This study aims to explore the potential of integrating Design of Expert (DOE) with Machine Learning (ML) to optimize and predict the adsorption process of solid adsorbent The prediction and optimization of adsorption performance can be improvised using statistical analysis and advanced predictive tools, resulting in substantial cost and energy savings. Firstly, the Response Surface Methodology-Central Composite Design (RSM-CCD) model was used to design and optimize the experiments on the adsorption of cationic dye using biomass-hydro char. Secondly, Random Forest (RF) was used to train the experimental results of RSM-CCD. It is well-suited for small datasets, withstands noise, and effectively reduces overfitting to predict adsorption performance. RF model demonstrated excellent accuracy, achieving a removal efficacy of 97.4 % with a significant R value of 0.9981 compared to the RSM-CCD, which had a removal efficiency of 95.6 % and R 0.9372. The physicochemical analysis also shows the novel hybrid hydrochar from fruit waste exhibits remarkable characteristics, including a higher content of carbon (78 %) and a surface area of 670 m/g. In summary, RSM-CCD with ML provides precise optimization and predictions of the adsorption efficacy of the novel hydrochar. This has significant value for industrial applications in the field of material discovery.

中文翻译：

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使用响应面方法和机器学习对源自水果废物的水炭进行优化驱动建模，用于吸附性能评估


本研究旨在探索将专家设计（DOE）与机器学习（ML）相结合来优化和预测固体吸附剂吸附过程的潜力。可以使用统计分析和先进的预测工具即兴预测和优化吸附性能，从而产生吸附性能。显着节省成本和能源。首先，采用响应面法-中心复合设计（RSM-CCD）模型对生物质-水炭吸附阳离子染料的实验进行设计和优化。其次，使用随机森林（RF）来训练RSM-CCD的实验结果。它非常适合小数据集，能够承受噪声，并有效减少过度拟合来预测吸附性能。 RF 模型表现出出色的准确性，与 RSM-CCD 相比，去除效率为 97.4%，R 值为 0.9981，RSM-CCD 的去除效率为 95.6%，R 值为 0.9372。物理化学分析还表明，从水果废物中提取的新型混合水炭具有显着的特性，包括更高的碳含量 (78%) 和 670 m/g 的表面积。总之，带有 ML 的 RSM-CCD 可以对新型水炭的吸附效率进行精确优化和预测。这对于材料发现领域的工业应用具有重要价值。