Pyrolysis techniques have served to be a groundbreaking technique for
effectively utilising natural and man-made biomass products like plastics,
wood, crop residue, fruit peels etc. Recent advancements have shown a greater
yield of essential products like biochar, bio-oil and other non-condensable
gases by blending different biomasses in a certain ratio. This synergy effect
of combining two pyrolytic raw materials i.e co-pyrolysis of algae and wood
biomass has been systematically studied and grouped into 3 phases in this
research paper-kinetic analysis of co-pyrolysis, correlation among proximate
and ultimate analysis with bio-char yield and lastly grouping of different
weight ratios based on biochar yield up to a certain percentage. Different ML
and DL algorithms have been utilized for regression and classification
techniques to give a comprehensive overview of the effect of the synergy of two
different biomass materials on biochar yield. For the first phase, the best
prediction of biochar yield was obtained by using a decision tree regressor
with a perfect MSE score of 0.00, followed by a gradient-boosting regressor.
The second phase was analyzed using both ML and DL techniques. Within ML, SVR
proved to be the most convenient model with an accuracy score of 0.972 with DNN
employed for deep learning technique. Finally, for the third phase, binary
classification was applied to biochar yield with and without heating rate for
biochar yield percentage above and below 40%. The best technique for ML was
Support Vector followed by Random forest while ANN was the most suitable Deep
Learning Technique.

利用热解技术有效利用天然和人造生物质产品，如塑料，木材，废弃物，果皮等，通过混合不同的生物质以一定比例，获得了更高产的生物炭、生物油和其他非冷凝气体的关联性分析和各生物炭产率的分组，此外还利用了机器学习（ML）和深度学习（DL）算法，综合地研究了两种不同生物质材料对生物炭产量的协同作用。

利用机器学习对藻类和木材共热解进行生物炭产率的三相协同效应分析

A Three-Phase Analysis of Synergistic Effects During Co-pyrolysis of  Algae and Wood for Biochar Yield Using Machine Learning

Co-pyrolysis of biomass feedstocks with polymeric wastes is a promising
strategy for improving the quantity and quality parameters of the resulting
liquid fuel. Numerous experimental measurements are typically conducted to find
the optimal operating conditions. However, performing co-pyrolysis experiments
is highly challenging due to the need for costly and lengthy procedures.
Machine learning (ML) provides capabilities to cope with such issues by
leveraging on existing data. This work aims to introduce an evolutionary ML
approach to quantify the (by)products of the biomass-polymer co-pyrolysis
process. A comprehensive dataset covering various biomass-polymer mixtures
under a broad range of process conditions is compiled from the qualified
literature. The database was subjected to statistical analysis and mechanistic
discussion. The input features are constructed using an innovative approach to
reflect the physics of the process. The constructed features are subjected to
principal component analysis to reduce their dimensionality. The obtained
scores are introduced into six ML models. Gaussian process regression model
tuned by particle swarm optimization algorithm presents better prediction
performance (R2 > 0.9, MAE < 0.03, and RMSE < 0.06) than other developed
models. The multi-objective particle swarm optimization algorithm successfully
finds optimal independent parameters.

利用机器学习方法，数量化生物质和聚合物共热解过程中的产品，并实现预测，该研究发现高斯过程回归模型调整粒子群优化算法的预测性能最优，是一种更有效的方法。