Optimization of Palm Oil Mill Effluent (POME) Solubilization Using Linguistic Fuzzy Logic and Machine Learning Techniques
Springer Proceedings in Mathematics and Statistics, ISSN: 2194-1017, Vol: 413, Page: 225-242
2023
- 3Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures3
- Readers3
Conference Paper Description
The continuous hike in the price of the edible vegetable oil has directly impacted upon the price growth of the palm oil. In this palm oil production, Indonesia and Malaysia are in top leading position at present. It is found that an increase in palm oil mill setup is radically increasing the produce of effluent discharge which is a severe threat to our environment. Hence to maintain the balance of our already affected ecosystem, the proper treatment of this residual product is becoming the dire need of the hour. The conventional method used for solubilization of palm oil mill effluent (POME) is thermal alkaline pre-treatment. In this paper, the linguistic fuzzy logic (LFL) and machine learning (ML) techniques have been used to analyze the data, and type-2 fuzzy logic controller (T2FLC) has been used to optimize the solubilization of POME. The effect of reaction time, NaOH concentration, and temperature on the solubilization has been evaluated. From the investigation of the surface plot, which developed in T2FLC environment, it has been observed that NaOH concentration has a significant effect on the solubilization of POME. The prediction efficiency of T2FLC then has been compared with T1FLC and RSM. By evaluating some statistical analyses, the sensitivity and validity of the proposed model have finally been measured.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85163280962&origin=inward; http://dx.doi.org/10.1007/978-981-19-8194-4_18; https://link.springer.com/10.1007/978-981-19-8194-4_18; https://dx.doi.org/10.1007/978-981-19-8194-4_18; https://link.springer.com/chapter/10.1007/978-981-19-8194-4_18
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know