Summary of A Three-phase Analysis Of Synergistic Effects During Co-pyrolysis Of Algae and Wood For Biochar Yield Using Machine Learning, by Subhadeep Chakrabarti and Saish Shinde
A Three-Phase Analysis of Synergistic Effects During Co-pyrolysis of Algae and Wood for Biochar Yield Using Machine Learning
by Subhadeep Chakrabarti, Saish Shinde
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Pyrolysis techniques have been revolutionizing the utilization of biomass products, such as plastics, wood, and crop residue. Recent advancements in blending different biomasses have shown a significant increase in essential products like biochar, bio-oil, and non-condensable gases. This study systematically investigates the synergy effect of co-pyrolyzing algae and wood biomass, grouping results into three phases: kinetic analysis, correlation between proximate and ultimate analysis with bio-char yield, and grouping different weight ratios based on biochar yield. Machine learning (ML) and deep learning (DL) algorithms are employed for regression and classification techniques to analyze the effect of biomass blending on biochar yield. The best ML prediction was obtained using a decision tree regressor with a perfect MSE score of 0.00, followed by gradient-boosting regressor. For the second phase, both ML and DL techniques were used, with SVR proving most suitable for ML (accuracy score: 0.972) and DNN employed for deep learning technique. Finally, binary classification was applied to biochar yield with and without heating rate for biochar yield percentage above and below 40%. The best ML technique was Support Vector, followed by Random forest, while ANN was the most suitable DL Technique. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores a new way to use natural and man-made materials like plastics, wood, and crop residue. By mixing different types of biomass together in a special ratio, scientists can create more useful products like biochar, bio-oil, and gases. The researchers looked at how combining algae and wood biomass affects the amount of these products they get. They used machine learning algorithms to analyze the results and found that certain methods are better than others for predicting the outcome. This study helps us understand how we can use different biomasses together to create more valuable products. |
Keywords
» Artificial intelligence » Boosting » Classification » Decision tree » Deep learning » Machine learning » Mse » Random forest » Regression
