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