Haarlemmer Geert, Roubaud Anne
CEA/LITEN/DTCH, Université Grenoble Alpes, Grenoble, 38000, France.
Open Res Eur. 2022 Dec 15;2:111. doi: 10.12688/openreseurope.14915.2. eCollection 2022.
Food wastes are an abundant resource that can be effectively valorised by hydrothermal liquefaction to produce bio-fuels. The objective of the European project WASTE2ROAD is to demonstrate the complete value chain from waste collection to engine tests. The principle of hydrothermal liquefaction is well known but there are still many factors that make the science very empirical. Most experiments in the literature are performed on batch reactors. Comparison of results from batch reactors with experiments with continuous reactors are rare in the literature. Various food wastes were transformed by hydrothermal liquefaction. The resources used and the products from the experiments have been extensively analysed. Two different experimental reactors have been used, a batch reactor and a continuous reactor. This paper presents a dataset of fully documented experiments performed in this project, on food wastes with different compositions, conditions and solvents. The data set is extended with data from the literature. The data was analysed using machine learning analysis and regression techniques. This paper presents experimental results on various food wastes as well as modelling and analysis with machine learning algorithms. The experimental results were used to attempt to establish a link between batch and continuous experiments. The molecular weight of bio-oil from continuous experiments appear higher than that of batch experiments. This may be due to the configuration of our reactor. This paper shows how the use of regression models help with understanding the results, and the importance of process variables and resource composition. A novel data analysis technique gives an insight on the accuracy that can be obtained from these models.
食物垃圾是一种丰富的资源，可通过水热液化有效地转化为生物燃料。欧洲项目WASTE2ROAD的目标是展示从垃圾收集到发动机测试的完整价值链。水热液化的原理是众所周知的，但仍有许多因素使得该科学非常依赖经验。文献中的大多数实验是在间歇式反应器上进行的。间歇式反应器的结果与连续式反应器实验结果的比较在文献中很少见。通过水热液化对各种食物垃圾进行了转化。对实验中使用的资源和产物进行了广泛分析。使用了两种不同的实验反应器，一种是间歇式反应器，另一种是连续式反应器。本文展示了该项目中针对不同成分、条件和溶剂的食物垃圾进行的充分记录实验的数据集。该数据集通过文献数据进行了扩充。使用机器学习分析和回归技术对数据进行了分析。本文展示了各种食物垃圾的实验结果以及使用机器学习算法进行的建模和分析。实验结果用于尝试建立间歇式和连续式实验之间的联系。连续式实验中生物油的分子量似乎高于间歇式实验。这可能是由于我们反应器的配置。本文展示了回归模型的使用如何有助于理解结果，以及过程变量和资源组成的重要性。一种新颖的数据分析技术深入了解了从这些模型中可以获得的准确性。
