

Rintyarna¹,

Sarno²,

Fitrianto³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

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The assessment process of Technology Readiness Level using the questionnaire-based tool for Indonesian university's academic papers is considered to be labor-intensive. This paper introduces a new method of determining the TRL of an academic paper based on a text mining technique. The content of the research paper represented by their abstract published by university lecturers is justified to represent the technology maturity of research. Abstracts of papers were collected from the nine most reputable universities in Indonesia. By utilizing Labelled Latent Dirichlet Allocation, the abstracts were categorized into 1 of 9 levels of TRL. To determine the prior label of LLDA, we built a corpus of keywords representing each TRL level based on Bloom Taxonomy. Beforehand, Helmoltz principle was utilized to select the text feature. Since Bloom Taxonomy has only six levels, we split the keywords into 9 level. Afterward, the reputation score is calculated using our formula. Lastly, the university ranking is generated according to the extracted academic reputation score. To evaluate the proposed method, we compare our rank with QS's. We calculate the ranking gap and Pearson correlation to evaluate the result. Helmholtz has successfully pruned 86% of features. The utilization of Helmholtz significantly improves the Pearson correlation of our proposed method. In short, the new insight of university ranking introduced in this work is promising. For all indicator experiments, LLDA-Helmholtz performed better results indicated by 0.95 Pearson correlation between two rankings, while for LLDA without Helmhotz, the correlation is 0.78.

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Section: Introductionmentioning

confidence: 99%